Nearly Linear-Time Model-Based Compressive Sensing. - Chinmay Hegde; Piotr Indyk; Ludwig Schmidt (ICALP (1), 2014)

Automatic fault localization using the generalized Earth Mover's distance. - Ludwig Schmidt; Chinmay Hegde; Piotr Indyk; Jonathan Kane; Ligang Lu; Detlef Hohl (ICASSP, 2014)

Large-scale speaker identification. - Ludwig Schmidt; Matthew Sharifi; Ignacio López-Moreno (ICASSP, 2014)

A fast approximation algorithm for tree-sparse recovery. - Chinmay Hegde; Piotr Indyk; Ludwig Schmidt (ISIT, 2014)

Approximation-Tolerant Model-Based Compressive Sensing. - Chinmay Hegde; Piotr Indyk; Ludwig Schmidt (SODA, 2014)

Recent Developments in the Sparse Fourier Transform: A compressed Fourier transform for big data. - Anna C. Gilbert; Piotr Indyk; Mark A. Iwen; Ludwig Schmidt (IEEE Signal Process. Mag., 2014)

Seismic feature extraction using steiner tree methods. - Ludwig Schmidt; Chinmay Hegde; Piotr Indyk; Ligang Lu; Xingang Chi; Detlef Hohl (ICASSP, 2015)

A Nearly-Linear Time Framework for Graph-Structured Sparsity. - Chinmay Hegde; Piotr Indyk; Ludwig Schmidt (ICML, 2015)

Practical and Optimal LSH for Angular Distance. - Alexandr Andoni; Piotr Indyk; Thijs Laarhoven; Ilya P. Razenshteyn; Ludwig Schmidt (NIPS, 2015)

Differentially Private Learning of Structured Discrete Distributions. - Ilias Diakonikolas; Moritz Hardt; Ludwig Schmidt (NIPS, 2015)

Fast and Near-Optimal Algorithms for Approximating Distributions by Histograms. - Jayadev Acharya; Ilias Diakonikolas; Chinmay Hegde; Jerry Zheng Li; Ludwig Schmidt (PODS, 2015)

A Nearly Optimal and Agnostic Algorithm for Properly Learning a Mixture of k Gaussians, for any Constant k. - Jerry Zheng Li; Ludwig Schmidt (CoRR, 2015)

Fast Algorithms for Structured Sparsity. - Chinmay Hegde; Piotr Indyk; Ludwig Schmidt (Bull. EATCS, 2015)

Approximation Algorithms for Model-Based Compressive Sensing. - Chinmay Hegde; Piotr Indyk; Ludwig Schmidt (IEEE Trans. Inf. Theory, 2015)

Fast Algorithms for Segmented Regression. - Jayadev Acharya; Ilias Diakonikolas; Jerry Li 001; Ludwig Schmidt (ICML, 2016)

Fast recovery from a union of subspaces. - Chinmay Hegde; Piotr Indyk; Ludwig Schmidt (NIPS, 2016)

Robust and Proper Learning for Mixtures of Gaussians via Systems of Polynomial Inequalities. - Jerry Li 001; Ludwig Schmidt (COLT, 2017)

On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks. - Arturs Backurs; Piotr Indyk; Ludwig Schmidt (NIPS, 2017)

Communication-Efficient Distributed Learning of Discrete Distributions. - Ilias Diakonikolas; Elena Grigorescu; Jerry Li 001; Abhiram Natarajan; Krzysztof Onak; Ludwig Schmidt (NIPS, 2017)

Sample-Optimal Density Estimation in Nearly-Linear Time. - Jayadev Acharya; Ilias Diakonikolas; Jerry Li 001; Ludwig Schmidt (SODA, 2017)

Better Approximations for Tree Sparsity in Nearly-Linear Time. - Arturs Backurs; Piotr Indyk; Ludwig Schmidt (SODA, 2017)

Towards Understanding the Dynamics of Generative Adversarial Networks. - Jerry Li 001; Aleksander Madry; John Peebles; Ludwig Schmidt (CoRR, 2017)

A Classification-Based Perspective on GAN Distributions. - Shibani Santurkar; Ludwig Schmidt; Aleksander Madry (CoRR, 2017)

A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations. - Logan Engstrom; Dimitris Tsipras; Ludwig Schmidt; Aleksander Madry (CoRR, 2017)

