Vladimir Braverman

ORCID

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 162

Top Venues

Publications

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
A collective AI via lifelong learning and sharing at the edge. Nat. Mac. Intell. 6 (3) (2024)
Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? ICLR (2024)
Sunghan Lee, Jeonghwan Koh, Guangyao Zheng, Vladimir Braverman, In Cheol Jeong
Exploring the Possibility of Arrhythmia Interpretation of Time Domain ECG Using XAI: A Preliminary Study. AIME (2) (2024)
Zirui Liu, Jiayi Yuan, Hongye Jin, Shaochen Zhong, Zhaozhuo Xu, Vladimir Braverman, Beidi Chen, Xia Hu
KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache. CoRR (2024)
Vladimir Braverman, Prathamesh Dharangutte, Vihan Shah, Chen Wang
Learning-augmented Maximum Independent Set. CoRR (2024)
Meghana Madhyastha, Tamas Budavari, Vladimir Braverman, Joshua T. Vogelstein, Randal C. Burns
T-Rex (Tree-Rectangles): Reformulating Decision Tree Traversal as Hyperrectangle Enclosure. ICDE (2024)
Vladimir Braverman, Kevin Garbe, Eli Jaffe, Rafail Ostrovsky
Private Computations on Streaming Data. IACR Cryptol. ePrint Arch. 2024 (2024)
Vladimir Braverman, Joel Manning, Zhiwei Steven Wu, Samson Zhou
Private Data Stream Analysis for Universal Symmetric Norm Estimation. CoRR (2023)
Jingfeng Wu, Vladimir Braverman, Jason D. Lee
Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability. NeurIPS (2023)
Vladimir Braverman, Joel Manning, Zhiwei Steven Wu, Samson Zhou
Private Data Stream Analysis for Universal Symmetric Norm Estimation. APPROX/RANDOM (2023)
Guangyao Zheng, Shuhao Lai, Vladimir Braverman, Michael A. Jacobs, Vishwa S. Parekh
Multi-environment lifelong deep reinforcement learning for medical imaging. CoRR (2023)
Vladimir Braverman, Robert Krauthgamer, Aditya Krishnan, Shay Sapir
Lower Bounds for Pseudo-Deterministic Counting in a Stream. CoRR (2023)
Alaa Maalouf, Murad Tukan, Vladimir Braverman, Daniela Rus
AutoCoreset: An Automatic Practical Coreset Construction Framework. CoRR (2023)
Jingfeng Wu, Wennan Zhu, Peter Kairouz, Vladimir Braverman
Private Federated Frequency Estimation: Adapting to the Hardness of the Instance. CoRR (2023)
Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? CoRR (2023)
Vladimir Braverman, Dan Feldman, Harry Lang, Daniela Rus, Adiel Statman
Least-Mean-Squares Coresets for Infinite Streams. IEEE Trans. Knowl. Data Eng. 35 (9) (2023)
Enayat Ullah, Harry Lang, Raman Arora, Vladimir Braverman
Clustering using Approximate Nearest Neighbour Oracles. Trans. Mach. Learn. Res. 2023 (2023)
Guangyao Zheng, Samson Zhou, Vladimir Braverman, Michael A. Jacobs, Vishwa Sanjay Parekh
Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging. MIDL (2023)
Zhuolong Yu, Bowen Su, Wei Bai, Shachar Raindel, Vladimir Braverman, Xin Jin
Understanding the Micro-Behaviors of Hardware Offloaded Network Stacks with Lumina. SIGCOMM (2023)
Vladimir Braverman, Robert Krauthgamer, Aditya Krishnan, Shay Sapir
Lower Bounds for Pseudo-Deterministic Counting in a Stream. ICALP (2023)
Guangyao Zheng, Shuhao Lai, Vladimir Braverman, Michael A. Jacobs, Vishwa S. Parekh
A framework for dynamically training and adapting deep reinforcement learning models to different, low-compute, and continuously changing radiology deployment environments. CoRR (2023)
Murad Tukan, Samson Zhou, Alaa Maalouf, Daniela Rus, Vladimir Braverman, Dan Feldman
Provable Data Subset Selection For Efficient Neural Network Training. CoRR (2023)
Sanae Amani, Khushbu Pahwa, Vladimir Braverman, Lin F. Yang
Scaling Distributed Multi-task Reinforcement Learning with Experience Sharing. CoRR (2023)
Haoran Li, Jingfeng Wu, Vladimir Braverman
Fixed Design Analysis of Regularization-Based Continual Learning. CoRR (2023)
Murad Tukan, Samson Zhou, Alaa Maalouf, Daniela Rus, Vladimir Braverman, Dan Feldman
Provable Data Subset Selection For Efficient Neural Networks Training. ICML (2023)
Jingfeng Wu, Wennan Zhu, Peter Kairouz, Vladimir Braverman
Private Federated Frequency Estimation: Adapting to the Hardness of the Instance. NeurIPS (2023)
Murad Tukan, Fares Fares, Yotam Grufinkle, Ido Talmor, Loay Mualem, Vladimir Braverman, Dan Feldman
ORBSLAM3-Enhanced Autonomous Toy Drones: Pioneering Indoor Exploration. CoRR (2023)
Alaa Maalouf, Murad Tukan, Vladimir Braverman, Daniela Rus
AutoCoreset: An Automatic Practical Coreset Construction Framework. ICML (2023)
Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
Learning High-Dimensional Single-Neuron ReLU Networks with Finite Samples. CoRR (2023)
Guangyao Zheng, Michael A. Jacobs, Vladimir Braverman, Vishwa S. Parekh
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging. CoRR (2023)
Jingfeng Wu, Vladimir Braverman, Jason D. Lee
Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability. CoRR (2023)
Guangyao Zheng, Samson Zhou, Vladimir Braverman, Michael A. Jacobs, Vishwa S. Parekh
Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging. CoRR (2023)
Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron. ICML (2023)
Haoran Li, Jingfeng Wu, Vladimir Braverman
Fixed Design Analysis of Regularization-Based Continual Learning. CoLLAs (2023)
Orion Weller, Marc Marone, Vladimir Braverman, Dawn J. Lawrie, Benjamin Van Durme
Pretrained Models for Multilingual Federated Learning. NAACL-HLT (2022)
Vladimir Braverman, Aditya Krishnan, Christopher Musco
Sublinear time spectral density estimation. STOC (2022)
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime. NeurIPS (2022)
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime. CoRR (2022)
Miklós Ajtai, Vladimir Braverman, T. S. Jayram, Sandeep Silwal, Alec Sun, David P. Woodruff, Samson Zhou
The White-Box Adversarial Data Stream Model. CoRR (2022)
Ben Mussay, Dan Feldman, Samson Zhou, Vladimir Braverman, Margarita Osadchy
Data-Independent Structured Pruning of Neural Networks via Coresets. IEEE Trans. Neural Networks Learn. Syst. 33 (12) (2022)
Shir Landau Feibish, Zaoxing Liu, Nikita Ivkin, Xiaoqi Chen, Vladimir Braverman, Jennifer Rexford
Flow-level loss detection with Δ-sketches. SOSR (2022)
Ningyuan Huang, Soledad Villar, Carey E. Priebe, Da Zheng, Chengyue Huang, Lin Yang, Vladimir Braverman
From Local to Global: Spectral-Inspired Graph Neural Networks. CoRR (2022)
Murad Tukan, Xuan Wu, Samson Zhou, Vladimir Braverman, Dan Feldman
New Coresets for Projective Clustering and Applications. AISTATS (2022)
Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression. ICML (2022)
Ali Abbasi, Parsa Nooralinejad, Vladimir Braverman, Hamed Pirsiavash, Soheil Kolouri
Sparsity and Heterogeneous Dropout for Continual Learning in the Null Space of Neural Activations. CoLLAs (2022)
Murad Tukan, Xuan Wu, Samson Zhou, Vladimir Braverman, Dan Feldman
New Coresets for Projective Clustering and Applications. CoRR (2022)
Ali Abbasi, Parsa Nooralinejad, Vladimir Braverman, Hamed Pirsiavash, Soheil Kolouri
Sparsity and Heterogeneous Dropout for Continual Learning in the Null Space of Neural Activations. CoRR (2022)
Jingfeng Wu, Vladimir Braverman, Lin Yang
Gap-Dependent Unsupervised Exploration for Reinforcement Learning. AISTATS (2022)
Orion Weller, Marc Marone, Vladimir Braverman, Dawn J. Lawrie, Benjamin Van Durme
Pretrained Models for Multilingual Federated Learning. CoRR (2022)
Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift. CoRR (2022)
Nikita Ivkin, Edo Liberty, Kevin J. Lang, Zohar S. Karnin, Vladimir Braverman
Streaming Quantiles Algorithms with Small Space and Update Time. Sensors 22 (24) (2022)
