Better Malware Ground Truth: Techniques for Weighting Anti-Virus Vendor Labels. - Alex Kantchelian; Michael Carl Tschantz; Sadia Afroz 001; Brad Miller 002; Vaishaal Shankar; Rekha Bachwani; Anthony D. Joseph; J. Doug Tygar (AISec@CCS, 2015)

A Modern Student Experience inSystems Programming. - Vaishaal Shankar; David E. Culler (L@S, 2015)

Back to the Future: Malware Detection with Temporally Consistent Labels. - Brad Miller 002; Alex Kantchelian; Michael Carl Tschantz; Sadia Afroz 001; Rekha Bachwani; Riyaz Faizullabhoy; Ling Huang 001; Vaishaal Shankar; Tony Wu 002; George Yiu; Anthony D. Joseph; J. D. Tygar (CoRR, 2015)

Reviewer Integration and Performance Measurement for Malware Detection. - Brad Miller 002; Alex Kantchelian; Michael Carl Tschantz; Sadia Afroz 001; Rekha Bachwani; Riyaz Faizullabhoy; Ling Huang 001; Vaishaal Shankar; Tony Wu 002; George Yiu; Anthony D. Joseph; J. D. Tygar (DIMVA, 2016)

Approximate Subgraph Isomorphism for Image Localization. - Vaishaal Shankar; Jordan Zhang; Jerry Chen; Christopher Dinh; Matthew Clements; Avideh Zakhor (Image Processing: Algorithms and Systems, 2016)

Flare Prediction Using Photospheric and Coronal Image Data. - Eric Jonas; Monica G. Bobra; Vaishaal Shankar; J. Todd Hoeksema; Benjamin Recht (CoRR, 2017)

Ground Control to Major Tom: the importance of field surveys in remotely sensed data analysis. - Ian Bolliger; Tamma Carleton; Solomon Hsiang; Jonathan Kadish; Jonathan Proctor; Benjamin Recht; Esther Rolf; Vaishaal Shankar (CoRR, 2017)

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

numpywren: serverless linear algebra. - Vaishaal Shankar; Karl Krauth; Qifan Pu; Eric Jonas; Shivaram Venkataraman; Ion Stoica; Benjamin Recht; Jonathan Ragan-Kelley (CoRR, 2018)

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

Cloud Programming Simplified: A Berkeley View on Serverless Computing. - Eric Jonas; Johann Schleier-Smith; Vikram Sreekanti; Chia-Che Tsai; Anurag Khandelwal; Qifan Pu; Vaishaal Shankar; João Carreira; Karl Krauth; Neeraja Jayant Yadwadkar; Joseph E. Gonzalez; Raluca Ada Popa; et al. (CoRR, 2019)

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

Serverless linear algebra. - Vaishaal Shankar; Karl Krauth; Kailas Vodrahalli; Qifan Pu; Benjamin Recht; Ion Stoica; Jonathan Ragan-Kelley; Eric Jonas; Shivaram Venkataraman (SoCC, 2020)

Serverless Straggler Mitigation using Error-Correcting Codes. - Vipul Gupta; Dominic Carrano; Yaoqing Yang; Vaishaal Shankar; Thomas A. Courtade; Kannan Ramchandran (ICDCS, 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)

Serverless Straggler Mitigation using Local Error-Correcting Codes. - Vipul Gupta; Dominic Carrano; Yaoqing Yang; Vaishaal Shankar; Thomas A. Courtade; Kannan Ramchandran (CoRR, 2020)

A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery. - Esther Rolf; Jonathan Proctor; Tamma Carleton; Ian Bolliger; Vaishaal Shankar; Miyabi Ishihara; Benjamin Recht; Solomon Hsiang (CoRR, 2020)

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

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)

Masked Autoencoding Does Not Help Natural Language Supervision at Scale. - Floris Weers; Vaishaal Shankar; Angelos Katharopoulos; Yinfei Yang; Tom Gunter (CVPR, 2023)

Pre-trained Language Models Do Not Help Auto-regressive Text-to-Image Generation. - Yuhui Zhang; Brandon McKinzie; Zhe Gan; Vaishaal Shankar; Alexander Toshev (ICBINB, 2023)

Robustness in Multimodal Learning under Train-Test Modality Mismatch. - Brandon McKinzie; Vaishaal Shankar; Joseph Yitan Cheng; Yinfei Yang; Jonathon Shlens; Alexander T. Toshev (ICML, 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)

Self Supervision Does Not Help Natural Language Supervision at Scale. - Floris Weers; Vaishaal Shankar; Angelos Katharopoulos; Yinfei Yang; Tom Gunter (CoRR, 2023)

On Robustness in Multimodal Learning. - Brandon McKinzie; Joseph Yitan Cheng; Vaishaal Shankar; Yinfei Yang; Jonathon Shlens; Alexander Toshev (CoRR, 2023)

Robust multimodal models have outlier features and encode more concepts. - Jonathan Crabbé; Pau Rodríguez; Vaishaal Shankar; Luca Zappella; Arno Blaas (CoRR, 2023)

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

TiC-CLIP: Continual Training of CLIP Models. - Saurabh Garg; Mehrdad Farajtabar; Hadi Pouransari; Raviteja Vemulapalli; Sachin Mehta; Oncel Tuzel; Vaishaal Shankar; Fartash Faghri (ICLR, 2024)

Scalable Pre-training of Large Autoregressive Image Models. - Alaaeldin El-Nouby; Michal Klein; Shuangfei Zhai; Miguel Ángel Bautista 001; Vaishaal Shankar; Alexander T. Toshev; Joshua M. Susskind; Armand Joulin (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)

Dataset Decomposition: Faster LLM Training with Variable Sequence Length Curriculum. - Hadi Pouransari; Chun-Liang Li; Jen-Hao Rick Chang; Pavan Kumar Anasosalu Vasu; Cem Koc; Vaishaal Shankar; Oncel Tuzel (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)

Apple Intelligence Foundation Language Models. - Tom Gunter; Zirui Wang; Chong Wang; Ruoming Pang; Andy Narayanan; Aonan Zhang; Bowen Zhang 002; Chen Chen 005; Chung-Cheng Chiu; David Qiu; Deepak Gopinath; Dian Ang Yap; et al. (CoRR, 2024)

Interpreting CLIP: Insights on the Robustness to ImageNet Distribution Shifts. - Jonathan Crabbé; Pau Rodríguez; Vaishaal Shankar; Luca Zappella; Arno Blaas (Trans. Mach. Learn. Res., 2024)

TiC-LM: A Web-Scale Benchmark for Time-Continual LLM Pretraining. - Jeffrey Li; Mohammadreza Armandpour; Iman Mirzadeh; Sachin Mehta; Vaishaal Shankar; Raviteja Vemulapalli; Samy Bengio; Oncel Tuzel; Mehrdad Farajtabar; Hadi Pouransari; Fartash Faghri (ACL (1), 2025)

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)

MobileCLIP2: Improving Multi-Modal Reinforced Training. - Fartash Faghri; Pavan Kumar Anasosalu Vasu; Cem Koc; Vaishaal Shankar; Alexander T. Toshev; Oncel Tuzel; Hadi Pouransari (Trans. Mach. Learn. Res., 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)

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)