Ludwig Schmidt (original) (raw)

The papers below give an overview of topics I have worked on. For a full list of publications, please see Google Scholar.

My group also contributes to open source via code repositories and datasets, e.g., OpenCLIP, OpenFlamingo, LAION-5B, and the DataComp datasets.

Selected publications

• DataComp-LM: In search of the next generation of training sets for language models
  Jeffrey Li, Alex Fang, Georgios Smyrnis, Maor Ivgi, Matt Jordan, Samir Gadre, Hritik Bansal, Etash Guha, Sedrick Keh, Kushal Arora, Saurabh Garg, Rui Xin, Niklas Muennighoff, Reinhard Heckel, Jean Mercat, Mayee Chen, Suchin Gururangan, Mitchell Wortsman, Alon Albalak, Yonatan Bitton, Marianna Nezhurina, Amro Abbas, Cheng-Yu Hsieh, Dhruba Ghosh, Josh Gardner, Maciej Kilian, Hanlin Zhang, Rulin Shao, Sarah Pratt, Sunny Sanyal, Gabriel Ilharco, Giannis Daras, Kalyani Marathe, Aaron Gokaslan, Jieyu Zhang, Khyathi Chandu, Thao Nguyen, Igor Vasiljevic, Sham Kakade, Shuran Song, Sujay Sanghavi, Fartash Faghri, Sewoong Oh, Luke Zettlemoyer, Kyle Lo, Alaaeldin El-Nouby, Hadi Pouransari, Alexander Toshev, Stephanie Wang, Dirk Groeneveld, Luca Soldaini, Pang Wei Koh, Jenia Jitsev, Thomas Kollar, Alexandros G. Dimakis, Yair Carmon, Achal Dave, Ludwig Schmidt, Vaishaal Shankar
  NeurIPS 2024, datasets & benchmarks track
• DataComp: In search of the next generation of multimodal datasets
  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 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
  NeurIPS 2023, datasets & benchmarks track
• LAION-5B: An open large-scale dataset for training next generation image-text models
  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
  NeurIPS 2022, datasets & benchmarks track
  Outstanding paper award
• Robust fine-tuning of zero-shot models
  Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo-Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt
  CVPR 2022
  Best paper finalist
• Retiring Adult: New Datasets for Fair Machine Learning
  Frances Ding, Moritz Hardt, John Miller, Ludwig Schmidt
  NeurIPS 2021 & EAAMO 2021
  New Horizons Award
• Measuring Robustness to Natural Distribution Shifts in Image Classification
  Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, Ludwig Schmidt
  NeurIPS 2020
• Do ImageNet Classifiers Generalize to ImageNet?
  Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar
  ICML 2019
• Towards Deep Learning Models Resistant to Adversarial Attacks
  Aleksander Mądry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu
  ICLR 2018
• Algorithms Above the Noise Floor
  Ludwig Schmidt
  PhD thesis, 2018
  George M. Sprowls Award (for best PhD theses in computer science at MIT)
• Practical and Optimal LSH for Angular Distance
  Alexandr Andoni, Piotr Indyk, Thijs Laarhoven, Ilya Razenshteyn, Ludwig Schmidt
  NIPS 2015
• A Nearly-Linear Time Framework for Graph-Structured Sparsity
  Chinmay Hegde, Piotr Indyk, Ludwig Schmidt
  ICML 2015
  Best paper award