Testing Visual Attention in Dynamic Environments. - Philip Bachman; David Krueger 001; Doina Precup (CoRR, 2015)

NICE: Non-linear Independent Components Estimation. - Laurent Dinh; David Krueger 001; Yoshua Bengio (ICLR (Workshop), 2015)

Zero-bias autoencoders and the benefits of co-adapting features. - Roland Memisevic; Kishore Reddy Konda; David Krueger 001 (ICLR (Poster), 2015)

Regularizing RNNs by Stabilizing Activations. - David Krueger 001; Roland Memisevic (ICLR, 2016)

Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations. - David Krueger 001; Tegan Maharaj; János Kramár; Mohammad Pezeshki; Nicolas Ballas; Nan Rosemary Ke; Anirudh Goyal; Yoshua Bengio; Hugo Larochelle; Aaron C. Courville; Chris Pal (CoRR, 2016)

Nested LSTMs. - Joel Ruben Antony Moniz; David Krueger 001 (ACML, 2017)

Deep Nets Don't Learn via Memorization. - David Krueger 001; Nicolas Ballas; Stanislaw Jastrzebski; Devansh Arpit; Maxinder S. Kanwal; Tegan Maharaj; Emmanuel Bengio; Asja Fischer; Aaron C. Courville (ICLR (Workshop), 2017)

Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations. - David Krueger 001; Tegan Maharaj; János Kramár; Mohammad Pezeshki; Nicolas Ballas; Nan Rosemary Ke; Anirudh Goyal; Yoshua Bengio; Aaron C. Courville; Christopher J. Pal (ICLR (Poster), 2017)

A Closer Look at Memorization in Deep Networks. - Devansh Arpit; Stanislaw Jastrzebski; Nicolas Ballas; David Krueger 001; Emmanuel Bengio; Maxinder S. Kanwal; Tegan Maharaj; Asja Fischer; Aaron C. Courville; Yoshua Bengio; Simon Lacoste-Julien (ICML, 2017)

Bayesian Hypernetworks. - David Krueger 001; Chin-Wei Huang; Riashat Islam; Ryan Turner; Alexandre Lacoste; Aaron C. Courville (CoRR, 2017)

Deep Prior. - Alexandre Lacoste; Thomas Boquet; Negar Rostamzadeh; Boris N. Oreshkin; Wonchang Chung; David Krueger 001 (CoRR, 2017)

Neural Autoregressive Flows. - Chin-Wei Huang; David Krueger 001; Alexandre Lacoste; Aaron C. Courville (ICML, 2018)

Uncertainty in Multitask Transfer Learning. - Alexandre Lacoste; Boris N. Oreshkin; Wonchang Chung; Thomas Boquet; Negar Rostamzadeh; David Krueger 001 (CoRR, 2018)

Scalable agent alignment via reward modeling: a research direction. - Jan Leike; David Krueger 001; Tom Everitt; Miljan Martic; Vishal Maini; Shane Legg (CoRR, 2018)

Out-of-Distribution Generalization via Risk Extrapolation (REx). - David Krueger 001; Ethan Caballero; Jörn-Henrik Jacobsen; Amy Zhang 001; Jonathan Binas; Rémi Le Priol; Aaron C. Courville (CoRR, 2020)

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims. - Miles Brundage; Shahar Avin; Jasmine Wang; Haydn Belfield; Gretchen Krueger; Gillian K. Hadfield; Heidy Khlaaf; Jingying Yang; Helen Toner; Ruth Fong; Tegan Maharaj; Pang Wei Koh; et al. (CoRR, 2020)

AI Research Considerations for Human Existential Safety (ARCHES). - Andrew Critch; David Krueger 001 (CoRR, 2020)

Hidden Incentives for Auto-Induced Distributional Shift. - David Krueger 001; Tegan Maharaj; Jan Leike (CoRR, 2020)

Active Reinforcement Learning: Observing Rewards at a Cost. - David Krueger 001; Jan Leike; Owain Evans; John Salvatier (CoRR, 2020)

Out-of-Distribution Generalization via Risk Extrapolation (REx). - David Krueger 001; Ethan Caballero; Jörn-Henrik Jacobsen; Amy Zhang 001; Jonathan Binas; Dinghuai Zhang; Rémi Le Priol; Aaron C. Courville (ICML, 2021)

Filling gaps in trustworthy development of AI. - Shahar Avin; Haydn Belfield; Miles Brundage; Gretchen Krueger; Jasmine Wang; Adrian Weller; Markus Anderljung; Igor Krawczuk; David Krueger 001; Jonathan Lebensold; Tegan Maharaj; Noa Zilberman (CoRR, 2021)

Multi-Domain Balanced Sampling Improves Out-of-Distribution Generalization of Chest X-ray Pathology Prediction Models. - Enoch Tetteh; Joseph D. Viviano; Yoshua Bengio; David Krueger 001; Joseph Paul Cohen (CoRR, 2021)

Goal Misgeneralization in Deep Reinforcement Learning. - Lauro Langosco di Langosco; Jack Koch; Lee D. Sharkey; Jacob Pfau; David Krueger 001 (ICML, 2022)

Defining and Characterizing Reward Gaming. - Joar Skalse; Nikolaus H. R. Howe; Dmitrii Krasheninnikov; David Krueger 001 (NeurIPS, 2022)

Defining and Characterizing Reward Hacking. - Joar Skalse; Nikolaus H. R. Howe; Dmitrii Krasheninnikov; David Krueger 001 (CoRR, 2022)

Towards Out-of-Distribution Adversarial Robustness. - Adam Ibrahim; Charles Guille-Escuret; Ioannis Mitliagkas; Irina Rish; David Krueger 001; Pouya Bashivan (CoRR, 2022)

Domain Generalization for Robust Model-Based Offline Reinforcement Learning. - Alan Clark; Shoaib Ahmed Siddiqui; Robert Kirk; Usman Anwar; Stephen Chung; David Krueger 001 (CoRR, 2022)

Characterizing Manipulation from AI Systems. - Micah Carroll; Alan Chan; Henry Ashton; David Krueger 001 (EAAMO, 2023)

Harms from Increasingly Agentic Algorithmic Systems. - Alan Chan; Rebecca Salganik; Alva Markelius; Chris Pang; Nitarshan Rajkumar; Dmitrii Krasheninnikov; Lauro Langosco; Zhonghao He; Yawen Duan; Micah Carroll; Michelle P. Lin; Alex Mayhew; et al. (FAccT, 2023)

Broken Neural Scaling Laws. - Ethan Caballero; Kshitij Gupta; Irina Rish; David Krueger 001 (ICLR, 2023)

Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics. - Shoaib Ahmed Siddiqui; Nitarshan Rajkumar; Tegan Maharaj; David Krueger 001; Sara Hooker (ICLR, 2023)

Mechanistic Mode Connectivity. - Ekdeep Singh Lubana; Eric J. Bigelow; Robert P. Dick; David Scott Krueger; Hidenori Tanaka (ICML, 2023)

Thinker: Learning to Plan and Act. - Stephen Chung; Ivan Anokhin; David Krueger 001 (NeurIPS, 2023)

What Mechanisms Does Knowledge Distillation Distill? - Cindy Wu; Ekdeep Singh Lubana; Bruno Kacper Mlodozeniec; Robert Kirk; David Krueger 001 (UniReps, 2023)

On The Fragility of Learned Reward Functions. - Lev McKinney; Yawen Duan; David Krueger 001; Adam Gleave (CoRR, 2023)

Unifying Grokking and Double Descent. - Xander Davies; Lauro Langosco; David Krueger 001 (CoRR, 2023)

Investigating the Nature of 3D Generalization in Deep Neural Networks. - Shoaib Ahmed Siddiqui; David Krueger 001; Thomas M. Breuel (CoRR, 2023)

Meta- (out-of-context) learning in neural networks. - Dmitrii Krasheninnikov; Egor Krasheninnikov; Bruno Mlodozeniec; David Krueger 001 (CoRR, 2023)

