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no code implementations • ICML 2020 • Vaishaal Shankar, Rebecca Roelofs, Horia Mania, Alex Fang, Benjamin Recht, Ludwig Schmidt

We perform an in-depth evaluation of human accuracy on the ImageNet dataset.

1 code implementation • 4 Sep 2021 • Mitchell Wortsman, Gabriel Ilharco, Mike Li, Jong Wook Kim, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt

Compared to standard fine-tuning, the resulting weight-space ensembles provide large accuracy improvements out-of-distribution, while matching or improving in-distribution accuracy.

1 code implementation • NeurIPS 2021 • Frances Ding, Moritz Hardt, John Miller, Ludwig Schmidt

Our primary contribution is a suite of new datasets derived from US Census surveys that extend the existing data ecosystem for research on fair machine learning.

2 code implementations • NeurIPS 2021 • Timo Milbich, Karsten Roth, Samarth Sinha, Ludwig Schmidt, Marzyeh Ghassemi, Björn Ommer

Finally, we propose few-shot DML as an efficient way to consistently improve generalization in response to unknown test shifts presented in ooDML.

1 code implementation • 9 Jul 2021 • John Miller, Rohan Taori, aditi raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, Ludwig Schmidt

For machine learning systems to be reliable, we must understand their performance in unseen, out-of-distribution environments.

no code implementations • ICCV 2021 • Devin Guillory, Vaishaal Shankar, Sayna Ebrahimi, Trevor Darrell, Ludwig Schmidt

Our work connects techniques from domain adaptation and predictive uncertainty literature, and allows us to predict model accuracy on challenging unseen distributions without access to labeled data.

1 code implementation • ICCV 2021 • Klemen Kotar, Gabriel Ilharco, Ludwig Schmidt, Kiana Ehsani, Roozbeh Mottaghi

In the past few years, we have witnessed remarkable breakthroughs in self-supervised representation learning.

1 code implementation • NeurIPS 2020 • Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, Ludwig Schmidt

We study how robust current ImageNet models are to distribution shifts arising from natural variations in datasets.

no code implementations • ICML 2020 • John Miller, Karl Krauth, Benjamin Recht, Ludwig Schmidt

We build four new test sets for the Stanford Question Answering Dataset (SQuAD) and evaluate the ability of question-answering systems to generalize to new data.

1 code implementation • ICML 2020 • Vaishaal Shankar, Alex Fang, Wenshuo Guo, Sara Fridovich-Keil, Ludwig Schmidt, Jonathan Ragan-Kelley, Benjamin Recht

We investigate the connections between neural networks and simple building blocks in kernel space.

no code implementations • NeurIPS 2019 • Rebecca Roelofs, Vaishaal Shankar, Benjamin Recht, Sara Fridovich-Keil, Moritz Hardt, John Miller, Ludwig Schmidt

By systematically comparing the public ranking with the final ranking, we assess how much participants adapted to the holdout set over the course of a competition.

no code implementations • 25 Sep 2019 • Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, Ludwig Schmidt

We conduct a large experimental comparison of various robustness metrics for image classification.

1 code implementation • ICCV 2021 • Vaishaal Shankar, Achal Dave, Rebecca Roelofs, Deva Ramanan, Benjamin Recht, Ludwig Schmidt

Additionally, we evaluate three detection models and show that natural perturbations induce both classification as well as localization errors, leading to a median drop in detection mAP of 14 points.

5 code implementations • NeurIPS 2019 • Yair Carmon, aditi raghunathan, Ludwig Schmidt, Percy Liang, John C. Duchi

We demonstrate, theoretically and empirically, that adversarial robustness can significantly benefit from semisupervised learning.

no code implementations • NeurIPS 2019 • Horia Mania, John Miller, Ludwig Schmidt, Moritz Hardt, Benjamin Recht

Excessive reuse of test data has become commonplace in today's machine learning workflows.

no code implementations • ICML Workshop Deep_Phenomen 2019 • Vaishaal Shankar, Achal Dave, Rebecca Roelofs, Deva Ramanan, Benjamin Recht, Ludwig Schmidt

We introduce a systematic framework for quantifying the robustness of classifiers to naturally occurring perturbations of images found in videos.

1 code implementation • NeurIPS Workshop ImageNet_PPF 2021 • Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar

We evaluate a broad range of models and find accuracy drops of 3% - 15% on CIFAR-10 and 11% - 14% on ImageNet.

no code implementations • 13 Jul 2018 • Smitha Milli, Ludwig Schmidt, Anca D. Dragan, Moritz Hardt

We show through theory and experiment that gradient-based explanations of a model quickly reveal the model itself.

