Vladimir Braverman

236 papers A* 60A 9B 1C 2Misc 6Journal 125Unranked 32

Year	Rank	Type	Title / Venue / Authors
2026	—	`conf`	FaithLM: Towards Faithful Explanations for Large Language Models. EACL (Volume 1: Long Papers) Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang, Ruixiang Tang, Shaochen Zhong, Fan Yang, Andrew Wen, Mengnan Du, Xuanting Cai, Vladimir Braverman, Xia Hu
2026	J	`jnl`	High-Dimensional Robust Mean Estimation with Untrusted Batches. CoRR Maryam Aliakbarpour, Vladimir Braverman, Yuhan Liu, Junze Yin
2026	A*	`conf`	Online Learning with Limited Information in the Sliding Window Model. SODA Vladimir Braverman, Sumegha Garg, Chen Wang, David P. Woodruff, Samson Zhou
2026	J	`jnl`	Online Learning with Limited Information in the Sliding Window Model. CoRR Vladimir Braverman, Sumegha Garg, Chen Wang, David P. Woodruff, Samson Zhou
2025	A*	`conf`	Adversarially Robust Quantum State Learning and Testing. FOCS Maryam Aliakbarpour, Vladimir Braverman, Nai-Hui Chia, Yuhan Liu
2025	J	`jnl`	Adversarially robust quantum state learning and testing. CoRR Maryam Aliakbarpour, Vladimir Braverman, Nai-Hui Chia, Yuhan Liu
2025	J	`jnl`	AutoL2S: Auto Long-Short Reasoning for Efficient Large Language Models. CoRR Feng Luo, Yu-Neng Chuang, Guanchu Wang, Hoang Anh Duy Le, Shaochen Zhong, Hongyi Liu, Jiayi Yuan, Yang Sui, Vladimir Braverman, Vipin Chaudhary, Xia Hu
2025	—	`conf`	CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systems. ACL (Findings) Haochen Zhang, Tianyi Zhang, Junze Yin, Oren Gal, Anshumali Shrivastava, Vladimir Braverman
2025	J	`jnl`	CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systems. CoRR Haochen Zhang, Tianyi Zhang, Junze Yin, Oren Gal, Anshumali Shrivastava, Vladimir Braverman
2025	J	`jnl`	Confident or Seek Stronger: Exploring Uncertainty-Based On-device LLM Routing From Benchmarking to Generalization. CoRR Yu-Neng Chuang, Leisheng Yu, Guanchu Wang, Lizhe Zhang, Zirui Liu, Xuanting Cai, Yang Sui, Vladimir Braverman, Xia Ben Hu
2025	J	`jnl`	DTS: Enhancing Large Reasoning Models via Decoding Tree Sketching. CoRR Zicheng Xu, Guanchu Wang, Yu-Neng Chuang, Guangyao Zheng, Alexander S. Szalay, Zirui Liu, Vladimir Braverman
2025	A	`conf`	Fully Dynamic Adversarially Robust Correlation Clustering in Polylogarithmic Update Time. AISTATS Vladimir Braverman, Prathamesh Dharangutte, Shreyas Pai, Vihan Shah, Chen Wang
2025	—	`conf`	Generative Counterfactual Augmentation for Bias Mitigation. ICCVW Jason Uwaeze, Pranav Kulkarni, Vladimir Braverman, Michael A. Jacobs, Vishwa S. Parekh
2025	J	`jnl`	I3S: Importance Sampling Subspace Selection for Low-Rank Optimization in LLM Pretraining. CoRR Haochen Zhang, Junze Yin, Guanchu Wang, Zirui Liu, Tianyi Zhang, Anshumali Shrivastava, Lin Yang, Vladimir Braverman
2025	A*	`conf`	Learning-Augmented Hierarchical Clustering. ICML Vladimir Braverman, Jon C. Ergun, Chen Wang, Samson Zhou
2025	J	`jnl`	Learning-Augmented Hierarchical Clustering. CoRR Vladimir Braverman, Jon C. Ergun, Chen Wang, Samson Zhou
2025	J	`jnl`	Memory-Statistics Tradeoff in Continual Learning with Structural Regularization. CoRR Haoran Li, Jingfeng Wu, Vladimir Braverman
2025	J	`jnl`	Optimizing beat-wise input for arrhythmia detection using 1-D convolutional neural networks: A real-world ECG study. Comput. Methods Programs Biomed. Sunghan Lee, Guangyao Zheng, Jeonghwan Koh, Haoran Li, Zicheng Xu, Sung Pil Cho, Sung Il Im, Vladimir Braverman, In Cheol Jeong
2025	A*	`conf`	Relative Error Fair Clustering in the Weak-Strong Oracle Model. ICML Vladimir Braverman, Prathamesh Dharangutte, Shaofeng H.-C. Jiang, Hoai-An Nguyen, Chen Wang, Yubo Zhang, Samson Zhou
2025	J	`jnl`	Relative Error Fair Clustering in the Weak-Strong Oracle Model. CoRR Vladimir Braverman, Prathamesh Dharangutte, Shaofeng H.-C. Jiang, Hoai-An Nguyen, Chen Wang, Yubo Zhang, Samson Zhou
2025	—	`conf`	Self-Ensemble: Mitigating Confidence Distortion for Large Language Models. EMNLP (Findings) Zicheng Xu, Guanchu Wang, Guangyao Zheng, Yu-Neng Chuang, Alex Szalay, Xia Hu, Vladimir Braverman
2025	J	`jnl`	Self-ensemble: Mitigating Confidence Distortion for Large Language Models. CoRR Zicheng Xu, Guanchu Wang, Guangyao Zheng, Yu-Neng Chuang, Alexander S. Szalay, Xia Ben Hu, Vladimir Braverman
2025	J	`jnl`	Shadow Tomography Against Adversaries. CoRR Maryam Aliakbarpour, Vladimir Braverman, Nai-Hui Chia, Chia-Ying Lin, Yuhan Liu, Aadil Oufkir, Yu-Ching Shen
2025	J	`jnl`	Support Basis: Fast Attention Beyond Bounded Entries. CoRR Maryam Aliakbarpour, Vladimir Braverman, Junze Yin, Haochen Zhang
2025	J	`jnl`	Towards Fair Medical AI: Adversarial Debiasing of 3D CT Foundation Embeddings. CoRR Guangyao Zheng, Michael A. Jacobs, Vladimir Braverman, Vishwa S. Parekh
