V. Braverman

发表

Zero-one frequency laws

R. Ostrovsky, V. Braverman, 2010, STOC '10.

Jaqen: A High-Performance Switch-Native Approach for Detecting and Mitigating Volumetric DDoS Attacks with Programmable Switches

Minlan Yu, Vladimir Braverman, Vyas Sekar, 2021, USENIX Security Symposium.

Smooth Histograms for Sliding Windows

R. Ostrovsky, V. Braverman, 2007, FOCS 2007.

Open Problems in Data Streams, Property Testing, and Related Topics

David P. Woodruff, T. S. Jayram, Michael W. Mahoney, 2011 .

FetchSGD: Communication-Efﬁcient Federated Learning with Sketching

Vladimir Braverman, Joseph Gonzalez, Raman Arora, 2022 .

D S ] 1 7 Ju l 2 01 9 Improved Algorithms for Time Decay Streams

V. Braverman, Enayat Ullah, Harry Lang, 2019 .

Measuring $k$-Wise Independence of Streaming Data under $L_2$ Norm

R. Ostrovsky, V. Braverman, 2008 .

BPTree: an $\ell_2$ heavy hitters algorithm using constant memory

David P. Woodruff, Stephen R. Chestnut, Jelani Nelson, 2016 .

Online Factorization and Partition of Complex Networks From Random Walks

Lin F. Yang, T. Zhao, Mengdi Wang, 2017 .

DETERMINISTIC CONSTRUCTION OF SYNCHRONIZATION STRING OVER SMALL ALPHABET

M. Dinitz, Philipp Koehn, Mark Dredze, 2017 .

Obtaining Regularization for Free via Iterate Averaging

Lin F. Yang, V. Braverman, Jingfeng Wu, 2019 .

Longitudinal functional and imaging outcome measures in FKRP limb-girdle muscular dystrophy

Alex E. Bocchieri, V. Braverman, M. Jacobs, 2020, BMC Neurology.

Workshop Report on Programmable Networks Organizers :

M. Alizadeh, V. Braverman, Xin Jin, 2019 .

Coresets for Clustering with Missing Values pdf

Robert Krauthgamer, Vladimir Braverman, Shaofeng H.-C. Jiang, 2021, NeurIPS.

Convex hull for intersections of random lines

D. Berend, V. Braverman, 2004 .

Pretrained Models for Multilingual Federated Learning

Benjamin Van Durme, Benjamin Van Durme, V. Braverman, 2022, North American Chapter of the Association for Computational Linguistics.

Near Optimal Linear Algebra in the Online and Sliding Window Models

David P. Woodruff, Cameron Musco, V. Braverman, 2018, 2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS).

Data-Independent Neural Pruning via Coresets

Margarita Osadchy, V. Braverman, Dan Feldman, 2019, ICLR.

The Benefits of Implicit Regularization from SGD in Least Squares Problems

Dean P. Foster, S. Kakade, Dean Phillips Foster, 2021, NeurIPS.

The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift

S. Kakade, Quanquan Gu, V. Braverman, 2022, NeurIPS.

Sparsity and Heterogeneous Dropout for Continual Learning in the Null Space of Neural Activations

H. Pirsiavash, V. Braverman, S. Kolouri, 2022, CoLLAs.

P OSTER : FLLE DGE : F EDERATED L IFELONG L EARNING ON E DGE D EVICES

Jingfeng Wu, V. Braverman, Ziang Song, 2022 .

The White-Box Adversarial Data Stream Model

David P. Woodruff, T. S. Jayram, M. Ajtai, 2022, PODS.

Near Optimal Linear Algebra in the Online and Sliding Window Models

David P. Woodruff, Cameron Musco, V. Braverman, 2018, 2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS).

Flow-level loss detection with Δ-sketches

Shir Landau Feibish, J. Rexford, V. Braverman, 2022, SOSR.

The Power of Uniform Sampling for Coresets

Robert Krauthgamer, V. Braverman, S. Jiang, 2022, 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS).

Measuring $k$-Wise Independence of Streaming Data pdf

Rafail Ostrovsky, Vladimir Braverman, R. Ostrovsky, 2008, ArXiv.

Coresets for Clustering in Excluded-minor Graphs and Beyond pdf

Robert Krauthgamer, Vladimir Braverman, Xuan Wu, 2020, SODA.

Sliding Window Algorithms

Vladimir Braverman, V. Braverman, 2016, Encyclopedia of Algorithms.

Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression

S. Kakade, Quanquan Gu, V. Braverman, 2021, ICML.

From Local to Global: Spectral-Inspired Graph Neural Networks

Carey E. Priebe, N. Huang, V. Braverman, 2022, ArXiv.

Bidirectional Adaptive Communication for Heterogeneous Distributed Learning

V. Braverman, Nikita Ivkin, Dmitrii Avdiukhin, 2022 .

A scalable and unbiased discordance metric with H+

Daniel N. Baker, B. Langmead, V. Braverman, 2022, bioRxiv.

Obtaining Adjustable Regularization for Free via Iterate Averaging pdf

Vladimir Braverman, Lin F. Yang, Jingfeng Wu, 2020, ICML.

On the Noisy Gradient Descent that Generalizes as SGD

Zhanxing Zhu, Vladimir Braverman, Haoyi Xiong, 2019, ICML.

Sketch and Scale Geo-distributed tSNE and UMAP pdf

Vladimir Braverman, Nikita Ivkin, Alexander Szalay, 2020, 2020 IEEE International Conference on Big Data (Big Data).

Memory-Efficient Performance Monitoring on Programmable Switches with Lean Algorithms

Vladimir Braverman, Jennifer Rexford, Samson Zhou, 2019, APOCS.

I Know What You Did Last Summer

Roy Friedman, Vladimir Braverman, Ran Ben Basat, 2019, Proc. ACM Meas. Anal. Comput. Syst..

V. Braverman, Xin Jin, Nikita Ivkin, 2019, Proceedings of the 15th International Conference on Emerging Networking Experiments And Technologies.

QPipe: quantiles sketch fully in the data plane

Vladimir Braverman, Xin Jin, Zhuolong Yu, 2019, CoNEXT.

Nitrosketch: robust and general sketch-based monitoring in software switches

Roy Friedman, Vladimir Braverman, Vyas Sekar, 2019, SIGCOMM.

Communication-efficient distributed SGD with Sketching

Vladimir Braverman, Ion Stoica, Raman Arora, 2019, NeurIPS.

DistCache: Provable Load Balancing for Large-Scale Storage Systems with Distributed Caching

Xiaozhou Li, Xin Jin, Vladimir Braverman, 2019, FAST.

DreamNLP: Novel NLP System for Clinical Report Metadata Extraction using Count Sketch Data Streaming Algorithm: Preliminary Results pdf

Vladimir Braverman, Nikita Ivkin, Michael A. Jacobs, 2018, ArXiv.

Nearly Optimal Distinct Elements and Heavy Hitters on Sliding Windows pdf

David P. Woodruff, Vladimir Braverman, Samson Zhou, 2018, APPROX-RANDOM.

Scalable streaming tools for analyzing N-body simulations: Finding halos and investigating excursion sets in one pass

Alexander S. Szalay, Vladimir Braverman, Lin F. Yang, 2017, Astron. Comput..

BPTree: An ℓ2 Heavy Hitters Algorithm Using Constant Memory

David P. Woodruff, Vladimir Braverman, Zhengyu Wang, 2016, PODS.

One Sketch to Rule Them All: Rethinking Network Flow Monitoring with UnivMon

Vladimir Braverman, Gregory Vorsanger, Vyas Sekar, 2016, SIGCOMM.

Clustering Problems on Sliding Windows

Vladimir Braverman, Morteza Monemizadeh, Harry Lang, 2016, SODA.

Beating CountSketch for heavy hitters in insertion streams

David P. Woodruff, Vladimir Braverman, Nikita Ivkin, 2015, STOC.

Enabling a "RISC" Approach for Software-Defined Monitoring using Universal Streaming

Vladimir Braverman, Gregory Vorsanger, Vyas Sekar, 2015, HotNets.

A Unified Approach for Clustering Problems on Sliding Windows pdf

Vladimir Braverman, Morteza Monemizadeh, Harry Lang, 2015, ArXiv.

Universal Streaming pdf

Rafail Ostrovsky, Vladimir Braverman, Alan Roytman, 2014, ArXiv.

How to catch L2-heavy-hitters on sliding windows

Rafail Ostrovsky, Vladimir Braverman, Ran Gelles, 2010, Theor. Comput. Sci..

Measuring independence of datasets

Rafail Ostrovsky, Vladimir Braverman, R. Ostrovsky, 2009, STOC '10.

AMS Without 4-Wise Independence on Product Domains

Rafail Ostrovsky, Vladimir Braverman, Kai-Min Chung, 2008, STACS.

Coresets for Clustering in Graphs of Bounded Treewidth

Robert Krauthgamer, Vladimir Braverman, Xuan Wu, 2019, ICML.

