Ludwig Schmidt

Publication Activity (10 Years)

Years Active: 2014-2023
Publications (10 Years): 128

Top Venues

CoRR

NeurIPS

ICML

NIPS

Massachusetts Institute of Technology, Cambridge, USA (PHD)

Education History

Massachusetts Institute of Technology, Cambridge, USA

PHD

Publications

Thao Nguyen, Samir Yitzhak Gadre, Gabriel Ilharco, Sewoong Oh, Ludwig Schmidt
Improving Multimodal Datasets with Image Captioning. CoRR (2023)
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)
Jeffrey Li, Jieyu Zhang, Ludwig Schmidt, Alexander J. Ratner
Characterizing the Impacts of Semi-supervised Learning for Weak Supervision. NeurIPS (2023)
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. NeurIPS (2023)
Matthew Wallingford, Vivek Ramanujan, Alex Fang, Aditya Kusupati, Roozbeh Mottaghi, Aniruddha Kembhavi, Ludwig Schmidt, Ali Farhadi
Neural Priming for Sample-Efficient Adaptation. NeurIPS (2023)
Vivek Ramanujan, Thao Nguyen, Sewoong Oh, Ali Farhadi, Ludwig Schmidt
On the Connection between Pre-training Data Diversity and Fine-tuning Robustness. NeurIPS (2023)
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)
Alex Fang, Simon Kornblith, Ludwig Schmidt
Does progress on ImageNet transfer to real-world datasets? CoRR (2023)
Alex Fang, Simon Kornblith, Ludwig Schmidt
Does progress on ImageNet transfer to real-world datasets? NeurIPS (2023)
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)
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 (2023)
Gabriel Ilharco, Marco Túlio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannaneh Hajishirzi, Ali Farhadi
Editing models with task arithmetic. ICLR (2023)
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. NeurIPS (2023)
Dhruba Ghosh, Hanna Hajishirzi, Ludwig Schmidt
GenEval: An Object-Focused Framework for Evaluating Text-to-Image Alignment. CoRR (2023)
Mitchell Wortsman, Tim Dettmers, Luke Zettlemoyer, Ari Morcos, Ali Farhadi, Ludwig Schmidt
Stable and low-precision training for large-scale vision-language models. CoRR (2023)
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)
Rahim Entezari, Mitchell Wortsman, Olga Saukh, Moein Shariatnia, Hanie Sedghi, Ludwig Schmidt
The Role of Pre-training Data in Transfer Learning. CoRR (2023)
Mitchell Wortsman, Tim Dettmers, Luke Zettlemoyer, Ari Morcos, Ali Farhadi, Ludwig Schmidt
Stable and low-precision training for large-scale vision-language models. NeurIPS (2023)
Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A. Smith, Mike Lewis
Measuring and Narrowing the Compositionality Gap in Language Models. EMNLP (Findings) (2023)
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 (2023)
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 (2023)
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 (2023)
Yonatan Bitton, Hritik Bansal, Jack Hessel, Rulin Shao, Wanrong Zhu, Anas Awadalla, Josh Gardner, Rohan Taori, Ludwig Schmidt
VisIT-Bench: A Dynamic Benchmark for Evaluating Instruction-Following Vision-and-Language Models. NeurIPS (2023)
Matthew Wallingford, Vivek Ramanujan, Alex Fang, Aditya Kusupati, Roozbeh Mottaghi, Aniruddha Kembhavi, Ludwig Schmidt, Ali Farhadi
Neural Priming for Sample-Efficient Adaptation. CoRR (2023)
Josh Gardner, Zoran Popovic, Ludwig Schmidt
Benchmarking Distribution Shift in Tabular Data with TableShift. NeurIPS (2023)
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 J. 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. NeurIPS (2023)
Dhruba Ghosh, Hannaneh Hajishirzi, Ludwig Schmidt
GenEval: An object-focused framework for evaluating text-to-image alignment. NeurIPS (2023)
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 (2023)
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 (2023)
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)
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. ICCV (2023)
Thao Nguyen, Samir Yitzhak Gadre, Gabriel Ilharco, Sewoong Oh, Ludwig Schmidt
Improving multimodal datasets with image captioning. NeurIPS (2023)
