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03/30/2023
Whose Opinions Do Language Models Reflect?
Language models (LMs) are increasingly being used in open-ended contexts...
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02/06/2023
Data Selection for Language Models via Importance Resampling
Selecting a suitable training dataset is crucial for both general-domain...
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11/16/2022
Holistic Evaluation of Language Models
Language models (LMs) are becoming the foundation for almost all major l...
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12/02/2021
Editing a classifier by rewriting its prediction rules
We present a methodology for modifying the behavior of a classifier by d...
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05/11/2021
Leveraging Sparse Linear Layers for Debuggable Deep Networks
We show how fitting sparse linear models over learned deep feature repre...
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08/11/2020
BREEDS: Benchmarks for Subpopulation Shift
We develop a methodology for assessing the robustness of models to subpo...
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05/25/2020
Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO
We study the roots of algorithmic progress in deep policy gradient algor...
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05/22/2020
From ImageNet to Image Classification: Contextualizing Progress on Benchmarks
Building rich machine learning datasets in a scalable manner often neces...
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05/19/2020
Identifying Statistical Bias in Dataset Replication
Dataset replication is a useful tool for assessing whether improvements ...
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06/06/2019
Image Synthesis with a Single (Robust) Classifier
We show that the basic classification framework alone can be used to tac...
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06/06/2019
Computer Vision with a Single (Robust) Classifier
We show that the basic classification framework alone can be used to tac...
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06/03/2019
Learning Perceptually-Aligned Representations via Adversarial Robustness
Many applications of machine learning require models that are human-alig...
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05/06/2019
Adversarial Examples Are Not Bugs, They Are Features
Adversarial examples have attracted significant attention in machine lea...
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11/06/2018
Are Deep Policy Gradient Algorithms Truly Policy Gradient Algorithms?
We study how the behavior of deep policy gradient algorithms reflects th...
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05/30/2018
There Is No Free Lunch In Adversarial Robustness (But There Are Unexpected Benefits)
We provide a new understanding of the fundamental nature of adversariall...
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05/29/2018
How Does Batch Normalization Help Optimization? (No, It Is Not About Internal Covariate Shift)
Batch Normalization (BatchNorm) is a widely adopted technique that enabl...
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04/30/2018
Adversarially Robust Generalization Requires More Data
Machine learning models are often susceptible to adversarial perturbatio...
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11/02/2017
A Classification-Based Perspective on GAN Distributions
A fundamental, and still largely unanswered, question in the context of ...
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03/04/2017
Generative Compression
Traditional image and video compression algorithms rely on hand-crafted ...
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02/23/2017
Toward Streaming Synapse Detection with Compositional ConvNets
Connectomics is an emerging field in neuroscience that aims to reconstru...
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11/20/2016