Managing AI Risks in an Era of Rapid Progress. - Yoshua Bengio; Geoffrey E. Hinton; Andrew Yao; Dawn Song; Pieter Abbeel; Yuval Noah Harari; Ya-Qin Zhang; Lan Xue; Shai Shalev-Shwartz; Gillian K. Hadfield; Jeff Clune; Tegan Maharaj; et al. (CoRR, 2023)

Hazards from Increasingly Accessible Fine-Tuning of Downloadable Foundation Models. - Alan Chan; Ben Bucknall; Herbie Bradley; David Krueger 001 (CoRR, 2023)

Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback. - Stephen Casper; Xander Davies; Claudia Shi; Thomas Krendl Gilbert; Jérémy Scheurer; Javier Rando; Rachel Freedman; Tomasz Korbak; David Lindner; Pedro Freire; Tony Tong Wang; Samuel Marks; et al. (Trans. Mach. Learn. Res., 2023)

Implicitly Bayesian Prediction Rules in Deep Learning. - Bruno Mlodozeniec; David Krueger 001; Richard E. Turner (AABI, 2024)

Black-Box Access is Insufficient for Rigorous AI Audits. - Stephen Casper; Carson Ezell; Charlotte Siegmann; Noam Kolt; Taylor Lynn Curtis; Benjamin Bucknall; Andreas A. Haupt; Kevin Wei; Jérémy Scheurer; Marius Hobbhahn; Lee Sharkey; Satyapriya Krishna; et al. (FAccT, 2024)

Visibility into AI Agents. - Alan Chan; Carson Ezell; Max Kaufmann; Kevin Wei; Lewis Hammond; Herbie Bradley; Emma Bluemke; Nitarshan Rajkumar; David Krueger 001; Noam Kolt; Lennart Heim; Markus Anderljung (FAccT, 2024)

Reward Model Ensembles Help Mitigate Overoptimization. - Thomas Coste; Usman Anwar; Robert Kirk; David Krueger 001 (ICLR, 2024)

Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks. - Samyak Jain; Robert Kirk; Ekdeep Singh Lubana; Robert P. Dick; Hidenori Tanaka; Tim Rocktäschel; Edward Grefenstette; David Scott Krueger (ICLR, 2024)

Implicit meta-learning may lead language models to trust more reliable sources. - Dmitrii Krasheninnikov; Egor Krasheninnikov; Bruno Kacper Mlodozeniec; Tegan Maharaj; David Krueger 001 (ICML, 2024)

A Generative Model of Symmetry Transformations. - James Urquhart Allingham; Bruno Mlodozeniec; Shreyas Padhy; Javier Antorán; David Krueger 001; Richard E. Turner; Eric T. Nalisnick; José Miguel Hernández-Lobato (NeurIPS, 2024)

Predicting Future Actions of Reinforcement Learning Agents. - Stephen Chung; Scott Niekum; David Krueger 001 (NeurIPS, 2024)

Stress-Testing Capability Elicitation With Password-Locked Models. - Ryan Greenblatt; Fabien Roger; Dmitrii Krasheninnikov; David Krueger 001 (NeurIPS, 2024)

Interpreting Learned Feedback Patterns in Large Language Models. - Luke Marks; Amir Abdullah; Clement Neo; Rauno Arike; David Krueger 001; Philip Torr 001; Fazl Barez (NeurIPS, 2024)

Safety Cases: How to Justify the Safety of Advanced AI Systems. - Joshua Clymer; Nick Gabrieli; David Krueger 001; Thomas Larsen (CoRR, 2024)

Foundational Challenges in Assuring Alignment and Safety of Large Language Models. - Usman Anwar; Abulhair Saparov; Javier Rando; Daniel Paleka; Miles Turpin; Peter Hase; Ekdeep Singh Lubana; Erik Jenner; Stephen Casper; Oliver Sourbut; Benjamin L. Edelman; Zhaowei Zhang; et al. (CoRR, 2024)

IDs for AI Systems. - Alan Chan; Noam Kolt; Peter Wills; Usman Anwar; Christian Schröder de Witt; Nitarshan Rajkumar; Lewis Hammond; David Krueger 001; Lennart Heim; Markus Anderljung (CoRR, 2024)

