932 citations · 2.2k across the 38 of their papers we have counts for
41 papers
Inferring couplings in networks across order-disorder phase transitions
Vudtiwat Ngampruetikorn, Vedant Sachdeva, Johanna Torrence +3
Statistical inference is central to many scientific endeavors, yet how it works remains unresolved. Answering this requires a quantitative understanding of the intrinsic interplay…
Perturbation Theory for the Information Bottleneck
Vudtiwat Ngampruetikorn, David J. Schwab
Extracting relevant information from data is crucial for all forms of learning. The information bottleneck (IB) method formalizes this, offering a mathematically precise and concep…
Understanding Species Abundance Distributions in Complex Ecosystems of Interacting Species
Jim Wu, Pankaj Mehta, David Schwab
Niche and neutral theory are two prevailing, yet much debated, ideas in ecology proposed to explain the patterns of biodiversity. Whereas niche theory emphasizes selective differen…
Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations
Chaitanya K. Ryali, David J. Schwab, Ari S. Morcos
Recent progress in self-supervised learning has demonstrated promising results in multiple visual tasks. An important ingredient in high-performing self-supervised methods is the u…
Are all negatives created equal in contrastive instance discrimination?
Tiffany Tianhui Cai, Jonathan Frankle, David J. Schwab +1
Self-supervised learning has recently begun to rival supervised learning on computer vision tasks. Many of the recent approaches have been based on contrastive instance discriminat…
Learning Optimal Representations with the Decodable Information Bottleneck
Yann Dubois, Douwe Kiela, David J. Schwab +1
We address the question of characterizing and finding optimal representations for supervised learning. Traditionally, this question has been tackled using the Information Bottlenec…