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2020 – today
- 2023
[j4]Mitchell Wortsman, Suchin Gururangan, Shen Li, Ali Farhadi, Ludwig Schmidt, Michael G. Rabbat, Ari S. Morcos:
lo-fi: distributed fine-tuning without communication. Trans. Mach. Learn. Res. 2023 (2023)- [c55]Mike A. Merrill, Esteban Safranchik, Arinbjörn Kolbeinsson, Piyusha Gade, Ernesto Ramirez, Ludwig Schmidt, Luca Foshchini, Tim Althoff:
Homekit2020: A Benchmark for Time Series Classification on a Large Mobile Sensing Dataset with Laboratory Tested Ground Truth of Influenza Infections. CHIL 2023: 207-228 - [c54]Mehdi Cherti, Romain Beaumont, Ross Wightman, Mitchell Wortsman, Gabriel Ilharco, Cade Gordon, Christoph Schuhmann, Ludwig Schmidt, Jenia Jitsev:
Reproducible Scaling Laws for Contrastive Language-Image Learning. CVPR 2023: 2818-2829 - [c53]Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, Ali Farhadi:
Objaverse: A Universe of Annotated 3D Objects. CVPR 2023: 13142-13153 - [c52]Samir Yitzhak Gadre, Mitchell Wortsman, Gabriel Ilharco, Ludwig Schmidt, Shuran Song:
CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigation. CVPR 2023: 23171-23181 - [c51]Gabriel Ilharco, Marco Túlio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannaneh Hajishirzi, Ali Farhadi:
Editing models with task arithmetic. ICLR 2023 - [i57]Alex Fang, Simon Kornblith, Ludwig Schmidt:
Does progress on ImageNet transfer to real-world datasets? CoRR abs/2301.04644 (2023) - [i56]Zhouxing Shi, Nicholas Carlini, Ananth Balashankar, Ludwig Schmidt, Cho-Jui Hsieh, Alex Beutel, Yao Qin:
Effective Robustness against Natural Distribution Shifts for Models with Different Training Data. CoRR abs/2302.01381 (2023) - [i55]Rahim Entezari, Mitchell Wortsman, Olga Saukh, Moein Shariatnia, Hanie Sedghi, Ludwig Schmidt:
The Role of Pre-training Data in Transfer Learning. CoRR abs/2302.13602 (2023) - [i54]Nitzan Bitton Guetta, Yonatan Bitton, Jack Hessel, Ludwig Schmidt, Yuval Elovici, Gabriel Stanovsky, Roy Schwartz:
Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images. CoRR abs/2303.07274 (2023) - [i53]Wanrong Zhu, Jack Hessel, Anas Awadalla, Samir Yitzhak Gadre, Jesse Dodge, Alex Fang, Youngjae Yu, Ludwig Schmidt, William Yang Wang, Yejin Choi:
Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved With Text. CoRR abs/2304.06939 (2023) - [i52]Mitchell Wortsman, Tim Dettmers, Luke Zettlemoyer, Ari Morcos, Ali Farhadi, Ludwig Schmidt:
Stable and low-precision training for large-scale vision-language models. CoRR abs/2304.13013 (2023) - [i51]Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, Jieyu Zhang, Eyal Orgad, Rahim Entezari, Giannis Daras, Sarah M. Pratt, Vivek Ramanujan, Yonatan Bitton, Kalyani Marathe, Stephen Mussmann, Richard Vencu, Mehdi Cherti, Ranjay Krishna, Pang Wei Koh, Olga Saukh, Alexander Ratner, Shuran Song, Hannaneh Hajishirzi, Ali Farhadi, Romain Beaumont, Sewoong Oh, Alex Dimakis, Jenia Jitsev, Yair Carmon, Vaishaal Shankar, Ludwig Schmidt:
DataComp: In search of the next generation of multimodal datasets. CoRR abs/2304.14108 (2023) - [i50]Matthew Wallingford, Vivek Ramanujan, Alex Fang, Aditya Kusupati, Roozbeh Mottaghi, Aniruddha Kembhavi, Ludwig Schmidt, Ali Farhadi:
Neural Priming for Sample-Efficient Adaptation. CoRR abs/2306.10191 (2023) - [i49]Nicholas Carlini, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski, Irena Gao, Anas Awadalla, Pang Wei Koh, Daphne Ippolito, Katherine Lee, Florian Tramèr, Ludwig Schmidt:
Are aligned neural networks adversarially aligned? CoRR abs/2306.15447 (2023) - [i48]Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Christian Laforte, Vikram Voleti, Samir Yitzhak Gadre, Eli VanderBilt, Aniruddha Kembhavi, Carl Vondrick, Georgia Gkioxari, Kiana Ehsani, Ludwig Schmidt, Ali Farhadi:
Objaverse-XL: A Universe of 10M+ 3D Objects. CoRR abs/2307.05663 (2023) - [i47]Thao Nguyen, Samir Yitzhak Gadre, Gabriel Ilharco, Sewoong Oh, Ludwig Schmidt:
Improving Multimodal Datasets with Image Captioning. CoRR abs/2307.10350 (2023) - [i46]Vivek Ramanujan, Thao Nguyen, Sewoong Oh, Ludwig Schmidt, Ali Farhadi:
On the Connection between Pre-training Data Diversity and Fine-tuning Robustness. CoRR abs/2307.12532 (2023) - [i45]Anas Awadalla, Irena Gao, Josh Gardner, Jack Hessel, Yusuf Hanafy, Wanrong Zhu, Kalyani Marathe, Yonatan Bitton, Samir Yitzhak Gadre, Shiori Sagawa, Jenia Jitsev, Simon Kornblith, Pang Wei Koh, Gabriel Ilharco, Mitchell Wortsman, Ludwig Schmidt:
OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models. CoRR abs/2308.01390 (2023) - [i44]Yonatan Bitton, Hritik Bansal, Jack Hessel, Rulin Shao, Wanrong Zhu, Anas Awadalla, Josh Gardner, Rohan Taori, Ludwig Schmidt:
VisIT-Bench: A Benchmark for Vision-Language Instruction Following Inspired by Real-World Use. CoRR abs/2308.06595 (2023) - 2022
- [c50]Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt:
Robust fine-tuning of zero-shot models. CVPR 2022: 7949-7961 - [c49]Anas Awadalla, Mitchell Wortsman, Gabriel Ilharco, Sewon Min, Ian Magnusson, Hannaneh Hajishirzi, Ludwig Schmidt:
Exploring The Landscape of Distributional Robustness for Question Answering Models. EMNLP (Findings) 2022: 5971-5987 - [c48]Rediet Abebe, Moritz Hardt, Angela Jin, John Miller, Ludwig Schmidt, Rebecca Wexler:
Adversarial Scrutiny of Evidentiary Statistical Software. FAccT 2022: 1733-1746 - [c47]Alex Fang, Gabriel Ilharco, Mitchell Wortsman, Yuhao Wan, Vaishaal Shankar, Achal Dave, Ludwig Schmidt:
Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP). ICML 2022: 6216-6234 - [c46]Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, Rebecca Roelofs, Raphael Gontijo Lopes, Ari S. Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, Ludwig Schmidt:
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. ICML 2022: 23965-23998 - [c45]Josh Gardner, Zoran Popovic, Ludwig Schmidt:
Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation. NeurIPS 2022 - [c44]Gabriel Ilharco, Mitchell Wortsman, Samir Yitzhak Gadre, Shuran Song, Hannaneh Hajishirzi, Simon Kornblith, Ali Farhadi, Ludwig Schmidt:
Patching open-vocabulary models by interpolating weights. NeurIPS 2022 - [c43]Thao Nguyen, Gabriel Ilharco, Mitchell Wortsman, Sewoong Oh, Ludwig Schmidt:
Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP. NeurIPS 2022 - [c42]Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, Patrick Schramowski, Srivatsa Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, Jenia Jitsev:
LAION-5B: An open large-scale dataset for training next generation image-text models. NeurIPS 2022 - [i43]Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, Rebecca Roelofs, Raphael Gontijo Lopes, Ari S. Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, Ludwig Schmidt:
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. CoRR abs/2203.05482 (2022) - [i42]Samir Yitzhak Gadre, Mitchell Wortsman, Gabriel Ilharco, Ludwig Schmidt, Shuran Song:
CLIP on Wheels: Zero-Shot Object Navigation as Object Localization and Exploration. CoRR abs/2203.10421 (2022) - [i41]Alex Fang, Gabriel Ilharco, Mitchell Wortsman, Yuhao Wan, Vaishaal Shankar, Achal Dave, Ludwig Schmidt:
Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP). CoRR abs/2205.01397 (2022) - [i40]Rediet Abebe, Moritz Hardt, Angela Jin, John Miller, Ludwig Schmidt, Rebecca Wexler:
Adversarial Scrutiny of Evidentiary Statistical Software. CoRR abs/2206.09305 (2022) - [i39]Thao Nguyen, Gabriel Ilharco, Mitchell Wortsman, Sewoong Oh, Ludwig Schmidt:
Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP. CoRR abs/2208.05516 (2022) - [i38]Gabriel Ilharco, Mitchell Wortsman, Samir Yitzhak Gadre, Shuran Song, Hannaneh Hajishirzi, Simon Kornblith, Ali Farhadi, Ludwig Schmidt:
Patching open-vocabulary models by interpolating weights. CoRR abs/2208.05592 (2022) - [i37]Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A. Smith, Mike Lewis:
Measuring and Narrowing the Compositionality Gap in Language Models. CoRR abs/2210.03350 (2022) - [i36]Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, Patrick Schramowski, Srivatsa Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, Jenia Jitsev:
LAION-5B: An open large-scale dataset for training next generation image-text models. CoRR abs/2210.08402 (2022) - [i35]Mitchell Wortsman, Suchin Gururangan, Shen Li, Ali Farhadi, Ludwig Schmidt, Michael G. Rabbat, Ari S. Morcos:
lo-fi: distributed fine-tuning without communication. CoRR abs/2210.11948 (2022) - [i34]Anas Awadalla, Mitchell Wortsman, Gabriel Ilharco, Sewon Min, Ian Magnusson, Hannaneh Hajishirzi, Ludwig Schmidt:
Exploring The Landscape of Distributional Robustness for Question Answering Models. CoRR abs/2210.12517 (2022) - [i33]Josh Gardner, Zoran Popovic, Ludwig Schmidt:
Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation. CoRR abs/2211.12703 (2022) - [i32]Gabriel Ilharco, Marco Túlio Ribeiro, Mitchell Wortsman, Suchin Gururangan, Ludwig Schmidt, Hannaneh Hajishirzi, Ali Farhadi:
Editing Models with Task Arithmetic. CoRR abs/2212.04089 (2022) - [i31]Mehdi Cherti, Romain Beaumont, Ross Wightman, Mitchell Wortsman, Gabriel Ilharco, Cade Gordon, Christoph Schuhmann, Ludwig Schmidt, Jenia Jitsev:
Reproducible scaling laws for contrastive language-image learning. CoRR abs/2212.07143 (2022) - [i30]Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, Ali Farhadi:
Objaverse: A Universe of Annotated 3D Objects. CoRR abs/2212.08051 (2022) - 2021
- [c41]Devin Guillory, Vaishaal Shankar, Sayna Ebrahimi, Trevor Darrell, Ludwig Schmidt:
Predicting with Confidence on Unseen Distributions. ICCV 2021: 1114-1124 - [c40]Vaishaal Shankar, Achal Dave, Rebecca Roelofs, Deva Ramanan
, Benjamin Recht, Ludwig Schmidt:
Do Image Classifiers Generalize Across Time? ICCV 2021: 9641-9649 - [c39]Klemen Kotar, Gabriel Ilharco, Ludwig Schmidt, Kiana Ehsani, Roozbeh Mottaghi:
Contrasting Contrastive Self-Supervised Representation Learning Pipelines. ICCV 2021: 9929-9939 - [c38]John Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, Ludwig Schmidt:
Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization. ICML 2021: 7721-7735 - [c37]Frances Ding, Moritz Hardt, John Miller, Ludwig Schmidt:
Retiring Adult: New Datasets for Fair Machine Learning. NeurIPS 2021: 6478-6490 - [c36]Thomas Liao, Rohan Taori, Deborah Raji, Ludwig Schmidt:
Are We Learning Yet? A Meta Review of Evaluation Failures Across Machine Learning. NeurIPS Datasets and Benchmarks 2021 - [c35]Timo Milbich, Karsten Roth, Samarth Sinha, Ludwig Schmidt, Marzyeh Ghassemi, Björn Ommer:
Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning. NeurIPS 2021: 25006-25018 - [i29]Klemen Kotar, Gabriel Ilharco, Ludwig Schmidt, Kiana Ehsani, Roozbeh Mottaghi:
Contrasting Contrastive Self-Supervised Representation Learning Models. CoRR abs/2103.14005 (2021) - [i28]Devin Guillory, Vaishaal Shankar, Sayna Ebrahimi, Trevor Darrell, Ludwig Schmidt:
Predicting with Confidence on Unseen Distributions. CoRR abs/2107.03315 (2021) - [i27]John Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, Ludwig Schmidt:
Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization. CoRR abs/2107.04649 (2021) - [i26]Timo Milbich, Karsten Roth, Samarth Sinha, Ludwig Schmidt, Marzyeh Ghassemi, Björn Ommer:
Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning. CoRR abs/2107.09562 (2021) - [i25]Frances Ding, Moritz Hardt, John Miller, Ludwig Schmidt:
Retiring Adult: New Datasets for Fair Machine Learning. CoRR abs/2108.04884 (2021) - [i24]Mitchell Wortsman, Gabriel Ilharco, Mike Li, Jong Wook Kim, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt:
Robust fine-tuning of zero-shot models. CoRR abs/2109.01903 (2021) - 2020
- [c34]John Miller, Karl Krauth, Benjamin Recht, Ludwig Schmidt:
The Effect of Natural Distribution Shift on Question Answering Models. ICML 2020: 6905-6916 - [c33]Vaishaal Shankar, Alex Fang, Wenshuo Guo, Sara Fridovich-Keil, Jonathan Ragan-Kelley, Ludwig Schmidt, Benjamin Recht:
Neural Kernels Without Tangents. ICML 2020: 8614-8623 - [c32]Vaishaal Shankar, Rebecca Roelofs, Horia Mania, Alex Fang, Benjamin Recht, Ludwig Schmidt:
Evaluating Machine Accuracy on ImageNet. ICML 2020: 8634-8644 - [c31]Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, Ludwig Schmidt:
Measuring Robustness to Natural Distribution Shifts in Image Classification. NeurIPS 2020 - [i23]Vaishaal Shankar, Alex Fang, Wenshuo Guo, Sara Fridovich-Keil, Ludwig Schmidt, Jonathan Ragan-Kelley, Benjamin Recht:
Neural Kernels Without Tangents. CoRR abs/2003.02237 (2020) - [i22]John Miller, Karl Krauth, Benjamin Recht, Ludwig Schmidt:
The Effect of Natural Distribution Shift on Question Answering Models. CoRR abs/2004.14444 (2020) - [i21]Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, Ludwig Schmidt:
Measuring Robustness to Natural Distribution Shifts in Image Classification. CoRR abs/2007.00644 (2020)
2010 – 2019
- 2019
- [c30]Smitha Milli, Ludwig Schmidt, Anca D. Dragan, Moritz Hardt:
Model Reconstruction from Model Explanations. FAT 2019: 1-9 - [c29]Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, Aleksander Madry:
Exploring the Landscape of Spatial Robustness. ICML 2019: 1802-1811 - [c28]Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar:
Do ImageNet Classifiers Generalize to ImageNet? ICML 2019: 5389-5400 - [c27]Rebecca Roelofs, Vaishaal Shankar, Benjamin Recht, Sara Fridovich-Keil, Moritz Hardt, John Miller, Ludwig Schmidt:
A Meta-Analysis of Overfitting in Machine Learning. NeurIPS 2019: 9175-9185 - [c26]Horia Mania, John Miller, Ludwig Schmidt, Moritz Hardt, Benjamin Recht:
Model Similarity Mitigates Test Set Overuse. NeurIPS 2019: 9993-10002 - [c25]Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C. Duchi, Percy Liang:
Unlabeled Data Improves Adversarial Robustness. NeurIPS 2019: 11190-11201 - [i20]Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar:
Do ImageNet Classifiers Generalize to ImageNet? CoRR abs/1902.10811 (2019) - [i19]Horia Mania, John Miller, Ludwig Schmidt, Moritz Hardt, Benjamin Recht:
Model Similarity Mitigates Test Set Overuse. CoRR abs/1905.12580 (2019) - [i18]Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, John C. Duchi:
Unlabeled Data Improves Adversarial Robustness. CoRR abs/1905.13736 (2019) - [i17]Vaishaal Shankar, Achal Dave, Rebecca Roelofs, Deva Ramanan, Benjamin Recht, Ludwig Schmidt:
A systematic framework for natural perturbations from videos. CoRR abs/1906.02168 (2019) - 2018
- [b1]Ludwig Schmidt:
Algorithms above the noise floor. Massachusetts Institute of Technology, Cambridge, USA, 2018 - [c24]Aleksander Madry, Slobodan Mitrovic, Ludwig Schmidt:
A Fast Algorithm for Separated Sparsity via Perturbed Lagrangians. AISTATS 2018: 20-28 - [c23]Ilias Diakonikolas, Jerry Li, Ludwig Schmidt:
Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms. COLT 2018: 819-842 - [c22]Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu:
Towards Deep Learning Models Resistant to Adversarial Attacks. ICLR (Poster) 2018 - [c21]Jerry Li, Aleksander Madry, John Peebles, Ludwig Schmidt:
On the Limitations of First-Order Approximation in GAN Dynamics. ICML 2018: 3011-3019 - [c20]Shibani Santurkar, Ludwig Schmidt, Aleksander Madry:
A Classification-Based Study of Covariate Shift in GAN Distributions. ICML 2018: 4487-4496 - [c19]Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Madry:
Adversarially Robust Generalization Requires More Data. NeurIPS 2018: 5019-5031 - [i16]Ilias Diakonikolas, Jerry Li, Ludwig Schmidt:
Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms. CoRR abs/1802.08513 (2018) - [i15]Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Madry:
Adversarially Robust Generalization Requires More Data. CoRR abs/1804.11285 (2018) - [i14]Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar:
Do CIFAR-10 Classifiers Generalize to CIFAR-10? CoRR abs/1806.00451 (2018) - [i13]Smitha Milli, Ludwig Schmidt, Anca D. Dragan, Moritz Hardt:
Model Reconstruction from Model Explanations. CoRR abs/1807.05185 (2018) - 2017
- [c18]Jerry Li, Ludwig Schmidt:
Robust and Proper Learning for Mixtures of Gaussians via Systems of Polynomial Inequalities. COLT 2017: 1302-1382 - [c17]Arturs Backurs, Piotr Indyk, Ludwig Schmidt:
On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks. NIPS 2017: 4308-4318 - [c16]Ilias Diakonikolas, Elena Grigorescu, Jerry Li, Abhiram Natarajan, Krzysztof Onak, Ludwig Schmidt:
Communication-Efficient Distributed Learning of Discrete Distributions. NIPS 2017: 6391-6401 - [c15]Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt:
Sample-Optimal Density Estimation in Nearly-Linear Time. SODA 2017: 1278-1289 - [c14]Arturs Backurs, Piotr Indyk, Ludwig Schmidt:
Better Approximations for Tree Sparsity in Nearly-Linear Time. SODA 2017: 2215-2229 - [i12]Arturs Backurs, Piotr Indyk, Ludwig Schmidt:
On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks. CoRR abs/1704.02958 (2017) - [i11]Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu:
Towards Deep Learning Models Resistant to Adversarial Attacks. CoRR abs/1706.06083 (2017) - [i10]Jerry Li, Aleksander Madry, John Peebles, Ludwig Schmidt:
Towards Understanding the Dynamics of Generative Adversarial Networks. CoRR abs/1706.09884 (2017) - [i9]Shibani Santurkar, Ludwig Schmidt, Aleksander Madry:
A Classification-Based Perspective on GAN Distributions. CoRR abs/1711.00970 (2017) - [i8]Logan Engstrom, Dimitris Tsipras, Ludwig Schmidt, Aleksander Madry:
A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations. CoRR abs/1712.02779 (2017) - [i7]Alexander LeNail, Ludwig Schmidt, Johnathan Li, Tobias Ehrenberger, Karen Sachs, Stefanie Jegelka, Ernest Fraenkel:
Graph-Sparse Logistic Regression. CoRR abs/1712.05510 (2017) - [i6]Aleksander Madry, Slobodan Mitrovic, Ludwig Schmidt:
A Fast Algorithm for Separated Sparsity via Perturbed Lagrangians. CoRR abs/1712.08130 (2017) - 2016
- [c13]Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt:
Fast Algorithms for Segmented Regression. ICML 2016: 2878-2886 - [c12]Chinmay Hegde, Piotr Indyk, Ludwig Schmidt:
A Nearly-Linear Time Framework for Graph-Structured Sparsity. IJCAI 2016: 4165-4169 - [c11]Chinmay Hegde, Piotr Indyk, Ludwig Schmidt:
Fast recovery from a union of subspaces. NIPS 2016: 4394-4402 - [i5]Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt:
Fast Algorithms for Segmented Regression. CoRR abs/1607.03990 (2016) - 2015
- [j3]Chinmay Hegde, Piotr Indyk, Ludwig Schmidt:
Fast Algorithms for Structured Sparsity. Bull. EATCS 117 (2015) - [j2]Chinmay Hegde, Piotr Indyk, Ludwig Schmidt:
Approximation Algorithms for Model-Based Compressive Sensing. IEEE Trans. Inf. Theory 61(9): 5129-5147 (2015) - [c10]Ludwig Schmidt, Chinmay Hegde, Piotr Indyk, Ligang Lu, Xingang Chi, Detlef Hohl:
Seismic feature extraction using steiner tree methods. ICASSP 2015: 1647-1651 - [c9]Chinmay Hegde, Piotr Indyk, Ludwig Schmidt:
A Nearly-Linear Time Framework for Graph-Structured Sparsity. ICML 2015: 928-937 - [c8]Alexandr Andoni, Piotr Indyk, Thijs Laarhoven, Ilya P. Razenshteyn, Ludwig Schmidt:
Practical and Optimal LSH for Angular Distance. NIPS 2015: 1225-1233 - [c7]Ilias Diakonikolas, Moritz Hardt, Ludwig Schmidt:
Differentially Private Learning of Structured Discrete Distributions. NIPS 2015: 2566-2574 - [c6]Jayadev Acharya, Ilias Diakonikolas, Chinmay Hegde, Jerry Zheng Li, Ludwig Schmidt:
Fast and Near-Optimal Algorithms for Approximating Distributions by Histograms. PODS 2015: 249-263 - [i4]Jayadev Acharya, Ilias Diakonikolas, Jerry Zheng Li, Ludwig Schmidt:
Sample-Optimal Density Estimation in Nearly-Linear Time. CoRR abs/1506.00671 (2015) - [i3]Jerry Zheng Li, Ludwig Schmidt:
A Nearly Optimal and Agnostic Algorithm for Properly Learning a Mixture of k Gaussians, for any Constant k. CoRR abs/1506.01367 (2015) - [i2]Alexandr Andoni, Piotr Indyk, Thijs Laarhoven, Ilya P. Razenshteyn, Ludwig Schmidt:
Practical and Optimal LSH for Angular Distance. CoRR abs/1509.02897 (2015) - 2014
- [j1]Anna C. Gilbert, Piotr Indyk, Mark A. Iwen, Ludwig Schmidt:
Recent Developments in the Sparse Fourier Transform: A compressed Fourier transform for big data. IEEE Signal Process. Mag. 31(5): 91-100 (2014) - [c5]Chinmay Hegde, Piotr Indyk, Ludwig Schmidt:
Nearly Linear-Time Model-Based Compressive Sensing. ICALP (1) 2014: 588-599 - [c4]Ludwig Schmidt, Matthew Sharifi, Ignacio Lopez-Moreno:
Large-scale speaker identification. ICASSP 2014: 1650-1654 - [c3]Ludwig Schmidt, Chinmay Hegde, Piotr Indyk, Jonathan Kane, Ligang Lu, Detlef Hohl:
Automatic fault localization using the generalized Earth Mover's distance. ICASSP 2014: 8134-8138 - [c2]Chinmay Hegde, Piotr Indyk, Ludwig Schmidt:
A fast approximation algorithm for tree-sparse recovery. ISIT 2014: 1842-1846 - [c1]Chinmay Hegde, Piotr Indyk, Ludwig Schmidt:
Approximation-Tolerant Model-Based Compressive Sensing. SODA 2014: 1544-1561 - [i1]Chinmay Hegde, Piotr Indyk, Ludwig Schmidt:
Approximation Algorithms for Model-Based Compressive Sensing. CoRR abs/1406.1579 (2014)
Coauthor Index