Dami Choi - RankMe

18 papers A* 6Journal 10Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Predictive Concept Decoders: Training Scalable End-to-End Interpretability Assistants. CoRR Vincent Huang, Dami Choi, Daniel D. Johnson, Sarah Schwettmann, Jacob Steinhardt
2024	A*	`conf`	Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data. NeurIPS Johannes Treutlein, Dami Choi, Jan Betley, Samuel Marks, Cem Anil, Roger B. Grosse, Owain Evans
2024	J	`jnl`	Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data. CoRR Johannes Treutlein, Dami Choi, Jan Betley, Cem Anil, Samuel Marks, Roger Baker Grosse, Owain Evans
2024	A*	`conf`	LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language. NeurIPS James Requeima, John Bronskill, Dami Choi, Richard E. Turner, David Kristjanson Duvenaud
2024	J	`jnl`	LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language. CoRR James Requeima, John Bronskill, Dami Choi, Richard E. Turner, David Duvenaud
2023	A*	`conf`	Order Matters in the Presence of Dataset Imbalance for Multilingual Learning. NeurIPS Dami Choi, Derrick Xin, Hamid Dadkhahi, Justin Gilmer, Ankush Garg, Orhan Firat, Chih-Kuan Yeh, Andrew M. Dai, Behrooz Ghorbani
2023	J	`jnl`	Order Matters in the Presence of Dataset Imbalance for Multilingual Learning. CoRR Dami Choi, Derrick Xin, Hamid Dadkhahi, Justin Gilmer, Ankush Garg, Orhan Firat, Chih-Kuan Yeh, Andrew M. Dai, Behrooz Ghorbani
2023	A*	`conf`	Tools for Verifying Neural Models' Training Data. NeurIPS Dami Choi, Yonadav Shavit, David Kristjanson Duvenaud
2023	J	`jnl`	Tools for Verifying Neural Models' Training Data. CoRR Dami Choi, Yonadav Shavit, David Duvenaud
2020	A*	`conf`	Gradient Estimation with Stochastic Softmax Tricks. NeurIPS Max B. Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, Chris J. Maddison
2020	J	`jnl`	Gradient Estimation with Stochastic Softmax Tricks. CoRR Max B. Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, Chris J. Maddison
2020	—	`conf`	Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. ICBINB@NeurIPS Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig
2020	J	`jnl`	Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. CoRR Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig
2019	J	`jnl`	Faster Neural Network Training with Data Echoing. CoRR Dami Choi, Alexandre Passos, Christopher J. Shallue, George E. Dahl
2019	A*	`conf`	Guided evolutionary strategies: augmenting random search with surrogate gradients. ICML Niru Maheswaranathan, Luke Metz, George Tucker, Dami Choi, Jascha Sohl-Dickstein
2019	J	`jnl`	On Empirical Comparisons of Optimizers for Deep Learning. CoRR Dami Choi, Christopher J. Shallue, Zachary Nado, Jaehoon Lee, Chris J. Maddison, George E. Dahl
2018	—	`conf`	Backpropagation through the Void: Optimizing control variates for black-box gradient estimation. ICLR (Poster) Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud
2017	J	`jnl`	Backpropagation through the Void: Optimizing control variates for black-box gradient estimation. CoRR Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud