95 citations · 421 across the 33 of their papers we have counts for
33 papers
Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms
Qian Yu, Yining Wang, Baihe Huang +2
In stochastic zeroth-order optimization, a problem of practical relevance is understanding how to fully exploit the local geometry of the underlying objective function. We consider…
Reconstructing Training Data from Model Gradient, Provably
Zihan Wang, Jason D. Lee, Qi Lei
Understanding when and how much a model gradient leaks information about the training sample is an important question in privacy. In this paper, we present a surprising result: eve…
Optimization for Amortized Inverse Problems
Tianci Liu, Tong Yang, Quan Zhang +1
Incorporating a deep generative model as the prior distribution in inverse problems has established substantial success in reconstructing images from corrupted observations. Notwit…
Efficient Medical Image Assessment via Self-supervised Learning
Chun-Yin Huang, Qi Lei, Xiaoxiao Li
High-performance deep learning methods typically rely on large annotated training datasets, which are difficult to obtain in many clinical applications due to the high cost of medi…
Nearly Minimax Algorithms for Linear Bandits with Shared Representation
Jiaqi Yang, Qi Lei, Jason D. Lee +1
We give novel algorithms for multi-task and lifelong linear bandits with shared representation. Specifically, we consider the setting where we play linear bandits with dimensio…
Sample Efficiency of Data Augmentation Consistency Regularization
Shuo Yang, Yijun Dong, Rachel Ward +3
Data augmentation is popular in the training of large neural networks; currently, however, there is no clear theoretical comparison between different algorithmic choices on how to…