Josh Gardner, Zoran Popovic, Ludwig Schmidt
Benchmarking Distribution Shift in Tabular Data with TableShift. CoRR (2023)
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. NeurIPS (2023)
Nicholas Carlini, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski, Irena Gao, Pang Wei Koh, Daphne Ippolito, Florian Tramèr, Ludwig Schmidt
Are aligned neural networks adversarially aligned? NeurIPS (2023)
Alex Fang, Albin Madappally Jose, Amit Jain, Ludwig Schmidt, Alexander Toshev, Vaishaal Shankar
Data Filtering Networks. CoRR (2023)
Vivek Ramanujan, Thao Nguyen, Sewoong Oh, Ludwig Schmidt, Ali Farhadi
On the Connection between Pre-training Data Diversity and Fine-tuning Robustness. CoRR (2023)
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 (2023)
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 (2023)
Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A. Smith, Mike Lewis
Measuring and Narrowing the Compositionality Gap in Language Models. CoRR (2022)
Samir Yitzhak Gadre, Mitchell Wortsman, Gabriel Ilharco, Ludwig Schmidt, Shuran Song
CLIP on Wheels: Zero-Shot Object Navigation as Object Localization and Exploration. CoRR (2022)
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)
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)
Mitchell Wortsman, Gabriel Ilharco, Samir Ya 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)
Gabriel Ilharco, Marco Túlio Ribeiro, Mitchell Wortsman, Suchin Gururangan, Ludwig Schmidt, Hannaneh Hajishirzi, Ali Farhadi
Editing Models with Task Arithmetic. CoRR (2022)
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 (2022)
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)
Rediet Abebe, Moritz Hardt, Angela Jin, John Miller, Ludwig Schmidt, Rebecca Wexler
Adversarial Scrutiny of Evidentiary Statistical Software. CoRR (2022)
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 (2022)
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)
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 (2022)
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 (2022)
Rediet Abebe, Moritz Hardt, Angela Jin, John Miller, Ludwig Schmidt, Rebecca Wexler
Adversarial Scrutiny of Evidentiary Statistical Software. FAccT (2022)
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)
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 (2022)
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 (2022)
Thao Nguyen, Gabriel Ilharco, Mitchell Wortsman, Sewoong Oh, Ludwig Schmidt
Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP. CoRR (2022)
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)
Josh Gardner, Zoran Popovic, Ludwig Schmidt
Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation. NeurIPS (2022)
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 (2022)
Josh Gardner, Zoran Popovic, Ludwig Schmidt
Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation. CoRR (2022)
Mitchell Wortsman, Suchin Gururangan, Shen Li, Ali Farhadi, Ludwig Schmidt, Michael G. Rabbat, Ari S. Morcos
lo-fi: distributed fine-tuning without communication. CoRR (2022)
Devin Guillory, Vaishaal Shankar, Sayna Ebrahimi, Trevor Darrell, Ludwig Schmidt
Predicting with Confidence on Unseen Distributions. CoRR (2021)
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)
Timo Milbich, Karsten Roth, Samarth Sinha, Ludwig Schmidt, Marzyeh Ghassemi, Björn Ommer
Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning. CoRR (2021)
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 (2021)
Vaishaal Shankar, Achal Dave, Rebecca Roelofs, Deva Ramanan, Benjamin Recht, Ludwig Schmidt
Do Image Classifiers Generalize Across Time? ICCV (2021)
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 (2021)
John P. 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)
Klemen Kotar, Gabriel Ilharco, Ludwig Schmidt, Kiana Ehsani, Roozbeh Mottaghi
Contrasting Contrastive Self-Supervised Representation Learning Pipelines. ICCV (2021)