3 code implementations • 1 Jun 2018 • Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar

Although we ensure that the new test set is as close to the original data distribution as possible, we find a large drop in accuracy (4% to 10%) for a broad range of deep learning models.

no code implementations • NeurIPS 2018 • Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Mądry

We postulate that the difficulty of training robust classifiers stems, at least partially, from this inherently larger sample complexity.

no code implementations • 23 Feb 2018 • Ilias Diakonikolas, Jerry Li, Ludwig Schmidt

We give an algorithm for this learning problem that uses $n = \tilde{O}_d(k/\epsilon^2)$ samples and runs in time $\tilde{O}_d(n)$.

no code implementations • ICLR 2018 • Ludwig Schmidt, Kunal Talwar

Based on our experiments, we propose a number of training modifications that lead to significantly better datasets for nearest neighbor algorithms.

no code implementations • ICLR 2018 • Shibani Santurkar, Ludwig Schmidt, Aleksander Madry

A fundamental, and still largely unanswered, question in the context of Generative Adversarial Networks (GANs) is whether GANs are actually able to capture the key characteristics of the datasets they are trained on.

no code implementations • ICLR 2018 • Jerry Li, Aleksander Madry, John Peebles, Ludwig Schmidt

This suggests that such usage of the first order approximation of the discriminator, which is a de-facto standard in all the existing GAN dynamics, might be one of the factors that makes GAN training so challenging in practice.

1 code implementation • 15 Dec 2017 • Alexander LeNail, Ludwig Schmidt, Johnathan Li, Tobias Ehrenberger, Karen Sachs, Stefanie Jegelka, Ernest Fraenkel

We introduce Graph-Sparse Logistic Regression, a new algorithm for classification for the case in which the support should be sparse but connected on a graph.

2 code implementations • 7 Dec 2017 • Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, Aleksander Madry

The study of adversarial robustness has so far largely focused on perturbations bound in p-norms.

no code implementations • NeurIPS 2017 • Ilias Diakonikolas, Elena Grigorescu, Jerry Li, Abhiram Natarajan, Krzysztof Onak, Ludwig Schmidt

For the case of structured distributions, such as k-histograms and monotone distributions, we design distributed learning algorithms that achieve significantly better communication guarantees than the naive ones, and obtain tight upper and lower bounds in several regimes.

no code implementations • ICML 2018 • Shibani Santurkar, Ludwig Schmidt, Aleksander Mądry

A basic, and still largely unanswered, question in the context of Generative Adversarial Networks (GANs) is whether they are truly able to capture all the fundamental characteristics of the distributions they are trained on.

no code implementations • ICML 2018 • Jerry Li, Aleksander Madry, John Peebles, Ludwig Schmidt

While Generative Adversarial Networks (GANs) have demonstrated promising performance on multiple vision tasks, their learning dynamics are not yet well understood, both in theory and in practice.

43 code implementations • ICLR 2018 • Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu

Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal.

Ranked #2 on Robust classification on CIFAR-10

no code implementations • NeurIPS 2017 • Arturs Backurs, Piotr Indyk, Ludwig Schmidt

We also give similar hardness results for computing the gradient of the empirical loss, which is the main computational burden in many non-convex learning tasks.

no code implementations • NeurIPS 2016 • Chinmay Hegde, Piotr Indyk, Ludwig Schmidt

We address the problem of recovering a high-dimensional but structured vector from linear observations in a general setting where the vector can come from an arbitrary union of subspaces.

no code implementations • 14 Jul 2016 • Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt

We study the fixed design segmented regression problem: Given noisy samples from a piecewise linear function $f$, we want to recover $f$ up to a desired accuracy in mean-squared error.

no code implementations • NeurIPS 2015 • Ilias Diakonikolas, Moritz Hardt, Ludwig Schmidt

We investigate the problem of learning an unknown probability distribution over a discrete population from random samples.

1 code implementation • NeurIPS 2015 • Alexandr Andoni, Piotr Indyk, Thijs Laarhoven, Ilya Razenshteyn, Ludwig Schmidt

Our lower bound implies that the above LSH family exhibits a trade-off between evaluation time and quality that is close to optimal for a natural class of LSH functions.

no code implementations • 3 Jun 2015 • Jerry Li, Ludwig Schmidt

One notion of learning a GMM is proper learning: here, the goal is to find a mixture of $k$ Gaussians $\mathcal{M}$ that is close to the density $f$ of the unknown distribution from which we draw samples.

no code implementations • 1 Jun 2015 • Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt

Let $f$ be the density function of an arbitrary univariate distribution, and suppose that $f$ is $\mathrm{OPT}$-close in $L_1$-distance to an unknown piecewise polynomial function with $t$ interval pieces and degree $d$.

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