Vladimir Braverman, Robert Krauthgamer, Lin F. Yang
Universal Streaming of Subset Norms. Theory Comput. 18 (2022)
Miklós Ajtai, Vladimir Braverman, T. S. Jayram, Sandeep Silwal, Alec Sun, David P. Woodruff, Samson Zhou
The White-Box Adversarial Data Stream Model. PODS (2022)
Vladimir Braverman, Vincent Cohen-Addad, Shaofeng H.-C. Jiang, Robert Krauthgamer, Chris Schwiegelshohn, Mads Bech Toftrup, Xuan Wu
The Power of Uniform Sampling for Coresets. FOCS (2022)
Vladimir Braverman, Vincent Cohen-Addad, Shaofeng H.-C. Jiang, Robert Krauthgamer, Chris Schwiegelshohn, Mads Bech Toftrup, Xuan Wu
The Power of Uniform Sampling for Coresets. CoRR (2022)
Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift. NeurIPS (2022)
Vladimir Braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu
Coresets for Clustering in Excluded-minor Graphs and Beyond. SODA (2021)
Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression. CoRR (2021)
Vladimir Braverman, Avinatan Hassidim, Yossi Matias, Mariano Schain, Sandeep Silwal, Samson Zhou
Adversarial Robustness of Streaming Algorithms through Importance Sampling. NeurIPS (2021)
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
Benign Overfitting of Constant-Stepsize SGD for Linear Regression. CoRR (2021)
Zhuolong Yu, Chuheng Hu, Jingfeng Wu, Xiao Sun, Vladimir Braverman, Mosharaf Chowdhury, Zhenhua Liu, Xin Jin
Programmable packet scheduling with a single queue. SIGCOMM (2021)
Vladimir Braverman, Viska Wei, Samson Zhou
Symmetric Norm Estimation and Regression on Sliding Windows. COCOON (2021)
Vishwa S. Parekh, Shuhao Lai, Vladimir Braverman, Jeff Leal, Steven Rowe, Jay J. Pillai, Michael A. Jacobs
Cross-Domain Federated Learning in Medical Imaging. CoRR (2021)
Vladimir Braverman, Avinatan Hassidim, Yossi Matias, Mariano Schain, Sandeep Silwal, Samson Zhou
Adversarial Robustness of Streaming Algorithms through Importance Sampling. CoRR (2021)
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham M. Kakade
The Benefits of Implicit Regularization from SGD in Least Squares Problems. CoRR (2021)
Jingfeng Wu, Vladimir Braverman, Lin F. Yang
Gap-Dependent Unsupervised Exploration for Reinforcement Learning. CoRR (2021)
Vladimir Braverman, Viska Wei, Samson Zhou
Symmetric Norm Estimation and Regression on Sliding Windows. CoRR (2021)
Vladimir Braverman, Harry Lang, Keith Levin, Yevgeniy Rudoy
Metric k-median clustering in insertion-only streams. Discret. Appl. Math. 304 (2021)
Vladimir Braverman, Aditya Krishnan, Christopher Musco
Linear and Sublinear Time Spectral Density Estimation. CoRR (2021)
Vladimir Braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu
Coresets for Clustering with Missing Values. NeurIPS (2021)
Haoran Li, Aditya Krishnan, Jingfeng Wu, Soheil Kolouri, Praveen K. Pilly, Vladimir Braverman
Lifelong Learning with Sketched Structural Regularization. CoRR (2021)
Haoran Li, Aditya Krishnan, Jingfeng Wu, Soheil Kolouri, Praveen K. Pilly, Vladimir Braverman
Lifelong Learning with Sketched Structural Regularization. ACML (2021)
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
Benign Overfitting of Constant-Stepsize SGD for Linear Regression. COLT (2021)
Daniel N. Baker, Nathan Dyjack, Vladimir Braverman, Stephanie C. Hicks, Ben Langmead
-means clustering with various distances. BCB (2021)
Zhuolong Yu, Jingfeng Wu, Vladimir Braverman, Ion Stoica, Xin Jin
Twenty Years After: Hierarchical Core-Stateless Fair Queueing. NSDI (2021)
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham M. Kakade
The Benefits of Implicit Regularization from SGD in Least Squares Problems. NeurIPS (2021)
Vladimir Braverman, Robert Krauthgamer, Aditya Krishnan, Shay Sapir
Near-Optimal Entrywise Sampling of Numerically Sparse Matrices. COLT (2021)