2025	J	`jnl`	Why and How Auxiliary Tasks Improve JEPA Representations. CoRR Jiacan Yu, Siyi Chen, Mingrui Liu, Nono Horiuchi, Vladimir Braverman, Zicheng Xu, Dan Haramati, Randall Balestriero
2024	J	`jnl`	A collective AI via lifelong learning and sharing at the edge. Nat. Mac. Intell. Andrea Soltoggio, Eseoghene Ben-Iwhiwhu, Vladimir Braverman, Eric Eaton, Benjamin Epstein, Yunhao Ge, Lucy Halperin, Jonathan P. How, Laurent Itti, Michael A. Jacobs, Pavan Kantharaju, Long Le, Steven Lee, Xinran Liu, Sildomar T. Monteiro, David Musliner, Saptarshi Nath, Priyadarshini Panda, Christos Peridis, Hamed Pirsiavash, Vishwa S. Parekh, Kaushik Roy, Shahaf S. Shperberg, Hava T. Siegelmann, Peter Stone, Kyle Vedder, Jingfeng Wu, Lin Yang, Guangyao Zheng, Soheil Kolouri
2024	J	`jnl`	Assessing and Enhancing Large Language Models in Rare Disease Question-answering. CoRR Guanchu Wang, Junhao Ran, Ruixiang Tang, Chia-Yuan Chang, Yu-Neng Chuang, Zirui Liu, Vladimir Braverman, Zhandong Liu, Xia Hu
2024	—	`conf`	Exploring the Possibility of Arrhythmia Interpretation of Time Domain ECG Using XAI: A Preliminary Study. AIME (2) Sunghan Lee, Jeonghwan Koh, Guangyao Zheng, Vladimir Braverman, In Cheol Jeong
2024	J	`jnl`	Federated Learning Clients Clustering with Adaptation to Data Drifts. CoRR Minghao Li, Dmitrii Avdiukhin, Rana Shahout, Nikita Ivkin, Vladimir Braverman, Minlan Yu
2024	J	`jnl`	Fully Dynamic Adversarially Robust Correlation Clustering in Polylogarithmic Update Time. CoRR Vladimir Braverman, Prathamesh Dharangutte, Shreyas Pai, Vihan Shah, Chen Wang
2024	A*	`conf`	How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? ICLR Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett
2024	A*	`conf`	KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache. ICML Zirui Liu, Jiayi Yuan, Hongye Jin, Shaochen (Henry) Zhong, Zhaozhuo Xu, Vladimir Braverman, Beidi Chen, Xia Hu
2024	J	`jnl`	KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache. CoRR Zirui Liu, Jiayi Yuan, Hongye Jin, Shaochen (Henry) Zhong, Zhaozhuo Xu, Vladimir Braverman, Beidi Chen, Xia Hu
2024	A	`conf`	Learning-Augmented Maximum Independent Set. APPROX/RANDOM Vladimir Braverman, Prathamesh Dharangutte, Vihan Shah, Chen Wang
2024	J	`jnl`	Learning-augmented Maximum Independent Set. CoRR Vladimir Braverman, Prathamesh Dharangutte, Vihan Shah, Chen Wang
2024	J	`jnl`	Private Computations on Streaming Data. IACR Cryptol. ePrint Arch. Vladimir Braverman, Kevin Garbe, Eli Jaffe, Rafail Ostrovsky
2024	A*	`conf`	T-Rex (Tree-Rectangles): Reformulating Decision Tree Traversal as Hyperrectangle Enclosure. ICDE Meghana Madhyastha, Tamas Budavari, Vladimir Braverman, Joshua T. Vogelstein, Randal C. Burns
2024	—	`conf`	Towards a Collective Medical Imaging AI: Enabling Continual Learning from Peers. MIDL Guangyao Zheng, Vladimir Braverman, Jeffrey Leal, Steven Rowe, Doris G. Leung, Michael A. Jacobs, Vishwa Sanjay Parekh
2023	J	`jnl`	A framework for dynamically training and adapting deep reinforcement learning models to different, low-compute, and continuously changing radiology deployment environments. CoRR Guangyao Zheng, Shuhao Lai, Vladimir Braverman, Michael A. Jacobs, Vishwa S. Parekh
2023	J	`jnl`	Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging. CoRR Guangyao Zheng, Michael A. Jacobs, Vladimir Braverman, Vishwa S. Parekh
2023	A*	`conf`	AutoCoreset: An Automatic Practical Coreset Construction Framework. ICML Alaa Maalouf, Murad Tukan, Vladimir Braverman, Daniela Rus
2023	J	`jnl`	AutoCoreset: An Automatic Practical Coreset Construction Framework. CoRR Alaa Maalouf, Murad Tukan, Vladimir Braverman, Daniela Rus
2023	J	`jnl`	Benign Overfitting of Constant-Stepsize SGD for Linear Regression. J. Mach. Learn. Res. Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2023	J	`jnl`	Clustering using Approximate Nearest Neighbour Oracles. Trans. Mach. Learn. Res. Enayat Ullah, Harry Lang, Raman Arora, Vladimir Braverman
2023	A*	`conf`	Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron. ICML Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2023	—	`conf`	Fixed Design Analysis of Regularization-Based Continual Learning. CoLLAs Haoran Li, Jingfeng Wu, Vladimir Braverman
2023	J	`jnl`	Fixed Design Analysis of Regularization-Based Continual Learning. CoRR Haoran Li, Jingfeng Wu, Vladimir Braverman
2023	J	`jnl`	How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? CoRR Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett
2023	A*	`conf`	Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability. NeurIPS Jingfeng Wu, Vladimir Braverman, Jason D. Lee
2023	J	`jnl`	Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability. CoRR Jingfeng Wu, Vladimir Braverman, Jason D. Lee
2023	J	`jnl`	Learning High-Dimensional Single-Neuron ReLU Networks with Finite Samples. CoRR Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2023	J	`jnl`	Least-Mean-Squares Coresets for Infinite Streams. IEEE Trans. Knowl. Data Eng. Vladimir Braverman, Dan Feldman, Harry Lang, Daniela Rus, Adiel Statman
2023	A*	`conf`	Lower Bounds for Pseudo-Deterministic Counting in a Stream. ICALP Vladimir Braverman, Robert Krauthgamer, Aditya Krishnan, Shay Sapir
2023	J	`jnl`	Lower Bounds for Pseudo-Deterministic Counting in a Stream. CoRR Vladimir Braverman, Robert Krauthgamer, Aditya Krishnan, Shay Sapir
2023	J	`jnl`	Multi-environment lifelong deep reinforcement learning for medical imaging. CoRR Guangyao Zheng, Shuhao Lai, Vladimir Braverman, Michael A. Jacobs, Vishwa S. Parekh
2023	J	`jnl`	ORBSLAM3-Enhanced Autonomous Toy Drones: Pioneering Indoor Exploration. CoRR Murad Tukan, Fares Fares, Yotam Grufinkle, Ido Talmor, Loay Mualem, Vladimir Braverman, Dan Feldman
2023	A	`conf`	Private Data Stream Analysis for Universal Symmetric Norm Estimation. APPROX/RANDOM Vladimir Braverman, Joel Manning, Zhiwei Steven Wu, Samson Zhou
2023	J	`jnl`	Private Data Stream Analysis for Universal Symmetric Norm Estimation. CoRR Vladimir Braverman, Joel Manning, Zhiwei Steven Wu, Samson Zhou
2023	A*	`conf`	Private Federated Frequency Estimation: Adapting to the Hardness of the Instance. NeurIPS Jingfeng Wu, Wennan Zhu, Peter Kairouz, Vladimir Braverman
2023	J	`jnl`	Private Federated Frequency Estimation: Adapting to the Hardness of the Instance. CoRR Jingfeng Wu, Wennan Zhu, Peter Kairouz, Vladimir Braverman
2023	J	`jnl`	Provable Data Subset Selection For Efficient Neural Network Training. CoRR Murad Tukan, Samson Zhou, Alaa Maalouf, Daniela Rus, Vladimir Braverman, Dan Feldman
2023	A*	`conf`	Provable Data Subset Selection For Efficient Neural Networks Training. ICML Murad Tukan, Samson Zhou, Alaa Maalouf, Daniela Rus, Vladimir Braverman, Dan Feldman
2023	J	`jnl`	Scaling Distributed Multi-task Reinforcement Learning with Experience Sharing. CoRR Sanae Amani, Khushbu Pahwa, Vladimir Braverman, Lin F. Yang
2023	—	`conf`	Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging. MIDL Guangyao Zheng, Samson Zhou, Vladimir Braverman, Michael A. Jacobs, Vishwa Sanjay Parekh
2023	J	`jnl`	Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging. CoRR Guangyao Zheng, Samson Zhou, Vladimir Braverman, Michael A. Jacobs, Vishwa S. Parekh
2023	A*	`conf`	Understanding the Micro-Behaviors of Hardware Offloaded Network Stacks with Lumina. SIGCOMM Zhuolong Yu, Bowen Su, Wei Bai, Shachar Raindel, Vladimir Braverman, Xin Jin
2022	J	`jnl`	Data-Independent Structured Pruning of Neural Networks via Coresets. IEEE Trans. Neural Networks Learn. Syst. Ben Mussay, Dan Feldman, Samson Zhou, Vladimir Braverman, Margarita Osadchy
2022	—	`conf`	Flow-level loss detection with Δ-sketches. SOSR Shir Landau Feibish, Zaoxing Liu, Nikita Ivkin, Xiaoqi Chen, Vladimir Braverman, Jennifer Rexford
2022	J	`jnl`	From Local to Global: Spectral-Inspired Graph Neural Networks. CoRR Ningyuan Huang, Soledad Villar, Carey E. Priebe, Da Zheng, Chengyue Huang, Lin Yang, Vladimir Braverman
2022	A	`conf`	Gap-Dependent Unsupervised Exploration for Reinforcement Learning. AISTATS Jingfeng Wu, Vladimir Braverman, Lin Yang
2022	A*	`conf`	Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression. ICML Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2022	A	`conf`	New Coresets for Projective Clustering and Applications. AISTATS Murad Tukan, Xuan Wu, Samson Zhou, Vladimir Braverman, Dan Feldman
2022	J	`jnl`	New Coresets for Projective Clustering and Applications. CoRR Murad Tukan, Xuan Wu, Samson Zhou, Vladimir Braverman, Dan Feldman
2022	—	`conf`	Pretrained Models for Multilingual Federated Learning. NAACL-HLT Orion Weller, Marc Marone, Vladimir Braverman, Dawn J. Lawrie, Benjamin Van Durme
2022	J	`jnl`	Pretrained Models for Multilingual Federated Learning. CoRR Orion Weller, Marc Marone, Vladimir Braverman, Dawn J. Lawrie, Benjamin Van Durme
2022	A*	`conf`	Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime. NeurIPS Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2022	J	`jnl`	Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime. CoRR Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2022	—	`conf`	Sparsity and Heterogeneous Dropout for Continual Learning in the Null Space of Neural Activations. CoLLAs Ali Abbasi, Parsa Nooralinejad, Vladimir Braverman, Hamed Pirsiavash, Soheil Kolouri
2022	J	`jnl`	Sparsity and Heterogeneous Dropout for Continual Learning in the Null Space of Neural Activations. CoRR Ali Abbasi, Parsa Nooralinejad, Vladimir Braverman, Hamed Pirsiavash, Soheil Kolouri
2022	J	`jnl`	Streaming Quantiles Algorithms with Small Space and Update Time. Sensors Nikita Ivkin, Edo Liberty, Kevin J. Lang, Zohar S. Karnin, Vladimir Braverman
2022	A*	`conf`	Sublinear time spectral density estimation. STOC Vladimir Braverman, Aditya Krishnan, Christopher Musco
2022	A*	`conf`	The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift. NeurIPS Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2022	J	`jnl`	The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift. CoRR Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2022	A*	`conf`	The Power of Uniform Sampling for Coresets. FOCS Vladimir Braverman, Vincent Cohen-Addad, Shaofeng H.-C. Jiang, Robert Krauthgamer, Chris Schwiegelshohn, Mads Bech Toftrup, Xuan Wu
2022	J	`jnl`	The Power of Uniform Sampling for Coresets. CoRR Vladimir Braverman, Vincent Cohen-Addad, Shaofeng H.-C. Jiang, Robert Krauthgamer, Chris Schwiegelshohn, Mads Bech Toftrup, Xuan Wu
2022	A*	`conf`	The White-Box Adversarial Data Stream Model. PODS Miklós Ajtai, Vladimir Braverman, T. S. Jayram, Sandeep Silwal, Alec Sun, David P. Woodruff, Samson Zhou
2022	J	`jnl`	The White-Box Adversarial Data Stream Model. CoRR Miklós Ajtai, Vladimir Braverman, T. S. Jayram, Sandeep Silwal, Alec Sun, David P. Woodruff, Samson Zhou
2022	J	`jnl`	Universal Streaming of Subset Norms. Theory Comput. Vladimir Braverman, Robert Krauthgamer, Lin F. Yang
2021	A*	`conf`	Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning. NeurIPS Jingfeng Wu, Vladimir Braverman, Lin Yang
2021	A*	`conf`	Adversarial Robustness of Streaming Algorithms through Importance Sampling. NeurIPS Vladimir Braverman, Avinatan Hassidim, Yossi Matias, Mariano Schain, Sandeep Silwal, Samson Zhou
2021	J	`jnl`	Adversarial Robustness of Streaming Algorithms through Importance Sampling. CoRR Vladimir Braverman, Avinatan Hassidim, Yossi Matias, Mariano Schain, Sandeep Silwal, Samson Zhou
2021	A*	`conf`	Benign Overfitting of Constant-Stepsize SGD for Linear Regression. COLT Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2021	J	`jnl`	Benign Overfitting of Constant-Stepsize SGD for Linear Regression. CoRR Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2021	A*	`conf`	Coresets for Clustering in Excluded-minor Graphs and Beyond. SODA Vladimir Braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu
2021	A*	`conf`	Coresets for Clustering with Missing Values. NeurIPS Vladimir Braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu
2021	J	`jnl`	Coresets for Clustering with Missing Values. CoRR Vladimir Braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu
2021	J	`jnl`	Cross-Domain Federated Learning in Medical Imaging. CoRR Vishwa S. Parekh, Shuhao Lai, Vladimir Braverman, Jeff Leal, Steven Rowe, Jay J. Pillai, Michael A. Jacobs
2021	A*	`conf`	Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate. ICLR Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu
2021	C	`conf`	Efficient Coreset Constructions via Sensitivity Sampling. ACML Vladimir Braverman, Dan Feldman, Harry Lang, Adiel Statman, Samson Zhou
2021	—	`conf`	Fast and memory-efficient scRNA-seq BCB Daniel N. Baker, Nathan Dyjack, Vladimir Braverman, Stephanie C. Hicks, Ben Langmead
2021	J	`jnl`	Gap-Dependent Unsupervised Exploration for Reinforcement Learning. CoRR Jingfeng Wu, Vladimir Braverman, Lin F. Yang
2021	A*	`conf`	Jaqen: A High-Performance Switch-Native Approach for Detecting and Mitigating Volumetric DDoS Attacks with Programmable Switches. USENIX Security Symposium Zaoxing Liu, Hun Namkung, Georgios Nikolaidis, Jeongkeun Lee, Changhoon Kim, Xin Jin, Vladimir Braverman, Minlan Yu, Vyas Sekar
2021	J	`jnl`	Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression. CoRR Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2021	C	`conf`	Lifelong Learning with Sketched Structural Regularization. ACML Haoran Li, Aditya Krishnan, Jingfeng Wu, Soheil Kolouri, Praveen K. Pilly, Vladimir Braverman
2021	J	`jnl`	Lifelong Learning with Sketched Structural Regularization. CoRR Haoran Li, Aditya Krishnan, Jingfeng Wu, Soheil Kolouri, Praveen K. Pilly, Vladimir Braverman
2021	J	`jnl`	Linear and Sublinear Time Spectral Density Estimation. CoRR Vladimir Braverman, Aditya Krishnan, Christopher Musco
2021	J	`jnl`	Metric k-median clustering in insertion-only streams. Discret. Appl. Math. Vladimir Braverman, Harry Lang, Keith D. Levin, Yevgeniy Rudoy
2021	A*	`conf`	Near-Optimal Entrywise Sampling of Numerically Sparse Matrices. COLT Vladimir Braverman, Robert Krauthgamer, Aditya Krishnan, Shay Sapir
2021	A*	`conf`	Programmable packet scheduling with a single queue. SIGCOMM Zhuolong Yu, Chuheng Hu, Jingfeng Wu, Xiao Sun, Vladimir Braverman, Mosharaf Chowdhury, Zhenhua Liu, Xin Jin
