Johnson, Daniel D.

12 publications

ICML 2025 Eliciting Language Model Behaviors with Investigator Agents Xiang Lisa Li, Neil Chowdhury, Daniel D. Johnson, Tatsunori Hashimoto, Percy Liang, Sarah Schwettmann, Jacob Steinhardt

TMLR 2024 A Density Estimation Perspective on Learning from Pairwise Human Preferences Vincent Dumoulin, Daniel D. Johnson, Pablo Samuel Castro, Hugo Larochelle, Yann Dauphin

ICLRW 2024 Experts Don't Cheat: Learning What You Don't Know by Predicting Pairs Daniel D. Johnson, Daniel Tarlow, David Duvenaud, Chris J. Maddison

ICML 2024 Experts Don’t Cheat: Learning What You Don’t Know by Predicting Pairs Daniel D. Johnson, Daniel Tarlow, David Duvenaud, Chris J. Maddison

ICMLW 2024 Penzai + Treescope: A Toolkit for Interpreting, Visualizing, and Editing Models as Data Daniel D. Johnson

ICLR 2023 Contrastive Learning Can Find an Optimal Basis for Approximately View-Invariant Functions Daniel D. Johnson, Ayoub El Hanchi, Chris J. Maddison

ICML 2023 R-U-SURE? Uncertainty-Aware Code Suggestions by Maximizing Utility Across Random User Intents Daniel D. Johnson, Daniel Tarlow, Christian Walder

ICMLW 2022 Contrastive Learning Can Find an Optimal Basis for Approximately Invariant Functions Daniel D. Johnson, Ayoub El Hanchi, Chris J. Maddison

ICMLW 2021 Beyond In-Place Corruption: Insertion and Deletion in Denoising Probabilistic Models Daniel D. Johnson, Jacob Austin, Rianne van den Berg, Daniel Tarlow

NeurIPS 2021 Learning Generalized Gumbel-Max Causal Mechanisms Guy Lorberbom, Daniel D. Johnson, Chris J Maddison, Daniel Tarlow, Tamir Hazan

NeurIPS 2021 Structured Denoising Diffusion Models in Discrete State-Spaces Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, Rianne van den Berg

ICLR 2017 Learning Graphical State Transitions Daniel D. Johnson