The following is a list of papers from the field of explainable AI. Please feel free to submit a pull request to contribute to this list.

2021

  • A. Akula, K Wang, C. Liu, S. Saba, H. Lu, S. Todorovic, J. Y. Chai, S-C. Zhu, “X-ToM: Explaining with Theory-of-Mind for Gaining Justified Human Trust”, iScience Cell Press (minor revision) 2021.
  • A. Ray, M. Cogswell, X. Lin, K. Alipour, A. Divakaran, Y. Yao and G. T. Burachas, "Knowing What VQA Does Not: Pointing to Error-Inducing Regions to Improve Explanation Helpfulness," in arXiv:2103.14712, 2021.
  • Alec G. Moore, Ryan P. McMahan, Hailiang Dong, and Nicholas Ruozzi. Personal Identifiability of User Tracking Data During VR Training. In IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VR Workshops 2021, Lisbon, Portugal, March 27 - April 1, 2021, pages 556–557. IEEE, 2021
  • Antonio Vergari, YooJung Choi, Anji Liu, Stefano Teso, and Guy Van den Broeck. A Compositional Atlas of Tractable Circuit Operations: From Simple Transformations to Complex Information-Theoretic Queries. CoRR, abs/2102.06137, 2021. eprint: 2102.06137
  • Chiradeep Roy, Tahrima Rahman, Hailiang Dong, Nicholas Ruozzi, and Vibhav Gogate. Dynamic Cutset Networks. In Arindam Banerjee and Kenji Fukumizu, editors, The 24th International Conference on Artificial Intelligence and Statistics, AISTATS 2021, April 13-15, 2021, Virtual Event, volume 130 of Proceedings of Machine Learning Research, pages 3106–3114. PMLR, 2021
  • Druce, Jeff, Michael Harradon, and James Tittle. "Explainable Artificial Intelligence (XAI) for Increasing User Trust in Deep Reinforcement Learning Driven Autonomous Systems." arXiv preprint arXiv:2106.03775 (2021).
  • Druce, Jeff, et al. "Brittle AI, Causal Confusion, and Bad Mental Models: Challenges and Successes in the XAI Program." arXiv preprint arXiv:2106.05506 (2021).
  • Eric Wang, Pasha Khosravi, and Guy Van den Broeck. Probabilistic Sufficient Explanations. CoRR, abs/2105.10118, 2021. eprint: 2105.10118
  • Folke, T., Yang, S.C-H., Anderson, S., & Shafto, P. (2021). Explainable AI for medical imaging: explaining pneumothorax diagnoses with Bayesian teaching. Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III. doi: 10.1117/12.2585967
  • Guy Van den Broeck, Anton Lykov, Maximilian Schleich, and Dan Suciu. On the Tractability of SHAP Explanations. In Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2021
  • Guy Van den Broeck. From Probabilistic Circuits to Probabilistic Programs and Back. In Ana Paula Rocha, Luc Steels, and H. Jaap van den Herik, editors, Proceedings of the 13th International Conference on Agents and Artificial Intelligence, ICAART 2021, Volume 1, Online Streaming, February 4-6, 2021, page 9. SCITEPRESS, 2021
  • Hoffman, R.R. (2021)."Eggsplaining AI." Concept Blog at [https://www.ihmc.us/hoffmans-concept-blog/]
  • Honghua Zhang, Brendan Juba, and Guy Van den Broeck. Probabilistic Generating Circuits. CoRR, abs/2102.09768, 2021. eprint: 2102.09768
  • Jialin Wu, Liyan Chen, Raymond J. Mooney “Improving VQA and its Explanations by Comparing Competing Explanations” The AAAI Workshop on Explainable Agency in Artificial Intelligence, February 2021. http://www.cs.utexas.edu/~ai-lab/pub-view.php?PubID=127839
  • Jonathan Dodge, Roli Khanna, Jed Irvine, Kin-Ho Lam, Theresa Mai, Zhengxian Lin, Nicholas Kiddle, Evan Newman, Andrew Anderson, Sai Raja, Caleb Matthews, Christopher Perdriau, Margaret Burnett, and Alan Fern. (to appear). After-Action Review for AI (AAR/AI). ACM Transactions on Interactive Intelligent Systems.
  • Kareem Ahmed, Eric Wang, Guy Van den Broeck, and Kai-Wei Chang. Leveraging Unlabeled Data for Entity-Relation Extraction through Probabilistic Constraint Satisfaction. CoRR, abs/2103.11062, 2021. eprint: 2103.11062
  • Klein, G. (2021). "The Discovery Platform: A Tool for Exploring Intelligent Systems." Psychology Today Blog.
  • Mahsan Nourani, Chiradeep Roy, Jeremy E. Block, Donald R. Honeycutt, Tahrima Rahman, Eric D. Ragan, and Vibhav Gogate. Anchoring Bias Affects Mental Model Formation and User Reliance in Explainable AI Systems. In Tracy Hammond, Katrien Verbert, Dennis Parra, Bart P. Knijnenburg, John O’Donovan, and Paul Teale, editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17
  • Margaret Burnett. (2021). Doing Remote Controlled Studies with Humans: Tales from the COVID Trenches. ACM/IEEE 14th International Conference on Cooperative and Human Aspects of Software Engineering (CHASE 2021).
  • Martin Erwig and Prashant Kumar. (2021). Explainable Dynamic Programming. Journal of Functional Programming, Volume 31, e10, 2021.
  • Mohamad Danesh, Anurag Koul, Alan Fern, and Saeed Khorram. (2021). Re-Understanding Finite-State Representations of Recurrent Policy Networks. International Conference on Machine Learning (ICML-2021).
  • Mueller, S.T., Veinott, E.S., Hoffman, R.R., Klein, G., Alam, L. Mamun, T, & Clancey, W.J. (2021). Principles of explanation in human-machine systems. In AAAI-2021 Workshop on Explainable Agency in Artificial of explanation in human-machine systems. In AAAI-2021 Workshop on Explainable Agency in Artificial [https://www.researchgate.net/publication/349145196_Principles_of_Explanation_in_Human-AI_Systems]
  • Olson, M., Khanna, R., Neal, L., Li, F. and Wong, W-K. (2021). Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep Learning. Artificial Intelligence, 295.
  • Saeed Khorram, Tyler Lawson, and Li Fuxin. (2021). iGOS++: Integrated Gradient Optimized Saliency by Bilateral Perturbations. ACM Conference on Health, Inference and Learning (CHIL).
  • Sina Mohseni, Jeremy E. Block, and Eric D. Ragan. Quantitative Evaluation of Machine Learning Explanations: A Human-Grounded Benchmark. In Tracy Hammond, Katrien Verbert, Dennis Parra, Bart P. Knijnenburg, John O’Donovan, and Paul Teale, editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 22–31. ACM, 2021
  • Sina Mohseni, Niloofar Zarei, Eric Ragan. A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems. ACM Trans. Interact. Intell. Syst. (TiiS) Special Issue on Interactive Visual Analytics for Making Explainable and Accountable Decisions.
  • Wenzhe Li, Zhe Zeng, Antonio Vergari, and Guy Van den Broeck. Tractable Computation of Expected Kernels by Circuits. CoRR, abs/2102.10562, 2021. eprint: 2102.10562
  • Yang, S.,C-H., Folke, T., & Shafto, P. (preprint). Abstraction, validation, and generalization for explainable artificial intelligence.
  • Yang, S.,C-H., Vong, W-K., Sojitra, R.B., Folke, T., & Shafto, P. (2021). Mitigating belief projection in explainable artificial intelligence via Bayesian Teaching. Scientific Reports.
  • Yipeng Huang, Steven Holtzen, Todd D. Millstein, Guy Van den Broeck, and Margaret Martonosi. Logical abstractions for noisy variational Quantum algorithm simulation. In Tim Sherwood, Emery Berger, and Christos Kozyrakis, editors, ASPLOS ’21: 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Virtual Event, USA, April 19-23, 2021, pages 456–472. ACM, 2021
  • YooJung Choi, Meihua Dang, and Guy Van den Broeck. Group Fairness by Probabilistic Modeling with Latent Fair Decisions. In Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2021
  • Zhengxian Lin, Kim-Ho Lam, and Alan Fern. (2021). Contrastive Explanations for Reinforcement Learning via Embedded Self Predictions. International Conference on Learning Representations (ICLR-2021).
  • Zhengyang Wang, Yaochen Xie, and Shuiwang Ji. "Global voxel transformer networks for augmented microscopy." Nature Machine Intelligence 3.2 (2021): 161-171.
  • Zhongang Qi, Saeed Khorram, Li Fuxin. (2021). Embedding Deep Networks into Visual Explanations. Artificial Intelligence, 292.
  • ˙Ismail ˙Ilkan Ceylan, Adnan Darwiche, and Guy Van den Broeck. Open-world probabilistic databases: Semantics, algorithms, complexity. Artif. Intell., 295:103474, 2021

2020

  • A. Akula, S. Gella, O. Yaser, S. Reddy, S-C. Zhu, “On the Robustness of Visual Referring Expressions”, submitted to Association for Computational Linguistics (ACL) 2020.
  • A. Akula, S. Wang, S-C. Zhu, “CoCo-X: Generating Conceptual and Counterfactual Explanations via Fault-Lines”, Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2020.
  • Q. Zhang, J. Ren, G. Huang, R. Cao, Y. N. Wu, and S-C. Zhu, “Mining Interpretable AOG Representations from Convolutional Networks via Active Question Answering”, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI, under minor revision), 2020.
  • Q. Zhang, X. Wang, R. Cao, Y. N. Wu, F. Shi, and S-C. Zhu, “Extracting an Explanatory Graph to Interpret a CNN”, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI, under minor revision), 2020.
  • Q. Zhang, X. Wang, Y. N. Wu, H. Zhou, and S-C. Zhu, “Interpretable CNNs for Object Classification”, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Accepted, 2020.
