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Conference and Workshop Papers
- 2023
[c6]Dami Choi, Yonadav Shavit, David Kristjanson Duvenaud:
Tools for Verifying Neural Models' Training Data. NeurIPS 2023- [c5]Dami Choi, Derrick Xin, Hamid Dadkhahi, Justin Gilmer, Ankush Garg, Orhan Firat, Chih-Kuan Yeh, Andrew M. Dai, Behrooz Ghorbani:
Order Matters in the Presence of Dataset Imbalance for Multilingual Learning. NeurIPS 2023 - 2020
- [c4]Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig:
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. ICBINB@NeurIPS 2020: 60-69 - [c3]Max B. Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, Chris J. Maddison:
Gradient Estimation with Stochastic Softmax Tricks. NeurIPS 2020 - 2019
- [c2]Niru Maheswaranathan, Luke Metz, George Tucker, Dami Choi, Jascha Sohl-Dickstein:
Guided evolutionary strategies: augmenting random search with surrogate gradients. ICML 2019: 4264-4273 - 2018
- [c1]Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud:
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation. ICLR (Poster) 2018
Informal and Other Publications
- 2024
- [i9]James Requeima, John Bronskill, Dami Choi, Richard E. Turner, David Duvenaud:
LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language. CoRR abs/2405.12856 (2024) - [i8]Johannes Treutlein, Dami Choi, Jan Betley, Cem Anil, Samuel Marks, Roger Baker Grosse, Owain Evans:
Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data. CoRR abs/2406.14546 (2024) - 2023
- [i7]Dami Choi, Yonadav Shavit, David Duvenaud:
Tools for Verifying Neural Models' Training Data. CoRR abs/2307.00682 (2023) - [i6]Dami Choi, Derrick Xin, Hamid Dadkhahi, Justin Gilmer, Ankush Garg, Orhan Firat, Chih-Kuan Yeh, Andrew M. Dai, Behrooz Ghorbani:
Order Matters in the Presence of Dataset Imbalance for Multilingual Learning. CoRR abs/2312.06134 (2023) - 2020
- [i5]Max B. Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, Chris J. Maddison:
Gradient Estimation with Stochastic Softmax Tricks. CoRR abs/2006.08063 (2020) - [i4]Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig:
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. CoRR abs/2011.04803 (2020) - 2019
- [i3]Dami Choi, Alexandre Passos, Christopher J. Shallue, George E. Dahl:
Faster Neural Network Training with Data Echoing. CoRR abs/1907.05550 (2019) - [i2]Dami Choi, Christopher J. Shallue, Zachary Nado, Jaehoon Lee, Chris J. Maddison, George E. Dahl:
On Empirical Comparisons of Optimizers for Deep Learning. CoRR abs/1910.05446 (2019) - 2017
- [i1]Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud:
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation. CoRR abs/1711.00123 (2017)
Coauthor Index
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