Vladimir Braverman, Dan Feldman, Harry Lang, Adiel Statman, Samson Zhou
Efficient Coreset Constructions via Sensitivity Sampling. ACML (2021)
Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu
Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate. ICLR (2021)
Jingfeng Wu, Vladimir Braverman, Lin Yang
Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning. NeurIPS (2021)
Zaoxing Liu, Hun Namkung, Georgios Nikolaidis, Jeongkeun Lee, Changhoon Kim, Xin Jin, Vladimir Braverman, Minlan Yu, Vyas Sekar
Jaqen: A High-Performance Switch-Native Approach for Detecting and Mitigating Volumetric DDoS Attacks with Programmable Switches. USENIX Security Symposium (2021)
Vladimir Braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu
Coresets for Clustering with Missing Values. CoRR (2021)
Nikita Ivkin, Ran Ben Basat, Zaoxing Liu, Gil Einziger, Roy Friedman, Vladimir Braverman
I Know What You Did Last Summer: Network Monitoring using Interval Queries. SIGMETRICS (Abstracts) (2020)
Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu
Direction Matters: On the Implicit Regularization Effect of Stochastic Gradient Descent with Moderate Learning Rate. CoRR (2020)
Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Vladimir Braverman, Zhanxing Zhu
On the Noisy Gradient Descent that Generalizes as SGD. ICML (2020)
Vladimir Braverman, Moses Charikar, William Kuszmaul, Lin F. Yang
The one-way communication complexity of dynamic time warping distance. J. Comput. Geom. 11 (2) (2020)
Daniel N. Baker, Vladimir Braverman, Lingxiao Huang, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu
Coresets for Clustering in Graphs of Bounded Treewidth. ICML (2020)
Vladimir Braverman, Robert Krauthgamer, Aditya Krishnan, Roi Sinoff
Schatten Norms in Matrix Streams: Hello Sparsity, Goodbye Dimension. ICML (2020)
Daniel Rothchild, Ashwinee Panda, Enayat Ullah, Nikita Ivkin, Ion Stoica, Vladimir Braverman, Joseph Gonzalez, Raman Arora
FetchSGD: Communication-Efficient Federated Learning with Sketching. ICML (2020)
Ben Mussay, Margarita Osadchy, Vladimir Braverman, Samson Zhou, Dan Feldman
Data-Independent Neural Pruning via Coresets. ICLR (2020)
Vladimir Braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu
Coresets for Clustering in Excluded-minor Graphs and Beyond. CoRR (2020)
Zhuolong Yu, Yiwen Zhang, Vladimir Braverman, Mosharaf Chowdhury, Xin Jin
NetLock: Fast, Centralized Lock Management Using Programmable Switches. SIGCOMM (2020)
Viska Wei, Nikita Ivkin, Vladimir Braverman, Alexander S. Szalay
Sketch and Scale Geo-distributed tSNE and UMAP. IEEE BigData (2020)
Viska Wei, Nikita Ivkin, Vladimir Braverman, Alexander S. Szalay
Sketch and Scale: Geo-distributed tSNE and UMAP. CoRR (2020)
Vladimir Braverman, Robert Krauthgamer, Aditya Krishnan, Shay Sapir
Near-Optimal Entrywise Sampling of Numerically Sparse Matrices. CoRR (2020)
Jingfeng Wu, Vladimir Braverman, Lin F. Yang
Obtaining Adjustable Regularization for Free via Iterate Averaging. CoRR (2020)
Zaoxing Liu, Samson Zhou, Ori Rottenstreich, Vladimir Braverman, Jennifer Rexford
Memory-Efficient Performance Monitoring on Programmable Switches with Lean Algorithms. APOCS (2020)
Vladimir Braverman, Dan Feldman, Harry Lang, Daniela Rus, Adiel Statman
Sparse Coresets for SVD on Infinite Streams. CoRR (2020)
Jingfeng Wu, Vladimir Braverman, Lin Yang
Obtaining Adjustable Regularization for Free via Iterate Averaging. ICML (2020)
Ben Mussay, Dan Feldman, Samson Zhou, Vladimir Braverman, Margarita Osadchy
Data-Independent Structured Pruning of Neural Networks via Coresets. CoRR (2020)

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Publication Activity (10 Years)

Top Topics

Top Venues

Vladimir Braverman