  • Adnan Darwiche and Auguste Hirth. On the Reasons Behind Decisions. In Giuseppe De Giacomo, Alejandro Catal´a, Bistra Dilkina, Michela Milano, Sen´en Barro, Alberto Bugar´ın, and J´erˆome Lang, editors, ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020 - Including 10th Conference on Prestigious Applications of Artificial Int
  • Adnan Darwiche. An Advance on Variable Elimination with Applications to Tensor-Based Computation. In Giuseppe De Giacomo, Alejandro Catal´a, Bistra Dilkina, Michela Milano, Sen´en Barro, Alberto Bugar´ın, and J´erˆome Lang, editors, ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020 - Including 10th Conference on Prestigious
  • Adnan Darwiche. Three Modern Roles for Logic in AI. In Dan Suciu, Yufei Tao, and Zhewei Wei, editors, Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, PODS 2020, Portland, OR, USA, June 14-19, 2020, pages 229–243. ACM, 2020
  • Aishwarya Sivaraman, Golnoosh Farnadi, Todd D. Millstein, and Guy Van den Broeck. Counterexample- Guided Learning of Monotonic Neural Networks. In Hugo Larochelle, Marc’Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin, editors, Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, 2020
  • Alec G. Moore, Ryan P. McMahan, Hailiang Dong, and Nicholas Ruozzi. Extracting Velocity-Based User-Tracking Features to Predict Learning Gains in a Virtual Reality Training Application. In 2020 IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2020, Recife/Porto de Galinhas, Brazil, November 9-13, 2020, pages 694–703. IEEE, 2020
  • Andrew Anderson, Jonathan Dodge, Amrita Sadarangani, Zoe Juozapaitis, Evan Newman, Jed Irvine, Souti Chattopadhyay, Alan Fern, Margaret Burnett. (accepted). Mental Models of Mere Mortals with Explanations of Reinforcement Learning. ACM Transactions on Interactive Intelligent Systems (TiiS).
  • Anji Liu, Yitao Liang, Ji Liu, Guy Van den Broeck, and Jianshu Chen. On Effective Parallelization of Monte Carlo Tree Search. CoRR, abs/2006.08785, 2020. eprint: 2006.08785
  • Anji Liu, Yitao Liang, and Guy Van den Broeck. Off-Policy Deep Reinforcement Learning with Analogous Disentangled Exploration. In Amal El Fallah Seghrouchni, Gita Sukthankar, Bo An, and Neil Yorke-Smith, editors, Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS ’20, Auckland, New Zealand, May 9-13, 2020, pages 753–761. International Foundation for Autonomous Agents and Multia
  • Arnab Kumar Mondal, Himanshu Asnani, Parag Singla, and Prathosh AP. To Regularize or Not To Regularize? The Bias Variance Trade-off in Regularized AEs. CoRR, abs/2006.05838, 2020. eprint: 2006.05838
  • Arnab Kumar Mondal, Sankalan Pal Chowdhury, Aravind Jayendran, Himanshu Asnani, Parag Singla, and Prathosh A. P. MaskAAE: Latent space optimization for Adversarial Auto-Encoders. In Ryan P. Adams and Vibhav Gogate, editors, Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, UAI 2020, virtual online, August 3-6, 2020, volume 124 of Proceedings of Machine Learning Research, pages 689–698. AUAI
  • Arthur Choi, Andy Shih, Anchal Goyanka, and Adnan Darwiche. On Symbolically Encoding the Behavior of Random Forests. CoRR, abs/2007.01493, 2020. eprint: 2007.01493
  • Brett Benda, Shaghayegh Esmaeili, and Eric D. Ragan. Determining Detection Thresholds for Fixed Positional Offsets for Virtual Hand Remapping in Virtual Reality. In 2020 IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2020, Recife/Porto de Galinhas, Brazil, November 9-13, 2020, pages 269–278. IEEE, 2020
  • Chen Ziwen, Wenxuan Wu, Zhongang Qi, and Li Fuxin. (2020). Visualizing Point Cloud Classifiers by Curvature Smoothing. British Machine Vision Conference.
  • David Bau, Jun-Yan Zhu, Hendrik Strobelt, Agata Lapedriza, Bolei Zhou, Antonio Torralba. “Understanding the role of individual units in a deep neural network” Proceedings of the National Academy of Sciences (PNAS), 2020
  • Devendra Singh Chaplot, Lisa Lee, Ruslan Salakhutdinov, Devi Parikh, Dhruv Batra, “Embodied Multimodal Multitask Learning”, International Joint Conference on Artificial Intelligence (IJCAI), 2020
  • Dhrubo Jyoti Paul and Eric D. Ragan. Subtle Gaze Direction with Asymmetric Field-of-View Modulation in Headworn Virtual Reality. In 2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VR Workshops, Atlanta, GA, USA, March 22-26, 2020, pages 569–570. IEEE, 2020
  • Dinu, Jonathan, Jeffrey Bigham, and J. Zico Kolter. "Challenging common interpretability assumptions in feature attribution explanations." arXiv preprint arXiv:2012.02748 (2020).
  • Donald R. Honeycutt, Mahsan Nourani, and Eric D. Ragan. Soliciting Human-in-the-Loop User Feedback for Interactive Machine Learning Reduces User Trust and Impressions of Model Accuracy. CoRR, abs/2008.12735, 2020. eprint: 2008.12735
  • Eric D. Ragan, Andrew Pachuilo, John R. Goodall, and Felipe Bacim. Preserving Contextual Awareness during Selection of Moving Targets in Animated Stream Visualizations. In Genny Tortora, Giuliana Vitiello, and MarcoWinckler, editors, AVI ’20: International Conference on Advanced Visual Interfaces, Island of Ischia, Italy, September 28 - October 2, 2020, pages 28:1–28:9. ACM, 2020
  • Eric D. Ragan, Andrew S. Stamps, and John R. Goodall. Empirical Study of Focus-Plus-Context and Aggregation Techniques for the Visualization of Streaming Data. In Genny Tortora, Giuliana Vitiello, and Marco Winckler, editors, AVI ’20: International Conference on Advanced Visual Interfaces, Island of Ischia, Italy, September 28 - October 2, 2020, pages 55:1–55:5. ACM, 2020
  • Erik Wijmans, Abhishek Kadian, Ari Morcos, Stefan Lee, Irfan Essa, Devi Parikh, Manolis Savva, Dhruv Batra “Decentralized Distributed PPO: Solving PointGoal Navigation”, International Conference on Learning Representations (ICLR), 2020
  • Fabian Bolte, Mahsan Nourani, Eric D. Ragan, and Stefan Bruckner. SplitStreams: A Visual Metaphor for Evolving Hierarchies. CoRR, abs/2002.03891, 2020. eprint: 2002.03891
  • Fan Yang, Ninghao Liu, Mengnan Du, Kaixiong Zhou, Shuiwang Ji, Xia Hu. " Deep Neural Networks with Knowledge Instillation." Proceedings of the 2020 SIAM international conference on data mining (SDM). Society for Industrial and Applied Mathematics, 2020.
  • Hamed Shahbazi, Xiaoli Fern, and Prasad Tadepalli. (2020). Relation Extraction with Explanation. ACL 2020.
  • Hao Xiong and Nicholas Ruozzi. General Purpose MRF Learning with Neural Network Potentials. In Christian Bessiere, editor, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, pages 2769–2776. ijcai.org, 2020
  • Hao Yuan, Jiliang Tang, Xia Hu, and Shuiwang Ji. "Xgnn: Towards model-level explanations of graph neural networks." In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 430-438. 2020.
  • Hao Yuan, Lei Cai, Xia Hu, Jie Wang, and Shuiwang Ji. "Interpreting image classifiers by generating discrete masks." IEEE Transactions on Pattern Analysis and Machine Intelligence (2020).
  • Honghua Zhang, Steven Holtzen, and Guy Van den Broeck. On the Relationship Between Probabilistic Circuits and Determinantal Point Processes. In Ryan P. Adams and Vibhav Gogate, editors, Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, UAI 2020, virtual online, August 3-6, 2020, volume 124 of Proceedings of Machine Learning Research, pages 1188–1197. AUAI Press, 2020
  • Jeremy E. Block and Eric D. Ragan. Micro-entries: Encouraging Deeper Evaluation of Mental Models Over Time for Interactive Data Systems. In Anastasia Bezerianos, Kyle Wm Hall, Samuel Huron, Matthew Kay, Miriah Meyer, and Michael Sedlmair, editors, IEEE Workshop on Evaluation and Beyond - Methodological Approaches to Visualization,
  • Jiasen Lu, Vedanuj Goswami, Marcus Rohrbach, Devi Parikh, Stefan Lee “12-in-1: Multi-Task Vision and Language Representation Learning” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020
  • Jonathan Dodge and Margaret Burnett. (2020). Position: We Can Measure XAI Explanations Better with "Templates". IUI ExSS-ATEC Workshop.
  • K. Alipour, A. Ray, X. Lin, J. P. Schulze, Y. Yao and G. T. Burachas, "The Impact of Explanations on AI Competency Prediction in VQA.," in HCCAI, 2020.
  • K. Alipour, J. P. Schulze, Y. Yao, A. Ziskind and G. Burachas, "A study on multimodal and interactive explanations for visual question answering," in arXiv:2003.00431.
  • Kasra Rahimi Moghadam, Colin Banigan, and Eric D. Ragan. Scene Transitions and Teleportation in Virtual Reality and the Implications for Spatial Awareness and Sickness. IEEE Trans. Vis. Comput. Graph., 26(6):2273–2287, 2020
  • Kin-Ho Lam, Zhengxian Lin, Jed Irvine, Jonathan Dodge, Zeyad T Shureih, Roli Khanna, Minsuk Kahng, and Alan Fern. (2020). Identifying Reasoning Flaws in Planning-Based RL Using Tree Explanations. IJCAI-PRICAI 2020 Workshop on Explainable Artificial Intelligence.
  • Klein, G. (2020). "AIQ: Artificial Intelligence Quotient: Helping People Get Smarter About Smart Machines" Psychology Today Blog.
  • Krzysztof Gajowniczek, Yitao Liang, Tal Friedman, Tomasz Zabkowski, and Guy Van den Broeck. Semantic and Generalized Entropy Loss Functions for Semi-Supervised Deep Learning. Entropy, 22(3):334, 2020
  • Laura Isabel Galindez Olascoaga, Wannes Meert, Nimish Shah, Guy Van den Broeck, and Marian Verhelst. Discriminative Bias for Learning Probabilistic Sentential Decision Diagrams. In Michael R. Berthold, Ad Feelders, and Georg Krempl, editors, Advances in Intelligent Data Analysis XVIII - 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27-29, 2020, Proceedings, volume 12080 of Lectu
  • Liu, Steven, Tongzhou Wang, David Bau, Jun-Yan Zhu, and Antonio Torralba “Diverse Image Generation via Self-Conditioned GANs” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14286-14295, 2020
  • Lovish Madaan, Soumya Sharma, and Parag Singla. Transfer Learning for Related Languages: Submissions to the WMT20 Similar Language Translation Task. In Lo¨ıc Barrault, Ondrej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-juss`a, Christian Federmann, Mark Fishel, Alexander Fraser, Yvette Graham, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno-Yepes, Philipp Koehn, Andr´e Martins, Makoto Morishita, Christof M
  • Lu, C-K & Shafto, P. (preprint). Multi-source deep Gaussian Process kernel learning.
  • Lu, C-K, Yang, S, C-H, Hao, X., & Shafto, P. (2020). Interpretable deep Gaussian Processes with moments. Proceedings of the 23rd international conference on Artificial Intelligence and Statistics (AISTATS).
  • Mahsan Nourani, Chiradeep Roy, Tahrima Rahman, Eric D. Ragan, Nicholas Ruozzi, and Vibhav Gogate. Don’t Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition. CoRR, abs/2005.02335, 2020. eprint: 2005.02335
  • Mahsan Nourani, Donald R. Honeycutt, Jeremy E. Block, Chiradeep Roy, Tahrima Rahman, Eric D. Ragan, and Vibhav Gogate. Investigating the Importance of First Impressions and Explainable AI with Interactive Video Analysis. In Regina Bernhaupt, Florian ’Floyd’ Mueller, David Verweij, Josh Andres, Joanna McGrenere, Andy Cockburn, Ignacio Avellino, Alix Goguey, Pernille Bjøn, Shengdong Zhao, Briane Paul Samson, and Rafal Kocielnik
  • Mahsan Nourani, Joanie T. King, and Eric D. Ragan. The Role of Domain Expertise in User Trust and the Impact of First Impressions with Intelligent Systems. CoRR, abs/2008.09100, 2020. eprint: 2008.09100
  • Margaret Burnett. (2020). Abstract: Explaining AI: Fairly? Well?. Extended Abstract of Keynote at ACM IUI’20.
  • Martin Erwig, Prashant Kumar, and Alan Fern. (2020). Explanations for Dynamic Programming. Symposium on Practical Aspects of Declarative Languages (PADL).
  • Meihua Dang, Antonio Vergari, and Guy Van den Broeck. Strudel: Learning structured-decomposable probabilistic circuits. In Manfred Jaeger and Thomas Dyhre Nielsen, editors, Proceedings of the 10th International Conference on Probabilistic Graphical Models, volume 138 of Proceedings of Machine Learning Research, pages 137–148. PMLR, 23–25 Sep 2020
  • Mengnan Du, Ninghao Liu, Xia Hu, "Techniques for Interpretable Machine Learning", Communications of the ACM (CACM), 2020
  • Mitsopoulos K., Somers, S., Thomson, R., Lebiere, C. “Cognitive Architectures for Introspecting Deep Reinforcement Learning Agents.” Accepted for presentation at the workshop on Bridging AI and Cognitive Science, at the 8th International Conference on Learning Representations (ICLR2020). 
  • Mohamad Danesh, Anurag Koul, Alan Fern. (2020). and Understanding Finite-State Representations of Recurrent Policy Networks. ICML 2020 Workshop on XXAI: Extending Explainable AI Beyond Deep Models and Classifiers
  • Mueller, S.T. (2020, March). Cognitive anthropomorphism of AI: How humans and computers classify images. Ergonomics in Design, pp. 1-8. Santa Monica, CA: Human Factors and Ergonomics Society. [DOI:10.1177/1064804620920870]
  • Mueller, S.T., Agarwal, P., Linja, A., Alam, L. (2020). The unreasonable ineptitude of deep image classification networks. In Proceedings of the Annual Meeting of the Human Factors and Ergonomics Society, pp. 184-188. Santa Monica, CA: Human Factors and Ergonomics Society.
  • Neale Ratzlaff, Qinxun Bai, Li Fuxin, Wei Xu. (2020). Implicit Generative Modeling for Efficient Exploration. International Conference on Machine Learning (ICML-20).
  • Nirbhay Modhe, Prithvijit Chattopadhyay, Mohit Sharma, Abhishek Das, Devi Parikh, Dhruv Batra, Ramakrishna Vedantam “IR-VIC: Unsupervised Discovery of Sub-goals for Transfer in RL” International Joint Conference on Artificial Intelligence (IJCAI), 2020
  • Pasha Khosravi, Antonio Vergari, YooJung Choi, Yitao Liang, and Guy Van den Broeck. Handling missing data in decision trees: A probabilistic approach. In The Art of Learning with Missing Values Workshop at ICML (Artemiss), jul 2020
  • Ramprasaath R. Selvaraju, Purva Tendulkar, Devi Parikh, Eric Horvitz, Marco Ribeiro, Besmira Nushi, Ece Kamar “SQuINTing at VQA Models: Interrogating VQA Models with Sub-Questions” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020
  • Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Guy Van den Broeck, Kristian Kersting, and Zoubin Ghahramani. Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, volume 119 of Proceedings of Machine Learning Research, pages 7563–7574. PMLR,
  • Ronak R. Mohanty, Ricardo M. Castillo, Eric D. Ragan, and Vinayak R. Krishnamurthy. Investigating Force-Feedback in Mid-Air Sketching of Multi-Planar Three-Dimensional Curve-Soups. J. Comput. Inf. Sci. Eng., 20(1), 2020
  • Sameer Dharur, Purva Tendulkar, Dhruv Batra, Devi Parikh, Ramprasaath R. Selvaraju “SOrT-ing VQA Models: Improving Consistency via Gradient Alignment” Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020
  • Sara Rouhani, Tahrima Rahman, and Vibhav Gogate. A Novel Approach for Constrained Optimization in Graphical Models. In Hugo Larochelle, Marc’Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin, editors, Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, 2020
  • Sean Penney, Jonathan Dodge, Andrew Anderson, Claudia Hilderbrand, Logan Simpson, and Margaret Burnett. (2020). The Shoutcasters, the Game Enthusiasts, and the AI: Foraging for Explanations of Real-Time Strategy Players. ACM Transactions on Interactive Intelligent Systems.
  • Shaghayegh Esmaeili, Brett Benda, and Eric D. Ragan. Detection of Scaled Hand Interactions in Virtual Reality: The Effects of Motion Direction and Task Complexity. In IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2010, Atlanta, GA, USA, March 22-26, 2020, pages 453–462. IEEE, 2020
  • Sina Mohseni, Eric Ragan, Ben Hu, Shuiwang Ji, Fan Yang, Mengnan Du, Shiva Pentyala, Yi Liu and Nic Lupfer. "Trust Evolution Over Time in Explainable AI for Fake News Detection", Fair & Responsible AI Workshop at CHI 2020.
  • Sina Mohseni, Eric Ragan. Quantitative Evaluation of Machine Learning Explanations: A Human-Grounded Approach. CHI 2020 Workshop AI4HCI.
  • Sina Mohseni, Fan Yang, Shiva K. Pentyala, Mengnan Du, Yi Liu, Nic Lupfer, Xia Hu, Shuiwang Ji, and Eric D. Ragan. Machine Learning Explanations to Prevent Overtrust in Fake News Detection. CoRR, abs/2007.12358, 2020. eprint: 2007.12358
  • Steven Holtzen, Guy Van den Broeck, and Todd D. Millstein. Dice: Compiling Discrete Probabilistic Programs for Scalable Inference. CoRR, abs/2005.09089, 2020. eprint: 2005.09089
  • Steven Holtzen, Guy Van den Broeck, and Todd Millstein. Scaling exact inference for discrete probabilistic programs. Proc. ACM Program. Lang., 4(OOPSLA), November 2020
  • Sun, Mingjie, Siddhant Agarwal, and J. Zico Kolter. "Poisoned classifiers are not only backdoored, they are fundamentally broken." arXiv preprint arXiv:2010.09080 (2020).
  • Tal Friedman and Guy Van den Broeck. Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings. In Ryan P. Adams and Vibhav Gogate, editors, Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, UAI 2020, virtual online, August 3-6, 2020, volume 124 of Proceedings of Machine Learning Research, pages 1268–1277. AUAI Press, 2020
  • Theresa Mai, Roli Khanna, Jonathan Dodge, Jed Irvine, Kin-Ho Lam, Zhengxian Lin, Nicholas Kiddle, Evan Newman, Sai Raja, Caleb Matthews, Christopher Perdriau, Margaret Burnett, and Alan Fern. (2020). Keeping It "Organized and Logical": After Action Review for AI (AAR/AI). ACM Conference on Intelligent User Interfaces (IUI).
  • Thomson, R., & Schoenherr, J. (2020) Knowledge-to-Information Translation Training (KITT): An Adaptive Approach to Explainable Artificial Intelligence. Human Computer Interaction International Annual Conference.
  • Wang, J., Wang, P., & Shafto, P. (2020). Sequential cooperative Bayesian inference. International Conference on Machine Learning (ICML).
  • Wang, P., Givchi, A., & Shafto, P. (2020). Manifold learning from a teacher’s demonstrations. NeurIPS workshop: TDA and beyond.
  • Wang, P., Wang, J., Paranamana, P., & Shafto, P. (2020). A mathematical theory of cooperative communication. Advances in Neural Information Processing Systems (NeurIPS). arXiv (oral presentation, <1.5% acceptance rate)
  • Weijia Shi, Andy Shih, Adnan Darwiche, and Arthur Choi. On Tractable Representations of Binary Neural Networks. In Diego Calvanese, Esra Erdem, and Michael Thielscher, editors, Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning, KR 2020, Rhodes, Greece, September 12-18, 2020, pages 882–892, 2020
  • Xingyi Li, Zhongang Qi, Xiaoli Fern, LI Fuxin. (2020). ScaleNet - Improve CNNs through Recursively Scaling Objects. AAAI Conference on Artificial Intelligence (AAAI-2020).