ASAP: Fast, Approximate Graph Pattern Mining at Scale

Xin Jin, Vladimir Braverman, Ion Stoica, 2018, OSDI.

minicore: Fast scRNA-seq clustering with various distances

Daniel N. Baker, B. Langmead, V. Braverman, 2021, bioRxiv.

Schatten Norms in Matrix Streams: Hello Sparsity, Goodbye Dimension

Robert Krauthgamer, Vladimir Braverman, Aditya Krishnan, 2019, ICML.

Approximations of Schatten Norms via Taylor Expansions pdf

Vladimir Braverman, V. Braverman, 2018, CSR.

An Optimal Algorithm for Large Frequency Moments Using O(n^(1-2/k)) Bits

Vladimir Braverman, Gregory Vorsanger, Jonathan Katzman, 2014, APPROX-RANDOM.

A phase Ib/IIa, open‐label, multiple ascending‐dose trial of domagrozumab in fukutin‐related protein limb‐girdle muscular dystrophy

Alex E. Bocchieri, V. Braverman, M. Jacobs, 2021, Muscle & nerve.

Universal Streaming of Subset Norms

Lin F. Yang, Robert Krauthgamer, V. Braverman, 2018, Theory Comput..

Zero-One Laws for Sliding Windows and Universal Sketches

Rafail Ostrovsky, Vladimir Braverman, Alan Roytman, 2015, APPROX-RANDOM.

Approximating Large Frequency Moments with O(n1-2/k) Bits pdf

Vladimir Braverman, Gregory Vorsanger, Jonathan Katzman, 2014, ArXiv.

Generalizing the Layering Method of Indyk and Woodruff: Recursive Sketches for Frequency-Based Vectors on Streams

Rafail Ostrovsky, Vladimir Braverman, R. Ostrovsky, 2013, APPROX-RANDOM.

Approximating Large Frequency Moments with Pick-and-Drop Sampling

Rafail Ostrovsky, Vladimir Braverman, R. Ostrovsky, 2012, APPROX-RANDOM.

Recursive Sketching For Frequency Moments pdf

Rafail Ostrovsky, Vladimir Braverman, R. Ostrovsky, 2010, ArXiv.

Twenty Years After: Hierarchical Core-Stateless Fair Queueing

Vladimir Braverman, Ion Stoica, Zhuolong Yu, 2021, NSDI.

NetLock: Fast, Centralized Lock Management Using Programmable Switches

Vladimir Braverman, Xin Jin, Mosharaf Chowdhury, 2020, SIGCOMM.

Benign Overfitting of Constant-Stepsize SGD for Linear Regression pdf

Vladimir Braverman, Sham M. Kakade, Quanquan Gu, 2021, COLT.

Cross-Domain Federated Learning in Medical Imaging pdf

Vladimir Braverman, Jay J Pillai, Michael A Jacobs, 2021, ArXiv.

Programmable packet scheduling with a single queue

Vladimir Braverman, Mosharaf Chowdhury, Zhuolong Yu, 2021, SIGCOMM.

From Local to Global: Spectral-Inspired Graph Neural Networks

Carey E. Priebe, N. Huang, V. Braverman, 2022, ArXiv.

Provable Data Subset Selection For Efficient Neural Network Training

V. Braverman, Dan Feldman, D. Rus, 2023, arXiv.org.

New Coresets for Projective Clustering and Applications

V. Braverman, Dan Feldman, Samson Zhou, 2022, AISTATS.

New Frameworks for Offline and Streaming Coreset Constructions pdf

Vladimir Braverman, Dan Feldman, Harry Lang, 2016, ArXiv.

AutoCoreset: An Automatic Practical Coreset Construction Framework

V. Braverman, D. Rus, Alaa Maalouf, 2023, ICML.

Near Optimal Linear Algebra in the Online and Sliding Window Models

David P. Woodruff, Cameron Musco, V. Braverman, 2018, 2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS).

Effective Computations on Sliding Windows

Rafail Ostrovsky, Vladimir Braverman, R. Ostrovsky, 2010, SIAM J. Comput..

Optimal sampling from sliding windows

Rafail Ostrovsky, Carlo Zaniolo, Vladimir Braverman, 2009, J. Comput. Syst. Sci..

A framework for dynamically training and adapting deep reinforcement learning models to different, low-compute, and continuously changing radiology deployment environments

V. Braverman, M. Jacobs, Shuhao Lai, 2023, ArXiv.

Streaming k-means on well-clusterable data

Rafail Ostrovsky, Vladimir Braverman, Adam Meyerson, 2011, SODA '11.

