560 citations · 820 across the 9 of their papers we have counts for
9 papers
Grass: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients
Aashiq Muhamed, Oscar Li, David Woodruff +2
Large language model (LLM) training and finetuning are often bottlenecked by limited GPU memory. While existing projection-based optimization methods address this by projecting gra…
OmniPred: Language Models as Universal Regressors
Xingyou Song, Oscar Li, Chansoo Lee +4
Regression is a powerful tool to accurately predict the outcome metric of a system given a set of parameters, but has traditionally been restricted to methods which are only applic…
Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies
Oscar Li, James Harrison, Jascha Sohl-Dickstein +2
Unrolled computation graphs are prevalent throughout machine learning but present challenges to automatic differentiation (AD) gradient estimation methods when their loss functions…
Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution
Amrith Setlur, Oscar Li, Virginia Smith
We categorize meta-learning evaluation into two settings: [ID], in which the train and test tasks are sampled from the same underlying tas…
Label Leakage and Protection in Two-party Split Learning
Oscar Li, Jiankai Sun, Xin Yang +5
Two-party split learning is a popular technique for learning a model across feature-partitioned data. In this work, we explore whether it is possible for one party to steal the pri…
Is Support Set Diversity Necessary for Meta-Learning?
Amrith Setlur, Oscar Li, Virginia Smith
Meta-learning is a popular framework for learning with limited data in which an algorithm is produced by training over multiple few-shot learning tasks. For classification problems…
Interpretable Image Recognition with Hierarchical Prototypes
Peter Hase, Chaofan Chen, Oscar Li +1
Vision models are interpretable when they classify objects on the basis of features that a person can directly understand. Recently, methods relying on visual feature prototypes ha…
This Looks Like That: Deep Learning for Interpretable Image Recognition
Chaofan Chen, Oscar Li, Chaofan Tao +3
When we are faced with challenging image classification tasks, we often explain our reasoning by dissecting the image, and pointing out prototypical aspects of one class or another…
Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions
Oscar Li, Hao Liu, Chaofan Chen +1
Deep neural networks are widely used for classification. These deep models often suffer from a lack of interpretability -- they are particularly difficult to understand because of…