Affirmative safety: An approach to risk management for high-risk AI. - Akash R. Wasil; Joshua Clymer; David Krueger 001; Emily Dardaman; Siméon Campos; Evan R. Murphy (CoRR, 2024)

The Perils of Optimizing Learned Reward Functions: Low Training Error Does Not Guarantee Low Regret. - Lukas Fluri; Leon Lang; Alessandro Abate; Patrick Forré; David Krueger 001; Joar Skalse (CoRR, 2024)

A deeper look at depth pruning of LLMs. - Shoaib Ahmed Siddiqui; Xin Dong 009; Greg Heinrich; Thomas M. Breuel; Jan Kautz; David Krueger 001; Pavlo Molchanov 001 (CoRR, 2024)

Permissive Information-Flow Analysis for Large Language Models. - Shoaib Ahmed Siddiqui; Radhika Gaonkar; Boris Köpf; David Krueger 001; Andrew Paverd; Ahmed Salem 001; Shruti Tople; Lukas Wutschitz; Menglin Xia; Santiago Zanella-Béguelin (CoRR, 2024)

Sparse Autoencoders Reveal Universal Feature Spaces Across Large Language Models. - Michael Lan; Philip Torr 001; Austin Meek; Ashkan Khakzar; David Krueger 001; Fazl Barez (CoRR, 2024)

Exploring the design space of deep-learning-based weather forecasting systems. - Shoaib Ahmed Siddiqui; Jean Kossaifi; Boris Bonev; Christopher B. Choy; Jan Kautz; David Krueger 001; Kamyar Azizzadenesheli (CoRR, 2024)

PoisonBench: Assessing Large Language Model Vulnerability to Data Poisoning. - Tingchen Fu; Mrinank Sharma; Philip Torr 001; Shay B. Cohen; David Krueger 001; Fazl Barez (CoRR, 2024)

Towards Reliable Evaluation of Behavior Steering Interventions in LLMs. - Itamar Pres; Laura Ruis; Ekdeep Singh Lubana; David Krueger 001 (CoRR, 2024)

Integrating uncertainty quantification into randomized smoothing based robustness guarantees. - Sina Däubener; Kira Maag; David Krueger 001; Asja Fischer (CoRR, 2024)

Enhancing Neural Network Interpretability with Feature-Aligned Sparse Autoencoders. - Luke Marks; Alasdair Paren; David Krueger 001; Fazl Barez (CoRR, 2024)

Noisy Zero-Shot Coordination: Breaking The Common Knowledge Assumption In Zero-Shot Coordination Games. - Usman Anwar; Ashish Pandian; Jia Wan; David Krueger 001; Jakob N. Foerster (CoRR, 2024)

Adversarial Robustness of In-Context Learning in Transformers for Linear Regression. - Usman Anwar; Johannes von Oswald; Louis Kirsch; David Krueger 001; Spencer Frei (CoRR, 2024)

Comparing Bottom-Up and Top-Down Steering Approaches on In-Context Learning Tasks. - Madeline Brumley; Joe Kwon; David Krueger 001; Dmitrii Krasheninnikov; Usman Anwar (CoRR, 2024)

Learning to Forget using Hypernetworks. - Jose Miguel Lara Rangel; Stefan Schoepf; Jack Foster; David Krueger 001; Usman Anwar (CoRR, 2024)

Foundational Challenges in Assuring Alignment and Safety of Large Language Models. - Usman Anwar; Abulhair Saparov; Javier Rando; Daniel Paleka; Miles Turpin; Peter Hase; Ekdeep Singh Lubana; Erik Jenner; Stephen Casper; Oliver Sourbut; Benjamin L. Edelman; Zhaowei Zhang; et al. (Trans. Mach. Learn. Res., 2024)

Blockwise Self-Supervised Learning at Scale. - Shoaib Ahmed Siddiqui; David Krueger 001; Yann LeCun; Stéphane Deny (Trans. Mach. Learn. Res., 2024)

Protecting against simultaneous data poisoning attacks. - Neel Alex; Shoaib Ahmed Siddiqui; Amartya Sanyal; David Krueger 001 (ICLR, 2025)