  • Yangming Shi, Jing Du, and Eric D. Ragan. Review visual attention and spatial memory in building inspection: Toward a cognition-driven information system. Adv. Eng. Informatics, 44:101061, 2020
  • Yatin Nandwani, Ankesh Gupta, Aman Agrawal, Mayank Singh Chauhan, Parag Singla, and Mausam. OxKBC: Outcome Explanation for Factorization Based Knowledge Base Completion. In Dipanjan Das, Hannaneh Hajishirzi, Andrew McCallum, and Sameer Singh, editors, Conference on Automated Knowledge Base Construction, AKBC 2020, Virtual, June 22-24, 2020, 2020
  • Yatin Nandwani, Deepanshu Jindal, Mausam, and Parag Singla. Neural Learning of One-of-Many Solutions for Combinatorial Problems in Structured Output Spaces. CoRR, abs/2008.11990, 2020. eprint: 2008.11990
  • YooJung Choi, Golnoosh Farnadi, Behrouz Babaki, and Guy Van den Broeck. Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New Yor
  • Yujia Shen, Arthur Choi, and Adnan Darwiche. A New Perspective on Learning Context-Specific Independence. CoRR, abs/2006.06896, 2020. eprint: 2006.06896
  • Yuqiao Chen, Yibo Yang, Sriraam Natarajan, and Nicholas Ruozzi. Lifted Hybrid Variational Inference. In Christian Bessiere, editor, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, pages 4237–4244. ijcai.org, 2020
  • Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari, and Guy Van den Broeck. Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations. In Hugo Larochelle, Marc’Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin, editors, Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12,
  • Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari, and Guy Van den Broeck. Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, volume 119 of Proceedings of Machine Learning Research, pages 10990–11000. PMLR, 2020
  • Zhengxian Lin, Kim-Ho Lam, and Alan Fern. (2020). Contrastive Explanations for Reinforcement Learning via Embedded Self Predictions. ICML 2020 Workshop on XXAI: Extending Explainable AI Beyond Deep Models and Classifiers
  • Zhengyang Wang, Na Zou, Dinggang Shen, and Shuiwang Ji. "Non-local U-Nets for biomedical image segmentation." In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 04, pp. 6315-6322. 2020.
  • Zhengyang Wang, and Shuiwang Ji. "Second-order pooling for graph neural networks." IEEE Transactions on Pattern Analysis and Machine Intelligence (2020).
  • Zhongang Qi, Saeed Khorram, Li Fuxin. (2020). Visualizing Deep Networks by Optimizing with Integrated Gradients. AAAI Conference on Artificial Intelligence (AAAI-2020).

2019

  • M. Edmonds*, F. Gao*, H. Liu*, X. Xie*, S. Qi, B. Rothrock, Y. Zhu, Y.N. Wu, H. Lu, and S.-C. Zhu, “A Tale of Two Explanations: Enhancing Human Trust by Explaining Robot Behavior”, Science Robotics, Vol. 4, No. 37, eaay4663, 2019.
  • A. Ray, Y. Yao, R. Kumar, A. Divakaran and G. Burachas, "Can you explain that? Lucid explanations help human-AI collaborative image retrieval," in HCOMP, 2019.
  • Aishwarya Sivaraman, Tianyi Zhang, Guy Van den Broeck, and Miryung Kim. Active inductive logic programming for code search. In Joanne M. Atlee, Tevfik Bultan, and Jon Whittle, editors, Proceedings of the 41st International Conference on Software Engineering, ICSE 2019, Montreal, QC, Canada, May 25-31, 2019, pages 292–303. IEEE / ACM, 2019
  • Albert Zhao, Tong He, Yitao Liang, Haibin Huang, Guy Van den Broeck, and Stefano Soatto. LaTeS: Latent Space Distillation for Teacher-Student Driving Policy Learning. CoRR, abs/1912.02973, 2019. eprint: 1912.02973
  • Alyssa M. Pena, Ehsanul Haque Nirjhar, Andrew Pachuilo, Theodora Chaspari, and Eric D. Ragan. Detecting Changes in User Behavior to Understand Interaction Provenance during Visual Data Analysis. In Christoph Trattner, Denis Parra, and Nathalie Riche, editors, Joint Proceedings of the ACM IUI 2019 Workshops co-located with the 24th ACM Conference on Intelligent User Interfaces (ACM IUI 2019), Los Angeles, USA, March 20, 2019,
  • Alyssa M. Pena, Ehsanul Haque Nirjhar, Andrew Pachuilo, Theodora Chaspari, and Eric D. Ragan. "Detecting Changes in User Behavior to Understand Interaction Provenance during Visual Data Analysis." In IUI Workshops. 2019.
  • Alyssa M. Pe˜na, Eric D. Ragan, and Julian Kang. Designing Educational Virtual Environments for Construction Safety: A Case Study in Contextualizing Incident Reports and Engaging Learners. In Jessie Y. C. Chen and Gino Fragomeni, editors, Virtual, Augmented and Mixed Reality. Applications and Case Studies - 11th International Conference, VAMR 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Orlando, FL,
  • Andrew Anderson, Jonathan Dodge, Amrita Sadarangani, Zoe Juozapaitis, Evan Newman, Jed Irvine, Souti Chattopadhyay, Alan Fern, and Margaret Burnett. (2019). Explaining Reinforcement Learning to Mere Mortals: An Empirical Study. International Joint Conference on Artificial Intelligence (IJCAI’19).
  • Andy Shih, Adnan Darwiche, and Arthur Choi. Verifying Binarized Neural Networks by Angluin-Style Learning. In Mikol´as Janota and Inˆes Lynce, editors, Theory and Applications of Satisfiability Testing - SAT 2019 - 22nd International Conference, SAT 2019, Lisbon, Portugal, July 9-12, 2019, Proceedings, volume 11628 of Lecture Notes in Computer Science, pages 354–370. Springer, 2019
  • Andy Shih, Arthur Choi, and Adnan Darwiche. Compiling Bayesian Network Classifiers into Decision Graphs. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019, pages 7966–7974.
  • Andy Shih, Guy Van den Broeck, Paul Beame, and Antoine Amarilli. Smoothing Structured Decomposable Circuits. In Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d’Alch´e Buc, Emily B. Fox, and Roman Garnett, editors, Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, pages 11412–11422, 20
  • Anurag Koul, Sam Greydanus, and Alan Fern. (2019). Learning Finite State Representations of Recurrent Policy Networks. International Conference on Learning Representations (ICLR).
  • Arcchit Jain, Tal Friedman, Ondrej Kuzelka, Guy Van den Broeck, and Luc De Raedt. Scalable Rule Learning in Probabilistic Knowledge Bases. In 1st Conference on Automated Knowledge Base Construction, AKBC 2019, Amherst, MA, USA, May 20-22, 2019, 2019
  • Arnab Kumar Mondal, Sankalan Pal Chowdhury, Aravind Jayendran, Parag Singla, Himanshu Asnani, and Prathosh A. P. Towards Latent Space Optimality for Auto-Encoder Based Generative Models. CoRR, abs/1912.04564, 2019. eprint: 1912.04564
  • Arthur Choi, Ruocheng Wang, and Adnan Darwiche. On the relative expressiveness of Bayesian and neural networks. Int. J. Approx. Reason., 113:303–323, 2019
  • Bau, David "GAN Dissection: Visualizing and Understanding Generative Adversarial Networks." ICLR 2019arXiv preprint arXiv:1811.10597 https://openreview.net/pdf?id=Hyg_X2C5FX
  • Bau, David, Jun-Yan Zhu, Jonas Wulff, William Peebles, Hendrik Strobelt, Bolei Zhou, and Antonio Torralba “Seeing What a GAN Cannot Generate” International Conference on Computer Vision (ICCV), 2019 http://ganseeing.csail.mit.edu/papers/seeing.pdf Keywords: Generative Adversarial Networks (GANs), mode collapse, image segmentation
  • C.-H. Chang, E. Creager, A. Goldenberg and D. Duvenaud, "Explaining image classifiers by counterfactual generation," in ICLR, 2019.
  • Chiradeep Roy, Mahesh Shanbhag, Mahsan Nourani, Tahrima Rahman, Samia Kabir, Vibhav Gogate, Nicholas Ruozzi, and Eric D. Ragan. Explainable Activity Recognition in Videos. In Christoph Trattner, Denis Parra, and Nathalie Riche, editors, Joint Proceedings of the ACM IUI 2019 Workshops co-located with the 24th ACM Conference on Intelligent User Interfaces (ACM IUI 2019), Los Angeles, USA, March 20, 2019, volume 2327 of CEUR Wor
  • Da Tang, Dawen Liang, Nicholas Ruozzi, and Tony Jebara. Learning Correlated Latent Representations with Adaptive Priors. CoRR, abs/1906.06419, 2019. eprint: 1906.06419
  • Da Tang, Dawen Liang, Tony Jebara, and Nicholas Ruozzi. Correlated Variational Auto-Encoders. In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, volume 97 of Proceedings of Machine Learning Research, pages 6135–6144. PMLR, 2019
  • Fan Yang, Shiva K. Pentyala, Sina Mohseni, Mengnan Du, Hao Yuan, Rhema Linder, Eric D. Ragan, Shuiwang Ji, and Xia (Ben) Hu. XFake: Explainable Fake News Detector with Visualizations. In Ling Liu, Ryen W. White, Amin Mantrach, Fabrizio Silvestri, Julian J. McAuley, Ricardo Baeza-Yates, and Leila Zia, editors, The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13-17, 2019, pages 3600–3604. ACM, 2019
  • Fan Yang, Shiva K. Pentyala, Sina Mohseni, Mengnan Du, Hao Yuan, Rhema Linder, Eric D. Ragan, Shuiwang Ji, and Xia Ben Hu. "XFake: Explainable Fake News Detector with Visualizations." In The World Wide Web Conference (demo track), pp. 3600-3604. ACM, 2019.
  • Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., Yang, G. “XAI – Explainable Artificial Intelligence.” Science Robotics, 18 December 2019. Vol. 4, issue 37.
  • Hao Xiong, Yuanzhen Guo, Yibo Yang, and Nicholas Ruozzi. One-Shot Inference in Markov Random Fields. In Amir Globerson and Ricardo Silva, editors, Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2019, Tel Aviv, Israel, July 22-25, 2019, volume 115 of Proceedings of Machine Learning Research, pages 102–112. AUAI Press, 2019
  • Hao Yuan, Yongjun Chen, Xia Hu, and Shuiwang Ji. "Interpreting Deep Models for Text Analysis via Optimization and Regularization Methods." In AAAI Conference on Artificial Intelligence (AAAI). 2019.
  • Happy Mittal, Ayush Bhardwaj, Vibhav Gogate, and Parag Singla. Domain-Size Aware Markov Logic Networks. In Kamalika Chaudhuri and Masashi Sugiyama, editors, The 22nd International Conference on Artificial Intelligence and Statistics, AISTATS 2019, 16-18 April 2019, Naha, Okinawa, Japan, volume 89 of Proceedings of Machine Learning Research, pages 3216–3224. PMLR, 2019
  • Harsh Agrawal, Karan Desai, Yufei Wang, Xinlei Chen, Rishabh Jain, Mark Johnson, Dhruv Batra, Devi Parikh, Stefan Lee, Peter Anderson “nocaps: novel object captioning at scale” International Conference on Computer Vision (ICCV), 2019 https://arxiv.org/abs/1812.08658 Keywords: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
  • Hongyang Gao, Hao Yuan, Zhengyang Wang, and Shuiwang Ji. "Pixel Transposed Convolutional Networks." IEEE transactions on pattern analysis and machine intelligence (2019).
