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Vladimir Braverman
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- affiliation: Johns Hopkins University
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2020 – today
- 2024
- [j18]Andrea Soltoggio
, Eseoghene Ben-Iwhiwhu
A collective AI via lifelong learning and sharing at the edge. Nat. Mac. Intell. 6(3): 251-264 (2024) - [c100]Sunghan Lee
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
KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache. ICML 2024 - [i88]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) - [i87]Vladimir Braverman, Prathamesh Dharangutte, Vihan Shah, Chen Wang:
Learning-augmented Maximum Independent Set. CoRR abs/2407.11364 (2024) - [i86]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) - [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
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
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
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. Theory Comput. 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
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
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
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
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
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
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
New Bounds for the CLIQUE-GAP Problem Using Graph Decomposition Theory. Algorithmica 80(2): 652-667 (2018) - [j7]Nikita Ivkin, Zaoxing Liu
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
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
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
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
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
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
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
Batched disk scheduling with delays. SIGMETRICS Perform. Evaluation Rev. 33(4): 36-41 (2006)
Coauthor Index
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