2021	Misc	`conf`	Symmetric Norm Estimation and Regression on Sliding Windows. COCOON Vladimir Braverman, Viska Wei, Samson Zhou
2021	J	`jnl`	Symmetric Norm Estimation and Regression on Sliding Windows. CoRR Vladimir Braverman, Viska Wei, Samson Zhou
2021	A*	`conf`	The Benefits of Implicit Regularization from SGD in Least Squares Problems. NeurIPS Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham M. Kakade
2021	J	`jnl`	The Benefits of Implicit Regularization from SGD in Least Squares Problems. CoRR Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham M. Kakade
2021	Misc	`conf`	Twenty Years After: Hierarchical Core-Stateless Fair Queueing. NSDI Zhuolong Yu, Jingfeng Wu, Vladimir Braverman, Ion Stoica, Xin Jin
2020	J	`jnl`	Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning. CoRR Jingfeng Wu, Vladimir Braverman, Lin F. Yang
2020	J	`jnl`	Coresets for Clustering in Excluded-minor Graphs and Beyond. CoRR Vladimir Braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu
2020	A*	`conf`	Coresets for Clustering in Graphs of Bounded Treewidth. ICML Daniel N. Baker, Vladimir Braverman, Lingxiao Huang, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu
2020	A*	`conf`	Data-Independent Neural Pruning via Coresets. ICLR Ben Mussay, Margarita Osadchy, Vladimir Braverman, Samson Zhou, Dan Feldman
2020	J	`jnl`	Data-Independent Structured Pruning of Neural Networks via Coresets. CoRR Ben Mussay, Dan Feldman, Samson Zhou, Vladimir Braverman, Margarita Osadchy
2020	J	`jnl`	Direction Matters: On the Implicit Regularization Effect of Stochastic Gradient Descent with Moderate Learning Rate. CoRR Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu
2020	A*	`conf`	FetchSGD: Communication-Efficient Federated Learning with Sketching. ICML Daniel Rothchild, Ashwinee Panda, Enayat Ullah, Nikita Ivkin, Ion Stoica, Vladimir Braverman, Joseph Gonzalez, Raman Arora
2020	J	`jnl`	FetchSGD: Communication-Efficient Federated Learning with Sketching. CoRR Daniel Rothchild, Ashwinee Panda, Enayat Ullah, Nikita Ivkin, Ion Stoica, Vladimir Braverman, Joseph Gonzalez, Raman Arora
2020	—	`conf`	I Know What You Did Last Summer: Network Monitoring using Interval Queries. SIGMETRICS (Abstracts) Nikita Ivkin, Ran Ben Basat, Zaoxing Liu, Gil Einziger, Roy Friedman, Vladimir Braverman
2020	—	`conf`	Memory-Efficient Performance Monitoring on Programmable Switches with Lean Algorithms. APOCS Zaoxing Liu, Samson Zhou, Ori Rottenstreich, Vladimir Braverman, Jennifer Rexford
2020	—	`conf`	Multitask radiological modality invariant landmark localization using deep reinforcement learning. MIDL Vishwa S. Parekh, Alex E. Bocchieri, Vladimir Braverman, Michael A. Jacobs
2020	A*	`conf`	Near Optimal Linear Algebra in the Online and Sliding Window Models. FOCS Vladimir Braverman, Petros Drineas, Cameron Musco, Christopher Musco, Jalaj Upadhyay, David P. Woodruff, Samson Zhou
2020	J	`jnl`	Near-Optimal Entrywise Sampling of Numerically Sparse Matrices. CoRR Vladimir Braverman, Robert Krauthgamer, Aditya Krishnan, Shay Sapir
2020	A*	`conf`	NetLock: Fast, Centralized Lock Management Using Programmable Switches. SIGCOMM Zhuolong Yu, Yiwen Zhang, Vladimir Braverman, Mosharaf Chowdhury, Xin Jin
2020	A*	`conf`	Obtaining Adjustable Regularization for Free via Iterate Averaging. ICML Jingfeng Wu, Vladimir Braverman, Lin Yang
2020	J	`jnl`	Obtaining Adjustable Regularization for Free via Iterate Averaging. CoRR Jingfeng Wu, Vladimir Braverman, Lin F. Yang
2020	A*	`conf`	On the Noisy Gradient Descent that Generalizes as SGD. ICML Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Vladimir Braverman, Zhanxing Zhu
2020	A*	`conf`	Schatten Norms in Matrix Streams: Hello Sparsity, Goodbye Dimension. ICML Vladimir Braverman, Robert Krauthgamer, Aditya Krishnan, Roi Sinoff
2020	—	`conf`	Sketch and Scale Geo-distributed tSNE and UMAP. IEEE BigData Viska Wei, Nikita Ivkin, Vladimir Braverman, Alexander S. Szalay
2020	J	`jnl`	Sketch and Scale: Geo-distributed tSNE and UMAP. CoRR Viska Wei, Nikita Ivkin, Vladimir Braverman, Alexander S. Szalay
2020	J	`jnl`	Sparse Coresets for SVD on Infinite Streams. CoRR Vladimir Braverman, Dan Feldman, Harry Lang, Daniela Rus, Adiel Statman
2020	J	`jnl`	The one-way communication complexity of dynamic time warping distance. J. Comput. Geom. Vladimir Braverman, Moses Charikar, William Kuszmaul, Lin F. Yang
2019	Misc	`conf`	Approximations of Schatten Norms via Taylor Expansions. CSR Vladimir Braverman
2019	—	`conf`	Attack Time Localization using Interval Queries. SIGCOMM Posters and Demos Nikita Ivkin, Ran Ben Basat, Zaoxing Liu, Gil Einziger, Roy Friedman, Vladimir Braverman
2019	A*	`conf`	Communication-efficient Distributed SGD with Sketching. NeurIPS Nikita Ivkin, Daniel Rothchild, Enayat Ullah, Vladimir Braverman, Ion Stoica, Raman Arora
2019	J	`jnl`	Communication-efficient distributed SGD with Sketching. CoRR Nikita Ivkin, Daniel Rothchild, Enayat Ullah, Vladimir Braverman, Ion Stoica, Raman Arora
2019	J	`jnl`	Coresets for Clustering in Graphs of Bounded Treewidth. CoRR Vladimir Braverman, Lingxiao Huang, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu
2019	A*	`conf`	Coresets for Ordered Weighted Clustering. ICML Vladimir Braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu
2019	J	`jnl`	Coresets for Ordered Weighted Clustering. CoRR Vladimir Braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu
2019	A	`conf`	DistCache: Provable Load Balancing for Large-Scale Storage Systems with Distributed Caching. FAST Zaoxing Liu, Zhihao Bai, Zhenming Liu, Xiaozhou Li, Changhoon Kim, Vladimir Braverman, Xin Jin, Ion Stoica
2019	—	`conf`	DistCache: Provable Load Balancing for Large-Scale Storage Systems with Distributed Caching. USENIX ATC Zaoxing Liu, Zhihao Bai, Zhenming Liu, Xiaozhou Li, Changhoon Kim, Vladimir Braverman, Xin Jin, Ion Stoica
2019	J	`jnl`	DistCache: Provable Load Balancing for Large-Scale Storage Systems with Distributed Caching. CoRR Zaoxing Liu, Zhihao Bai, Zhenming Liu, Xiaozhou Li, Changhoon Kim, Vladimir Braverman, Xin Jin, Ion Stoica
2019	J	`jnl`	I Know What You Did Last Summer: Network Monitoring using Interval Queries. Proc. ACM Meas. Anal. Comput. Syst. Nikita Ivkin, Ran Ben Basat, Zaoxing Liu, Gil Einziger, Roy Friedman, Vladimir Braverman
2019	—	`conf`	Improved Algorithms for Time Decay Streams. APPROX-RANDOM Vladimir Braverman, Harry Lang, Enayat Ullah, Samson Zhou
2019	J	`jnl`	Improved Algorithms for Time Decay Streams. CoRR Vladimir Braverman, Harry Lang, Enayat Ullah, Samson Zhou
2019	J	`jnl`	Memory-Efficient Performance Monitoring on Programmable Switches with Lean Algorithms. CoRR Zaoxing Liu, Samson Zhou, Ori Rottenstreich, Vladimir Braverman, Jennifer Rexford
2019	J	`jnl`	Multiparametric Deep Learning Tissue Signatures for Muscular Dystrophy: Preliminary Results. CoRR Alex E. Bocchieri, Vishwa S. Parekh, Kathryn R. Wagner, Shivani Ahlawat, Vladimir Braverman, Doris G. Leung, Michael A. Jacobs
2019	A*	`conf`	Nitrosketch: robust and general sketch-based monitoring in software switches. SIGCOMM Zaoxing Liu, Ran Ben-Basat, Gil Einziger, Yaron Kassner, Vladimir Braverman, Roy Friedman, Vyas Sekar
2019	J	`jnl`	On Activation Function Coresets for Network Pruning. CoRR Ben Mussay, Samson Zhou, Vladimir Braverman, Dan Feldman
2019	A	`conf`	Online Factorization and Partition of Complex Networks by Random Walk. UAI Lin F. Yang, Zheng Yu, Vladimir Braverman, Tuo Zhao, Mengdi Wang
2019	A	`conf`	QPipe: quantiles sketch fully in the data plane. CoNEXT Nikita Ivkin, Zhuolong Yu, Vladimir Braverman, Xin Jin
2019	J	`jnl`	Schatten Norms in Matrix Streams: Hello Sparsity, Goodbye Dimension. CoRR Vladimir Braverman, Robert Krauthgamer, Aditya Krishnan, Roi Sinoff
2019	—	`conf`	Streaming Coreset Constructions for M-Estimators. APPROX-RANDOM Vladimir Braverman, Dan Feldman, Harry Lang, Daniela Rus
2019	J	`jnl`	Streaming Quantiles Algorithms with Small Space and Update Time. CoRR Nikita Ivkin, Edo Liberty, Kevin J. Lang, Zohar S. Karnin, Vladimir Braverman
2019	A*	`conf`	The One-Way Communication Complexity of Dynamic Time Warping Distance. SoCG Vladimir Braverman, Moses Charikar, William Kuszmaul, David P. Woodruff, Lin F. Yang
2019	J	`jnl`	The One-Way Communication Complexity of Dynamic Time Warping Distance. CoRR Vladimir Braverman, Moses Charikar, William Kuszmaul, David P. Woodruff, Lin F. Yang
2018	A*	`conf`	ASAP: Fast, Approximate Graph Pattern Mining at Scale. OSDI Anand Padmanabha Iyer, Zaoxing Liu, Xin Jin, Shivaram Venkataraman, Vladimir Braverman, Ion Stoica
2018	A*	`conf`	Approximate Convex Hull of Data Streams. ICALP Avrim Blum, Vladimir Braverman, Ananya Kumar, Harry Lang, Lin F. Yang
2018	J	`jnl`	Approximations of Schatten Norms via Taylor Expansions. CoRR Vladimir Braverman
2018	A*	`conf`	Differentially Private Robust Low-Rank Approximation. NeurIPS Raman Arora, Vladimir Braverman, Jalaj Upadhyay
2018	J	`jnl`	DreamNLP: Novel NLP System for Clinical Report Metadata Extraction using Count Sketch Data Streaming Algorithm: Preliminary Results. CoRR Sanghyun Choi, Nikita Ivkin, Vladimir Braverman, Michael A. Jacobs
2018	A*	`conf`	Matrix Norms in Data Streams: Faster, Multi-Pass and Row-Order. ICML Vladimir Braverman, Stephen R. Chestnut, Robert Krauthgamer, Yi Li, David P. Woodruff, Lin F. Yang
2018	J	`jnl`	Near Optimal Linear Algebra in the Online and Sliding Window Models CoRR Vladimir Braverman, Petros Drineas, Cameron Musco, Christopher Musco, Jalaj Upadhyay, David P. Woodruff, Samson Zhou
2018	—	`conf`	Nearly Optimal Distinct Elements and Heavy Hitters on Sliding Windows. APPROX-RANDOM Vladimir Braverman, Elena Grigorescu, Harry Lang, David P. Woodruff, Samson Zhou
2018	J	`jnl`	Nearly Optimal Distinct Elements and Heavy Hitters on Sliding Windows. CoRR Vladimir Braverman, Elena Grigorescu, Harry Lang, David P. Woodruff, Samson Zhou
2018	J	`jnl`	New Bounds for the CLIQUE-GAP Problem Using Graph Decomposition Theory. Algorithmica Vladimir Braverman, Zaoxing Liu, Tejasvam Singh, N. V. Vinodchandran, Lin F. Yang
2018	A*	`conf`	Revisiting Frequency Moment Estimation in Random Order Streams. ICALP Vladimir Braverman, Emanuele Viola, David P. Woodruff, Lin F. Yang
2018	J	`jnl`	Revisiting Frequency Moment Estimation in Random Order Streams. CoRR Vladimir Braverman, Emanuele Viola, David P. Woodruff, Lin F. Yang
2018	J	`jnl`	Scalable streaming tools for analyzing N-body simulations: Finding halos and investigating excursion sets in one pass. Astron. Comput. Nikita Ivkin, Zaoxing Liu, Lin F. Yang, S. S. Kumar, Gerard Lemson, Mark Neyrinck, Alexander S. Szalay, Vladimir Braverman, Tamas Budavari
2018	A*	`conf`	The Physical Systems Behind Optimization Algorithms. NeurIPS Lin F. Yang, Raman Arora, Vladimir Braverman, Tuo Zhao
2018	—	`conf`	Towards Fast and Scalable Graph Pattern Mining. HotCloud Anand Padmanabha Iyer, Zaoxing Liu, Xin Jin, Shivaram Venkataraman, Vladimir Braverman, Ion Stoica
2018	J	`jnl`	Universal Streaming of Subset Norms. CoRR Vladimir Braverman, Robert Krauthgamer, Lin F. Yang
2017	—	`conf`	Accurate Low-Space Approximation of Metric k-Median for Insertion-Only Streams. CALDAM Vladimir Braverman, Harry Lang, Keith D. Levin
2017	J	`jnl`	Approximate Convex Hull of Data Streams. CoRR Avrim Blum, Vladimir Braverman, Ananya Kumar, Harry Lang, Lin F. Yang
2017	A*	`conf`	BPTree: An ℓ PODS Vladimir Braverman, Stephen R. Chestnut, Nikita Ivkin, Jelani Nelson, Zhengyu Wang, David P. Woodruff
2017	A*	`conf`	Clustering High Dimensional Dynamic Data Streams. ICML Vladimir Braverman, Gereon Frahling, Harry Lang, Christian Sohler, Lin F. Yang
2017	J	`jnl`	Clustering High Dimensional Dynamic Data Streams. CoRR Vladimir Braverman, Gereon Frahling, Harry Lang, Christian Sohler, Lin F. Yang
2017	J	`jnl`	Dynamic Factorization and Partition of Complex Networks. CoRR Lin F. Yang, Vladimir Braverman, Tuo Zhao, Mengdi Wang
2017	J	`jnl`	Processing Big Data Streams (NII Shonan Meeting 2017-7). NII Shonan Meet. Rep. Vladimir Braverman, David P. Woodruff, Ke Yi
2017	A*	`conf`	Streaming symmetric norms via measure concentration. STOC Jaroslaw Blasiok, Vladimir Braverman, Stephen R. Chestnut, Robert Krauthgamer, Lin F. Yang
2016	—	`conf`	Approximating Subadditive Hadamard Functions on Implicit Matrices. APPROX-RANDOM Vladimir Braverman, Alan Roytman, Gregory Vorsanger
2016	J	`jnl`	BPTree: an ℓ CoRR Vladimir Braverman, Stephen R. Chestnut, Nikita Ivkin, Jelani Nelson, Zhengyu Wang, David P. Woodruff
2016	A*	`conf`	Beating CountSketch for heavy hitters in insertion streams. STOC Vladimir Braverman, Stephen R. Chestnut, Nikita Ivkin, David P. Woodruff
2016	A*	`conf`	Clustering Problems on Sliding Windows. SODA Vladimir Braverman, Harry Lang, Keith D. Levin, Morteza Monemizadeh
2016	J	`jnl`	New Frameworks for Offline and Streaming Coreset Constructions. CoRR Vladimir Braverman, Dan Feldman, Harry Lang
2016	A*	`conf`	One Sketch to Rule Them All: Rethinking Network Flow Monitoring with UnivMon. SIGCOMM Zaoxing Liu, Antonis Manousis, Gregory Vorsanger, Vyas Sekar, Vladimir Braverman
2016	J	`jnl`	Sketches for Matrix Norms: Faster, Smaller and More General. CoRR Vladimir Braverman, Stephen R. Chestnut, Robert Krauthgamer, Lin F. Yang
2016	—	`ch.`	Sliding Window Algorithms. Encyclopedia of Algorithms Vladimir Braverman
2016	A*	`conf`	Streaming Space Complexity of Nearly All Functions of One Variable on Frequency Vectors. PODS Vladimir Braverman, Stephen R. Chestnut, David P. Woodruff, Lin F. Yang
2016	J	`jnl`	Streaming Space Complexity of Nearly All Functions of One Variable on Frequency Vectors. CoRR Vladimir Braverman, Stephen R. Chestnut, David P. Woodruff, Lin F. Yang
2016	J	`jnl`	The Physical Systems Behind Optimization Algorithms. CoRR Lin F. Yang, Raman Arora, Vladimir Braverman, Tuo Zhao
2015	J	`jnl`	A Unified Approach for Clustering Problems on Sliding Windows. CoRR Vladimir Braverman, Harry Lang, Keith D. Levin, Morteza Monemizadeh
2015	J	`jnl`	Approximating Subadditive Hadamard Functions on Implicit Matrices. CoRR Vladimir Braverman, Alan Roytman, Gregory Vorsanger
2015	J	`jnl`	Beating CountSketch for Heavy Hitters in Insertion Streams. CoRR Vladimir Braverman, Stephen R. Chestnut, Nikita Ivkin, David P. Woodruff
2015	—	`conf`	Clustering on Sliding Windows in Polylogarithmic Space. FSTTCS Vladimir Braverman, Harry Lang, Keith D. Levin, Morteza Monemizadeh
