research
∙
02/15/2023
Dataset Interfaces: Diagnosing Model Failures Using Controllable Counterfactual Generation
Distribution shifts are a major source of failure of deployed machine le...
share
research
∙
02/01/2022
Datamodels: Predicting Predictions from Training Data
We present a conceptual framework, datamodeling, for analyzing the behav...
research
∙
06/17/2020
Noise or Signal: The Role of Image Backgrounds in Object Recognition
We assess the tendency of state-of-the-art object recognition models to ...
research
∙
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...
research
∙
05/22/2020
From ImageNet to Image Classification: Contextualizing Progress on Benchmarks
Building rich machine learning datasets in a scalable manner often neces...
research
∙
05/19/2020
Identifying Statistical Bias in Dataset Replication
Dataset replication is a useful tool for assessing whether improvements ...
research
∙
06/06/2019
Image Synthesis with a Single (Robust) Classifier
We show that the basic classification framework alone can be used to tac...
research
∙
06/06/2019
Computer Vision with a Single (Robust) Classifier
We show that the basic classification framework alone can be used to tac...
research
∙
06/03/2019
Learning Perceptually-Aligned Representations via Adversarial Robustness
Many applications of machine learning require models that are human-alig...
research
∙
05/06/2019
Adversarial Examples Are Not Bugs, They Are Features
Adversarial examples have attracted significant attention in machine lea...
research
∙
11/06/2018
Are Deep Policy Gradient Algorithms Truly Policy Gradient Algorithms?
We study how the behavior of deep policy gradient algorithms reflects th...
research
∙
07/26/2018
Evaluating and Understanding the Robustness of Adversarial Logit Pairing
We evaluate the robustness of Adversarial Logit Pairing, a recently prop...
research
∙
07/20/2018
Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors
We introduce a framework that unifies the existing work on black-box adv...
research
∙
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...
research
∙
04/23/2018
Black-box Adversarial Attacks with Limited Queries and Information
Current neural network-based classifiers are susceptible to adversarial ...
research
∙
12/19/2017
Query-Efficient Black-box Adversarial Examples
Current neural network-based image classifiers are susceptible to advers...
research
∙
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...
research
∙
07/24/2017