  • Hye-Chung Kum, Eric D. Ragan, Gurudev Ilangovan, Mahin Ramezani, Qinbo Li, and Cason Schmit. Enhancing Privacy through an Interactive On-demand Incremental Information Disclosure Interface: Applying Privacy-by-Design to Record Linkage. In Heather Richter Lipford, editor, Fifteenth Symposium on Usable Privacy and Security, SOUPS 2019, Santa Clara, CA, USA, August 11-13, 2019. USENIX Association, 2019
  • Hye-Chung Kum, Eric D. Ragan, Gurudev Ilangovan, Mahin Ramezani, Qinbo Li, and Cason Schmit. "Enhancing Privacy through an Interactive On-demand Incremental Information Disclosure Interface: Applying Privacy-by-Design to Record Linkage." In Fifteenth Symposium on Usable Privacy and Security (SOUPS 2019). 2019.
  • J. Yu, Z. Lin, J. Yang, X. Shen, X. Lu and T. Huang, "Free-form image inpainting with gated convolution," in ICCV, 2019.
  • Jialin Wu, Raymond J Mooney “Faithful Multimodal Explanation for Visual Question Answering” https://arXiv:1809.02805.pdf
  • Jianwei Yang, Zhile Ren, Mingze Xu, Xinlei Chen, David Crandall, Devi Parikh, Dhruv Batra “Embodied Visual Recognition” International Conference on Computer Vision (ICCV), 2019 https://arxiv.org/abs/1904.04404 Keywords: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
  • Jiasen Lu, Dhruv Batra, Devi Parikh, Stefan Lee ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks Neural Information Processing Systems (NeurIPS), 2019 https://arxiv.org/abs/1908.02265 Keywords : Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
  • John R. Goodall, Eric D. Ragan, Chad A. Steed, Joel W. Reed, G. David Richardson, Kelly M. T. Huffer, Robert A. Bridges, and Jason A. Laska. Situ: Identifying and Explaining Suspicious Behavior in Networks. IEEE Trans. Vis. Comput. Graph., 25(1):204–214, 2019
  • Jonathan Dodge, Q. Vera Liao, Yunfeng Zhang, Rachel Bellamy, Casey Dugan, and Bhanukiran Vinzamuri . (2019). Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment. International Conference on Intelligent User Interfaces (IUI)
  • Jonathan Dodge, Q. Vera Liao, Yunfeng Zhang, Rachel K. E. Bellamy, and Casey Dugan. (2019). Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment. International Conference on Intelligent User Interfaces (IUI ’19).
  • Karthic Madanagopal, Eric D. Ragan, and Perakath C. Benjamin. Analytic Provenance in Practice: The Role of Provenance in Real-World Visualization and Data Analysis Environments. IEEE Computer Graphics and Applications, 39(6):30–45, 2019
  • Laura Isabel Galindez Olascoaga, Wannes Meert, Nimish Shah, Marian Verhelst, and Guy Van den Broeck. Towards Hardware-Aware Tractable Learning of Probabilistic Models. In Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d’Alch´e Buc, Emily B. Fox, and Roman Garnett, editors, Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, Decemb
  • Lovish Madaan, Ankur Sharma, Praneet Khandelwal, Shivank Goel, Parag Singla, and Aaditeshwar Seth. Price forecasting & anomaly detection for agricultural commodities in India. In Jay Chen, Jennifer Mankoff, and Carla P. Gomes, editors, Proceedings of the Conference on Computing & Sustainable Societies, COMPASS 2019, Accra, Ghana, July 3-5, 2019, pages 52–64. ACM, 2019
  • Mandana Hamidi-Haines, Zhongang Qi, Alan Fern, Fuxin Li, Prasad Tadepalli. (2019). Interactive Naming for Explaining Deep Neural Networks: A Formative Study. IUI Workshop on EXplainable Smart Systems (EXSS).
  • Meet Shah, Xinlei Chen, Marcus Rohrbach, Devi Parikh. “Cycle-Consistency for Robust Visual Question Answering.” CVPR 2019 https://arxiv.org/pdf/1902.05660.pdf Ramakrishna Vedantam, Karan Desai, Stefan Lee, Marcus Rohrbach, Dhruv Batra, Devi Parikh. "Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering." ICML Conference 2019 https://arXiv:1902.07864.ppdf
  • Mengnan Du, Ninghao Liu, Fan Yang, Shuiwang Ji, and Xia Hu. "On Attribution of Recurrent Neural Network Predictions via Additive Decomposition." In The World Wide Web Conference, pp. 383-393. ACM, 2019.
  • Mengnan Du, Ninghao Liu, Fan Yang, and Xia Hu. "Learning Credible Deep Neural Networks with Rationale Regularization." In IEEE International Conference on Data Mining (ICDM), IEEE, 2019.
  • Mingbo Ma, Renjie Zheng, and Liang Huang. (2019). Learning to Stop in Structured Prediction for Neural Machine Translation. Proceedings of NAACL.
  • Neale Ratzlaff and Li Fuxin. (2019). HyperGAN: A Generative Model for Diverse, Performant Neural Networks. International Conference on Machine Learning (ICML-2019).
  • Ninghao Liu, Mengnan Du, and Xia Hu. "Representation Interpretation with Spatial Encoding and Multimodal Analytics." In Proceedings of the 12th ACM International Conference on Web Search and Data Mining (WSDM), pp. 60-68. ACM, 2019.
  • Olson, M., Neal, L., Li, F. and Wong, W-K. (2019). Counterfactual States for Atari Agents via Generative Deep Learning. IJCAI Workshop on Explainable Artificial Intelligence.
  • Pasha Khosravi, Yitao Liang, YooJung Choi, and Guy Van den Broeck. What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features. In Sarit Kraus, editor, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019, pages 2716–2724. ijcai.org, 2019
  • Pasha Khosravi, YooJung Choi, Yitao Liang, Antonio Vergari, and Guy Van den Broeck. On Tractable Computation of Expected Predictions. In Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d’Alch´e Buc, Emily B. Fox, and Roman Garnett, editors, Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Cana
  • Pedro Guillermo Feij´oo Garc´ıa, Sishun Wang, Ju Cai, Naga Polavarapu, Christina Gardner-McCune, and Eric D. Ragan. Design and evaluation of a scaffolded block-based learning environment for hierarchical data structures. In Justin Smith, Christopher Bogart, Judith Good, and Scott D. Fleming, editors, 2019 IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC 2019, Memphis, Tennessee, USA, October 14-18, 2019,
  • Qinghong Xu and Eric D. Ragan. Effects of Character Guide in Immersive Virtual Reality Stories. In Jessie Y. C. Chen and Gino Fragomeni, editors, Virtual, Augmented and Mixed Reality. Multimodal Interaction - 11th International Conference, VAMR 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Orlando, FL, USA, July 26-31, 2019, Proceedings, Part I, volume 11574 of Lecture Notes in Computer Science, pages 37
  • Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, Dhruv Batra. “Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization.” IJCV 2019 Conference
  • Ramprasaath R. Selvaraju, Stefan Lee, Yilin Shen, Hongxia Jin, Dhruv Batra, Devi Parikh “Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded” International Conference on Computer Vision (ICCV), 2019 https://arxiv.org/abs/1902.03751 Keywords: Computer Vision and Pattern Recognition (cs.CV)
  • Rey Pocius, Lawrence Neal, and Alan Fern. (2019). Strategic Tasks for Explainable Reinforcement Learning. AAAI-2019 Student Abstract.
  • Reza Ghaeini, Xiaoli Z. Fern, Hamed Shahbazi, and Prasad Tadepalli. (2019). Saliency Learning: Teaching the Model Where to Pay Attention. NAACL HLT-2019.
  • S. Ghosh, G. Burachas, A. Ray and A. Ziskind, "Generating natural language explanations for visual question answering using scene graphs and visual attention," in arXiv:1902.05715, 2019.
  • S. W. Kim, M. Tapaswi and S. Fidler, "Visual reasoning by progressive module networks," in ICLR, 2019.
  • Saket Dingliwal, Ronak Agarwal, Happy Mittal, and Parag Singla. CVC4-SymBreak: Derived SMT solver at SMT Competition 2019. CoRR, abs/1908.00860, 2019. eprint: 1908.00860
  • Samyak Datta, Karan Sikka, Anirban Roy, Karuna Ahuja, Devi Parikh, Ajay Divakaran “Align2Ground: Weakly Supervised Phrase Grounding Guided by Image-Caption Alignment” International Conference on Computer Vision (ICCV), 2019 https://arxiv.org/abs/1903.11649 Keywords: Computer Vision and Pattern Recognition (cs.CV)
  • Shahab Shams, Nicholas Ruozzi, and P´eter Csikv´ari. Counting Homomorphisms in Bipartite Graphs. In IEEE International Symposium on Information Theory, ISIT 2019, Paris, France, July 7-12, 2019, pages 1487–1491. IEEE, 2019
  • Sina Mohseni, Eric D. Ragan, and Xia Hu. Open Issues in Combating Fake News: Interpretability as an Opportunity. CoRR, abs/1904.03016, 2019. eprint: 1904.03016
  • Sina Mohseni. "Toward Design and Evaluation Framework for Interpretable Machine Learning Systems." Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (AIES). 2019.
  • Sruti Srinivasa Ragavan, Mihai Codoban, David Piorkowski, Danny Dig, and Margaret Burnett. (to appear). Version Control Systems: An Information Foraging Perspective. IEEE Transactions on Software Engineering, to appear in 2019. 
  • Steven Holtzen, Todd D. Millstein, and Guy Van den Broeck. Generating and Sampling Orbits for Lifted Probabilistic Inference. In Amir Globerson and Ricardo Silva, editors, Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2019, Tel Aviv, Israel, July 22-25, 2019, volume 115 of Proceedings of Machine Learning Research, pages 985–994. AUAI Press, 2019
  • Tahrima Rahman, Shasha Jin, and Vibhav Gogate. Cutset Bayesian Networks: A New Representation for Learning Rao-Blackwellised Graphical Models. In Sarit Kraus, editor, Proceedings of the Twenty- Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019, pages 5751–5757. ijcai.org, 2019
  • Tahrima Rahman, Shasha Jin, and Vibhav Gogate. Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation. In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, volume 97 of Proceedings of Machine Learning Research, pages 5311–5320. PMLR, 2019
  • Tal Friedman and Guy Van den Broeck. On Constrained Open-World Probabilistic Databases. In Sarit Kraus, editor, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019, pages 5722–5729. ijcai.org, 2019
  • Travis Stebbins and Eric D. Ragan. Redirecting View Rotation in Immersive Movies with Washout Filters. In IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2019, Osaka, Japan, March 23-27, 2019, pages 377–385. IEEE, 2019
  • Vishvak Murahari, Prithvijit Chattopadhyay, Dhruv Batra, Devi Parikh, Abhishek Das “Improving Generative Visual Dialog by Answering Diverse Questions” Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019 https://arxiv.org/abs/1909.10470 Keywords: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
  • Wang, P., Paranamana, P., & Shafto, P. (2019). Generalizing the theory of cooperative inference. Proceedings of the 22nd international conference on Artificial Intelligence and Statistics (AISTATS).
