Dimitris Tsipras | DeepAI

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∙ 11/16/2022

Holistic Evaluation of Language Models

Language models (LMs) are becoming the foundation for almost all major l...

21 Percy Liang, et al. ∙

∙ 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...

0 Shivam Garg, et al. ∙

∙ 12/02/2021

Editing a classifier by rewriting its prediction rules

We present a methodology for modifying the behavior of a classifier by d...

13 Shibani Santurkar, et al. ∙

∙ 10/15/2021

Combining Diverse Feature Priors

To improve model generalization, model designers often restrict the feat...

9 Saachi Jain, et al. ∙

∙ 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...

71 Micah Goldblum, et al. ∙

∙ 08/11/2020

BREEDS: Benchmarks for Subpopulation Shift

We develop a methodology for assessing the robustness of models to subpo...

5 Shibani Santurkar, et al. ∙

∙ 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...

0 Logan Engstrom, et al. ∙

∙ 05/22/2020

From ImageNet to Image Classification: Contextualizing Progress on Benchmarks

Building rich machine learning datasets in a scalable manner often neces...

24 Dimitris Tsipras, et al. ∙

∙ 05/19/2020

Identifying Statistical Bias in Dataset Replication

Dataset replication is a useful tool for assessing whether improvements ...

10 Logan Engstrom, et al. ∙

∙ 12/05/2019

Label-Consistent Backdoor Attacks

Deep neural networks have been demonstrated to be vulnerable to backdoor...

20 Alexander Turner, et al. ∙

∙ 06/06/2019

Image Synthesis with a Single (Robust) Classifier

We show that the basic classification framework alone can be used to tac...

0 Shibani Santurkar, et al. ∙

∙ 06/06/2019

Computer Vision with a Single (Robust) Classifier

We show that the basic classification framework alone can be used to tac...

0 Shibani Santurkar, et al. ∙

∙ 06/03/2019

Learning Perceptually-Aligned Representations via Adversarial Robustness

Many applications of machine learning require models that are human-alig...

0 Logan Engstrom, et al. ∙

∙ 05/06/2019

Adversarial Examples Are Not Bugs, They Are Features

Adversarial examples have attracted significant attention in machine lea...

0 Andrew Ilyas, et al. ∙

∙ 02/18/2019

On Evaluating Adversarial Robustness

Correctly evaluating defenses against adversarial examples has proven to...

10 Nicholas Carlini, et al. ∙

∙ 11/06/2018

Are Deep Policy Gradient Algorithms Truly Policy Gradient Algorithms?

We study how the behavior of deep policy gradient algorithms reflects th...

4 Andrew Ilyas, et al. ∙

∙ 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...

2 Dimitris Tsipras, et al. ∙

∙ 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...

0 Shibani Santurkar, et al. ∙

∙ 04/30/2018

Adversarially Robust Generalization Requires More Data

Machine learning models are often susceptible to adversarial perturbatio...

0 Ludwig Schmidt, et al. ∙

∙ 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...

0 Logan Engstrom, et al. ∙

∙ 06/19/2017

Towards Deep Learning Models Resistant to Adversarial Attacks

Recent work has demonstrated that neural networks are vulnerable to adve...

0 Aleksander Madry, et al. ∙

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