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cs.LG2024
Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence
Berfin Şimşek, Amire Bendjeddou, Daniel Hsu
This work focuses on the gradient flow dynamics of a neural network model that uses correlation loss to approximate a multi-index function on high-dimensional standard Gaussian dat…
cs.LG2024
Group-wise oracle-efficient algorithms for online multi-group learning
Samuel Deng, Daniel Hsu, Jingwen Liu
We study the problem of online multi-group learning, a learning model in which an online learner must simultaneously achieve small prediction regret on a large collection of (possi…
cs.LG2024
Multi-group Learning for Hierarchical Groups
Samuel Deng, Daniel Hsu
The multi-group learning model formalizes the learning scenario in which a single predictor must generalize well on multiple, possibly overlapping subgroups of interest. We extend…