Krueger, David

NICE: Non-linear Independent Components Estimation

Dinh, L., Krueger, D., & Bengio, Y. (2014) · arXiv

Generative Adversarial Networks and Image SynthesisGaussian Processes and Bayesian Inference

Cited by 1,493

A Closer Look at Memorization in Deep Networks

Arpit, D., Jastrzębski, S., Ballas, N., Krueger, D., Bengio, E., Kanwal, M. S., Maharaj, T., Fischer, A., Courville, A., Bengio, Y., et al. (2017) · arXiv

Stochastic Gradient Optimization TechniquesNeural Networks and Applications

Cited by 1,307

Out-of-Distribution Generalization via Risk Extrapolation (REx)

Krueger, D., Caballero, E., Jacobsen, J.-H., Zhang, A., Binas, J., Zhang, D., Priol, R. L., & Courville, A. (2020) · arXiv

Domain Adaptation and Few-Shot LearningAdversarial Robustness in Machine Learning

Cited by 407

Managing extreme AI risks amid rapid progress

Bengio, Y., Hinton, G., Yao, A., Song, D., Abbeel, P., Darrell, T., Harari, Y. N., Zhang, Y.-Q., Xue, L., Shalev-Shwartz, S., et al. (2024) · Science

Ethics and Social Impacts of AIDigitalization, Law, and Regulation

Cited by 349

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

Brundage, M., Avin, S., Wang, J., Belfield, H., Krueger, G., Hadfield, G., Khlaaf, H., Yang, J., Toner, H., Fong, R., et al. (2020) · arXiv

Ethics and Social Impacts of AISafety Systems Engineering in Autonomy

Cited by 306

Harms from Increasingly Agentic Algorithmic Systems

Chan, A., Salganik, R., Markelius, A., Pang, C., Rajkumar, N., Krasheninnikov, D., Langosco, L., He, Z., Duan, Y., Carroll, M., et al. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 149

Scalable agent alignment via reward modeling: a research direction

Leike, J., Krueger, D., Everitt, T., Martic, M., Maini, V., & Legg, S. (2018) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 121

Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Casper, S., Davies, X., Shi, C., Gilbert, T. K., Scheurer, J., Rando, J., Freedman, R., Korbak, T., Lindner, D., Freire, P., et al. (2023) · arXiv

Reinforcement Learning in RoboticsPsychiatry, Mental Health, Neuroscience

Cited by 101

Visibility into AI Agents

Chan, A., Ezell, C., Kaufmann, M., Wei, K., Hammond, L., Bradley, H., Bluemke, E., Rajkumar, N., Krueger, D., Kolt, N., et al. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency

Ethics and Social Impacts of AIDigitalization, Law, and Regulation

Cited by 84

AI Research Considerations for Human Existential Safety (ARCHES)

Critch, A., & Krueger, D. (2020) · arXiv

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 25

Foundational Challenges in Assuring Alignment and Safety of Large Language Models

Anwar, U., Saparov, A., Rando, J., Paleka, D., Turpin, M., Hase, P., Lubana, E. S., Jenner, E., Casper, S., Sourbut, O., et al. (2024) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 19

Hidden Incentives for Auto-Induced Distributional Shift

Krueger, D., Maharaj, T., & Leike, J. (2020) · arXiv

Ethics and Social Impacts of AIRecommender Systems and Techniques

Cited by 14

Safety Cases: How to Justify the Safety of Advanced AI Systems

Clymer, J., Gabrieli, N., Krueger, D., & Larsen, T. (2024) · arXiv

Ethics and Social Impacts of AISafety Systems Engineering in Autonomy

Cited by 5

Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Casper, S., Davies, X., Shi, C., Krendl Gilbert, T., Scheurer, J., Rando Ramirez, J., Freedman, R., Korbak, T., Lindner, D., Freire, P., et al. (2023) · ETH Zurich

Reinforcement Learning in RoboticsMachine Learning and Algorithms

Cited by 3

Quantifying Feature Space Universality Across Large Language Models via Sparse Autoencoders

Lan, M., Torr, P., Meek, A., Khakzar, A., Krueger, D., & Barez, F. (2024) · arXiv

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 2

Reward Model Ensembles Help Mitigate Overoptimization

Coste, T., Anwar, U., Kirk, R., & Krueger, D. (2023) · arXiv

Reinforcement Learning in RoboticsNatural Language Processing Techniques

Cited by 2

On The Fragility of Learned Reward Functions

McKinney, L., Duan, Y., Krueger, D., & Gleave, A. (2023) · arXiv

Reinforcement Learning in RoboticsMachine Learning and Algorithms

Cited by 1

Towards Reliable Evaluation of Behavior Steering Interventions in LLMs

Pres, I., Ruis, L., Lubana, E. S., & Krueger, D. (2024) · arXiv

Adversarial Robustness in Machine LearningExplainable Artificial Intelligence (XAI)

Cited by 0

Permissive Information-Flow Analysis for Large Language Models

Siddiqui, S. A., Gaonkar, R., Köpf, B., Krueger, D., Paverd, A., Salem, A., Tople, S., Wutschitz, L., Xia, M., & Zanella-Béguelin, S. (2024) · arXiv

Security and Verification in ComputingAdversarial Robustness in Machine Learning

Cited by 0