Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning. - Mitchell Wortsman; Kiana Ehsani; Mohammad Rastegari; Ali Farhadi; Roozbeh Mottaghi (CVPR, 2019)

Discovering Neural Wirings. - Mitchell Wortsman; Ali Farhadi; Mohammad Rastegari (NeurIPS, 2019)

What's Hidden in a Randomly Weighted Neural Network? - Vivek Ramanujan; Mitchell Wortsman; Aniruddha Kembhavi; Ali Farhadi; Mohammad Rastegari (CVPR, 2020)

Soft Threshold Weight Reparameterization for Learnable Sparsity. - Aditya Kusupati; Vivek Ramanujan; Raghav Somani; Mitchell Wortsman; Prateek Jain 002; Sham M. Kakade; Ali Farhadi (ICML, 2020)

Supermasks in Superposition. - Mitchell Wortsman; Vivek Ramanujan; Rosanne Liu; Aniruddha Kembhavi; Mohammad Rastegari; Jason Yosinski; Ali Farhadi (NeurIPS, 2020)

Deconstructing the Structure of Sparse Neural Networks. - Maxwell Van Gelder; Mitchell Wortsman; Kiana Ehsani (CoRR, 2020)

Learning Neural Network Subspaces. - Mitchell Wortsman; Maxwell Horton; Carlos Guestrin; Ali Farhadi; Mohammad Rastegari (ICML, 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)

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)

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)

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)

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)

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

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)

The Role of Pre-training Data in Transfer Learning. - Rahim Entezari; Mitchell Wortsman; Olga Saukh; Moein Shariatnia; Hanie Sedghi; 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)

Replacing softmax with ReLU in Vision Transformers. - Mitchell Wortsman; Jaehoon Lee 001; Justin Gilmer; Simon Kornblith (CoRR, 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)

OLMo: Accelerating the Science of Language Models. - Dirk Groeneveld; Iz Beltagy; Evan Pete Walsh; Akshita Bhagia; Rodney Kinney; Oyvind Tafjord; Ananya Harsh Jha; Hamish Ivison; Ian Magnusson; Yizhong Wang; Shane Arora; David Atkinson; et al. (ACL (1), 2024)

Small-scale proxies for large-scale Transformer training instabilities. - Mitchell Wortsman; Peter J. Liu; Lechao Xiao; Katie E. Everett; Alexander A. Alemi; Ben Adlam; John D. Co-Reyes; Izzeddin Gur; Abhishek Kumar; Roman Novak; Jeffrey Pennington; Jascha Sohl-Dickstein; et al. (ICLR, 2024)

Scaling Exponents Across Parameterizations and Optimizers. - Katie E. Everett; Lechao Xiao; Mitchell Wortsman; Alexander A. Alemi; Roman Novak; Peter J. Liu; Izzeddin Gur; Jascha Sohl-Dickstein; Leslie Pack Kaelbling; Jaehoon Lee 001; Jeffrey Pennington (ICML, 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)

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

OLMo: Accelerating the Science of Language Models. - Dirk Groeneveld; Iz Beltagy; Pete Walsh; Akshita Bhagia; Rodney Kinney; Oyvind Tafjord; Ananya Harsh Jha; Hamish Ivison; Ian Magnusson; Yizhong Wang; Shane Arora; David Atkinson; et al. (CoRR, 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)

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)

Robust and reliable large-scale transfer learning - Mitchell Wortsman (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. (ICLR, 2025)