| rTop-k: A Statistical Estimation Approach to Distributed SGD B Isik*, LP Barnes*, HA Inan*, A Özgür IEEE Journal on Selected Areas in Information Theory 1 (3), 897 - 907, 2020 | 67 | 2020 |
| Sparse random networks for communication-efficient federated learning B Isik, F Pase, D Gunduz, T Weissman, M Zorzi International Conference on Learning Representations (ICLR), 2023 | 59 | 2023 |
| An information-theoretic justification for model pruning B Isik, T Weissman, A No International Conference on Artificial Intelligence and Statistics, 3821-3846, 2022 | 51* | 2022 |
| Scaling laws for downstream task performance of large language models B Isik, N Ponomareva, H Hazimeh, D Paparas, S Vassilvitskii, S Koyejo International Conference on Learning Representations (ICLR), 2025 | 33 | 2025 |
| Lvac: Learned volumetric attribute compression for point clouds using coordinate based networks B Isik, P Chou, SJ Hwang, N Johnston, G Toderici Frontiers in Signal Processing, 65, 2022 | 31 | 2022 |
| Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation B Isik, WN Chen, A Ozgur, T Weissman, A No Advances in Neural Information Processing Systems 36, 2023 | 19 | 2023 |
| Neural network compression for noisy storage devices B Isik, K Choi, X Zheng, T Weissman, S Ermon, HSP Wong, A Alaghi ACM Transactions on Embedded Computing Systems 22 (3), 1-29, 2023 | 17* | 2023 |
| Adaptive Compression in Federated Learning via Side Information B Isik, F Pase, D Gunduz, S Koyejo, T Weissman, M Zorzi International Conference on Artificial Intelligence and Statistics, 487-495, 2024 | 14* | 2024 |
| Sandwiched video compression: Efficiently extending the reach of standard codecs with neural wrappers B Isik, OG Guleryuz, D Tang, J Taylor, PA Chou IEEE International Conference on Image Processing (ICIP), 2023 | 12 | 2023 |
| GPT-Zip: Deep Compression of Finetuned Large Language Models B Isik, H Kumbong, W Ning, X Yao, S Koyejo, C Zhang Workshop on Efficient Systems for Foundation Models @ICML2023, 2023 | 11 | 2023 |
| Lottery Ticket Adaptation: Mitigating Destructive Interference in LLMs A Panda, B Isik, X Qi, S Koyejo, T Weissman, P Mittal arXiv preprint arXiv:2406.16797, 2024 | 10* | 2024 |
| Neural 3d scene compression via model compression B Isik IEEE/CVF Computer Vision and Pattern Recognition Conference Workshops, 2021 | 10 | 2021 |
| Learning under storage and privacy constraints B Isik, T Weissman 2022 IEEE International Symposium on Information Theory (ISIT), 1844-1849, 2022 | 7 | 2022 |
| Lossy compression of noisy data for private and data-efficient learning B Isik, T Weissman IEEE Journal on Selected Areas in Information Theory 3 (4), 815-823, 2022 | 6 | 2022 |
| On Fairness of Low-Rank Adaptation of Large Models Z Ding, KZ Liu, P Peetathawatchai, B Isik, S Koyejo Conference on Language Modeling (COLM), 2024 | 3 | 2024 |
| An Information-Theoretic Understanding of Maximum Manifold Capacity Representations B Isik, V Lecomte, R Schaeffer, Y LeCun, M Khona, R Shwartz-Ziv, ... NeurIPS 2023, UniReps: the First Workshop on Unifying Representations in …, 2023 | 3* | 2023 |
| Improved Communication-Privacy Trade-offs in Mean Estimation under Streaming Differential Privacy WN Chen, B Isik, P Kairouz, A No, S Oh, Z Xu International Conference on Machine Learning (ICML), 2024 | 2 | 2024 |
| Sandwiched Compression: Repurposing Standard Codecs with Neural Network Wrappers OG Guleryuz, PA Chou, B Isik, H Hoppe, D Tang, R Du, J Taylor, ... arXiv preprint arXiv:2402.05887, 2024 | 2 | 2024 |
| Towards an Improved Understanding and Utilization of Maximum Manifold Capacity Representations R Schaeffer, V Lecomte, DB Pai, A Carranza, B Isik, A Unell, M Khona, ... arXiv preprint arXiv:2406.09366, 2024 | 2 | 2024 |
| Learned volumetric attribute compression using coordinate-based networks PA Chou, B Isik, SJ Hwang, NM Johnston, GD Toderici US Patent 11,900,525, 2024 | 1 | 2024 |
Tsachy WeissmanProfessor of Electrical Engineering at Stanford UniversityVerified email at stanford.edu