67 citations · 153 across the 8 of their papers we have counts for
8 papers
Provable Guarantees for Neural Networks via Gradient Feature Learning
Zhenmei Shi, Junyi Wei, Yingyu Liang
Neural networks have achieved remarkable empirical performance, while the current theoretical analysis is not adequate for understanding their success, e.g., the Neural Tangent Ker…
When and How Does Known Class Help Discover Unknown Ones? Provable Understanding Through Spectral Analysis
Yiyou Sun, Zhenmei Shi, Yingyu Liang +1
Novel Class Discovery (NCD) aims at inferring novel classes in an unlabeled set by leveraging prior knowledge from a labeled set with known classes. Despite its importance, there i…
Stratified Adversarial Robustness with Rejection
Jiefeng Chen, Jayaram Raghuram, Jihye Choi +3
Recently, there is an emerging interest in adversarially training a classifier with a rejection option (also known as a selective classifier) for boosting adversarial robustness. W…
The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning
Zhenmei Shi, Jiefeng Chen, Kunyang Li +4
Pre-training representations (a.k.a. foundation models) has recently become a prevalent learning paradigm, where one first pre-trains a representation using large-scale unlabeled d…
On the identifiability of mixtures of ranking models
Xiaomin Zhang, Xucheng Zhang, Po-Ling Loh +1
Mixtures of ranking models are standard tools for ranking problems. However, even the fundamental question of parameter identifiability is not fully understood: the identifiability…
Recovery Guarantee of Non-negative Matrix Factorization via Alternating Updates
Yuanzhi Li, Yingyu Liang, Andrej Risteski
Non-negative matrix factorization is a popular tool for decomposing data into feature and weight matrices under non-negativity constraints. It enjoys practical success but is poorl…