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Evaluating the Adversarial Robustness of Adaptive Test-time Defenses

Francesco Croce *, Sven Gowal, Thomas Brunner *, Evan Shelhamer, Matthias Hein *, Taylan Cemgil

ICML

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Fair Normalizing Flows

Mislav Balunovic *, Anian Ruoss, Martin Vechev *

ICLR

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Your Policy Regularizer is Secretly an Adversary

Rob Brekelmans *, Tim Genewein, Jordi Grau, Grégoire Delétang, Markus Kunesch, Shane Legg, Pedro Ortega

arXiv

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Defending Against Image Corruptions Through Adversarial Augmentations

Dan A. Calian, Florian Stimberg, Olivia Wiles, Sylvestre-Alvise Rebuffi, Andras Gyorgy, Timothy Mann, Sven Gowal

arXiv

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Equilibrium Refinements for Multi-Agent Influence Diagrams: Theory and Practice

James Fox *, Lewis Hammond *, Tom Everitt, Alessandro Abate *, Michael Wooldridge *

AAMAS

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Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming

Dj Dvijotham, Jonathan Uesato, Sumanth Dathathri, Rudy Bunel, Pushmeet Kohli

NeurIPS

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Avoiding Side Effects By Considering Future Tasks

Victoria Krakovna, Laurent Orseau, Richard Ngo, Miljan Martic, Shane Legg

NeurIPS

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Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples

Sven Gowal, Chongli Qin, Jonathan Uesato, Timothy Mann, Pushmeet Kohli

arXiv

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Pessimism About Unknown Unknowns Inspires Conservatism

Michael Cohen *, Marcus Hutter

arXiv

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Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors

Mike Dusenberry *, G Jerfel *, Y Wen *, Y Ma *, Jasper Snoek *, Katherine Heller *, Balaji Lakshminarayanan, D Tran *

ICML

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Pitfalls of learning a reward function online

Stuart Armstrong *, Jan Leike, Laurent Orseau, Shane Legg

arXiv

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An empirical investigation of the challenges of real-world reinforcement learning

Gabriel Dulac-Arnold *, Nir Levine, Daniel Mankowitz, Jerry Li, Cosmin Paduraru, Sven Gowal, Todd Hester

arXiv

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The Incentives that Shape Behaviour

Ryan Carey *, Eric Langlois, Tom Everitt, Shane Legg

arXiv

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Artificial Intelligence, Values and Alignment

Iason Gabriel

arXiv

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Deep Ensembles: A Loss Landscape Perspective

Stanislav Fort *, Huiyi Hu *, Balaji Lakshminarayanan

arXiv

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AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

Dan Hendrycks, Norman Mu *, E Cubuk *, Barret Zoph *, Justin Gilmer *, Balaji Lakshminarayanan

arXiv

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Towards Robust Image Classification Using Sequential Attention Models

Daniel Zoran, M Chrzanowski *, Po-Sen Huang, Sven Gowal, Alex Mott, Pushmeet Kohli

arXiv

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An Alternative Surrogate Loss for PGD-based Adversarial Testing

Sven Gowal, Jonathan Uesato, Chongli Qin, Po-Sen Huang, Timothy Mann, Pushmeet Kohli

arXiv

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Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation

Po-Sen Huang, Robert Stanforth, Johannes Welbl *, Chris Dyer, Dani Yogatama, Sven Gowal, Krishnamurthy (Dj) Dvijotham, Pushmeet Kohli

EMNLP

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Reward Tampering Problems and Solutions in Reinforcement Learning: A Causal Influence Diagram Perspective

Tom Everitt, Marcus Hutter

arXiv

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Adversarial Robustness through Local Linearization

Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan *, Krishnamurthy (Dj) Dvijotham, Alhussein Fawzi, Soham De, Robert Stanforth, Pushmeet Kohli

arXiv

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Modeling AGI Safety Frameworks with Causal Influence Diagrams

Tom Everitt, Ramana Kumar, Victoria Krakovna, Shane Legg

IJCAI

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Likelihood Ratios for Out-of-Distribution Detection

J Ren *, P Liu *, Emily Fertig *, Jasper Snoek *, Ryan Poplin *, M DePristo *, J Dillon *, Balaji Lakshminarayanan

arXiv

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Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift

Yaniv Ovadia *, Emily Fertig *, Z Nado *, N Nowozin *, J Dillon *, Balaji Lakshminarayanan, Jasper Snoek *

arXiv

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Training verified learners with learned verifiers

Krishnamurthy (Dj) Dvijotham, Sven Gowal, Robert Stanforth, Relja Arandjelovic, Brendan O'Donoghue, Jonathan Uesato, Pushmeet Kohli

arXiv

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