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ORCIDVladimir Braverman
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- affiliation: Johns Hopkins University
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2020 – today
- 2024
[j18]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): 251-264 (2024)- [c100]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: 288-295 - [c99]Vladimir Braverman, Prathamesh Dharangutte, Vihan Shah, Chen Wang:
Learning-Augmented Maximum Independent Set. APPROX/RANDOM 2024: 24:1-24:18 - [c98]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: 1792-1804 - [c97]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 - [c96]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. ICML 2024 - [i89]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 abs/2402.02750 (2024) - [i88]Vladimir Braverman, Prathamesh Dharangutte, Vihan Shah, Chen Wang:
Learning-augmented Maximum Independent Set. CoRR abs/2407.11364 (2024) - [i87]Guanchu Wang, Junhao Ran, Ruixiang Tang, Chia-Yuan Chang, Yu-Neng Chuang, Zirui Liu, Vladimir Braverman, Zhandong Liu, Xia Hu:
Assessing and Enhancing Large Language Models in Rare Disease Question-answering. CoRR abs/2408.08422 (2024) - [i86]Minghao Li, Dmitrii Avdiukhin, Rana Shahout, Nikita Ivkin, Vladimir Braverman, Minlan Yu:
Federated Learning Clients Clustering with Adaptation to Data Drifts. CoRR abs/2411.01580 (2024) - [i85]Vladimir Braverman, Kevin Garbe, Eli Jaffe, Rafail Ostrovsky:
Private Computations on Streaming Data. IACR Cryptol. ePrint Arch. 2024: 698 (2024) - 2023
- [j17]Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Benign Overfitting of Constant-Stepsize SGD for Linear Regression. J. Mach. Learn. Res. 24: 326:1-326:58 (2023) - [j16]Vladimir Braverman, Dan Feldman, Harry Lang, Daniela Rus, Adiel Statman:
Least-Mean-Squares Coresets for Infinite Streams. IEEE Trans. Knowl. Data Eng. 35(9): 8699-8712 (2023) - [j15]Enayat Ullah, Harry Lang, Raman Arora, Vladimir Braverman:
Clustering using Approximate Nearest Neighbour Oracles. Trans. Mach. Learn. Res. 2023 (2023) - [c95]Vladimir Braverman, Joel Manning, Zhiwei Steven Wu, Samson Zhou:
Private Data Stream Analysis for Universal Symmetric Norm Estimation. APPROX/RANDOM 2023: 45:1-45:24 - [c94]Haoran Li, Jingfeng Wu, Vladimir Braverman:
Fixed Design Analysis of Regularization-Based Continual Learning. CoLLAs 2023: 513-533 - [c93]Vladimir Braverman, Robert Krauthgamer, Aditya Krishnan, Shay Sapir:
Lower Bounds for Pseudo-Deterministic Counting in a Stream. ICALP 2023: 30:1-30:14 - [c92]Alaa Maalouf, Murad Tukan, Vladimir Braverman, Daniela Rus:
AutoCoreset: An Automatic Practical Coreset Construction Framework. ICML 2023: 23451-23466 - [c91]Murad Tukan, Samson Zhou, Alaa Maalouf, Daniela Rus, Vladimir Braverman, Dan Feldman:
Provable Data Subset Selection For Efficient Neural Networks Training. ICML 2023: 34533-34555 - [c90]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: 37919-37951 - [c89]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: 1751-1764 - [c88]Jingfeng Wu, Vladimir Braverman, Jason D. Lee:
Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability. NeurIPS 2023 - [c87]Jingfeng Wu, Wennan Zhu, Peter Kairouz, Vladimir Braverman:
Private Federated Frequency Estimation: Adapting to the Hardness of the Instance. NeurIPS 2023 - [c86]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: 1074-1087 - [i84]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 abs/2302.11510 (2023) - [i83]Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Learning High-Dimensional Single-Neuron ReLU Networks with Finite Samples. CoRR abs/2303.02255 (2023) - [i82]Murad Tukan, Samson Zhou, Alaa Maalouf, Daniela Rus, Vladimir Braverman, Dan Feldman:
Provable Data Subset Selection For Efficient Neural Network Training. CoRR abs/2303.05151 (2023) - [i81]Guangyao Zheng, Michael A. Jacobs, Vladimir Braverman, Vishwa S. Parekh:
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging. CoRR abs/2303.06783 (2023) - [i80]Haoran Li, Jingfeng Wu, Vladimir Braverman:
Fixed Design Analysis of Regularization-Based Continual Learning. CoRR abs/2303.10263 (2023) - [i79]Vladimir Braverman, Robert Krauthgamer, Aditya Krishnan, Shay Sapir:
Lower Bounds for Pseudo-Deterministic Counting in a Stream. CoRR abs/2303.16287 (2023) - [i78]Jingfeng Wu, Vladimir Braverman, Jason D. Lee:
Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability. CoRR abs/2305.11788 (2023) - [i77]Alaa Maalouf, Murad Tukan, Vladimir Braverman, Daniela Rus:
AutoCoreset: An Automatic Practical Coreset Construction Framework. CoRR abs/2305.11980 (2023) - [i76]Guangyao Zheng, Shuhao Lai, Vladimir Braverman, Michael A. Jacobs, Vishwa S. Parekh:
Multi-environment lifelong deep reinforcement learning for medical imaging. CoRR abs/2306.00188 (2023) - [i75]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 abs/2306.05310 (2023) - [i74]Jingfeng Wu, Wennan Zhu, Peter Kairouz, Vladimir Braverman:
Private Federated Frequency Estimation: Adapting to the Hardness of the Instance. CoRR abs/2306.09396 (2023) - [i73]Vladimir Braverman, Joel Manning, Zhiwei Steven Wu, Samson Zhou:
Private Data Stream Analysis for Universal Symmetric Norm Estimation. CoRR abs/2307.04249 (2023) - [i72]Sanae Amani, Khushbu Pahwa, Vladimir Braverman, Lin F. Yang:
Scaling Distributed Multi-task Reinforcement Learning with Experience Sharing. CoRR abs/2307.05834 (2023) - [i71]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 abs/2310.08391 (2023) - [i70]Murad Tukan, Fares Fares, Yotam Grufinkle, Ido Talmor, Loay Mualem, Vladimir Braverman, Dan Feldman:
ORBSLAM3-Enhanced Autonomous Toy Drones: Pioneering Indoor Exploration. CoRR abs/2312.13385 (2023) - 2022
- [j14]Nikita Ivkin, Edo Liberty, Kevin J. Lang, Zohar S. Karnin, Vladimir Braverman:
Streaming Quantiles Algorithms with Small Space and Update Time. Sensors 22(24): 9612 (2022) - [j13]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): 7829-7841 (2022) - [j12]Vladimir Braverman, Robert Krauthgamer, Lin F. Yang:
Universal Streaming of Subset Norms. Adv. Math. Commun. 18: 1-32 (2022) - [c85]Jingfeng Wu, Vladimir Braverman, Lin Yang:
Gap-Dependent Unsupervised Exploration for Reinforcement Learning. AISTATS 2022: 4109-4131 - [c84]Murad Tukan, Xuan Wu, Samson Zhou, Vladimir Braverman, Dan Feldman:
New Coresets for Projective Clustering and Applications. AISTATS 2022: 5391-5415 - [c83]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: 617-628 - [c82]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: 462-473 - [c81]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: 24280-24314 - [c80]Orion Weller, Marc Marone, Vladimir Braverman, Dawn J. Lawrie, Benjamin Van Durme:
Pretrained Models for Multilingual Federated Learning. NAACL-HLT 2022: 1413-1421 - [c79]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 - [c78]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 - [c77]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: 15-27 - [c76]Shir Landau Feibish, Zaoxing Liu, Nikita Ivkin, Xiaoqi Chen, Vladimir Braverman, Jennifer Rexford:
Flow-level loss detection with Δ-sketches. SOSR 2022: 25-32 - [c75]Vladimir Braverman, Aditya Krishnan, Christopher Musco:
Sublinear time spectral density estimation. STOC 2022: 1144-1157 - [i69]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 abs/2203.03159 (2022) - [i68]Murad Tukan, Xuan Wu, Samson Zhou, Vladimir Braverman, Dan Feldman:
New Coresets for Projective Clustering and Applications. CoRR abs/2203.04370 (2022) - [i67]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 abs/2203.06514 (2022) - [i66]Miklós Ajtai, Vladimir Braverman, T. S. Jayram, Sandeep Silwal, Alec Sun, David P. Woodruff, Samson Zhou:
The White-Box Adversarial Data Stream Model. CoRR abs/2204.09136 (2022) - [i65]Orion Weller, Marc Marone, Vladimir Braverman, Dawn J. Lawrie, Benjamin Van Durme:
Pretrained Models for Multilingual Federated Learning. CoRR abs/2206.02291 (2022) - [i64]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 abs/2208.01857 (2022) - [i63]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 abs/2209.01901 (2022) - [i62]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 abs/2209.12054 (2022) - 2021
- [j11]Vladimir Braverman, Harry Lang, Keith D. Levin, Yevgeniy Rudoy:
Metric k-median clustering in insertion-only streams. Discret. Appl. Math. 304: 164-180 (2021) - [c74]Vladimir Braverman, Dan Feldman, Harry Lang, Adiel Statman, Samson Zhou:
Efficient Coreset Constructions via Sensitivity Sampling. ACML 2021: 948-963 - [c73]Haoran Li, Aditya Krishnan, Jingfeng Wu, Soheil Kolouri, Praveen K. Pilly, Vladimir Braverman:
Lifelong Learning with Sketched Structural Regularization. ACML 2021: 985-1000 - [c72]Daniel N. Baker, Nathan Dyjack, Vladimir Braverman, Stephanie C. Hicks, Ben Langmead:
Fast and memory-efficient scRNA-seq k-means clustering with various distances. BCB 2021: 24:1-24:8 - [c71]Vladimir Braverman, Viska Wei, Samson Zhou:
Symmetric Norm Estimation and Regression on Sliding Windows. COCOON 2021: 528-539 - [c70]Vladimir Braverman, Robert Krauthgamer, Aditya Krishnan, Shay Sapir:
Near-Optimal Entrywise Sampling of Numerically Sparse Matrices. COLT 2021: 759-773 - [c69]Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Benign Overfitting of Constant-Stepsize SGD for Linear Regression. COLT 2021: 4633-4635 - [c68]Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu:
Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate. ICLR 2021 - [c67]Vladimir Braverman, Avinatan Hassidim, Yossi Matias, Mariano Schain, Sandeep Silwal, Samson Zhou:
Adversarial Robustness of Streaming Algorithms through Importance Sampling. NeurIPS 2021: 3544-3557 - [c66]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: 5456-5468 - [c65]Jingfeng Wu, Vladimir Braverman, Lin Yang:
Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning. NeurIPS 2021: 13112-13124 - [c64]Vladimir Braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu:
Coresets for Clustering with Missing Values. NeurIPS 2021: 17360-17372 - [c63]Zhuolong Yu, Jingfeng Wu, Vladimir Braverman, Ion Stoica, Xin Jin:
Twenty Years After: Hierarchical Core-Stateless Fair Queueing. NSDI 2021: 29-45 - [c62]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: 179-193 - [c61]Vladimir Braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu:
Coresets for Clustering in Excluded-minor Graphs and Beyond. SODA 2021: 2679-2696 - [c60]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: 3829-3846 - [i61]Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Benign Overfitting of Constant-Stepsize SGD for Linear Regression. CoRR abs/2103.12692 (2021) - [i60]Vladimir Braverman, Aditya Krishnan, Christopher Musco:
Linear and Sublinear Time Spectral Density Estimation. CoRR abs/2104.03461 (2021) - [i59]Haoran Li, Aditya Krishnan, Jingfeng Wu, Soheil Kolouri, Praveen K. Pilly, Vladimir Braverman:
Lifelong Learning with Sketched Structural Regularization. CoRR abs/2104.08604 (2021) - [i58]Vladimir Braverman, Avinatan Hassidim, Yossi Matias, Mariano Schain, Sandeep Silwal, Samson Zhou:
Adversarial Robustness of Streaming Algorithms through Importance Sampling. CoRR abs/2106.14952 (2021) - [i57]Vladimir Braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu:
Coresets for Clustering with Missing Values. CoRR abs/2106.16112 (2021) - [i56]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 abs/2108.04552 (2021) - [i55]Jingfeng Wu, Vladimir Braverman, Lin F. Yang:
Gap-Dependent Unsupervised Exploration for Reinforcement Learning. CoRR abs/2108.05439 (2021) - [i54]Vladimir Braverman, Viska Wei, Samson Zhou:
Symmetric Norm Estimation and Regression on Sliding Windows. CoRR abs/2109.01635 (2021) - [i53]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 abs/2110.06198 (2021) - [i52]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 abs/2112.10001 (2021) - 2020