Graph-Sparse Logistic Regression. - Alexander LeNail; Ludwig Schmidt; Johnathan Li; Tobias Ehrenberger; Karen Sachs; Stefanie Jegelka; Ernest Fraenkel (CoRR, 2017)

A Fast Algorithm for Separated Sparsity via Perturbed Lagrangians. - Aleksander Madry; Slobodan Mitrovic; Ludwig Schmidt (AISTATS, 2018)

Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms. - Ilias Diakonikolas; Jerry Li 001; Ludwig Schmidt (COLT, 2018)

Towards Deep Learning Models Resistant to Adversarial Attacks. - Aleksander Madry; Aleksandar Makelov; Ludwig Schmidt; Dimitris Tsipras; Adrian Vladu (ICLR (Poster), 2018)

On the Limitations of First-Order Approximation in GAN Dynamics. - Jerry Li 001; Aleksander Madry; John Peebles; Ludwig Schmidt (ICML, 2018)

A Classification-Based Study of Covariate Shift in GAN Distributions. - Shibani Santurkar; Ludwig Schmidt; Aleksander Madry (ICML, 2018)

Adversarially Robust Generalization Requires More Data. - Ludwig Schmidt; Shibani Santurkar; Dimitris Tsipras; Kunal Talwar; Aleksander Madry (NeurIPS, 2018)

Do CIFAR-10 Classifiers Generalize to CIFAR-10? - Benjamin Recht; Rebecca Roelofs; Ludwig Schmidt; Vaishaal Shankar (CoRR, 2018)

Algorithms above the noise floor. - Ludwig Schmidt (2018)

Model Reconstruction from Model Explanations. - Smitha Milli; Ludwig Schmidt; Anca D. Dragan; Moritz Hardt (FAT, 2019)

Exploring the Landscape of Spatial Robustness. - Logan Engstrom; Brandon Tran; Dimitris Tsipras; Ludwig Schmidt; Aleksander Madry (ICML, 2019)

Do ImageNet Classifiers Generalize to ImageNet? - Benjamin Recht; Rebecca Roelofs; Ludwig Schmidt; Vaishaal Shankar (ICML, 2019)

Unlabeled Data Improves Adversarial Robustness. - Yair Carmon; Aditi Raghunathan; Ludwig Schmidt; John C. Duchi; Percy Liang (NeurIPS, 2019)

Model Similarity Mitigates Test Set Overuse. - Horia Mania; John Miller 001; Ludwig Schmidt; Moritz Hardt; Benjamin Recht (NeurIPS, 2019)

A Meta-Analysis of Overfitting in Machine Learning. - Rebecca Roelofs; Vaishaal Shankar; Benjamin Recht; Sara Fridovich-Keil; Moritz Hardt; John Miller 001; Ludwig Schmidt (NeurIPS, 2019)

A systematic framework for natural perturbations from videos. - Vaishaal Shankar; Achal Dave; Rebecca Roelofs; Deva Ramanan; Benjamin Recht; Ludwig Schmidt (CoRR, 2019)

The Effect of Natural Distribution Shift on Question Answering Models. - John Miller 001; Karl Krauth; Benjamin Recht; Ludwig Schmidt (ICML, 2020)

Neural Kernels Without Tangents. - Vaishaal Shankar; Alex Fang; Wenshuo Guo; Sara Fridovich-Keil; Jonathan Ragan-Kelley; Ludwig Schmidt; Benjamin Recht (ICML, 2020)

Evaluating Machine Accuracy on ImageNet. - Vaishaal Shankar; Rebecca Roelofs; Horia Mania; Alex Fang; Benjamin Recht; Ludwig Schmidt (ICML, 2020)

Measuring Robustness to Natural Distribution Shifts in Image Classification. - Rohan Taori; Achal Dave; Vaishaal Shankar; Nicholas Carlini; Benjamin Recht; Ludwig Schmidt (NeurIPS, 2020)

Predicting with Confidence on Unseen Distributions. - Devin Guillory; Vaishaal Shankar; Sayna Ebrahimi; Trevor Darrell; Ludwig Schmidt (ICCV, 2021)

Contrasting Contrastive Self-Supervised Representation Learning Pipelines. - Klemen Kotar; Gabriel Ilharco; Ludwig Schmidt; Kiana Ehsani; Roozbeh Mottaghi (ICCV, 2021)

