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03/24/2023
TRAK: Attributing Model Behavior at Scale
The goal of data attribution is to trace model predictions back to train...
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11/22/2022
ModelDiff: A Framework for Comparing Learning Algorithms
We study the problem of (learning) algorithm comparison, where the goal ...
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05/06/2022
What Makes A Good Fisherman? Linear Regression under Self-Selection Bias
In the classical setting of self-selection, the goal is to learn k model...
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05/04/2022
Estimation of Standard Auction Models
We provide efficient estimation methods for first- and second-price auct...
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02/01/2022
Datamodels: Predicting Predictions from Training Data
We present a conceptual framework, datamodeling, for analyzing the behav...
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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 ...
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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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07/26/2018
Evaluating and Understanding the Robustness of Adversarial Logit Pairing
We evaluate the robustness of Adversarial Logit Pairing, a recently prop...
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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...
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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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04/23/2018
Black-box Adversarial Attacks with Limited Queries and Information
Current neural network-based classifiers are susceptible to adversarial ...
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12/26/2017
The Robust Manifold Defense: Adversarial Training using Generative Models
Deep neural networks are demonstrating excellent performance on several ...
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12/19/2017
Query-Efficient Black-box Adversarial Examples
Current neural network-based image classifiers are susceptible to advers...
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10/31/2017
Training GANs with Optimism
We address the issue of limit cycling behavior in training Generative Ad...
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10/31/2017
Extracting Syntactic Patterns from Databases
Many database columns contain string or numerical data that conforms to ...
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07/24/2017