- [j10]Vladimir Braverman, Moses Charikar, William Kuszmaul, Lin F. Yang:
The one-way communication complexity of dynamic time warping distance. J. Comput. Geom. 11(2): 62-93 (2020) - [c59]Zaoxing Liu, Samson Zhou, Ori Rottenstreich, Vladimir Braverman, Jennifer Rexford:
Memory-Efficient Performance Monitoring on Programmable Switches with Lean Algorithms. APOCS 2020: 31-44 - [c58]Viska Wei, Nikita Ivkin, Vladimir Braverman, Alexander S. Szalay:
Sketch and Scale Geo-distributed tSNE and UMAP. IEEE BigData 2020: 996-1003 - [c57]Vladimir Braverman, Petros Drineas, Cameron Musco, Christopher Musco, Jalaj Upadhyay, David P. Woodruff, Samson Zhou:
Near Optimal Linear Algebra in the Online and Sliding Window Models. FOCS 2020: 517-528 - [c56]Ben Mussay, Margarita Osadchy, Vladimir Braverman, Samson Zhou, Dan Feldman:
Data-Independent Neural Pruning via Coresets. ICLR 2020 - [c55]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: 569-579 - [c54]Vladimir Braverman, Robert Krauthgamer, Aditya Krishnan, Roi Sinoff:
Schatten Norms in Matrix Streams: Hello Sparsity, Goodbye Dimension. ICML 2020: 1100-1110 - [c53]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: 8253-8265 - [c52]Jingfeng Wu, Vladimir Braverman, Lin Yang:
Obtaining Adjustable Regularization for Free via Iterate Averaging. ICML 2020: 10344-10354 - [c51]Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Vladimir Braverman, Zhanxing Zhu:
On the Noisy Gradient Descent that Generalizes as SGD. ICML 2020: 10367-10376 - [c50]Vishwa S. Parekh, Alex E. Bocchieri, Vladimir Braverman, Michael A. Jacobs:
Multitask radiological modality invariant landmark localization using deep reinforcement learning. MIDL 2020: 588-600 - [c49]Zhuolong Yu, Yiwen Zhang, Vladimir Braverman, Mosharaf Chowdhury, Xin Jin:
NetLock: Fast, Centralized Lock Management Using Programmable Switches. SIGCOMM 2020: 126-138 - [c48]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: 61-62 - [i51]Vladimir Braverman, Dan Feldman, Harry Lang, Daniela Rus, Adiel Statman:
Sparse Coresets for SVD on Infinite Streams. CoRR abs/2002.06296 (2020) - [i50]Vladimir Braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu:
Coresets for Clustering in Excluded-minor Graphs and Beyond. CoRR abs/2004.07718 (2020) - [i49]Daniel Rothchild, Ashwinee Panda, Enayat Ullah, Nikita Ivkin, Ion Stoica, Vladimir Braverman, Joseph Gonzalez, Raman Arora:
FetchSGD: Communication-Efficient Federated Learning with Sketching. CoRR abs/2007.07682 (2020) - [i48]Jingfeng Wu, Vladimir Braverman, Lin F. Yang:
Obtaining Adjustable Regularization for Free via Iterate Averaging. CoRR abs/2008.06736 (2020) - [i47]Ben Mussay, Dan Feldman, Samson Zhou, Vladimir Braverman, Margarita Osadchy:
Data-Independent Structured Pruning of Neural Networks via Coresets. CoRR abs/2008.08316 (2020) - [i46]Vladimir Braverman, Robert Krauthgamer, Aditya Krishnan, Shay Sapir:
Near-Optimal Entrywise Sampling of Numerically Sparse Matrices. CoRR abs/2011.01777 (2020) - [i45]Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu:
Direction Matters: On the Implicit Regularization Effect of Stochastic Gradient Descent with Moderate Learning Rate. CoRR abs/2011.02538 (2020) - [i44]Viska Wei, Nikita Ivkin, Vladimir Braverman, Alexander S. Szalay:
Sketch and Scale: Geo-distributed tSNE and UMAP. CoRR abs/2011.06103 (2020) - [i43]Jingfeng Wu, Vladimir Braverman, Lin F. Yang:
Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning. CoRR abs/2011.13034 (2020)
2010 – 2019
- 2019
- [j9]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. Proc. ACM Meas. Anal. Comput. Syst. 3(3): 61:1-61:28 (2019) - [c47]Vladimir Braverman, Harry Lang, Enayat Ullah, Samson Zhou:
Improved Algorithms for Time Decay Streams. APPROX-RANDOM 2019: 27:1-27:17 - [c46]Vladimir Braverman, Dan Feldman, Harry Lang, Daniela Rus:
Streaming Coreset Constructions for M-Estimators. APPROX-RANDOM 2019: 62:1-62:15 - [c45]Vladimir Braverman, Moses Charikar, William Kuszmaul, David P. Woodruff, Lin F. Yang:
The One-Way Communication Complexity of Dynamic Time Warping Distance. SoCG 2019: 16:1-16:15 - [c44]Nikita Ivkin, Zhuolong Yu, Vladimir Braverman, Xin Jin:
QPipe: quantiles sketch fully in the data plane. CoNEXT 2019: 285-291 - [c43]Vladimir Braverman:
Approximations of Schatten Norms via Taylor Expansions. CSR 2019: 70-79 - [c42]Zaoxing Liu, Zhihao Bai, Zhenming Liu, Xiaozhou Li, Changhoon Kim, Vladimir Braverman, Xin Jin, Ion Stoica:
DistCache: Provable Load Balancing for Large-Scale Storage Systems with Distributed Caching. FAST 2019: 143-157 - [c41]Vladimir Braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu:
Coresets for Ordered Weighted Clustering. ICML 2019: 744-753 - [c40]Nikita Ivkin, Daniel Rothchild, Enayat Ullah, Vladimir Braverman, Ion Stoica, Raman Arora:
Communication-efficient Distributed SGD with Sketching. NeurIPS 2019: 13144-13154 - [c39]Nikita Ivkin, Ran Ben Basat, Zaoxing Liu, Gil Einziger, Roy Friedman, Vladimir Braverman:
Attack Time Localization using Interval Queries. SIGCOMM Posters and Demos 2019: 85-87 - [c38]Zaoxing Liu, Ran Ben-Basat, Gil Einziger, Yaron Kassner, Vladimir Braverman, Roy Friedman, Vyas Sekar:
Nitrosketch: robust and general sketch-based monitoring in software switches. SIGCOMM 2019: 334-350 - [c37]Lin F. Yang, Zheng Yu, Vladimir Braverman, Tuo Zhao, Mengdi Wang:
Online Factorization and Partition of Complex Networks by Random Walk. UAI 2019: 820-830 - [c36]Zaoxing Liu, Zhihao Bai, Zhenming Liu, Xiaozhou Li, Changhoon Kim, Vladimir Braverman, Xin Jin, Ion Stoica:
DistCache: Provable Load Balancing for Large-Scale Storage Systems with Distributed Caching. USENIX ATC 2019 - [i42]Zaoxing Liu, Zhihao Bai, Zhenming Liu, Xiaozhou Li, Changhoon Kim, Vladimir Braverman, Xin Jin, Ion Stoica:
DistCache: Provable Load Balancing for Large-Scale Storage Systems with Distributed Caching. CoRR abs/1901.08200 (2019) - [i41]Vladimir Braverman, Moses Charikar, William Kuszmaul, David P. Woodruff, Lin F. Yang:
The One-Way Communication Complexity of Dynamic Time Warping Distance. CoRR abs/1903.03520 (2019) - [i40]Vladimir Braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu:
Coresets for Ordered Weighted Clustering. CoRR abs/1903.04351 (2019) - [i39]Nikita Ivkin, Daniel Rothchild, Enayat Ullah, Vladimir Braverman, Ion Stoica, Raman Arora:
Communication-efficient distributed SGD with Sketching. CoRR abs/1903.04488 (2019) - [i38]Nikita Ivkin, Edo Liberty, Kevin J. Lang, Zohar S. Karnin, Vladimir Braverman:
Streaming Quantiles Algorithms with Small Space and Update Time. CoRR abs/1907.00236 (2019) - [i37]Ben Mussay, Samson Zhou, Vladimir Braverman, Dan Feldman:
On Activation Function Coresets for Network Pruning. CoRR abs/1907.04018 (2019) - [i36]Vladimir Braverman, Lingxiao Huang, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu:
Coresets for Clustering in Graphs of Bounded Treewidth. CoRR abs/1907.04733 (2019) - [i35]Vladimir Braverman, Robert Krauthgamer, Aditya Krishnan, Roi Sinoff:
Schatten Norms in Matrix Streams: Hello Sparsity, Goodbye Dimension. CoRR abs/1907.05457 (2019) - [i34]Vladimir Braverman, Harry Lang, Enayat Ullah, Samson Zhou:
Improved Algorithms for Time Decay Streams. CoRR abs/1907.07574 (2019) - [i33]Alex E. Bocchieri, Vishwa S. Parekh, Kathryn R. Wagner, Shivani Ahlawat, Vladimir Braverman, Doris G. Leung, Michael A. Jacobs:
Multiparametric Deep Learning Tissue Signatures for Muscular Dystrophy: Preliminary Results. CoRR abs/1908.00175 (2019) - [i32]Zaoxing Liu, Samson Zhou, Ori Rottenstreich, Vladimir Braverman, Jennifer Rexford:
Memory-Efficient Performance Monitoring on Programmable Switches with Lean Algorithms. CoRR abs/1911.06951 (2019) - 2018
- [j8]Vladimir Braverman, Zaoxing Liu, Tejasvam Singh, N. V. Vinodchandran, Lin F. Yang:
New Bounds for the CLIQUE-GAP Problem Using Graph Decomposition Theory. Algorithmica 80(2): 652-667 (2018) - [j7]Nikita Ivkin, Zaoxing Liu, Lin F. Yang, S. S. Kumar, Gerard Lemson, Mark Neyrinck, Alexander S. Szalay, Vladimir Braverman, Tamas Budavari:
Scalable streaming tools for analyzing N-body simulations: Finding halos and investigating excursion sets in one pass. Astron. Comput. 23: 166-179 (2018) - [c35]Vladimir Braverman, Elena Grigorescu, Harry Lang, David P. Woodruff, Samson Zhou:
Nearly Optimal Distinct Elements and Heavy Hitters on Sliding Windows. APPROX-RANDOM 2018: 7:1-7:22 - [c34]Anand Padmanabha Iyer, Zaoxing Liu, Xin Jin, Shivaram Venkataraman, Vladimir Braverman, Ion Stoica:
Towards Fast and Scalable Graph Pattern Mining. HotCloud 2018 - [c33]Avrim Blum, Vladimir Braverman, Ananya Kumar, Harry Lang, Lin F. Yang:
Approximate Convex Hull of Data Streams. ICALP 2018: 21:1-21:13 - [c32]Vladimir Braverman, Emanuele Viola, David P. Woodruff, Lin F. Yang:
Revisiting Frequency Moment Estimation in Random Order Streams. ICALP 2018: 25:1-25:14 - [c31]Vladimir Braverman, Stephen R. Chestnut, Robert Krauthgamer, Yi Li, David P. Woodruff, Lin F. Yang:
Matrix Norms in Data Streams: Faster, Multi-Pass and Row-Order. ICML 2018: 648-657 - [c30]Raman Arora, Vladimir Braverman, Jalaj Upadhyay:
Differentially Private Robust Low-Rank Approximation. NeurIPS 2018: 4141-4149 - [c29]Lin F. Yang, Raman Arora, Vladimir Braverman, Tuo Zhao:
The Physical Systems Behind Optimization Algorithms. NeurIPS 2018: 4377-4386 - [c28]Anand Padmanabha Iyer, Zaoxing Liu, Xin Jin, Shivaram Venkataraman, Vladimir Braverman, Ion Stoica:
ASAP: Fast, Approximate Graph Pattern Mining at Scale. OSDI 2018: 745-761 - [i31]Vladimir Braverman, Emanuele Viola, David P. Woodruff, Lin F. Yang:
Revisiting Frequency Moment Estimation in Random Order Streams. CoRR abs/1803.02270 (2018) - [i30]Vladimir Braverman, Elena Grigorescu, Harry Lang, David P. Woodruff, Samson Zhou:
Nearly Optimal Distinct Elements and Heavy Hitters on Sliding Windows. CoRR abs/1805.00212 (2018) - [i29]Vladimir Braverman, Petros Drineas, Cameron Musco, Christopher Musco, Jalaj Upadhyay, David P. Woodruff, Samson Zhou:
Near Optimal Linear Algebra in the Online and Sliding Window Models. CoRR abs/1805.03765 (2018) - [i28]Vladimir Braverman:
Approximations of Schatten Norms via Taylor Expansions. CoRR abs/1808.02348 (2018) - [i27]Sanghyun Choi, Nikita Ivkin, Vladimir Braverman, Michael A. Jacobs:
DreamNLP: Novel NLP System for Clinical Report Metadata Extraction using Count Sketch Data Streaming Algorithm: Preliminary Results. CoRR abs/1809.02665 (2018) - [i26]Vladimir Braverman, Robert Krauthgamer, Lin F. Yang:
Universal Streaming of Subset Norms. CoRR abs/1812.00241 (2018) - 2017
- [c27]Vladimir Braverman, Harry Lang, Keith D. Levin:
Accurate Low-Space Approximation of Metric k-Median for Insertion-Only Streams. CALDAM 2017: 72-82 - [c26]Vladimir Braverman, Gereon Frahling, Harry Lang, Christian Sohler, Lin F. Yang:
Clustering High Dimensional Dynamic Data Streams. ICML 2017: 576-585 - [c25]Vladimir Braverman, Stephen R. Chestnut, Nikita Ivkin, Jelani Nelson, Zhengyu Wang, David P. Woodruff:
BPTree: An ℓ2 Heavy Hitters Algorithm Using Constant Memory. PODS 2017: 361-376 - [c24]Jaroslaw Blasiok, Vladimir Braverman, Stephen R. Chestnut, Robert Krauthgamer, Lin F. Yang:
Streaming symmetric norms via measure concentration. STOC 2017: 716-729 - [i25]Lin F. Yang, Vladimir Braverman, Tuo Zhao, Mengdi Wang:
Dynamic Factorization and Partition of Complex Networks. CoRR abs/1705.07881 (2017) - [i24]Vladimir Braverman, Gereon Frahling, Harry Lang, Christian Sohler, Lin F. Yang:
Clustering High Dimensional Dynamic Data Streams. CoRR abs/1706.03887 (2017) - [i23]Avrim Blum, Vladimir Braverman, Ananya Kumar, Harry Lang, Lin F. Yang:
Approximate Convex Hull of Data Streams. CoRR abs/1712.04564 (2017) - [i22]Vladimir Braverman, David P. Woodruff, Ke Yi:
Processing Big Data Streams (NII Shonan Meeting 2017-7). NII Shonan Meet. Rep. 2017 (2017) - 2016
- [c23]Vladimir Braverman, Alan Roytman, Gregory Vorsanger:
Approximating Subadditive Hadamard Functions on Implicit Matrices. APPROX-RANDOM 2016: 25:1-25:19 - [c22]Vladimir Braverman, Stephen R. Chestnut, David P. Woodruff, Lin F. Yang:
Streaming Space Complexity of Nearly All Functions of One Variable on Frequency Vectors. PODS 2016: 261-276 - [c21]Zaoxing Liu, Antonis Manousis, Gregory Vorsanger, Vyas Sekar, Vladimir Braverman:
One Sketch to Rule Them All: Rethinking Network Flow Monitoring with UnivMon. SIGCOMM 2016: 101-114 - [c20]Vladimir Braverman, Harry Lang, Keith D. Levin, Morteza Monemizadeh:
Clustering Problems on Sliding Windows. SODA 2016: 1374-1390 - [c19]Vladimir Braverman, Stephen R. Chestnut, Nikita Ivkin, David P. Woodruff:
Beating CountSketch for heavy hitters in insertion streams. STOC 2016: 740-753 - [r1]Vladimir Braverman:
Sliding Window Algorithms. Encyclopedia of Algorithms 2016: 2006-2011 - [i21]Vladimir Braverman, Stephen R. Chestnut, David P. Woodruff, Lin F. Yang:
Streaming Space Complexity of Nearly All Functions of One Variable on Frequency Vectors. CoRR abs/1601.07473 (2016) - [i20]Vladimir Braverman, Stephen R. Chestnut, Nikita Ivkin, Jelani Nelson, Zhengyu Wang, David P. Woodruff:
BPTree: an ℓ2 heavy hitters algorithm using constant memory. CoRR abs/1603.00759 (2016) - [i19]Vladimir Braverman, Stephen R. Chestnut, Robert Krauthgamer, Lin F. Yang:
Sketches for Matrix Norms: Faster, Smaller and More General. CoRR abs/1609.05885 (2016) - [i18]Vladimir Braverman, Dan Feldman, Harry Lang:
New Frameworks for Offline and Streaming Coreset Constructions. CoRR abs/1612.00889 (2016) - [i17]Lin F. Yang, Raman Arora, Vladimir Braverman, Tuo Zhao:
The Physical Systems Behind Optimization Algorithms. CoRR abs/1612.02803 (2016) - 2015
- [j6]Vladimir Braverman, Rafail Ostrovsky, Gregory Vorsanger:
Weighted sampling without replacement from data streams. Inf. Process. Lett. 115(12): 923-926 (2015) - [c18]Vladimir Braverman, Rafail Ostrovsky, Alan Roytman:
Zero-One Laws for Sliding Windows and Universal Sketches. APPROX-RANDOM 2015: 573-590 - [c17]Vladimir Braverman, Stephen R. Chestnut:
Universal Sketches for the Frequency Negative Moments and Other Decreasing Streaming Sums. APPROX-RANDOM 2015: 591-605 - [c16]Zaoxing Liu, Nikita Ivkin, Lin Yang, Mark Neyrinck, Gerard Lemson, Alexander S. Szalay, Vladimir Braverman, Tamas Budavari, Randal C. Burns, Xin Wang:
Streaming Algorithms for Halo Finders. e-Science 2015: 342-351 - [c15]Vladimir Braverman, Harry Lang, Keith D. Levin, Morteza Monemizadeh:
Clustering on Sliding Windows in Polylogarithmic Space. FSTTCS 2015: 350-364 - [c14]Zaoxing Liu, Gregory Vorsanger, Vladimir Braverman, Vyas Sekar:
Enabling a "RISC" Approach for Software-Defined Monitoring using Universal Streaming. HotNets 2015: 21:1-21:7 - [c13]Vladimir Braverman, Zaoxing Liu, Tejasvam Singh, N. V. Vinodchandran, Lin F. Yang:
New Bounds for the CLIQUE-GAP Problem Using Graph Decomposition Theory. MFCS (2) 2015: 151-162 - [i16]Vladimir Braverman, Harry Lang, Keith D. Levin, Morteza Monemizadeh:
A Unified Approach for Clustering Problems on Sliding Windows. CoRR abs/1504.05553 (2015) - [i15]Vladimir Braverman, Rafail Ostrovsky, Gregory Vorsanger:
Weighted Sampling Without Replacement from Data Streams. CoRR abs/1506.01747 (2015) - [i14]Vladimir Braverman, Stephen R. Chestnut, Nikita Ivkin, David P. Woodruff:
Beating CountSketch for Heavy Hitters in Insertion Streams. CoRR abs/1511.00661 (2015) - [i13]Vladimir Braverman, Alan Roytman, Gregory Vorsanger:
Approximating Subadditive Hadamard Functions on Implicit Matrices. CoRR abs/1511.00838 (2015) - [i12]Vladimir Braverman, Stephen R. Chestnut, Robert Krauthgamer, Lin F. Yang:
Streaming Symmetric Norms via Measure Concentration. CoRR abs/1511.01111 (2015) - 2014