2015	Misc	`conf`	Enabling a "RISC" Approach for Software-Defined Monitoring using Universal Streaming. HotNets Zaoxing Liu, Gregory Vorsanger, Vladimir Braverman, Vyas Sekar
2015	—	`conf`	New Bounds for the CLIQUE-GAP Problem Using Graph Decomposition Theory. MFCS (2) Vladimir Braverman, Zaoxing Liu, Tejasvam Singh, N. V. Vinodchandran, Lin F. Yang
2015	B	`conf`	Streaming Algorithms for Halo Finders. e-Science Zaoxing Liu, Nikita Ivkin, Lin Yang, Mark Neyrinck, Gerard Lemson, Alexander S. Szalay, Vladimir Braverman, Tamas Budavari, Randal C. Burns, Xin Wang
2015	J	`jnl`	Streaming Symmetric Norms via Measure Concentration. CoRR Vladimir Braverman, Stephen R. Chestnut, Robert Krauthgamer, Lin F. Yang
2015	—	`conf`	Universal Sketches for the Frequency Negative Moments and Other Decreasing Streaming Sums. APPROX-RANDOM Vladimir Braverman, Stephen R. Chestnut
2015	J	`jnl`	Weighted Sampling Without Replacement from Data Streams. CoRR Vladimir Braverman, Rafail Ostrovsky, Gregory Vorsanger
2015	J	`jnl`	Weighted sampling without replacement from data streams. Inf. Process. Lett. Vladimir Braverman, Rafail Ostrovsky, Gregory Vorsanger
2015	—	`conf`	Zero-One Laws for Sliding Windows and Universal Sketches. APPROX-RANDOM Vladimir Braverman, Rafail Ostrovsky, Alan Roytman
2014	—	`conf`	An Optimal Algorithm for Large Frequency Moments Using O(n^(1-2/k)) Bits. APPROX-RANDOM Vladimir Braverman, Jonathan Katzman, Charles Seidell, Gregory Vorsanger
2014	J	`jnl`	Approximating Large Frequency Moments with O(n CoRR Vladimir Braverman, Jonathan Katzman, Charles Seidell, Gregory Vorsanger
2014	J	`jnl`	How to catch L Theor. Comput. Sci. Vladimir Braverman, Ran Gelles, Rafail Ostrovsky
2014	Misc	`conf`	Sampling from Dense Streams without Penalty - Improved Bounds for Frequency Moments and Heavy Hitters. COCOON Vladimir Braverman, Gregory Vorsanger
2014	J	`jnl`	Streaming sums in sublinear space. CoRR Vladimir Braverman, Stephen R. Chestnut
2014	J	`jnl`	Universal Streaming. CoRR Vladimir Braverman, Rafail Ostrovsky, Alan Roytman
2013	—	`conf`	Approximating Large Frequency Moments with Pick-and-Drop Sampling. APPROX-RANDOM Vladimir Braverman, Rafail Ostrovsky
2013	—	`conf`	Generalizing the Layering Method of Indyk and Woodruff: Recursive Sketches for Frequency-Based Vectors on Streams. APPROX-RANDOM Vladimir Braverman, Rafail Ostrovsky
2013	—	`conf`	How Hard Is Counting Triangles in the Streaming Model? ICALP (1) Vladimir Braverman, Rafail Ostrovsky, Dan Vilenchik
2013	J	`jnl`	How Hard is Counting Triangles in the Streaming Model CoRR Vladimir Braverman, Rafail Ostrovsky, Dan Vilenchik
2013	Misc	`conf`	How to Catch COCOON Vladimir Braverman, Ran Gelles, Rafail Ostrovsky
2012	J	`jnl`	Approximating Large Frequency Moments with Pick-and-Drop Sampling CoRR Vladimir Braverman, Rafail Ostrovsky
2012	J	`jnl`	Optimal sampling from sliding windows. J. Comput. Syst. Sci. Vladimir Braverman, Rafail Ostrovsky, Carlo Zaniolo
2011	A*	`conf`	Streaming k-means on Well-Clusterable Data. SODA Vladimir Braverman, Adam Meyerson, Rafail Ostrovsky, Alan Roytman, Michael Shindler, Brian Tagiku
2010	A	`conf`	AMS Without 4-Wise Independence on Product Domains. STACS Vladimir Braverman, Kai-Min Chung, Zhenming Liu, Michael Mitzenmacher, Rafail Ostrovsky
2010	J	`jnl`	Effective Computations on Sliding Windows. SIAM J. Comput. Vladimir Braverman, Rafail Ostrovsky
2010	J	`jnl`	How to Catch L_2-Heavy-Hitters on Sliding Windows CoRR Vladimir Braverman, Ran Gelles, Rafail Ostrovsky
2010	A*	`conf`	Measuring independence of datasets. STOC Vladimir Braverman, Rafail Ostrovsky
2010	J	`jnl`	Rademacher Chaos, Random Eulerian Graphs and The Sparse Johnson-Lindenstrauss Transform CoRR Vladimir Braverman, Rafail Ostrovsky, Yuval Rabani
2010	J	`jnl`	Recursive Sketching For Frequency Moments CoRR Vladimir Braverman, Rafail Ostrovsky
2010	A*	`conf`	Zero-one frequency laws. STOC Vladimir Braverman, Rafail Ostrovsky
2009	J	`jnl`	A linear algorithm for computing convex hulls for random lines. ACM Trans. Algorithms Daniel Berend, Vladimir Braverman
2009	J	`jnl`	Measuring Independence of Datasets CoRR Vladimir Braverman, Rafail Ostrovsky
2009	A*	`conf`	Optimal sampling from sliding windows. PODS Vladimir Braverman, Rafail Ostrovsky, Carlo Zaniolo
2008	J	`jnl`	Measuring $k$-Wise Independence of Streaming Data CoRR Vladimir Braverman, Rafail Ostrovsky
2007	A*	`conf`	Smooth Histograms for Sliding Windows. FOCS Vladimir Braverman, Rafail Ostrovsky
2007	J	`jnl`	Succinct Sampling on Streams CoRR Vladimir Braverman, Rafail Ostrovsky, Carlo Zaniolo
2006	J	`jnl`	Batched disk scheduling with delays. SIGMETRICS Perform. Evaluation Rev. Eitan Bachmat, Vladimir Braverman