Dimakis, Alex

51 publications

ICLR 2026 GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning Lakshya A Agrawal, Shangyin Tan, Dilara Soylu, Noah Ziems, Rishi Khare, Krista Opsahl-Ong, Arnav Singhvi, Herumb Shandilya, Michael J Ryan, Meng Jiang, Christopher Potts, Koushik Sen, Alex Dimakis, Ion Stoica, Dan Klein, Matei Zaharia, Omar Khattab

ICLR 2026 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, Zayne Rea Sprague, Ashima Suvarna, Benjamin Feuer, Leon Liangyu Chen, Zaid Khan, Eric Frankel, Sachin Grover, Caroline Choi, Niklas Muennighoff, Shiye Su, Wanjia Zhao, John Yang, Shreyas Pimpalgaonkar, Kartik Sharma, Charlie Cheng-Jie Ji, Yichuan Deng, Sarah M Pratt, Vivek Ramanujan, Jon Saad-Falcon, Stutee Acharya, Jeffrey Li, Achal Dave, Alon Albalak, Kushal Arora, Blake Wulfe, Chinmay Hegde, Greg Durrett, Sewoong Oh, Mohit Bansal, Saadia Gabriel, Aditya Grover, Kai-Wei Chang, Vaishaal Shankar, Aaron Gokaslan, Mike A Merrill, Tatsunori Hashimoto, Yejin Choi, Jenia Jitsev, Reinhard Heckel, Maheswaran Sathiamoorthy, Alex Dimakis, Ludwig Schmidt

ICLR 2026 Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces Mike A Merrill, Alexander Glenn Shaw, Nicholas Carlini, Boxuan Li, Harsh Raj, Ivan Bercovich, Lin Shi, Jeong Yeon Shin, Thomas Walshe, E. Kelly Buchanan, Junhong Shen, Guanghao Ye, Haowei Lin, Jason Poulos, Maoyu Wang, Marianna Nezhurina, Di Lu, Orfeas Menis Mastromichalakis, Zhiwei Xu, Zizhao Chen, Yue Liu, Robert Zhang, Leon Liangyu Chen, Anurag Kashyap, Jan-Lucas Uslu, Jeffrey Li, Jianbo Wu, Minghao Yan, Song Bian, Vedang Sharma, Ke Sun, Steven Dillmann, Akshay Anand, Andrew Lanpouthakoun, Bardia Koopah, Changran Hu, Etash Kumar Guha, Gabriel H. S. Dreiman, Jiacheng Zhu, Karl Krauth, Li Zhong, Niklas Muennighoff, Robert Kwesi Amanfu, Shangyin Tan, Shreyas Pimpalgaonkar, Tushar Aggarwal, Xiangning Lin, Xin Lan, Xuandong Zhao, Yiqing Liang, Yuanli Wang, Zilong Wang, Changzhi Zhou, David Heineman, Hange Liu, Harsh Trivedi, John Yang, Junhong Lin, Manish Shetty, Michael Yang, Nabil Omi, Negin Raoof, Shanda Li, Terry Yue Zhuo, Wuwei Lin, Yiwei Dai, Yuxin Wang, Wenhao Chai, Shang Zhou, Dariush Wahdany, Ziyu She, Jiaming Hu, Zhikang Dong, Yuxuan Zhu, Sasha Cui, Ahson Saiyed, Arinbjörn Kolbeinsson, Christopher Michael Rytting, Ryan Marten, Yixin Wang, Jenia Jitsev, Alex Dimakis, Andy Konwinski, Ludwig Schmidt

TMLR 2026 When Attention Collapses: How Degenerate Layers in LLMs Enable Smaller, Stronger Models Sunny Sanyal, Ravid Shwartz-Ziv, Alex Dimakis, Sujay Sanghavi

ICLR 2025 Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models Trained on Corrupted Data Asad Aali, Giannis Daras, Brett Levac, Sidharth Kumar, Alex Dimakis, Jon Tamir

ICML 2025 Geometric Median (GM) Matching for Robust K-Subset Selection from Noisy Data Anish Acharya, Sujay Sanghavi, Alex Dimakis, Inderjit S Dhillon

ICLR 2025 Infilling Score: A Pretraining Data Detection Algorithm for Large Language Models Negin Raoof, Litu Rout, Giannis Daras, Sujay Sanghavi, Constantine Caramanis, Sanjay Shakkottai, Alex Dimakis

ICLR 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, Igor Vasiljevic, Luca Soldaini, Jenia Jitsev, Alex Dimakis, Gabriel Ilharco, Pang Wei Koh, Shuran Song, Thomas Kollar, Yair Carmon, Achal Dave, Reinhard Heckel, Niklas Muennighoff, Ludwig Schmidt

CVPR 2025 Viewpoint Rosetta Stone: Unlocking Unpaired Ego-Exo Videos for View-Invariant Representation Learning Mi Luo, Zihui Xue, Alex Dimakis, Kristen Grauman

NeurIPS 2025 When Thinking Drifts: Evidential Grounding for Robust Video Reasoning Mi Luo, Zihui Xue, Alex Dimakis, Kristen Grauman

ECCV 2024 4Diff: 3D-Aware Diffusion Model for Third-to-First Viewpoint Translation Feng Cheng, Mi Luo, Huiyu Wang, Alex Dimakis, Lorenzo Torresani, Gedas Bertasius, Kristen Grauman

ICML 2024 Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data Giannis Daras, Alex Dimakis, Constantinos Costis Daskalakis

ICMLW 2024 Modeling Bilingual Disfluencies with Large Language Models Negin Raoof, Yating Wu, Carlos Bonilla, Junyi Jessy Li, Stephanie M Grasso, Alex Dimakis, Zoi Gkalitsiou

ECCV 2024 Put Myself in Your Shoes: Lifting the Egocentric Perspective from Exocentric Videos Mi Luo, Zihui Xue, Alex Dimakis, Kristen Grauman

NeurIPS 2024 SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors Vijay Lingam, Atula Tejaswi, Aditya Vavre, Aneesh Shetty, Gautham Krishna Gudur, Joydeep Ghosh, Alex Dimakis, Eunsol Choi, Aleksandar Bojchevski, Sujay Sanghavi

ICMLW 2024 SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors Vijay Lingam, Atula Tejaswi Neerkaje, Aditya Vavre, Aneesh Shetty, Gautham Krishna Gudur, Joydeep Ghosh, Alex Dimakis, Eunsol Choi, Aleksandar Bojchevski, Sujay Sanghavi

ICMLW 2024 SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors Vijay Lingam, Atula Tejaswi Neerkaje, Aditya Vavre, Aneesh Shetty, Gautham Krishna Gudur, Joydeep Ghosh, Eunsol Choi, Alex Dimakis, Aleksandar Bojchevski, Sujay Sanghavi

NeurIPSW 2024 SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors Vijay Lingam, Atula Tejaswi, Aditya Vavre, Aneesh Shetty, Gautham Krishna Gudur, Joydeep Ghosh, Alex Dimakis, Eunsol Choi, Aleksandar Bojchevski, Sujay Sanghavi

NeurIPS 2023 Ambient Diffusion: Learning Clean Distributions from Corrupted Data Giannis Daras, Kulin Shah, Yuval Dagan, Aravind Gollakota, Alex Dimakis, Adam Klivans

NeurIPSW 2023 Binding Oracle: Fine-Tuning from Stability to Binding Free Energy Chengyue Gong, Adam Klivans, Jordan Wells, James Loy, Qiang Liu, Alex Dimakis, Daniel Diaz

NeurIPS 2023 Consistent Diffusion Models: Mitigating Sampling Drift by Learning to Be Consistent Giannis Daras, Yuval Dagan, Alex Dimakis, Constantinos Daskalakis

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, Eyal Orgad, Rahim Entezari, Giannis Daras, Sarah Pratt, Vivek Ramanujan, Yonatan Bitton, Kalyani Marathe, Stephen Mussmann, Richard Vencu, Mehdi Cherti, Ranjay Krishna, Pang Wei W 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

ICLR 2023 HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing Tianlong Chen, Chengyue Gong, Daniel Jesus Diaz, Xuxi Chen, Jordan Tyler Wells, Qiang Liu, Zhangyang Wang, Andrew Ellington, Alex Dimakis, Adam Klivans

NeurIPSW 2023 Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models Sriram Ravula, Brett Levac, Ajil Jalal, Jon Tamir, Alex Dimakis

ICML 2023 Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis for DDIM-Type Samplers Sitan Chen, Giannis Daras, Alex Dimakis

TMLR 2023 Soft Diffusion: Score Matching with General Corruptions Giannis Daras, Mauricio Delbracio, Hossein Talebi, Alex Dimakis, Peyman Milanfar

NeurIPSW 2023 Solving Inverse Problems with Ambient Diffusion Giannis Daras, Alex Dimakis

NeurIPS 2023 Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models Litu Rout, Negin Raoof, Giannis Daras, Constantine Caramanis, Alex Dimakis, Sanjay Shakkottai

NeurIPSW 2022 Discovering the Hidden Vocabulary of DALLE-2 Giannis Daras, Alex Dimakis

NeurIPSW 2022 HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing Tianlong Chen, Chengyue Gong, Daniel Jesus Diaz, Xuxi Chen, Jordan Tyler Wells, Qiang Liu, Zhangyang Wang, Andrew Ellington, Alex Dimakis, Adam Klivans

NeurIPSW 2022 Multiresolution Textual Inversion Giannis Daras, Alex Dimakis

NeurIPS 2022 Multitasking Models Are Robust to Structural Failure: A Neural Model for Bilingual Cognitive Reserve Giannis Daras, Negin Raoof, Zoi Gkalitsiou, Alex Dimakis

ICML 2022 Score-Guided Intermediate Level Optimization: Fast Langevin Mixing for Inverse Problems Giannis Daras, Yuval Dagan, Alex Dimakis, Constantinos Daskalakis

NeurIPS 2022 Zonotope Domains for Lagrangian Neural Network Verification Matt Jordan, Jonathan Hayase, Alex Dimakis, Sewoong Oh

ICML 2021 Composing Normalizing Flows for Inverse Problems Jay Whang, Erik Lindgren, Alex Dimakis

ICML 2021 Fairness for Image Generation with Uncertain Sensitive Attributes Ajil Jalal, Sushrut Karmalkar, Jessica Hoffmann, Alex Dimakis, Eric Price

ICML 2021 Instance-Optimal Compressed Sensing via Posterior Sampling Ajil Jalal, Sushrut Karmalkar, Alex Dimakis, Eric Price

ICML 2021 Intermediate Layer Optimization for Inverse Problems Using Deep Generative Models Giannis Daras, Joseph Dean, Ajil Jalal, Alex Dimakis

ICML 2021 Provable Lipschitz Certification for Generative Models Matt Jordan, Alex Dimakis

NeurIPSW 2021 Robust Compressed Sensing MR Imaging with Deep Generative Priors Ajil Jalal, Marius Arvinte, Giannis Daras, Eric Price, Alex Dimakis, Jonathan Tamir

ICML 2021 Solving Inverse Problems with a Flow-Based Noise Model Jay Whang, Qi Lei, Alex Dimakis

NeurIPSW 2020 Approximate Probabilistic Inference with Composed Flows Jay Whang, Erik Lindgren, Alex Dimakis

AISTATS 2020 Communication-Efficient Asynchronous Stochastic Frank-Wolfe over Nuclear-Norm Balls Jiacheng Zhuo, Qi Lei, Alex Dimakis, Constantine Caramanis

NeurIPSW 2020 Compressed Sensing with Approximate Priors via Conditional Resampling Ajil Jalal, Sushrut Karmalkar, Alex Dimakis, Eric Price

NeurIPSW 2020 Compressed Sensing with Invertible Generative Models and Dependent Noise Jay Whang, Qi Lei, Alex Dimakis

NeurIPSW 2020 Intermediate Layer Optimization for Inverse Problems Using Deep Generative Models Joseph Dean, Giannis Daras, Alex Dimakis

ICML 2020 SGD Learns One-Layer Networks in WGANs Qi Lei, Jason Lee, Alex Dimakis, Constantinos Daskalakis

ICML 2019 Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling Shanshan Wu, Alex Dimakis, Sujay Sanghavi, Felix Yu, Daniel Holtmann-Rice, Dmitry Storcheus, Afshin Rostamizadeh, Sanjiv Kumar

ICML 2018 Gradient Coding from Cyclic MDS Codes and Expander Graphs Netanel Raviv, Rashish Tandon, Alex Dimakis, Itzhak Tamo

ICML 2017 Cost-Optimal Learning of Causal Graphs Murat Kocaoglu, Alex Dimakis, Sriram Vishwanath

ICML 2015 Stay on Path: PCA Along Graph Paths Megasthenis Asteris, Anastasios Kyrillidis, Alex Dimakis, Han-Gyol Yi, Bharath Chandrasekaran