Do Image Classifiers Generalize Across Time? - Vaishaal Shankar; Achal Dave; Rebecca Roelofs; Deva Ramanan; Benjamin Recht; Ludwig Schmidt (ICCV, 2021)

Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization. - John Miller 001; Rohan Taori; Aditi Raghunathan; Shiori Sagawa; Pang Wei Koh; Vaishaal Shankar; Percy Liang; Yair Carmon; Ludwig Schmidt (ICML, 2021)

Retiring Adult: New Datasets for Fair Machine Learning. - Frances Ding; Moritz Hardt; John Miller 001; Ludwig Schmidt (NeurIPS, 2021)

Are We Learning Yet? A Meta Review of Evaluation Failures Across Machine Learning. - Thomas Liao; Rohan Taori; Deborah Raji; Ludwig Schmidt (NeurIPS Datasets and Benchmarks, 2021)

Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning. - Timo Milbich; Karsten Roth; Samarth Sinha; Ludwig Schmidt; Marzyeh Ghassemi; Björn Ommer (NeurIPS, 2021)

Contrasting Contrastive Self-Supervised Representation Learning Models. - Klemen Kotar; Gabriel Ilharco; Ludwig Schmidt; Kiana Ehsani; Roozbeh Mottaghi (CoRR, 2021)

Robust fine-tuning of zero-shot models. - Mitchell Wortsman; Gabriel Ilharco; Mike Li; Jong Wook Kim; Hannaneh Hajishirzi; Ali Farhadi; Hongseok Namkoong; Ludwig Schmidt (CoRR, 2021)

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)

Exploring The Landscape of Distributional Robustness for Question Answering Models. - Anas Awadalla; Mitchell Wortsman; Gabriel Ilharco; Sewon Min; Ian Magnusson; Hannaneh Hajishirzi; Ludwig Schmidt (EMNLP (Findings), 2022)

Adversarial Scrutiny of Evidentiary Statistical Software. - Rediet Abebe; Moritz Hardt; Angela Jin; John Miller 001; Ludwig Schmidt; Rebecca Wexler (FAccT, 2022)

Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP). - Alex Fang; Gabriel Ilharco; Mitchell Wortsman; Yuhao Wan; Vaishaal Shankar; Achal Dave; Ludwig Schmidt (ICML, 2022)

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. - 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 (ICML, 2022)

Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation. - Josh Gardner 001; Zoran Popovic; Ludwig Schmidt (NeurIPS, 2022)

Patching open-vocabulary models by interpolating weights. - Gabriel Ilharco; Mitchell Wortsman; Samir Yitzhak Gadre; Shuran Song; Hannaneh Hajishirzi; Simon Kornblith; Ali Farhadi; Ludwig Schmidt (NeurIPS, 2022)

Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP. - Thao Nguyen; Gabriel Ilharco; Mitchell Wortsman; Sewoong Oh; Ludwig Schmidt (NeurIPS, 2022)

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; et al. (NeurIPS, 2022)

CLIP on Wheels: Zero-Shot Object Navigation as Object Localization and Exploration. - Samir Yitzhak Gadre; Mitchell Wortsman; Gabriel Ilharco; Ludwig Schmidt; Shuran Song (CoRR, 2022)

Editing Models with Task Arithmetic. - Gabriel Ilharco; Marco Túlio Ribeiro; Mitchell Wortsman; Suchin Gururangan; Ludwig Schmidt; Hannaneh Hajishirzi; Ali Farhadi (CoRR, 2022)

Homekit2020: A Benchmark for Time Series Classification on a Large Mobile Sensing Dataset with Laboratory Tested Ground Truth of Influenza Infections. - Mike A. Merrill; Esteban Safranchik; Arinbjörn Kolbeinsson; Piyusha Gade; Ernesto Ramirez; Ludwig Schmidt; Luca Foshchini; Tim Althoff (CHIL, 2023)

Reproducible Scaling Laws for Contrastive Language-Image Learning. - Mehdi Cherti; Romain Beaumont; Ross Wightman; Mitchell Wortsman; Gabriel Ilharco; Cade Gordon; Christoph Schuhmann; Ludwig Schmidt; Jenia Jitsev (CVPR, 2023)

Objaverse: A Universe of Annotated 3D Objects. - Matt Deitke; Dustin Schwenk; Jordi Salvador; Luca Weihs; Oscar Michel; Eli VanderBilt; Ludwig Schmidt; Kiana Ehsani; Aniruddha Kembhavi; Ali Farhadi (CVPR, 2023)

CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigation. - Samir Yitzhak Gadre; Mitchell Wortsman; Gabriel Ilharco; Ludwig Schmidt; Shuran Song (CVPR, 2023)

Measuring and Narrowing the Compositionality Gap in Language Models. - Ofir Press; Muru Zhang; Sewon Min; Ludwig Schmidt; Noah A. Smith; Mike Lewis (EMNLP (Findings), 2023)

Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images. - Nitzan Bitton Guetta; Yonatan Bitton; Jack Hessel; Ludwig Schmidt; Yuval Elovici; Gabriel Stanovsky; Roy Schwartz 001 (ICCV, 2023)

Editing models with task arithmetic. - Gabriel Ilharco; Marco Túlio Ribeiro; Mitchell Wortsman; Ludwig Schmidt; Hannaneh Hajishirzi; Ali Farhadi (ICLR, 2023)

VisIT-Bench: A Dynamic Benchmark for Evaluating Instruction-Following Vision-and-Language Models. - Yonatan Bitton; Hritik Bansal; Jack Hessel; Rulin Shao; Wanrong Zhu; Anas Awadalla; Josh Gardner 001; Rohan Taori; Ludwig Schmidt (NeurIPS, 2023)

Are aligned neural networks adversarially aligned? - Nicholas Carlini; Milad Nasr; Christopher A. Choquette-Choo; Matthew Jagielski; Irena Gao; Pang Wei Koh; Daphne Ippolito; Florian Tramèr; Ludwig Schmidt (NeurIPS, 2023)

Objaverse-XL: A Universe of 10M+ 3D Objects. - 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; et al. (NeurIPS, 2023)

Does progress on ImageNet transfer to real-world datasets? - Alex Fang; Simon Kornblith; Ludwig Schmidt (NeurIPS, 2023)

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 001; Eyal Orgad; Rahim Entezari; et al. (NeurIPS, 2023)

Benchmarking Distribution Shift in Tabular Data with TableShift. - Josh Gardner 001; Zoran Popovic; Ludwig Schmidt (NeurIPS, 2023)

GenEval: An object-focused framework for evaluating text-to-image alignment. - Dhruba Ghosh; Hannaneh Hajishirzi; Ludwig Schmidt (NeurIPS, 2023)

Characterizing the Impacts of Semi-supervised Learning for Weak Supervision. - Jeffrey Li; Jieyu Zhang 001; Ludwig Schmidt; Alexander J. Ratner (NeurIPS, 2023)

Improving multimodal datasets with image captioning. - Thao Nguyen; Samir Yitzhak Gadre; Gabriel Ilharco; Sewoong Oh; Ludwig Schmidt (NeurIPS, 2023)

On the Connection between Pre-training Data Diversity and Fine-tuning Robustness. - Vivek Ramanujan; Thao Nguyen; Sewoong Oh; Ali Farhadi; Ludwig Schmidt (NeurIPS, 2023)

Effective Robustness against Natural Distribution Shifts for Models with Different Training Data. - Zhouxing Shi; Nicholas Carlini; Ananth Balashankar; Ludwig Schmidt; Cho-Jui Hsieh; Alex Beutel; Yao Qin 001 (NeurIPS, 2023)

Neural Priming for Sample-Efficient Adaptation. - Matthew Wallingford; Vivek Ramanujan; Alex Fang; Aditya Kusupati; Roozbeh Mottaghi; Aniruddha Kembhavi; Ludwig Schmidt; Ali Farhadi (NeurIPS, 2023)

Stable and low-precision training for large-scale vision-language models. - Mitchell Wortsman; Tim Dettmers; Luke Zettlemoyer; Ari Morcos; Ali Farhadi; Ludwig Schmidt (NeurIPS, 2023)

Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved with Text. - Wanrong Zhu; Jack Hessel; Anas Awadalla; Samir Yitzhak Gadre; Jesse Dodge; Alex Fang; Youngjae Yu; Ludwig Schmidt; William Yang Wang; Yejin Choi 001 (NeurIPS, 2023)

The Role of Pre-training Data in Transfer Learning. - Rahim Entezari; Mitchell Wortsman; Olga Saukh; Moein Shariatnia; Hanie Sedghi; Ludwig Schmidt (CoRR, 2023)

Are aligned neural networks adversarially aligned? - 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 (CoRR, 2023)

OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models. - Anas Awadalla; Irena Gao; Josh Gardner 001; Jack Hessel; Yusuf Hanafy; Wanrong Zhu; Kalyani Marathe; Yonatan Bitton; Samir Yitzhak Gadre; Shiori Sagawa; Jenia Jitsev; Simon Kornblith; et al. (CoRR, 2023)

VisIT-Bench: A Benchmark for Vision-Language Instruction Following Inspired by Real-World Use. - Yonatan Bitton; Hritik Bansal; Jack Hessel; Rulin Shao; Wanrong Zhu; Anas Awadalla; Josh Gardner 001; Rohan Taori; Ludwig Schmidt (CoRR, 2023)

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. - Aarohi Srivastava; Abhinav Rastogi; Abhishek Rao; Abu Awal Md Shoeb; Abubakar Abid; Adam Fisch; Adam R. Brown; Adam Santoro; Aditya Gupta; Adrià Garriga-Alonso; Agnieszka Kluska; Aitor Lewkowycz; et al. (Trans. Mach. Learn. Res., 2023)

lo-fi: distributed fine-tuning without communication. - Mitchell Wortsman; Suchin Gururangan; Shen Li; Ali Farhadi; Ludwig Schmidt; Michael G. Rabbat; Ari S. Morcos (Trans. Mach. Learn. Res., 2023)

Getting it Right: Improving Spatial Consistency in Text-to-Image Models. - Agneet Chatterjee; Gabriela Ben Melech Stan; Estelle Aflalo; Sayak Paul; Dhruba Ghosh; Tejas Gokhale; Ludwig Schmidt; Hannaneh Hajishirzi; Vasudev Lal; Chitta Baral; Yezhou Yang (ECCV (22), 2024)

Better Alignment with Instruction Back-and-Forth Translation. - Thao Nguyen; Jeffrey Li; Sewoong Oh; Ludwig Schmidt; Jason Weston; Luke Zettlemoyer; Xian Li 003 (EMNLP (Findings), 2024)

Data Filtering Networks. - Alex Fang; Albin Madappally Jose; Amit Jain; Ludwig Schmidt; Alexander T. Toshev; Vaishaal Shankar (ICLR, 2024)

Large Scale Transfer Learning for Tabular Data via Language Modeling. - Josh Gardner 001; Juan C. Perdomo; Ludwig Schmidt (NeurIPS, 2024)

MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens. - Anas Awadalla; Le Xue; Oscar Lo; Manli Shu; Hannah Lee; Etash Kumar Guha; Sheng Shen 001; Mohamed Awadalla; Silvio Savarese; Caiming Xiong; Ran Xu 001; Yejin Choi 001; et al. (NeurIPS, 2024)

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 Yitzhak Gadre; Hritik Bansal; Etash Kumar Guha; Sedrick Scott Keh; Kushal Arora; Saurabh Garg; Rui Xin; et al. (NeurIPS, 2024)

Multilingual Diversity Improves Vision-Language Representations. - Thao Nguyen; Matthew Wallingford; Sebastin Santy; Wei-Chiu Ma; Sewoong Oh; Ludwig Schmidt; Pang Wei W. Koh; Ranjay Krishna (NeurIPS, 2024)

Resolving Discrepancies in Compute-Optimal Scaling of Language Models. - Tomer Porian; Mitchell Wortsman; Jenia Jitsev; Ludwig Schmidt; Yair Carmon (NeurIPS, 2024)

A Sober Look at the Robustness of CLIPs to Spurious Features. - Qizhou Wang; Yong Lin; Yongqiang Chen 002; Ludwig Schmidt; Bo Han 003; Tong Zhang 001 (NeurIPS, 2024)

Why are Visually-Grounded Language Models Bad at Image Classification? - Yuhui Zhang; Alyssa Unell; Xiaohan Wang; Dhruba Ghosh; Yuchang Su; Ludwig Schmidt; Serena Yeung (NeurIPS, 2024)

Language models scale reliably with over-training and on downstream tasks. - Samir Yitzhak Gadre; Georgios Smyrnis; Vaishaal Shankar; Suchin Gururangan; Mitchell Wortsman; Rulin Shao; Jean Mercat; Alex Fang; Jeffrey Li; Sedrick Keh; Rui Xin; Marianna Nezhurina; et al. (CoRR, 2024)

