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06/28/2023
Beyond NTK with Vanilla Gradient Descent: A Mean-Field Analysis of Neural Networks with Polynomial Width, Samples, and Time
Despite recent theoretical progress on the non-convex optimization of tw...
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05/27/2023
Beyond Positive Scaling: How Negation Impacts Scaling Trends of Language Models
Language models have been shown to exhibit positive scaling, where perfo...
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02/08/2023
Diagnosing and Rectifying Vision Models using Language
Recent multi-modal contrastive learning models have demonstrated the abi...
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11/27/2022
A Theoretical Study of Inductive Biases in Contrastive Learning
Understanding self-supervised learning is important but challenging. Pre...
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04/06/2022
Beyond Separability: Analyzing the Linear Transferability of Contrastive Representations to Related Subpopulations
Contrastive learning is a highly effective method which uses unlabeled d...
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04/01/2022
Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation
We consider unsupervised domain adaptation (UDA), where labeled data fro...
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02/28/2022
Amortized Proximal Optimization
We propose a framework for online meta-optimization of parameters that g...
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10/11/2021
Self-supervised Learning is More Robust to Dataset Imbalance
Self-supervised learning (SSL) is a scalable way to learn general visual...
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06/08/2021
Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss
Recent works in self-supervised learning have advanced the state-of-the-...
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11/03/2020
Meta-learning Transferable Representations with a Single Target Domain
Recent works found that fine-tuning and joint training—two popular appro...
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06/15/2020
Shape Matters: Understanding the Implicit Bias of the Noise Covariance
The noise in stochastic gradient descent (SGD) provides a crucial implic...
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06/26/2018
