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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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08/01/2022
What Can Transformers Learn In-Context? A Case Study of Simple Function Classes
In-context learning refers to the ability of a model to condition on a p...
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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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10/15/2021
Combining Diverse Feature Priors
To improve model generalization, model designers often restrict the feat...
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12/18/2020
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses
As machine learning systems grow in scale, so do their training data req...
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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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12/05/2019
Label-Consistent Backdoor Attacks
Deep neural networks have been demonstrated to be vulnerable to backdoor...
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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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02/18/2019
On Evaluating Adversarial Robustness
Correctly evaluating defenses against adversarial examples has proven to...
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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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12/07/2017
A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations
Recent work has shown that neural network-based vision classifiers exhib...
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06/19/2017
