[Paper] [BibTeX] "A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks." Hadi Salman, Greg Yang, Huan Zhang, Cho-Jui Hsieh and Pengchuan Zhang.
[Paper] [BibTeX] "Adaptation to Dangerous Environments Through Automated Reward Shaping." Joel Schlosser, Aaron Keech, Phillip Odom and Zsolt Kira.
[Paper] [BibTeX] "Adversarial Defense for Tree-Based Models." Hongge Chen, Huan Zhang, Duane Boning and Cho-Jui Hsieh.
[Paper] [BibTeX] "Adversarial Training with Voronoi Constraints." Marc Khoury and Dylan Hadfield-Menell.
[Paper] [BibTeX] "Analysis of Confident-Classifiers for Out-of-Distribution Detection." Sachin Vernekar, Ashish Gaurav, Taylor Denouden, Buu Phan, Vahdat Abdelzad, Rick Salay and Krzysztof Czarnecki.
[Paper] [BibTeX] "Attribution-driven Causal Analysis for Detection of Adversarial Examples." Susmit Jha, Sunny Raj, Steven Fernandes, Sumit Kumar Jha, Somesh Jha, Jalaian Brian, Gunjan Verma and Ananthram Swami.
[Paper] [BibTeX] "Bridging Adversarial Robustness and Gradient Interpretability." Beomsu Kim, Junghoon Seo and Taegyun Jeon.
[Paper] [BibTeX] "Constrained Policy Improvement for Safe and Efficient Reinforcement Learning." Elad Sarafian, Aviv Tamar and Sarit Kraus.
[Paper] [BibTeX] "Delegative Reinforcement Learning: learning to avoid traps with a little help." Vanessa Kosoy.
[Paper] [BibTeX] "Distributed generation of privacy preserving data with user customization." Xiao Chen, Thomas Navidi, Stefano Ermon and Ram Rajagopal.
[Paper] [BibTeX] "Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness." Saeed Mahloujifar, Xiao Zhang, Mohammad Mahmoody and David Evans.
[Paper] [BibTeX] "Evaluation of Model Robustness via Interpretable Counterfactuals." Shubham Sharma, Jette Henderson and Joydeep Ghosh.
[Paper] [BibTeX] "Evolutionary Search for Adversarially Robust Neural Networks." Mathieu Sinn, Martin Wistuba, Beat Buesser, Maria-Irina Nicolae and Minh Tran.
[Paper] [BibTeX] "Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness." Joern-Henrik Jacobsen, Jens Behrmann, Nicholas Carlini, Florian Tramèr and Nicolas Papernot.
[Paper] [BibTeX] "Exploring the Hyperparameter Landscape of Adversarial Robustness." Evelyn Duesterwald, Anupama Murthi, Ganesh Venkataraman, Mathieu Sinn and Deepak Vijaykeerthy.
[Paper] [BibTeX] "Fairness GAN: Generating Datasets with Fairness Properties using a Generative Adversarial Network." Prasanna Sattigeri, Samuel Hoffman, Vijil Chenthamarakshan and Kush Varshney.
[Paper] [BibTeX] "GAN-Based Generation and Automatic Selection of Explanations for Neural Networks." Saumitra Mishra, Daniel Stoller, Emmanouil Benetos, Bob Sturm and Simon Dixon.
[Paper] [BibTeX] "Generalizing from a few environments in safety-critical reinforcement learning." Zac Kenton, Angelos Filos, Yarin Gal and Owain Evans.
[Paper] [BibTeX] "Harnessing the Vulnerability of Latent Layers in Adversarially Trained Models." Mayank Singh, Abhishek Sinha, Nupur Kumari, Balaji Krishnamurthy, Vineeth N Balasubramanian and Harshitha Machiraju.
[Paper] [BibTeX] "How useful is quantilization for mitigating specification-gaming?." Ryan Carey.
[Paper] [BibTeX] "Maximum Weighted Loss Discrepancy." Fereshte Khani, Aditi Raghunathan and Percy Liang.
[Paper] [BibTeX] "Measuring Quality and Interpretability of Dimensionality Reduction Visualizations." Adrien Bibal and Benoît Frénay.
[Paper] [BibTeX] "Measuring the Robustness of Reinforcement Learning Algorithms." Stephanie Chan, Sam Fishman, John Canny, Anoop Korattikara and Sergio Guadarrama.
[Paper] [BibTeX] "Misleading meta-objectives and hidden incentives for distributional shift." David Krueger, Tegan Maharaj, Shane Legg and Jan Leike.
[Paper] [BibTeX] "MMA Training: Direct Input Space Margin Maximization through Adversarial Training." Gavin Weiguang Ding, Yash Sharma, Kry Yik Chau Lui and Ruitong Huang.
[Paper] [BibTeX] "OVERT: Verification of Nonlinear Dynamical Systems with Neural Network Controllers via Overapproximation." Chelsea Sidrane and Mykel J. Kochenderfer.
[Paper] [BibTeX] "Regulatory Markets for AI Safety." Gillian Hadfield and Jack Clark.
[Paper] [BibTeX] "RobBoost: A provable approach to boost the robustness of deep model ensemble." Huan Zhang, Minhao Cheng and Cho-Jui Hsieh.
[Paper] [BibTeX] "Safety-Guided Deep Reinforcement Learning via Online Gaussian Process Estimation." Jiameng Fan and Wenchao Li.
[Paper] [BibTeX] "Towards Few-Shot Out-of-Distribution Detection." Kuan-Chieh Wang, Chia-Cheng Liu, Paul Vicol and Richard Zemel.
[Paper] [BibTeX] "Towards Improved Agent Robustness Against Adversarial Environments." Richard Everett.
[Paper] [BibTeX] "Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems." Shalmali Joshi, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim and Joydeep Ghosh.
[Paper] [BibTeX] "Uncovering Surprising Behaviors in Reinforcement Learning via Worst-Case Analysis." Avraham Ruderman, Richard Everett, Bristy Sikder, Hubert Soyer, Charles Beattie, Jonathan Uesato, Ananya Kumar and Pushmeet Kohli.
[Paper] [BibTeX] "Using Pre-Training Can Improve Model Robustness and Uncertainty." Dan Hendrycks, Kimin Lee and Mantas Mazeika.
[Paper] [BibTeX] "Using Videos to Evaluate Image Model Robustness." Keren Gu, Brandon Yang, Jiquan Ngiam, Quoc Le and Jon Shlens.