Sublinear time spectral density estimation

V. Braverman, C. Musco, A. Krishnan, 2021, STOC.

Sampling from Dense Streams without Penalty - Improved Bounds for Frequency Moments and Heavy Hitters

Vladimir Braverman, Gregory Vorsanger, V. Braverman, 2014, COCOON.

Smooth Histograms for Sliding Windows

R. Ostrovsky, V. Braverman, 2007, 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07).

Streaming Quantiles Algorithms with Small Space and Update Time pdf

Vladimir Braverman, Edo Liberty, Nikita Ivkin, 2019, Sensors.

Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability

Jason D. Lee, V. Braverman, Jingfeng Wu, 2023, ArXiv.

Universal Sketches for the Frequency Negative Moments and Other Decreasing Streaming Sums

Vladimir Braverman, Stephen R. Chestnut, V. Braverman, 2014, APPROX-RANDOM.

Fixed Design Analysis of Regularization-Based Continual Learning

V. Braverman, Haoran Li, Jingfeng Wu, 2023, CoLLAs.

Fast and memory-efficient scRNA-seq k-means clustering with various distances

Vladimir Braverman, Stephanie C. Hicks, Ben Langmead, 2021, BCB.

Private Data Stream Analysis for Universal Symmetric Norm Estimation

Zhiwei Steven Wu, V. Braverman, Samson Zhou, 2023, APPROX/RANDOM.

Lifelong Learning with Sketched Structural Regularization pdf

Vladimir Braverman, Soheil Kolouri, Praveen K. Pilly, 2021, ACML.

Sketches for Matrix Norms: Faster, Smaller and More General pdf

Robert Krauthgamer, Vladimir Braverman, Lin F. Yang, 2016, ArXiv.

Streaming symmetric norms via measure concentration

Robert Krauthgamer, Vladimir Braverman, Lin F. Yang, 2015, STOC.

Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging

V. Braverman, M. Jacobs, V. Parekh, 2023, arXiv.org.

S PARSITY AND H ETEROGENEOUS D ROPOUT FOR C ONTINUAL L EARNING IN THE N ULL S PACE OF N EURAL A CTIVATIONS

H. Pirsiavash, V. Braverman, S. Kolouri, 2022 .

Multitask radiological modality invariant landmark localization using deep reinforcement learning

Vladimir Braverman, Vishwa S. Parekh, Michael A. Jacobs, 2020, MIDL.

Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate

Vladimir Braverman, Quanquan Gu, Difan Zou, 2020, ICLR.

Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron

S. Kakade, Quanquan Gu, V. Braverman, 2023, ICML.

Learning High-Dimensional Single-Neuron ReLU Networks with Finite Samples

S. Kakade, Quanquan Gu, V. Braverman, 2023, arXiv.org.

Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning

Lin F. Yang, V. Braverman, Jingfeng Wu, 2020, NeurIPS.

Gap-Dependent Unsupervised Exploration for Reinforcement Learning

V. Braverman, Lin F. Yang, Jingfeng Wu, 2021, AISTATS.

Towards Fast and Scalable Graph Pattern Mining

Xin Jin, Vladimir Braverman, Ion Stoica, 2018, HotCloud.

How Hard Is Counting Triangles in the Streaming Model? pdf

Rafail Ostrovsky, Vladimir Braverman, Dan Vilenchik, 2013, ICALP.

Data-Independent Structured Pruning of Neural Networks via Coresets pdf

Vladimir Braverman, Margarita Osadchy, Ben Mussay, 2020, IEEE Transactions on Neural Networks and Learning Systems.

Dynamic Partition of Complex Networks

Vladimir Braverman, Lin F. Yang, Tuo Zhao, 2017 .

New Bounds for the CLIQUE-GAP Problem Using Graph Decomposition Theory

Vladimir Braverman, Lin F. Yang, N. V. Vinodchandran, 2015, Algorithmica.

Private Federated Frequency Estimation: Adapting to the Hardness of the Instance

P. Kairouz, V. Braverman, Wennan Zhu, 2023, arXiv.org.

Coresets for Clustering in Excluded-minor Graphs and Beyond

Robert Krauthgamer, V. Braverman, S. Jiang, 2020, SODA.

Coresets for Ordered Weighted Clustering

Robert Krauthgamer, Vladimir Braverman, Xuan Wu, 2019, ICML.

Clustering High Dimensional Dynamic Data Streams

Vladimir Braverman, Christian Sohler, Lin F. Yang, 2017, ICML.

Metric k-median clustering in insertion-only streams

Vladimir Braverman, Keith Levin, Yevgeniy Rudoy, 2021, Discret. Appl. Math..

Accurate Low-Space Approximation of Metric k-Median for Insertion-Only Streams

Vladimir Braverman, Harry Lang, Keith Levin, 2017, CALDAM.

Streaming Coreset Constructions for M-Estimators

Vladimir Braverman, Dan Feldman, Daniela Rus, 2019, APPROX-RANDOM.

Clustering on Sliding Windows in Polylogarithmic Space

Vladimir Braverman, Morteza Monemizadeh, Harry Lang, 2015, FSTTCS.

Least-Mean-Squares Coresets for Infinite Streams

V. Braverman, Dan Feldman, D. Rus, 2023, IEEE Transactions on Knowledge and Data Engineering.

Numerical Linear Algebra in the Sliding Window Model

David P. Woodruff, Vladimir Braverman, Petros Drineas, 2018 .

Matrix Norms in Data Streams: Faster, Multi-Pass and Row-Order

David P. Woodruff, Robert Krauthgamer, Vladimir Braverman, 2016, ICML.

Approximating Subadditive Hadamard Functions on Implicit Matrices

Vladimir Braverman, Gregory Vorsanger, Alan Roytman, 2015, APPROX-RANDOM.

Linear and Sublinear Time Spectral Density Estimation

V. Braverman, C. Musco, A. Krishnan, 2021, ArXiv.

Adversarial Robustness of Streaming Algorithms through Importance Sampling pdf

Vladimir Braverman, Yossi Matias, Avinatan Hassidim, 2021, NeurIPS.

I Know What You Did Last Summer

V. Braverman, Zaoxing Liu, R. Friedman, 2020, Measurement and Modeling of Computer Systems.

I Know What You Did Last Summer: Network Monitoring using Interval Queries

Roy Friedman, Vladimir Braverman, Ran Ben Basat, 2020, Abstracts of the 2020 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems.

Communication-Efficient Federated Learning with Sketching

Vladimir Braverman, Joseph Gonzalez, Raman Arora, 2020, ICML 2020.

Improved Algorithms for Time Decay Streams pdf

Vladimir Braverman, Samson Zhou, Harry Lang, 2019, APPROX-RANDOM.

Succinct Sampling on Streams pdf

Rafail Ostrovsky, Carlo Zaniolo, Vladimir Braverman, 2007, ArXiv.

Clustering using Approximate Nearest Neighbour Oracles

R. Arora, V. Braverman, Enayat Ullah, 2023, Trans. Mach. Learn. Res..

FetchSGD: Communication-Efﬁcient Federated Learning with Sketching

Jeff Wu, Alec Radford, Dario Amodei, 2022 .

Symmetric Norm Estimation and Regression on Sliding Windows pdf

Vladimir Braverman, Samson Zhou, Viska Wei, 2021, International Computing and Combinatorics Conference.

On Activation Function Coresets for Network Pruning pdf

Vladimir Braverman, Dan Feldman, Ben Mussay, 2019, ArXiv.

Multiparametric Deep Learning Tissue Signatures for Muscular Dystrophy: Preliminary Results pdf

Vladimir Braverman, Vishwa S. Parekh, Michael A. Jacobs, 2019, ArXiv.

Dynamic Factorization and Partition of Complex Networks pdf

Vladimir Braverman, Lin F. Yang, Tuo Zhao, 2017, ArXiv.

Online Factorization and Partition of Complex Networks by Random Walk

Vladimir Braverman, Lin F. Yang, Zheng Yu, 2019, UAI.

Near-Optimal Entrywise Sampling of Numerically Sparse Matrices

Robert Krauthgamer, V. Braverman, A. Krishnan, 2020, COLT.

On the Noisy Gradient Descent that Generalizes as SGD

Wenqing Hu, V. Braverman, Zhanxing Zhu, 2019, ICML.

Direction Matters: On the Implicit Regularization Effect of Stochastic Gradient Descent with Moderate Learning Rate pdf

Vladimir Braverman, Quanquan Gu, Jingfeng Wu, 2020, ICLR.

Rademacher Chaos, Random Eulerian Graphs and The Sparse Johnson-Lindenstrauss Transform pdf

Rafail Ostrovsky, Yuval Rabani, Vladimir Braverman, 2010, ArXiv.

Understanding the Micro-Behaviors of Hardware Offloaded Network Stacks with Lumina

V. Braverman, Wei Bai, S. Raindel, 2023, SIGCOMM.