Duvenaud, David

Convolutional Networks on Graphs for Learning Molecular Fingerprints

Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., Gómez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A., & Adams, R. P. (2015) · arXiv

Computational Drug Discovery MethodsAdvanced Graph Neural Networks

Cited by 2,628

Neural ordinary differential equations

Chen, R. T. Q., Rubanova, Y., Bettencourt, J., & Duvenaud, D. (2018) · arXiv

Model Reduction and Neural NetworksNeural Networks and Applications

Cited by 2,097

Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach

Gómez-Bombarelli, R., Aguilera-Iparraguirre, J., Hirzel, T. D., Duvenaud, D., Maclaurin, D., Blood-Forsythe, M. A., Chae, H. S., Einzinger, M., Ha, D.-G., Wu, T., et al. (2016) · Nature Materials

Organic Light-Emitting Diodes ResearchMachine Learning in Materials Science

Cited by 1,060

FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models

Grathwohl, W., Chen, R. T. Q., Bettencourt, J., Sutskever, I., & Duvenaud, D. (2018) · arXiv

Generative Adversarial Networks and Image SynthesisMarkov Chains and Monte Carlo Methods

Cited by 796

Gradient-based Hyperparameter Optimization through Reversible Learning

Maclaurin, D., Duvenaud, D., & Adams, R. P. (2015) · arXiv

Machine Learning and Data ClassificationStochastic Gradient Optimization Techniques

Cited by 509

Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference

Roeder, G., Wu, Y., & Duvenaud, D. (2017) · arXiv

Gaussian Processes and Bayesian InferenceMarkov Chains and Monte Carlo Methods

Cited by 84

Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training

Hubinger, E., Denison, C., Mu, J., Lambert, M., Tong, M., MacDiarmid, M., Lanham, T., Ziegler, D. M., Maxwell, T., Cheng, N., et al. (2024) · arXiv

Adversarial Robustness in Machine LearningEthics and Social Impacts of AI

Cited by 46

Alignment faking in large language models

Greenblatt, R., Denison, C., Wright, B., Roger, F., MacDiarmid, M., Marks, S., Treutlein, J., Belonax, T., Chen, J., Duvenaud, D., et al. (2024) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 30

Many-shot Jailbreaking

Anil, C., Durmus, E., Panickssery, N., Sharma, M., Benton, J., Kundu, S., Batson, J., Tong, M., Mu, J., Ford, D., et al. (2024) · Advances in Neural Information Processing Systems 37

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 25

Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models

Denison, C., MacDiarmid, M., Barez, F., Duvenaud, D., Kravec, S., Marks, S., Schiefer, N., Soklaski, R., Tamkin, A., Kaplan, J., et al. (2024) · arXiv

Reinforcement Learning in RoboticsEthics and Social Impacts of AI

Cited by 11

Tools for Verifying Neural Models' Training Data

Choi, D., Shavit, Y., & Duvenaud, D. (2023) · arXiv

Software Testing and Debugging TechniquesFormal Methods in Verification

Cited by 8

A Definition of AGI

Hendrycks, D., Song, D., Szegedy, C., Lee, H., Gal, Y., Brynjolfsson, E., Li, S., Zou, A., Levine, L., Han, B., et al. (2025) · arXiv

Psychiatry, Mental Health, NeuroscienceArtificial Intelligence Applications

Cited by 3

Sabotage Evaluations for Frontier Models

Benton, J., Wagner, M., Christiansen, E., Anil, C., Perez, E., Srivastav, J., Durmus, E., Ganguli, D., Kravec, S., Shlegeris, B., et al. (2024) · arXiv

Adversarial Robustness in Machine LearningEthics and Social Impacts of AI

Cited by 2

Who's in Charge? Disempowerment Patterns in Real-World LLM Usage

Sharma, M., McCain, M., Douglas, R., & Duvenaud, D. (2026) · arXiv

Ethics and Social Impacts of AIInnovative Human-Technology Interaction

Cited by 2