rTop-k: A Statistical Estimation Approach to Distributed SGD. - Leighton Pate Barnes; Huseyin A. Inan; Berivan Isik; Ayfer Özgür (IEEE J. Sel. Areas Inf. Theory, 2020)

Successive Pruning for Model Compression via Rate Distortion Theory. - Berivan Isik; Albert No; Tsachy Weissman (CoRR, 2021)

Neural 3D Scene Compression via Model Compression. - Berivan Isik (CoRR, 2021)

LVAC: Learned Volumetric Attribute Compression for Point Clouds using Coordinate Based Networks. - Berivan Isik; Philip A. Chou; Sung Jin Hwang; Nick Johnston; George Toderici (CoRR, 2021)

An Information-Theoretic Justification for Model Pruning. - Berivan Isik; Tsachy Weissman; Albert No (AISTATS, 2022)

Learning under Storage and Privacy Constraints. - Berivan Isik; Tsachy Weissman (ISIT, 2022)

Upper bounds on the Rate of Uniformly-Random Codes for the Deletion Channel. - Berivan Isik; Francisco Pernice; Tsachy Weissman (CoRR, 2022)

Lossy Compression of Noisy Data for Private and Data-Efficient Learning. - Berivan Isik; Tsachy Weissman (IEEE J. Sel. Areas Inf. Theory, 2022)

Sandwiched Video Compression: Efficiently Extending the Reach of Standard Codecs with Neural Wrappers. - Berivan Isik; Onur G. Guleryuz; Danhang Tang; Jonathan Taylor 001; Philip A. Chou (ICIP, 2023)

Sparse Random Networks for Communication-Efficient Federated Learning. - Berivan Isik; Francesco Pase; Deniz Gündüz; Tsachy Weissman; Michele Zorzi (ICLR, 2023)

Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation. - Berivan Isik; Wei-Ning Chen; Ayfer Özgür; Tsachy Weissman; Albert No (NeurIPS, 2023)

Communication-Efficient Federated Learning through Importance Sampling. - Berivan Isik; Francesco Pase; Deniz Gündüz; Sanmi Koyejo; Tsachy Weissman; Michele Zorzi (CoRR, 2023)

Neural Network Compression for Noisy Storage Devices. - Berivan Isik; Kristy Choi; Xin Zheng 013; Tsachy Weissman; Stefano Ermon; H.-S. Philip Wong; Armin Alaghi (ACM Trans. Embed. Comput. Syst., 2023)

Adaptive Compression in Federated Learning via Side Information. - Berivan Isik; Francesco Pase; Deniz Gündüz; Sanmi Koyejo; Tsachy Weissman; Michele Zorzi (AISTATS, 2024)

Improved Communication-Privacy Trade-offs in L2 Mean Estimation under Streaming Differential Privacy. - Wei-Ning Chen; Berivan Isik; Peter Kairouz; Albert No; Sewoong Oh; Zheng Xu 002 (ICML, 2024)

Sketching for Distributed Deep Learning: A Sharper Analysis. - Mayank Shrivastava; Berivan Isik; Qiaobo Li; Sanmi Koyejo; Arindam Banerjee 001 (NeurIPS, 2024)

Scaling Laws for Downstream Task Performance of Large Language Models. - Berivan Isik; Natalia Ponomareva 001; Hussein Hazimeh 001; Dimitris Paparas; Sergei Vassilvitskii; Sanmi Koyejo (CoRR, 2024)

Sandwiched Compression: Repurposing Standard Codecs with Neural Network Wrappers. - Onur G. Guleryuz; Philip A. Chou; Berivan Isik; Hugues Hoppe; Danhang Tang; Ruofei Du; Jonathan Taylor 001; Philip Davidson; Sean Fanello (CoRR, 2024)

Improved Communication-Privacy Trade-offs in L2 Mean Estimation under Streaming Differential Privacy. - Wei-Ning Chen; Berivan Isik; Peter Kairouz; Albert No; Sewoong Oh; Zheng Xu 002 (CoRR, 2024)

On Fairness of Low-Rank Adaptation of Large Models. - Zhoujie Ding; Ken Ziyu Liu; Pura Peetathawatchai; Berivan Isik; Sanmi Koyejo (CoRR, 2024)

Towards an Improved Understanding and Utilization of Maximum Manifold Capacity Representations. - Rylan Schaeffer; Victor Lecomte; Dhruv Bhandarkar Pai; Andres Carranza; Berivan Isik; Alyssa Unell; Mikail Khona; Thomas E. Yerxa; Yann LeCun; SueYeon Chung; Andrey Gromov; Ravid Shwartz-Ziv; et al. (CoRR, 2024)

Lottery Ticket Adaptation: Mitigating Destructive Interference in LLMs. - Ashwinee Panda; Berivan Isik; Xiangyu Qi; Sanmi Koyejo; Tsachy Weissman; Prateek Mittal (CoRR, 2024)

Differentially Private Adaptation of Diffusion Models via Noisy Aggregated Embeddings. - Pura Peetathawatchai; Wei-Ning Chen; Berivan Isik; Sanmi Koyejo; Albert No (CoRR, 2024)

Mutual Information Upper Bounds for Uniform Inputs Through the Deletion Channel. - Francisco Pernice; Berivan Isik; Tsachy Weissman (IEEE Trans. Inf. Theory, 2024)

Scaling Laws for Downstream Task Performance in Machine Translation. - Berivan Isik; Natalia Ponomareva 001; Hussein Hazimeh 001; Dimitris Paparas; Sergei Vassilvitskii; Sanmi Koyejo (ICLR, 2025)

Leveraging Per-Instance Privacy for Machine Unlearning. - Nazanin Mohammadi Sepahvand; Anvith Thudi; Berivan Isik; Ashmita Bhattacharyya; Nicolas Papernot; Eleni Triantafillou; Daniel M. Roy 001; Gintare Karolina Dziugaite (CoRR, 2025)

Successive Halving with Learning Curve Prediction via Latent Kronecker Gaussian Processes. - Jihao Andreas Lin; Nicolas Mayoraz; Steffen Rendle; Dima Kuzmin; Emil Praun; Berivan Isik (CoRR, 2025)