  • Wenxuan Wu, Zhongang Qi, and LI Fuxin. PointConv: Deep Convolutional Networks on 3D Point Clouds. International Conference on Computer Vision and Pattern Recognition (CVPR-2019).
  • Wu, J. and Mooney, R.J. “Hidden State Guidance: Improving Image Captioning Using an Image Conditioned Autoencoder” Proceedings of the NeurIPS Workshop on Visually Grounded Interaction and Language (ViGIL), Vancouver, BC, Dec. 2019.
  • Wu, J. and Mooney, R.J. “Self-Critical Reasoning for Robust Visual Question Answering," in Proceedings of the Thirty-third Conference on Advances in Neural Information Processing Systems (NeurIPS) (Spotlight presentation), Vancouver, BC, Dec. 2019. https://arxiv.org/abs/1905.09998 Keywords: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
  • Yash Goyal, Ziyan Wu, Jan Ernst, Dhruv Batra, Devi Parikh, Stefan Lee. "Counterfactual Visual Explanations." https://arXiv:1904.07451.pdf (2019)
  • Yatin Nandwani, Abhishek Pathak, Mausam, and Parag Singla. A Primal Dual Formulation For Deep Learning With Constraints. In Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d’Alch´e Buc, Emily B. Fox, and Roman Garnett, editors, Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, pages 121
  • Yi Liu, Hao Yuan, and Shuiwang Ji. "Learning Local and Global Multi-context Representations for Document Classification." 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 2019.
  • Yitao Liang and Guy Van den Broeck. Learning Logistic Circuits. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019, pages 4277–4286. AAAI Press, 2019
  • Yuanzhen Guo, Hao Xiong, and Nicholas Ruozzi. Marginal Inference in Continuous Markov Random Fields Using Mixtures. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019, pages
  • Yujia Shen, Anchal Goyanka, Adnan Darwiche, and Arthur Choi. Structured Bayesian Networks: From Inference to Learning with Routes. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February
  • Yujia Shen, Haiying Huang, Arthur Choi, and Adnan Darwiche. Conditional Independence in Testing Bayesian Networks. In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, volume 97 of Proceedings of Machine Learning Research, pages 5701–5709. PMLR, 2019
  • Yuqiao Chen, Nicholas Ruozzi, and Sriraam Natarajan. Lifted Message Passing for Hybrid Probabilistic Inference. In Sarit Kraus, editor, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019, pages 5701–5707. ijcai.org, 2019
  • Zhe Zeng and Guy Van den Broeck. Efficient Search-Based Weighted Model Integration. In Amir Globerson and Ricardo Silva, editors, Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2019, Tel Aviv, Israel, July 22-25, 2019, volume 115 of Proceedings of Machine Learning Research, pages 175–185. AUAI Press, 2019
  • Zhe Zeng, Fanqi Yan, Paolo Morettin, Antonio Vergari, and Guy Van den Broeck. Hybrid Probabilistic Inference with Logical Constraints: Tractability and Message Passing. CoRR, abs/1909.09362, 2019. eprint: 1909.09362
  • Zoe Juozapaitis, Anurag Koul, Alan Fern, Martin Erwig and Finale Doshi-Velez. (2019). Explainable Reinforcement Learning via Reward Decomposition. IJCAI-2019 Workshop on XAI.

2018

  • A. Myronenko, "3D MRI brain tumor segmentation using autoencoder regularization," in International MICCAI Brainlesion Workshop, 2018.
  • Abhishek Das, Georgia Gkioxari, Stefan Lee, Devi Parikh, Dhruv Batra “Neural Modular Control for Embodied Question” CoRL 2018 https://arxiv.org/pdf/1810.11181.pdf
  • Adnan Darwiche. Human-level intelligence or animal-like abilities? Commun. ACM, 61(10):56–67, 2018
  • Aishwarya Agrawal, Dhruv Batra, Devi Parikh, and Aniruddha Kembhavi Don’t Just Assume; Look and “Answer: Overcoming Priors for Visual Question Answering” CVPR 2018 https://arxiv.org/pdf/1712.00377.pdf
  • Andy Shih, Arthur Choi, and Adnan Darwiche. A Symbolic Approach to Explaining Bayesian Network Classifiers. In J´erˆome Lang, editor, Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden, pages 5103–5111. ijcai.org, 2018
  • Andy Shih, Arthur Choi, and Adnan Darwiche. Formal Verification of Bayesian Network Classifiers. In Milan Studen´y and V´aclav Kratochv´ıl, editors, International Conference on Probabilistic Graphical Models, PGM 2018, 11-14 September 2018, Prague, Czech Republic, volume 72 of Proceedings of Machine Learning Research, pages 427–438. PMLR, 2018
  • Anurag Koul, Sam Greydanus, and Alan Fern. (2018). Toward Learning Finite State Representations of Recurrent Policy Networks. IJCAI Workshop on Explainable Artificial Intelligence.
  • Arthur Choi and Adnan Darwiche. On the Relative Expressiveness of Bayesian and Neural Networks. In Milan Studen´y and V´aclav Kratochv´ıl, editors, International Conference on Probabilistic Graphical Models, PGM 2018, 11-14 September 2018, Prague, Czech Republic, volume 72 of Proceedings of Machine Learning Research, pages 157–168. PMLR, 2018
  • Bahador Saket, Arjun Srinivasan, Eric D. Ragan, and Alex Endert. Evaluating Interactive Graphical Encodings for Data Visualization. IEEE Trans. Vis. Comput. Graph., 24(3):1316–1330, 2018
  • C. A., V. Prabhu, D. Yadav, P. Chattopadhyay and D. Parikh, "Do explanations make VQA models more predictable to a human?," in EMNLP, 2018.
  • Dinesh Khandelwal, Parag Singla, and Chetan Arora. Learning Higher Order Potentials for MRFs. In 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018, Lake Tahoe, NV, USA, March 12-15, 2018, pages 812–820. IEEE Computer Society, 2018
  • Dustin T. Han, Mohamed Suhail, and Eric D. Ragan. Evaluating Remapped Physical Reach for Hand Interactions with Passive Haptics in Virtual Reality. IEEE Trans. Vis. Comput. Graph., 24(4):1467– 1476, 2018
  • E. Kerfoot, J. Clough, I. Oksuz, J. Lee, A. P. King and J. A. Schnabel, "Left-ventricle quantification using residual U-Net," in International Workshop on Statistical Atlases and Computational Models of the Heart, 2018.
  • Eric D. Ragan, Hye-Chung Kum, Gurudev Ilangovan, and Han Wang. Balancing Privacy and Information Disclosure in Interactive Record Linkage with Visual Masking. In Regan L. Mandryk, Mark Hancock, Mark Perry, and Anna L. Cox, editors, Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, CHI 2018, Montreal, QC, Canada, April 21-26, 2018, page 326. ACM, 2018
  • Eunice Yuh-Jie Chen, Adnan Darwiche, and Arthur Choi. On pruning with the MDL Score. Int. J. Approx. Reason., 92:363–375, 2018
  • Fan Yang, Ninghao Liu, Suhang Wang, and Xia Hu. "Towards Interpretation of Recommender Systems with Sorted Explanation Paths." In 2018 IEEE International Conference on Data Mining (ICDM), pp. 667-676. IEEE, 2018.
  • Gagan Madan, Ankit Anand, Mausam, and Parag Singla. Block-Value Symmetries in Probabilistic Graphical Models. In Amir Globerson and Ricardo Silva, editors, Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, UAI 2018, Monterey, California, USA, August 6-10, 2018, pages 886–895. AUAI Press, 2018
  • Hao Yuan, Lei Cai, Zhengyang Wang, Xia Hu, Shaoting Zhang, and Shuiwang Ji. "Computational modeling of cellular structures using conditional deep generative networks." Bioinformatics 35, no. 12 (2018): 2141-2149.
  • Happy Mittal, Ayush Bhardwaj, Vibhav Gogate, and Parag Singla. Domain Aware Markov Logic Networks. CoRR, abs/1807.01082, 2018. eprint: 1807.01082
  • Harradon, Michael, Jeff Druce, and Brian Ruttenberg. "Causal learning and explanation of deep neural networks via autoencoded activations." arXiv preprint arXiv:1802.00541 (2018).
  • Helen Chen, Sophie Engle, Alark Joshi, Eric D. Ragan, Beste F. Yuksel, and Lane Harrison. Using Animation to Alleviate Overdraw in Multiclass Scatterplot Matrices. In Regan L. Mandryk, Mark Hancock, Mark Perry, and Anna L. Cox, editors, Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, CHI 2018, Montreal, QC, Canada, April 21-26, 2018, page 417. ACM, 2018
  • Hoffman, R.R., Mueller, S.T., Klein, G., and Clancey, W.J. (2018, May/June). Explaining Explanation Part 4: A Deep Dive on Deep Nets. IEEE: Intelligent Systems, pp. 87-95.
  • Hongyang Gao, Zhengyang Wang, and Shuiwang Ji. "Large-scale learnable graph convolutional networks." In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1416-1424. ACM, 2018.
  • J. Yu, Z. Lin, J. Yang, X. Shen, X. Lu and T. S. Huang, "Generative image inpainting with contextual attention," in CVPR, 2018.