Do CLIPs Always Generalize Better than ImageNet Models? - Qizhou Wang; Yong Lin; Yongqiang Chen 002; Ludwig Schmidt; Bo Han 003; Tong Zhang 001 (CoRR, 2024)

Multilingual Diversity Improves Vision-Language Representations. - Thao Nguyen; Matthew Wallingford; Sebastin Santy; Wei-Chiu Ma; Sewoong Oh; Ludwig Schmidt; Pang Wei Koh; Ranjay Krishna (CoRR, 2024)

MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens. - Anas Awadalla; Le Xue; Oscar Lo; Manli Shu; Hannah Lee; Etash Kumar Guha; Matt Jordan; Sheng Shen 001; Mohamed Awadalla; Silvio Savarese; Caiming Xiong; Ran Xu 001; et al. (CoRR, 2024)

xGen-MM (BLIP-3): A Family of Open Large Multimodal Models. - Le Xue; Manli Shu; Anas Awadalla; Jun Wang 090; An Yan; Senthil Purushwalkam; Honglu Zhou; Viraj Prabhu; Yutong Dai 002; Michael S. Ryoo; Shrikant Kendre; Jieyu Zhang 001; et al. (CoRR, 2024)

BLIP3-KALE: Knowledge Augmented Large-Scale Dense Captions. - Anas Awadalla; Le Xue; Manli Shu; An Yan; Jun Wang 090; Senthil Purushwalkam; Sheng Shen 001; Hannah Lee; Oscar Lo; Jae Sung Park; Etash Kumar Guha; Silvio Savarese; et al. (CoRR, 2024)

Detecting clinical medication errors with AI enabled wearable cameras. - Justin Chan; Solomon Nsumba; Mitchell Wortsman; Achal Dave; Ludwig Schmidt; Shyamnath Gollakota; Kelly E. Michaelsen (npj Digit. Medicine, 2024)

Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation. - Yuhui Zhang; Yuchang Su; Yiming Liu; Xiaohan Wang; James Burgess; Elaine Sui; Chenyu Wang 003; Josiah Aklilu; Alejandro Lozano; Anjiang Wei; Ludwig Schmidt; Serena Yeung-Levy (CVPR, 2025)

Should VLMs be Pre-trained with Image Data? - Sedrick Keh; Jean Mercat; Samir Yitzhak Gadre; Kushal Arora; Igor Vasiljevic; Benjamin Burchfiel; Shuran Song; Russ Tedrake; Thomas Kollar; Ludwig Schmidt; Achal Dave (ICLR, 2025)

Project Alexandria: Towards Freeing Scientific Knowledge from Copyright Burdens via LLMs. - Christoph Schuhmann; Gollam Rabby; Ameya Prabhu; Tawsif Ahmed; Andreas Hochlehnert; Huu Nguyen; Nick Akinci Heidrich; Ludwig Schmidt; Robert Kaczmarczyk; Sören Auer; Jenia Jitsev; Matthias Bethge (CoRR, 2025)

Datasets, Documents, and Repetitions: The Practicalities of Unequal Data Quality. - Alex Fang; Hadi Pouransari; Matt Jordan; Alexander Toshev; Vaishaal Shankar; Ludwig Schmidt; Tom Gunter (CoRR, 2025)

SWE-smith: Scaling Data for Software Engineering Agents. - John Yang 002; Kilian Leret; Carlos E. Jimenez; Alexander Wettig; Kabir Khandpur; Yanzhe Zhang; Binyuan Hui; Ofir Press; Ludwig Schmidt; Diyi Yang (CoRR, 2025)

OpenThoughts: Data Recipes for Reasoning Models. - Etash Kumar Guha; Ryan Marten; Sedrick Keh; Negin Raoof; Georgios Smyrnis; Hritik Bansal; Marianna Nezhurina; Jean Mercat; Trung Vu 002; Zayne Sprague; Ashima Suvarna; Benjamin Feuer; et al. (CoRR, 2025)

Recycling the Web: A Method to Enhance Pre-training Data Quality and Quantity for Language Models. - Thao Nguyen; Yang Li; Olga Golovneva; Luke Zettlemoyer; Sewoong Oh; Ludwig Schmidt; Xian Li 003 (CoRR, 2025)

Closing the Modality Gap for Mixed Modality Search. - Binxu Li; Yuhui Zhang; Xiaohan Wang; Weixin Liang; Ludwig Schmidt; Serena Yeung-Levy (CoRR, 2025)