Interpreting Emergent Planning in Model-Free Reinforcement Learning. - Thomas Bush; Stephen Chung; Usman Anwar; Adrià Garriga-Alonso; David Krueger 001 (ICLR, 2025)

Influence Functions for Scalable Data Attribution in Diffusion Models. - Bruno Kacper Mlodozeniec; Runa Eschenhagen; Juhan Bae; Alexander Immer; David Krueger 001; Richard E. Turner (ICLR, 2025)

Towards Interpreting Visual Information Processing in Vision-Language Models. - Clement Neo; Luke Ong; Philip Torr 001; Mor Geva; David Krueger 001; Fazl Barez (ICLR, 2025)

Input Space Mode Connectivity in Deep Neural Networks. - Jakub Vrábel; Ori Shem-Ur; Yaron Oz; David Krueger 001 (ICLR, 2025)

Analyzing (In)Abilities of SAEs via Formal Languages. - Abhinav Menon; Manish Shrivastava 001; David Krueger 001; Ekdeep Singh Lubana (NAACL (Long Papers), 2025)

Understanding In-Context Learning of Linear Models in Transformers Through an Adversarial Lens. - Usman Anwar; Johannes von Oswald; Louis Kirsch; David Krueger 001; Spencer Frei (Trans. Mach. Learn. Res., 2025)

Open Problems in Machine Unlearning for AI Safety. - Fazl Barez; Tingchen Fu; Ameya Prabhu; Stephen Casper; Amartya Sanyal; Adel Bibi; Aidan O'Gara; Robert Kirk; Ben Bucknall; Timothy Fist; Luke Ong; Philip Torr 001; et al. (CoRR, 2025)

Gradual Disempowerment: Systemic Existential Risks from Incremental AI Development. - Jan Kulveit; Raymond Douglas; Nora Ammann; Deger Turan; David Krueger 001; David Duvenaud (CoRR, 2025)

Pitfalls of Evidence-Based AI Policy. - Stephen Casper; David Krueger 001; Dylan Hadfield-Menell (CoRR, 2025)

Taxonomy, Opportunities, and Challenges of Representation Engineering for Large Language Models. - Jan Wehner; Sahar Abdelnabi; Daniel Tan; David Krueger 001; Mario Fritz (CoRR, 2025)

From Dormant to Deleted: Tamper-Resistant Unlearning Through Weight-Space Regularization. - Shoaib Ahmed Siddiqui; Adrian Weller; David Krueger 001; Gintare Karolina Dziugaite; Michael Curtis Mozer; Eleni Triantafillou (CoRR, 2025)

Understanding (Un)Reliability of Steering Vectors in Language Models. - Joschka Braun; Carsten Eickhoff; David Krueger 001; Seyed Ali Bahrainian; Dmitrii Krasheninnikov (CoRR, 2025)

Detecting High-Stakes Interactions with Activation Probes. - Alex McKenzie; Urja Pawar; Phil Blandfort; William Bankes; David Krueger 001; Ekdeep Singh Lubana; Dmitrii Krasheninnikov (CoRR, 2025)

Distributional Training Data Attribution. - Bruno Mlodozeniec; Isaac Reid; Sam Power; David Krueger 001; Murat A. Erdogdu; Richard E. Turner; Roger B. Grosse (CoRR, 2025)

Mitigating Goal Misgeneralization via Minimax Regret. - Karim Abdel Sadek; Matthew Farrugia-Roberts; Usman Anwar; Hannah Erlebach; Christian A. Schröder de Witt; David Krueger 001; Michael Dennis 001 (CoRR, 2025)

How Do LLMs Persuade? Linear Probes Can Uncover Persuasion Dynamics in Multi-Turn Conversations. - Brandon Jaipersaud; David Krueger 001; Ekdeep Singh Lubana (CoRR, 2025)

Rethinking Safety in LLM Fine-tuning: An Optimization Perspective. - Minseon Kim; Jin Myung Kwak; Lama Alssum; Bernard Ghanem; Philip Torr 001; David Krueger 001; Fazl Barez; Adel Bibi (CoRR, 2025)