  • Jianwei Yang, Jiasen Lu, Stefan Lee, Dhruv Batra, and Devi Parikh “Graph R-CNN for Scene Graph Generation” ECCV 2018 https://arxiv.org/pdf/1808.00191.pdf
  • Jianwei Yang, Jiasen Lu, Stefan Lee, Dhruv Batra, and Devi Parikh “Learning to Ask Questions to Learn Visual Recognition” CoRL 2018 https://arxiv.org/pdf/1810.00912.pdf
  • Jiasen Lu, Jianwei Yang, Dhruv Batra, and Devi Parikh “Neural Baby Talk” CVPR 2018 https://arxiv.org/pdf/1803.09845.pdf
  • Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang, and Guy Van den Broeck. A Semantic Loss Function for Deep Learning with Symbolic Knowledge. In Jennifer G. Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsm¨assan, Stockholm, Sweden, July 10-15, 2018, volume 80 of Proceedings of Machine Learning Research, pages 5498–5507. PMLR, 2018
  • Jinxue Zhang, Jingchao Sun, Rui Zhang, Yanchao Zhang, and Xia Hu. "Privacy-preserving social media data outsourcing." In IEEE INFOCOM 2018-IEEE Conference on Computer Communications, pp. 1106-1114. IEEE, 2018.
  • John R. Goodall, Eric D. Ragan, Chad A. Steed, Joel W. Reed, G. David Richardson, Kelly MT Huffer, Robert A. Bridges, and Jason A. Laska. "Situ: Identifying and explaining suspicious behavior in networks." IEEE transactions on visualization and computer graphics 25, no. 1 (2018): 204-214.
  • Jonathan Dodge, Sean Penney, Andrew Anderson, and Margaret Burnett. (2018). What Should Be in an XAI Explanation? What IFT Reveals. IUI Workshop on Explainable Smart Systems.
  • Juneki Hong and Liang Huang. (2018). Linear-Time Constituency Parsing with RNNs and Dynamic Programming. In Proceedings of ACL 2018.
  • Klein, G. (2018, September). Explaining Explanation, Part 3: The Causal Landscape. IEEE Intelligent Systems, pp. 83-88.
  • Lawrence Neal, Matthew Olson, Xiaoli Fern, Weng-Keen Wong, Fuxin Li. (2018). Open Set Learning with Counterfactual Images. European Conference on Computer Vision (ECCV).
  • Lei Cai, Zhengyang Wang, Hongyang Gao, Dinggang Shen, and Shuiwang Ji. "Deep adversarial learning for multi-modality missing data completion." In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1158-1166. ACM, 2018.
  • Li Chou, Pracheta Sahoo, Somdeb Sarkhel, Nicholas Ruozzi, and Vibhav Gogate. Automatic Parameter Tying: A New Approach for Regularized Parameter Learning in Markov Networks. In Sheila A. McIlraith and Kilian Q. Weinberger, editors, Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educatio
  • Li Chou, Wolfgang Gatterbauer, and Vibhav Gogate. Dissociation-Based Oblivious Bounds for Weighted Model Counting. In Amir Globerson and Ricardo Silva, editors, Proceedings of the Thirty- Fourth Conference on Uncertainty in Artificial Intelligence, UAI 2018, Monterey, California, USA, August 6-10, 2018, pages 866–875. AUAI Press, 2018
  • Lu, C-K. Yang, S.C-H., & Shafto, P. (2018). Standing wave decomposition Gaussian Process. Physical Review E, 98, 032303.
  • Martin Erwig, Alan Fern, Magesh Murali, and Anurag Koul. (2018). Explaining Deep Adaptive Programs via Reward Decomposition. IJCAI Workshop on Explainable Artificial Intelligence.
  • Md Amran Siddiqui, Alan Fern, Thomas Dietterich, and Weng-Keen Wong. (2018). Sequential Feature Explanations for Anomaly Detection. ACM Transactions on Knowledge Discovery from Data.
  • Mengnan Du, Ninghao Liu, Qingquan Song, and Xia Hu. "Towards explanation of dnn-based prediction with guided feature inversion." In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1358-1367. ACM, 2018.
  • Mohamed Suhail Mohamed Yousuf Sait, Shyam Prathish Sargunam, Dustin T. Han, and Eric D. Ragan. Physical hand interaction for controlling multiple virtual objects in virtual reality. In Prabhakaran Balakrishnan and Ryan P. McMahan, editors, Proceedings of the 3rd International Workshop on Interactive and Spatial Computing, IWISC 2018, Richardson, TX, USA, April 12-13, 2018, pages 64–74. ACM, 2018
  • Ninghao Liu, Donghwa Shin, and Xia Hu. "Contextual outlier interpretation." In Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp. 2461-2467. 2018.
  • Ninghao Liu, Hongxia Yang, and Xia Hu. "Adversarial detection with model interpretation." In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1803-1811. ACM, 2018.
  • Ninghao Liu, Xiao Huang, Jundong Li, and Xia Hu. "On interpretation of network embedding via taxonomy induction." In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1812-1820. ACM, 2018.
  • Ramakrishna Vedantam, Stefan Lee, Marcus Rohrbach, Dhruv Batra, and Devi Parikh “Variational Neural Module Networks” In Submission to ICLR 2018
  • Ramprasaath R. Selvaraju, Prithvijit Chattopadhyay, Mohamed Elhoseiny, Tilak Sharma, Dhruv Batra, Devi Parikh, Stefan Lee “Choose Your Neuron: Incorporating Domain Knowledge through Neuron Importance” ECCV 2018 https://arxiv.org/abs/1808.02861
  • Renjie Zheng, Mingbo Ma, Liang Huang. (2018). Multiple reference training with generated pseudo references. Conference on Empirical Methods in Natural Language Processing (EMNLP).
  • Renjie Zheng, Yilin Yang, Mingbo Ma, and Liang Huang. (2018). Ensemble Sequence Level Training for Multimodal MT: OSU-Baidu WMT18 Multimodal Machine Translation System Report. In Proceedings of WMT 2018.
  • Reza Ghaeini, Xiaoli Z. Fern, Prasad Tadepalli. (2018). Interpreting Recurrent and Attention-Based Neural Models: A Case Study on Natural Language Inference. Conference on Empirical Methods in Natural Language Processing (EMNLP).
  • Rhema Linder, Alexandria M. Stacy, Nic Lupfer, Andruid Kerne, and Eric D. Ragan. Pop the Feed Filter Bubble: Making Reddit Social Media a VR Cityscape. In Kiyoshi Kiyokawa, Frank Steinicke, Bruce H. Thomas, and Greg Welch, editors, 2018 IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2018, Tuebingen/Reutlingen, Germany, 18-22 March 2018, pages 619–620. IEEE Computer Society, 2018
  • S. Bakas, M. Reyes, A. Jakab, S. Bauer, M. Rempfler, A. Crimi, R. Shinohara, C. Berger, S. Ha, M. Rozycki and M. Prastawa, "Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge.," in arXiv:1811.02629, 2018.
  • Sam Greydanus, Anurag Koul, Jonathan Dodge, and Alan Fern. (2018). Visualizing and Understanding Atari Agents. International Conference on Machine Learning.
  • Sara Rouhani, Tahrima Rahman, and Vibhav Gogate. Algorithms for the Nearest Assignment Problem. In J´erˆome Lang, editor, Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden, pages 5096–5102. ijcai.org, 2018
  • Shyam Prathish Sargunam and Eric D. Ragan. Evaluating joystick control for view rotation in virtual reality with continuous turning, discrete turning, and field-of-view reduction. In Prabhakaran Balakrishnan and Ryan P. McMahan, editors, Proceedings of the 3rd International Workshop on Interactive and Spatial Computing, IWISC 2018, Richardson, TX, USA, April 12-13, 2018, pages 74–79. ACM, 2018
  • Sina Mohseni and Eric D. Ragan. A Human-Grounded Evaluation Benchmark for Local Explanations of Machine Learning. CoRR, abs/1801.05075, 2018. eprint: 1801.05075
  • Sina Mohseni and Eric D. Ragan. Combating Fake News with Interpretable News Feed Algorithm. CoRR, abs/1811.12349, 2018. eprint: 1811.12349
  • Sina Mohseni, Alyssa M. Pena, and Eric D. Ragan. ProvThreads: Analytic Provenance Visualization and Segmentation. CoRR, abs/1801.05469, 2018. eprint: 1801.05469
  • Sina Mohseni, Andrew Pachuilo, Ehsanul Haque Nirjhar, Rhema Linder, Alyssa M. Pena, and Eric D. Ragan. Analytic Provenance Datasets: A Data Repository of Human Analysis Activity and Interaction Logs. CoRR, abs/1801.05076, 2018. eprint: 1801.05076
  • Sina Mohseni, Niloofar Zarei, and Eric D. Ragan. A Survey of Evaluation Methods and Measures for Interpretable Machine Learning. CoRR, abs/1811.11839, 2018. eprint: 1811.11839
  • Somers, S., Mitsopoulos K., Thomson, R., Lebiere, C. “Cognitive-Level Salience for Explainable Artificial Intelligence.” Proceedings of the 17th International Conference on Cognitive Modeling (ICCM2018) (pp. 235-240), Madison, WI, USA. 
  • Somers, S., Mitsopoulos, K., Thomson, R., Lebiere, C. “Explaining Decisions of a Deep Reinforcement Learner with a Cognitive Architecture,” Proceedings of the 16th International Conference on Cognitive Modeling (ICCM2018) (pp. 144-149), Montreal, QC, Canada. 
  • Steven Holtzen, Guy Van den Broeck, and Todd D. Millstein. Sound Abstraction and Decomposition of Probabilistic Programs. In Jennifer G. Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsm¨assan, Stockholm, Sweden, July 10-15, 2018, volume 80 of Proceedings of Machine Learning Research, pages 2004–2013. PMLR, 2018
  • Tal Friedman and Guy Van den Broeck. Approximate Knowledge Compilation by Online Collapsed Importance Sampling. In Samy Bengio, Hanna M.Wallach, Hugo Larochelle, Kristen Grauman, Nicol`o Cesa-Bianchi, and Roman Garnett, editors, Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montr´eal, Canada, pages 8035–8045, 2018
  • Umut Oztok and Adnan Darwiche. An Exhaustive DPLL Algorithm for Model Counting. J. Artif. Intell. Res., 62:1–32, 2018
  • Vishal Sharma, Noman Ahmed Sheikh, Happy Mittal, Vibhav Gogate, and Parag Singla. Lifted Marginal MAP Inference. In Amir Globerson and Ricardo Silva, editors, Proceedings of the Thirty- Fourth Conference on Uncertainty in Artificial Intelligence, UAI 2018, Monterey, California, USA, August 6-10, 2018, pages 917–926. AUAI Press, 2018
  • Vong, W-K., Sojitra, R., Reyes, A., Yang, S.C-H., & Shafto, P. (2018). Bayesian teaching of image categories. Proceedings of the 40th annual conference of the Cognitive Science Society.
