Safety

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Score-based generative models learn manifold-like structures with constrained mixing

Kevin Li, Ben Moran

NeurIPS Workshop

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Latent Space Smoothing for Individually Fair Representations

Momchil Peychev *, Anian Ruoss, Mislav Balunovic *, Maximilian Baader *, Martin Vechev *

ECCV

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Beyond Bayes-optimality: meta-learning what you know you don’t know

Jordi Grau, Grégoire Delétang, Markus Kunesch, Tim Genewein, Kevin Li, Elliot Catt, Anian Ruoss, Chris Cundy, Joel Veness, Jane Wang, Marcus Hutter, Christopher Summerfield, Shane Legg

arXiv

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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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