Dami Choi

According to our database, Dami Choi authored at least 10 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data.

Johannes Treutlein, Dami Choi, Jan Betley, Cem Anil, Samuel Marks, Roger Baker Grosse, Owain Evans

CoRR, 2024

LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language.

James Requeima, John Bronskill, Dami Choi, Richard E. Turner, David Duvenaud

CoRR, 2024

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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Tools for Verifying Neural Models' Training Data.

Dami Choi, Yonadav Shavit, David Kristjanson Duvenaud

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2020

Gradient Estimation with Stochastic Softmax Tricks.

Max B. Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, Chris J. Maddison

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering.

Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig

Proceedings of the "I Can't Believe It's Not Better!" at NeurIPS Workshops, 2020

2019

On Empirical Comparisons of Optimizers for Deep Learning.

Dami Choi, Christopher J. Shallue, Zachary Nado, Jaehoon Lee, Chris J. Maddison, George E. Dahl

CoRR, 2019

Faster Neural Network Training with Data Echoing.

Dami Choi, Alexandre Passos, Christopher J. Shallue, George E. Dahl

CoRR, 2019

Guided evolutionary strategies: augmenting random search with surrogate gradients.

Niru Maheswaranathan, Luke Metz, George Tucker, Dami Choi, Jascha Sohl-Dickstein

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Backpropagation through the Void: Optimizing control variates for black-box gradient estimation.

Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud

Proceedings of the 6th International Conference on Learning Representations, 2018