  • Xiao Huang, Jundong Li, Na Zou, and Xia Hu. "A General Embedding Framework for Heterogeneous Information Learning in Large-Scale Networks." ACM Transactions on Knowledge Discovery from Data (TKDD) 12, no. 6 (2018): 70.
  • Xiao Huang, Qingquan Song, Jundong Li, and Xia Hu. "Exploring expert cognition for attributed network embedding." In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 270-278. ACM, 2018.
  • Yang, S.C-H., Yu, Y., Givchi, A., Wang, P., Vong, W.K., & Shafto, P. (2018). Optimal cooperative inference. Proceedings of the 21st international conference on Artificial Intelligence and Statistics (AISTATS).
  • Yibo Yang, Nicholas Ruozzi, and Vibhav Gogate. Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization. CoRR, abs/1806.05355, 2018. eprint: 1806.05355
  • Yilin Yang, Liang Huang, Mingbo Ma. (2018). Breaking the Beam Search Curse for Neural Machine Translation. Conference on Empirical Methods in Natural Language Processing (EMNLP).
  • YooJung Choi and Guy Van den Broeck. On Robust Trimming of Bayesian Network Classifiers. In J´erˆome Lang, editor, Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden, pages 5002–5009. ijcai.org, 2018
  • Yujia Shen, Arthur Choi, and Adnan Darwiche. Conditional PSDDs: Modeling and Learning With Modular Knowledge. In Sheila A. McIlraith and Kilian Q. Weinberger, editors, Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans,
  • Zhengyang Wang, and Shuiwang Ji. "Smoothed dilated convolutions for improved dense prediction." In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2486-2495. ACM, 2018.
  • Zhou, B., Sun, Y., Bau, D., and Torralba, A. “Revisiting the Importance of Individual Units in CNNs via Ablation” https://arXiv:1806.02891.pdf (2018)

2017

  • A. Das, H. Agrawal, L. Zitnick, D. Parikh and D. Batra, "Human attention in visual question answering: Do humans and deep networks look at the same regions?," Computer Vision and Image Understanding, 2017.
  • A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser and I. Polosukhin, "Attention is all you need.," in arXiv:1706.03762, 2017.
  • Alyssa M. Pena and Eric D. Ragan. Contextualizing construction accident reports in virtual environments for safety education. In Evan Suma Rosenberg, David M. Krum, Zachary Wartell, Betty J. Mohler, Sabarish V. Babu, Frank Steinicke, and Victoria Interrante, editors, 2017 IEEE Virtual Reality, VR 2017, Los Angeles, CA, USA, March 18-22, 2017, pages 389–390. IEEE Computer Society, 2017
  • Ankit Anand, Ritesh Noothigattu, Parag Singla, and Mausam. Non-Count Symmetries in Boolean & Multi-Valued Prob. Graphical Models. In Aarti Singh and Xiaojin (Jerry) Zhu, editors, Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, 20-22 April 2017, Fort Lauderdale, FL, USA, vol
  • Anna L. D. Latour, Behrouz Babaki, Anton Dries, Angelika Kimmig, Guy Van den Broeck, and Siegfried Nijssen. Combining Stochastic Constraint Optimization and Probabilistic Programming - From Knowledge Compilation to Constraint Solving. In J. Christopher Beck, editor, Principles and Practice of Constraint Programming - 23rd International Conference, CP 2017, Melbourne, VIC, Australia, August 28 - September 1, 2017, Proceedings,
  • Arthur Choi and Adnan Darwiche. On Relaxing Determinism in Arithmetic Circuits. In Doina Precup and Yee Whye Teh, editors, Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017, volume 70 of Proceedings of Machine Learning Research, pages 825–833. PMLR, 2017
  • Arthur Choi, Yujia Shen, and Adnan Darwiche. Tractability in Structured Probability Spaces. In Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M.Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett, editors, Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pages 3477–3485, 2017
  • Behrooz Mahasseni, Sinisa Todorovic, and Alan Fern. (2017). Budget-Aware Deep Semantic Video Segmentation. IEEE Conference on Computer Vision and Pattern Recognition.
  • Chao Ma, Janardhan Rao Doppa, Prasad Tadepalli, Hamed Shahbazi, and Xiaoli Fern. (2017). Multi-task Structured Prediction for Entity Analysis: Search-based Learning Algorithms. The 9th Asian Conference on Machine Learning.
  • Cullen Brown, Ghanshyam Bhutra, Mohamed Suhail, Qinghong Xu, and Eric D. Ragan. Coordinating attention and cooperation in multi-user virtual reality narratives. In Evan Suma Rosenberg, David M. Krum, Zachary Wartell, Betty J. Mohler, Sabarish V. Babu, Frank Steinicke, and Victoria Interrante, editors, 2017 IEEE Virtual Reality, VR
  • David B. Smith, Sara Rouhani, and Vibhav Gogate. Order Statistics for Probabilistic Graphical Models. In Carles Sierra, editor, Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017, pages 4625–4631. ijcai.org, 2017
  • David Piorkowski, Sean Penney, Austin Henley, Marco Pistoia, Margaret Burnett, Omer Tripp and Pietro Ferrara. (2017). Foraging Goes Mobile: Foraging While Debugging on Mobile Devices. IEEE Symposium on Visual Languages and Human-Centric Computing.
  • Dustin T. Han, Shyam Prathish Sargunam, and Eric D. Ragan. Simulating anthropomorphic upper body actions in virtual reality using head and hand motion data. In Evan Suma Rosenberg, David M. Krum, Zachary Wartell, Betty J. Mohler, Sabarish V. Babu, Frank Steinicke, and Victoria Interrante, editors, 2017 IEEE Virtual Reality, VR 2017, Los Angeles, CA, USA, March 18-22, 2017, pages 387–388. IEEE Computer Society, 2017
  • Eric D. Ragan, Siroberto Scerbo, Felipe Bacim, and Doug A. Bowman. Amplified Head Rotation in Virtual Reality and the Effects on 3D Search, Training Transfer, and Spatial Orientation. IEEE Trans. Vis. Comput. Graph., 23(8):1880–1895, 2017
  • Fei Tang, Ryan P. McMahan, Eric D. Ragan, and Tandra T. Allen. Subjective Evaluation of Tactile Fidelity for Single-Finger and Whole-Hand Touch Gestures. In Stephanie J. Lackey and Jessie Chen, editors, Virtual, Augmented and Mixed Reality - 9th International Conference, VAMR 2017, Held as Part of HCI International 2017, Vancouver, BC, Canada, July 9-14, 2017, Proceedings, volume 10280 of Lecture Notes in Computer Science, pages
  • Frederic Sala, Shahroze Kabir, Guy Van den Broeck, and Lara Dolecek. Don’t Fear the Bit Flips: Optimized Coding Strategies for Binary Classification. CoRR, abs/1703.02641, 2017. eprint: 1703.02641
  • Greg Van Buskirk, Benjamin Raichel, and Nicholas Ruozzi. Sparse Approximate Conic Hulls. In Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett, editors, Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pages 2534–2544, 2017
  • Guy Van den Broeck and Dan Suciu. Query Processing on Probabilistic Data: A Survey. Found. Trends Databases, 7(3-4):197–341, 2017
  • Haroun Habeeb, Ankit Anand, Mausam, and Parag Singla. Coarse-to-Fine Lifted MAP Inference in Computer Vision. In Carles Sierra, editor, Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017, pages 4595–4602. ijcai.org, 2017
  • Harvineet Singh, Amitabha Bagchi, and Parag Singla. Learning User Representations in Online Social Networks using Temporal Dynamics of Information Diffusion. CoRR, abs/1710.07622, 2017. eprint: 1710.07622
  • Hoffman, R.R., Mueller, S. T., and Klein, G. (2017, July/August). Explaining Explanation, Part 2: Empirical Foundations. IEEE Intelligent Systems, pp. 78-86.
  • Hoffman, R.R., and Klein, G. (2017, May/June). Explaining Explanation, Part 1: Theoretical Foundations. IEEE Intelligent Systems, pp. 68-73.
  • J. Dodge, S. Penney, C. Hilderbrand, A. Anderson, and M. Burnett. (2017). How the Experts Do It: Assessing and Explaining Agent Behaviors in Real-Time Strategy Games. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ‘18). ACM.
  • K. He, G. Gkioxari, P. Dollár and R. Girshick, "Mask R-CNN," in ICCV, 2017.
  • Kai Zhao and Liang Huang. Joint Syntacto-Discourse Parsing and the Syntacto-Discourse Treebank. In Proceedings of EMNLP 2017
  • Kasra Rahimi Moghadam and Eric D. Ragan. Towards understanding scene transition techniques in immersive 360 movies and cinematic experiences. In Evan Suma Rosenberg, David M. Krum, Zachary Wartell, Betty J. Mohler, Sabarish V. Babu, Frank Steinicke, and Victoria Interrante, editors, 2017 IEEE Virtual Reality, VR 2017, Los Angeles, CA, USA, March 18-22, 2017, pages 375–376. IEEE Computer Society, 2017
  • Liang Huang, Kai Zhao and Mingbo Ma. (2017). When to Finish? Optimal Beam Search for Neural Text Generation (modulo beam size). Proceedings of EMNLP.
  • Mandana Hamidi-Haines, Robby Goetshalchx, Prasad Tadepalli, and Alan Fern. (2017). Active Multi-Label Learning with Varying Query Sets. Picky Learners Workshop at the International Conference on Machine Learning.
  • Mandana Hamidi-Haines, Robby Goetshalchx, Prasad Tadepalli, and Alan Fern. (2017). Adaptive Submodularity with Varying Query Sets: An Application to Active Multi-label Learning. International Conference on Algorithmic Learning Theory.
  • Margaret Burnett, Todd Kulesza, Alannah Oleson, Shannon Ernst, Laura Beckwith, Jill Cao, William Jernigan, Valentina Grigoreanu . (2017). Toward Theory-Based End-User Software Engineering. in New Perspectives in End-User Development (F. Paterno and V. Wulf, eds.), 2017.
  • Mingbo Ma, Dapeng Li, Kai Zhao and Liang Huang. (2017). OSU Multimodal Machine Translation System Report. Proceedings of the Second Conference on Machine Translation.
  • Mingbo Ma, Kai Zhao, Liang Huang, Bing Xiang, Bowen Zhou. (2017). Jointly Trained Sequential Labeling and Classification by Sparse Attention Neural Networks. Proceedings of Interspeech.
  • Mingbo Ma, Liang Huang, Bing Xiang, Bowen Zhou. (2017). Group Sparse CNNs for Question Classification with Answer Sets. Proceedings of ACL 2017.
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