LEG processor for education. - Maxwell Waugaman; Zakkai Davidson; Samuel Dietrich; Daniel D. Johnson 001; Cassandra Meyer; Eric Storm; Avi Thaker; Ivan Wong (EWME, 2016)

Generating Polyphonic Music Using Tied Parallel Networks. - Daniel D. Johnson 001 (EvoMUSART, 2017)

Learning to Create Jazz Melodies Using a Product of Experts. - Daniel D. Johnson 001; Robert M. Keller; Nicholas Weintraut (ICCC, 2017)

Learning Graphical State Transitions. - Daniel D. Johnson 001 (ICLR, 2017)

Geometric Realizations of the 3D Associahedron (Multimedia Exposition). - Satyan L. Devadoss; Daniel D. Johnson 001; Justin Lee; Jackson Warley (SoCG, 2018)

Latent Gaussian Activity Propagation: Using Smoothness and Structure to Separate and Localize Sounds in Large Noisy Environments. - Daniel D. Johnson 001; Daniel Gorelik; Ross Mawhorter; Kyle Suver; Weiqing Gu; Steven Xing; Cody Gabriel; Peter Sankhagowit (NeurIPS, 2018)

Learning Graph Structure With A Finite-State Automaton Layer. - Daniel D. Johnson 001; Hugo Larochelle; Daniel Tarlow (NeurIPS, 2020)

Structured Denoising Diffusion Models in Discrete State-Spaces. - Jacob Austin; Daniel D. Johnson 001; Jonathan Ho; Daniel Tarlow; Rianne van den Berg (NeurIPS, 2021)

Learning Generalized Gumbel-max Causal Mechanisms. - Guy Lorberbom; Daniel D. Johnson 001; Chris J. Maddison; Daniel Tarlow; Tamir Hazan (NeurIPS, 2021)

Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Models. - Daniel D. Johnson 001; Jacob Austin; Rianne van den Berg; Daniel Tarlow (CoRR, 2021)

Getting to the point: index sets and parallelism-preserving autodiff for pointful array programming. - Adam Paszke; Daniel D. Johnson 001; David Duvenaud; Dimitrios Vytiniotis; Alexey Radul; Matthew J. Johnson 002; Jonathan Ragan-Kelley; Dougal Maclaurin (Proc. ACM Program. Lang., 2021)

A Library for Representing Python Programs as Graphs for Machine Learning. - David Bieber; Kensen Shi; Petros Maniatis; Charles Sutton; Vincent J. Hellendoorn; Daniel D. Johnson 001; Daniel Tarlow (CoRR, 2022)

Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions. - Daniel D. Johnson 001; Ayoub El Hanchi; Chris J. Maddison (ICLR, 2023)

R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents. - Daniel D. Johnson 001; Daniel Tarlow; Christian Walder (ICML, 2023)

Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs. - Daniel D. Johnson 001; Daniel Tarlow; David Duvenaud; Chris J. Maddison (ICML, 2024)

Penzai + Treescope: A Toolkit for Interpreting, Visualizing, and Editing Models As Data. - Daniel D. Johnson 001 (CoRR, 2024)

Parallel Algebraic Effect Handlers. - Ningning Xie; Daniel D. Johnson 001; Dougal Maclaurin; Adam Paszke (Proc. ACM Program. Lang., 2024)

A density estimation perspective on learning from pairwise human preferences. - Vincent Dumoulin; Daniel D. Johnson 001; Pablo Samuel Castro; Hugo Larochelle; Yann N. Dauphin (Trans. Mach. Learn. Res., 2024)

Eliciting Language Model Behaviors with Investigator Agents. - Xiang Lisa Li; Neil Chowdhury; Daniel D. Johnson 001; Tatsunori Hashimoto; Percy Liang; Sarah Schwettmann; Jacob Steinhardt (CoRR, 2025)