69 citations · 157 across the 10 of their papers we have counts for
3 papers · 1 filter
Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning
Liam Collins, Shanshan Wu, Sewoong Oh +1
In many applications of federated learning (FL), clients desire models that are personalized using their local data, yet are also robust in the sense that they retain general globa…
Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks
Liam Collins, Hamed Hassani, Mahdi Soltanolkotabi +2
An increasingly popular machine learning paradigm is to pretrain a neural network (NN) on many tasks offline, then adapt it to downstream tasks, often by re-training only the last…
InfoNCE Loss Provably Learns Cluster-Preserving Representations
Advait Parulekar, Liam Collins, Karthikeyan Shanmugam +2
The goal of contrasting learning is to learn a representation that preserves underlying clusters by keeping samples with similar content, e.g. the ``dogness'' of a dog, close to ea…