Backpropagation through the Void: Optimizing control variates for black-box gradient estimation. - Will Grathwohl; Dami Choi; Yuhuai Wu; Geoffrey Roeder; David Duvenaud (ICLR (Poster), 2018)

Guided evolutionary strategies: augmenting random search with surrogate gradients. - Niru Maheswaranathan; Luke Metz; George Tucker; Dami Choi; Jascha Sohl-Dickstein (ICML, 2019)

Faster Neural Network Training with Data Echoing. - Dami Choi; Alexandre Passos; Christopher J. Shallue; George E. Dahl (CoRR, 2019)

On Empirical Comparisons of Optimizers for Deep Learning. - Dami Choi; Christopher J. Shallue; Zachary Nado; Jaehoon Lee 001; Chris J. Maddison; George E. Dahl (CoRR, 2019)

Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. - Ricky T. Q. Chen; Dami Choi; Lukas Balles; David Duvenaud; Philipp Hennig (ICBINB@NeurIPS, 2020)

Gradient Estimation with Stochastic Softmax Tricks. - Max B. Paulus; Dami Choi; Daniel Tarlow; Andreas Krause 001; Chris J. Maddison (NeurIPS, 2020)

Tools for Verifying Neural Models' Training Data. - Dami Choi; Yonadav Shavit; David Kristjanson Duvenaud (NeurIPS, 2023)

Order Matters in the Presence of Dataset Imbalance for Multilingual Learning. - Dami Choi; Derrick Xin; Hamid Dadkhahi; Justin Gilmer; Ankush Garg; Orhan Firat; Chih-Kuan Yeh; Andrew M. Dai; Behrooz Ghorbani (NeurIPS, 2023)

LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language. - James Requeima; John Bronskill; Dami Choi; Richard E. Turner; David Kristjanson Duvenaud (NeurIPS, 2024)

Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data. - Johannes Treutlein; Dami Choi; Jan Betley; Samuel Marks; Cem Anil; Roger B. Grosse; Owain Evans (NeurIPS, 2024)