- [j5]Vladimir Braverman, Ran Gelles, Rafail Ostrovsky:
How to catch L2-heavy-hitters on sliding windows. Theor. Comput. Sci. 554: 82-94 (2014) - [c12]Vladimir Braverman, Jonathan Katzman, Charles Seidell, Gregory Vorsanger:
An Optimal Algorithm for Large Frequency Moments Using O(n^(1-2/k)) Bits. APPROX-RANDOM 2014: 531-544 - [c11]Vladimir Braverman, Gregory Vorsanger:
Sampling from Dense Streams without Penalty - Improved Bounds for Frequency Moments and Heavy Hitters. COCOON 2014: 13-24 - [i11]Vladimir Braverman, Jonathan Katzman, Charles Seidell, Gregory Vorsanger:
Approximating Large Frequency Moments with O(n1-2/k) Bits. CoRR abs/1401.1763 (2014) - [i10]Vladimir Braverman, Rafail Ostrovsky, Alan Roytman:
Universal Streaming. CoRR abs/1408.2604 (2014) - [i9]Vladimir Braverman, Stephen R. Chestnut:
Streaming sums in sublinear space. CoRR abs/1408.5096 (2014) - 2013
- [c10]Vladimir Braverman, Rafail Ostrovsky:
Approximating Large Frequency Moments with Pick-and-Drop Sampling. APPROX-RANDOM 2013: 42-57 - [c9]Vladimir Braverman, Rafail Ostrovsky:
Generalizing the Layering Method of Indyk and Woodruff: Recursive Sketches for Frequency-Based Vectors on Streams. APPROX-RANDOM 2013: 58-70 - [c8]Vladimir Braverman, Ran Gelles, Rafail Ostrovsky:
How to Catch L 2-Heavy-Hitters on Sliding Windows. COCOON 2013: 638-650 - [c7]Vladimir Braverman, Rafail Ostrovsky, Dan Vilenchik:
How Hard Is Counting Triangles in the Streaming Model? ICALP (1) 2013: 244-254 - [i8]Vladimir Braverman, Rafail Ostrovsky, Dan Vilenchik:
How Hard is Counting Triangles in the Streaming Model. CoRR abs/1304.1458 (2013) - 2012
- [j4]Vladimir Braverman, Rafail Ostrovsky, Carlo Zaniolo:
Optimal sampling from sliding windows. J. Comput. Syst. Sci. 78(1): 260-272 (2012) - [i7]Vladimir Braverman, Rafail Ostrovsky:
Approximating Large Frequency Moments with Pick-and-Drop Sampling. CoRR abs/1212.0202 (2012) - 2011
- [c6]Vladimir Braverman, Adam Meyerson, Rafail Ostrovsky, Alan Roytman, Michael Shindler, Brian Tagiku:
Streaming k-means on Well-Clusterable Data. SODA 2011: 26-40 - 2010
- [j3]Vladimir Braverman, Rafail Ostrovsky:
Effective Computations on Sliding Windows. SIAM J. Comput. 39(6): 2113-2131 (2010) - [c5]Vladimir Braverman, Kai-Min Chung, Zhenming Liu, Michael Mitzenmacher, Rafail Ostrovsky:
AMS Without 4-Wise Independence on Product Domains. STACS 2010: 119-130 - [c4]Vladimir Braverman, Rafail Ostrovsky:
Measuring independence of datasets. STOC 2010: 271-280 - [c3]Vladimir Braverman, Rafail Ostrovsky:
Zero-one frequency laws. STOC 2010: 281-290 - [i6]Vladimir Braverman, Rafail Ostrovsky:
Recursive Sketching For Frequency Moments. CoRR abs/1011.2571 (2010) - [i5]Vladimir Braverman, Rafail Ostrovsky, Yuval Rabani:
Rademacher Chaos, Random Eulerian Graphs and The Sparse Johnson-Lindenstrauss Transform. CoRR abs/1011.2590 (2010) - [i4]Vladimir Braverman, Ran Gelles, Rafail Ostrovsky:
How to Catch L_2-Heavy-Hitters on Sliding Windows. CoRR abs/1012.3130 (2010)
2000 – 2009
- 2009
- [j2]Daniel Berend, Vladimir Braverman:
A linear algorithm for computing convex hulls for random lines. ACM Trans. Algorithms 5(4): 42:1-42:21 (2009) - [c2]Vladimir Braverman, Rafail Ostrovsky, Carlo Zaniolo:
Optimal sampling from sliding windows. PODS 2009: 147-156 - [i3]Vladimir Braverman, Rafail Ostrovsky:
Measuring Independence of Datasets. CoRR abs/0903.0034 (2009) - 2008
- [i2]Vladimir Braverman, Rafail Ostrovsky:
Measuring $k$-Wise Independence of Streaming Data. CoRR abs/0806.4790 (2008) - 2007
- [c1]Vladimir Braverman, Rafail Ostrovsky:
Smooth Histograms for Sliding Windows. FOCS 2007: 283-293 - [i1]Vladimir Braverman, Rafail Ostrovsky, Carlo Zaniolo:
Succinct Sampling on Streams. CoRR abs/cs/0702151 (2007) - 2006
- [j1]Eitan Bachmat, Vladimir Braverman:
Batched disk scheduling with delays. SIGMETRICS Perform. Evaluation Rev. 33(4): 36-41 (2006)
Coauthor Index
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