Hyak Huskies at ICML 2023
We were delighted to see so many Huskies in attendance at the Fortieth International Conference on Machine Learning, which took place at the end of July. The researchers using Hyak are doing incredible work, and we wanted to say congratulations to those who had their papers accepted:
-
Ian Connick Covert, Wei Qiu, Mingyu Lu, Na Yoon Kim, Nathan J White, Su-In Lee: Learning to Maximize Mutual Information for Dynamic Feature Selection
-
Tim Dettmers, Luke Zettlemoyer: The case for 4-bit precision: k-bit Inference Scaling Laws
-
Runlong Zhou, Zhang Zihan, Simon Shaolei Du: Sharp Variance-Dependent Bounds in Reinforcement Learning: Best of Both Worlds in Stochastic and Deterministic Environments
-
Haotian Ye, Xiaoyu Chen, Liwei Wang, Simon Shaolei Du: On the Power of Pre-training for Generalization in RL: Provable Benefits and Hardness
-
Jikai Jin, Zhiyuan Li, Kaifeng Lyu, Simon Shaolei Du, Jason D. Lee: Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing
-
Runlong Zhou, Ruosong Wang, Simon Shaolei Du: Horizon-Free and Variance-Dependent Reinforcement Learning for Latent Markov Decision Processes
-
Yiping Wang, Yifang Chen, Kevin Jamieson, Simon Shaolei Du: Improved Active Multi-Task Representation Learning via Lasso
Also, special congratulations for those with accepted shadow papers:
