Braverman, Vladimir

46 publications

NeurIPS 2025 Breaking the Frozen Subspace: Importance Sampling for Low-Rank Optimization in LLM Pretraining Haochen Zhang, Junze Yin, Guanchu Wang, Zirui Liu, Lin Yang, Tianyi Zhang, Anshumali Shrivastava, Vladimir Braverman

AISTATS 2025 Fully Dynamic Adversarially Robust Correlation Clustering in Polylogarithmic Update Time Vladimir Braverman, Prathamesh Dharangutte, Shreyas Pai, Vihan Shah, Chen Wang

ICML 2025 Learning-Augmented Hierarchical Clustering Vladimir Braverman, Jon C. Ergun, Chen Wang, Samson Zhou

ICML 2025 Relative Error Fair Clustering in the Weak-Strong Oracle Model Vladimir Braverman, Prathamesh Dharangutte, Shaofeng H.-C. Jiang, Hoai-An Nguyen, Chen Wang, Yubo Zhang, Samson Zhou

ICLR 2024 How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter Bartlett

ICML 2024 KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache Zirui Liu, Jiayi Yuan, Hongye Jin, Shaochen Zhong, Zhaozhuo Xu, Vladimir Braverman, Beidi Chen, Xia Hu

MIDL 2024 Towards a Collective Medical Imaging AI: Enabling Continual Learning from Peers Guangyao Zheng, Vladimir Braverman, Jeffrey Leal, Steven Rowe, Doris Leung, Michael A. Jacobs, Vishwa Sanjay Parekh

ICML 2023 AutoCoreset: An Automatic Practical Coreset Construction Framework Alaa Maalouf, Murad Tukan, Vladimir Braverman, Daniela Rus

JMLR 2023 Benign Overfitting of Constant-Stepsize SGD for Linear Regression Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade

TMLR 2023 Clustering Using Approximate Nearest Neighbour Oracles Enayat Ullah, Harry Lang, Raman Arora, Vladimir Braverman

ICML 2023 Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade

CoLLAs 2023 Fixed Design Analysis of Regularization-Based Continual Learning Haoran Li, Jingfeng Wu, Vladimir Braverman

NeurIPS 2023 Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability Jingfeng Wu, Vladimir Braverman, Jason Lee

NeurIPS 2023 Private Federated Frequency Estimation: Adapting to the Hardness of the Instance Jingfeng Wu, Wennan Zhu, Peter Kairouz, Vladimir Braverman

ICML 2023 Provable Data Subset Selection for Efficient Neural Networks Training Murad Tukan, Samson Zhou, Alaa Maalouf, Daniela Rus, Vladimir Braverman, Dan Feldman

AISTATS 2022 Gap-Dependent Unsupervised Exploration for Reinforcement Learning Jingfeng Wu, Vladimir Braverman, Lin Yang

AISTATS 2022 New Coresets for Projective Clustering and Applications Murad Tukan, Xuan Wu, Samson Zhou, Vladimir Braverman, Dan Feldman

NeurIPSW 2022 Bidirectional Adaptive Communication for Heterogeneous Distributed Learning Dmitrii Avdiukhin, Vladimir Braverman, Nikita Ivkin, Sebastian U Stich

NeurIPSW 2022 From Local to Global: Spectral-Inspired Graph Neural Networks Ningyuan Teresa Huang, Soledad Villar, Carey Priebe, Da Zheng, Chengyue Huang, Lin Yang, Vladimir Braverman

ICML 2022 Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham Kakade

NeurIPS 2022 Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham Kakade

CoLLAs 2022 Sparsity and Heterogeneous Dropout for Continual Learning in the Null Space of Neural Activations Ali Abbasi, Parsa Nooralinejad, Vladimir Braverman, Hamed Pirsiavash, Soheil Kolouri

NeurIPS 2022 The Power and Limitation of Pretraining-Finetuning for Linear Regression Under Covariate Shift Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham Kakade

NeurIPS 2021 Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning Jingfeng Wu, Vladimir Braverman, Lin Yang

NeurIPS 2021 Adversarial Robustness of Streaming Algorithms Through Importance Sampling Vladimir Braverman, Avinatan Hassidim, Yossi Matias, Mariano Schain, Sandeep Silwal, Samson Zhou

ICMLW 2021 Adversarial Robustness of Streaming Algorithms Through Importance Sampling Vladimir Braverman, Avinatan Hassidim, Yossi Matias, Mariano Schain, Sandeep Silwal, Samson Zhou

COLT 2021 Benign Overfitting of Constant-Stepsize SGD for Linear Regression Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham Kakade

NeurIPS 2021 Coresets for Clustering with Missing Values Vladimir Braverman, Shaofeng Jiang, Robert Krauthgamer, Xuan Wu

ICLR 2021 Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu

ACML 2021 Efficient Coreset Constructions via Sensitivity Sampling Vladimir Braverman, Dan Feldman, Harry Lang, Adiel Statman, Samson Zhou

ACML 2021 Lifelong Learning with Sketched Structural Regularization Haoran Li, Aditya Krishnan, Jingfeng Wu, Soheil Kolouri, Praveen K. Pilly, Vladimir Braverman

COLT 2021 Near-Optimal Entrywise Sampling of Numerically Sparse Matrices Vladimir Braverman, Robert Krauthgamer, Aditya R. Krishnan, Shay Sapir

NeurIPS 2021 The Benefits of Implicit Regularization from SGD in Least Squares Problems Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham Kakade

ICML 2020 Coresets for Clustering in Graphs of Bounded Treewidth Daniel Baker, Vladimir Braverman, Lingxiao Huang, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu

ICLR 2020 Data-Independent Neural Pruning via Coresets Ben Mussay, Margarita Osadchy, Vladimir Braverman, Samson Zhou, Dan Feldman

ICML 2020 FetchSGD: Communication-Efficient Federated Learning with Sketching Daniel Rothchild, Ashwinee Panda, Enayat Ullah, Nikita Ivkin, Ion Stoica, Vladimir Braverman, Joseph Gonzalez, Raman Arora

ICML 2020 Obtaining Adjustable Regularization for Free via Iterate Averaging Jingfeng Wu, Vladimir Braverman, Lin Yang

ICML 2020 On the Noisy Gradient Descent That Generalizes as SGD Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Vladimir Braverman, Zhanxing Zhu

ICML 2020 Schatten Norms in Matrix Streams: Hello Sparsity, Goodbye Dimension Vladimir Braverman, Robert Krauthgamer, Aditya Krishnan, Roi Sinoff

NeurIPS 2019 Communication-Efficient Distributed SGD with Sketching Nikita Ivkin, Daniel Rothchild, Enayat Ullah, Vladimir Braverman, Ion Stoica, Raman Arora

ICML 2019 Coresets for Ordered Weighted Clustering Vladimir Braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu

UAI 2019 Online Factorization and Partition of Complex Networks by Random Walk Lin Yang, Zheng Yu, Vladimir Braverman, Tuo Zhao, Mengdi Wang

NeurIPS 2018 Differentially Private Robust Low-Rank Approximation Raman Arora, Vladimir Braverman, Jalaj Upadhyay

ICML 2018 Matrix Norms in Data Streams: Faster, Multi-Pass and Row-Order Vladimir Braverman, Stephen Chestnut, Robert Krauthgamer, Yi Li, David Woodruff, Lin Yang

NeurIPS 2018 The Physical Systems Behind Optimization Algorithms Lin Yang, Raman Arora, Vladimir Braverman, Tuo Zhao

ICML 2017 Clustering High Dimensional Dynamic Data Streams Vladimir Braverman, Gereon Frahling, Harry Lang, Christian Sohler, Lin F. Yang