Ludwig Schmidt

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time.

[BibT_eX]

[DOI]

Raphael Gontijo Lopes

Proceedings of the International Conference on Machine Learning, 2022

Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP).

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Adversarial Scrutiny of Evidentiary Statistical Software.

[BibT_eX]

[DOI]

Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

Exploring The Landscape of Distributional Robustness for Question Answering Models.

[BibT_eX]

[DOI]

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 2022

Robust fine-tuning of zero-shot models.

[BibT_eX]

[DOI]

Raphael Gontijo Lopes

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

Robust fine-tuning of zero-shot models.

[BibT_eX]

[DOI]

CoRR, 2021

Contrasting Contrastive Self-Supervised Representation Learning Models.

[BibT_eX]

[DOI]

CoRR, 2021

Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Are We Learning Yet? A Meta Review of Evaluation Failures Across Machine Learning.

[BibT_eX]

[DOI]

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Retiring Adult: New Datasets for Fair Machine Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

Do Image Classifiers Generalize Across Time?

[BibT_eX]

[DOI]

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Contrasting Contrastive Self-Supervised Representation Learning Pipelines.

[BibT_eX]

[DOI]

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Predicting with Confidence on Unseen Distributions.

[BibT_eX]

[DOI]

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020

Measuring Robustness to Natural Distribution Shifts in Image Classification.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Evaluating Machine Accuracy on ImageNet.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

Neural Kernels Without Tangents.

[BibT_eX]

[DOI]

Jonathan Ragan-Kelley

Benjamin Recht

Proceedings of the 37th International Conference on Machine Learning, 2020

The Effect of Natural Distribution Shift on Question Answering Models.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

A systematic framework for natural perturbations from videos.

[BibT_eX]

[DOI]

CoRR, 2019

A Meta-Analysis of Overfitting in Machine Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Model Similarity Mitigates Test Set Overuse.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Unlabeled Data Improves Adversarial Robustness.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Do ImageNet Classifiers Generalize to ImageNet?

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

Exploring the Landscape of Spatial Robustness.

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

Model Reconstruction from Model Explanations.

[BibT_eX]

[DOI]

Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019

2018

Algorithms above the noise floor.

[BibT_eX]

[DOI]

PhD thesis, 2018

Do CIFAR-10 Classifiers Generalize to CIFAR-10?

[BibT_eX]

[DOI]

CoRR, 2018

Adversarially Robust Generalization Requires More Data.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

A Classification-Based Study of Covariate Shift in GAN Distributions.

[BibT_eX]

[DOI]

Shibani Santurkar

Aleksander Madry

Proceedings of the 35th International Conference on Machine Learning, 2018

On the Limitations of First-Order Approximation in GAN Dynamics.

[BibT_eX]

[DOI]

Proceedings of the 35th International Conference on Machine Learning, 2018

Towards Deep Learning Models Resistant to Adversarial Attacks.

[BibT_eX]

[DOI]

Proceedings of the 6th International Conference on Learning Representations, 2018

Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms.

[BibT_eX]

[DOI]

Ilias Diakonikolas

Jerry Li

Proceedings of the Conference On Learning Theory, 2018

A Fast Algorithm for Separated Sparsity via Perturbed Lagrangians.

[BibT_eX]

[DOI]

Aleksander Madry

Slobodan Mitrovic

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Graph-Sparse Logistic Regression.

[BibT_eX]

[DOI]

CoRR, 2017

A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations.

[BibT_eX]

[DOI]

CoRR, 2017

A Classification-Based Perspective on GAN Distributions.

[BibT_eX]

[DOI]

Shibani Santurkar

Aleksander Madry

CoRR, 2017

Towards Understanding the Dynamics of Generative Adversarial Networks.

[BibT_eX]

[DOI]

CoRR, 2017

Better Approximations for Tree Sparsity in Nearly-Linear Time.

[BibT_eX]

[DOI]

Arturs Backurs

Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms, 2017

Sample-Optimal Density Estimation in Nearly-Linear Time.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms, 2017

Communication-Efficient Distributed Learning of Discrete Distributions.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks.

[BibT_eX]

[DOI]

Arturs Backurs

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Robust and Proper Learning for Mixtures of Gaussians via Systems of Polynomial Inequalities.

[BibT_eX]

[DOI]

Jerry Li

Proceedings of the 30th Conference on Learning Theory, 2017

2016

Fast recovery from a union of subspaces.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Fast Algorithms for Segmented Regression.

[BibT_eX]

[DOI]

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Approximation Algorithms for Model-Based Compressive Sensing.

[BibT_eX]

[DOI]

IEEE Trans. Inf. Theory, 2015

Fast Algorithms for Structured Sparsity.

[BibT_eX]

[DOI]

Bull. EATCS, 2015

A Nearly Optimal and Agnostic Algorithm for Properly Learning a Mixture of k Gaussians, for any Constant k.

[BibT_eX]

[DOI]

Jerry Zheng Li

CoRR, 2015

Fast and Near-Optimal Algorithms for Approximating Distributions by Histograms.

[BibT_eX]

[DOI]

Proceedings of the 34th ACM Symposium on Principles of Database Systems, 2015

Differentially Private Learning of Structured Discrete Distributions.

[BibT_eX]

[DOI]

Ilias Diakonikolas

Moritz Hardt

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Practical and Optimal LSH for Angular Distance.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

A Nearly-Linear Time Framework for Graph-Structured Sparsity.

[BibT_eX]

[DOI]

Proceedings of the 32nd International Conference on Machine Learning, 2015

Seismic feature extraction using steiner tree methods.

[BibT_eX]

[DOI]

Proceedings of the 2015 IEEE International Conference on Acoustics, 2015

2014

Recent Developments in the Sparse Fourier Transform: A compressed Fourier transform for big data.

[BibT_eX]

[DOI]

IEEE Signal Process. Mag., 2014

Approximation-Tolerant Model-Based Compressive Sensing.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms, 2014

A fast approximation algorithm for tree-sparse recovery.

[BibT_eX]

[DOI]

Proceedings of the 2014 IEEE International Symposium on Information Theory, Honolulu, HI, USA, June 29, 2014

Large-scale speaker identification.

[BibT_eX]

[DOI]

Matthew Sharifi

Ignacio López-Moreno

Proceedings of the IEEE International Conference on Acoustics, 2014

Automatic fault localization using the generalized Earth Mover's distance.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Acoustics, 2014

Nearly Linear-Time Model-Based Compressive Sensing.

[BibT_eX]

[DOI]