Devin Guillory, Vaishaal Shankar, Sayna Ebrahimi, Trevor Darrell, Ludwig Schmidt
Predicting with Confidence on Unseen Distributions. ICCV (2021)
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)
Frances Ding, Moritz Hardt, John Miller, Ludwig Schmidt
Retiring Adult: New Datasets for Fair Machine Learning. CoRR (2021)
Klemen Kotar, Gabriel Ilharco, Ludwig Schmidt, Kiana Ehsani, Roozbeh Mottaghi
Contrasting Contrastive Self-Supervised Representation Learning Models. CoRR (2021)
Frances Ding, Moritz Hardt, John Miller, Ludwig Schmidt
Retiring Adult: New Datasets for Fair Machine Learning. NeurIPS (2021)
Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, Ludwig Schmidt
Measuring Robustness to Natural Distribution Shifts in Image Classification. NeurIPS (2020)
Vaishaal Shankar, Rebecca Roelofs, Horia Mania, Alex Fang, Benjamin Recht, Ludwig Schmidt
Evaluating Machine Accuracy on ImageNet. ICML (2020)
Vaishaal Shankar, Alex Fang, Wenshuo Guo, Sara Fridovich-Keil, Ludwig Schmidt, Jonathan Ragan-Kelley, Benjamin Recht
Neural Kernels Without Tangents. CoRR (2020)
Vaishaal Shankar, Alex Fang, Wenshuo Guo, Sara Fridovich-Keil, Jonathan Ragan-Kelley, Ludwig Schmidt, Benjamin Recht
Neural Kernels Without Tangents. ICML (2020)
John Miller, Karl Krauth, Benjamin Recht, Ludwig Schmidt
The Effect of Natural Distribution Shift on Question Answering Models. ICML (2020)
Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, Ludwig Schmidt
Measuring Robustness to Natural Distribution Shifts in Image Classification. CoRR (2020)
John Miller, Karl Krauth, Benjamin Recht, Ludwig Schmidt
The Effect of Natural Distribution Shift on Question Answering Models. CoRR (2020)
Smitha Milli, Ludwig Schmidt, Anca D. Dragan, Moritz Hardt
Model Reconstruction from Model Explanations. FAT (2019)
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, John C. Duchi
Unlabeled Data Improves Adversarial Robustness. CoRR (2019)
Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, Aleksander Madry
Exploring the Landscape of Spatial Robustness. ICML (2019)
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar
Do ImageNet Classifiers Generalize to ImageNet? ICML (2019)
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar
Do ImageNet Classifiers Generalize to ImageNet? CoRR (2019)
Horia Mania, John Miller, Ludwig Schmidt, Moritz Hardt, Benjamin Recht
Model Similarity Mitigates Test Set Overuse. NeurIPS (2019)
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C. Duchi, Percy Liang
Unlabeled Data Improves Adversarial Robustness. NeurIPS (2019)
Vaishaal Shankar, Achal Dave, Rebecca Roelofs, Deva Ramanan, Benjamin Recht, Ludwig Schmidt
A systematic framework for natural perturbations from videos. CoRR (2019)
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)
Horia Mania, John Miller, Ludwig Schmidt, Moritz Hardt, Benjamin Recht
Model Similarity Mitigates Test Set Overuse. CoRR (2019)
Ilias Diakonikolas, Jerry Li, Ludwig Schmidt
Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms. CoRR (2018)
Ilias Diakonikolas, Jerry Li, Ludwig Schmidt
Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms. COLT (2018)
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Madry
Adversarially Robust Generalization Requires More Data. NeurIPS (2018)
Jerry Li, Aleksander Madry, John Peebles, Ludwig Schmidt
On the Limitations of First-Order Approximation in GAN Dynamics. ICML (2018)
Aleksander Madry, Slobodan Mitrovic, Ludwig Schmidt
A Fast Algorithm for Separated Sparsity via Perturbed Lagrangians. AISTATS (2018)
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu
Towards Deep Learning Models Resistant to Adversarial Attacks. ICLR (Poster) (2018)
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Madry
Adversarially Robust Generalization Requires More Data. CoRR (2018)
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar
Do CIFAR-10 Classifiers Generalize to CIFAR-10? CoRR (2018)

Ludwig Schmidt.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Ludwig Schmidt