Vladimir Braverman

research

∙ 07/11/2023

Scaling Distributed Multi-task Reinforcement Learning with Experience Sharing

Recently, DARPA launched the ShELL program, which aims to explore how ex...

0 Sanae Amani, et al. ∙

research

∙ 07/09/2023

Private Data Stream Analysis for Universal Symmetric Norm Estimation

We study how to release summary statistics on a data stream subject to t...

0 Vladimir Braverman, et al. ∙

research

∙ 06/15/2023

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

In federated frequency estimation (FFE), multiple clients work together ...

0 Jingfeng Wu, et al. ∙

research

∙ 06/08/2023

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

While Deep Reinforcement Learning has been widely researched in medical ...

0 Guangyao Zheng, et al. ∙

research

∙ 05/31/2023

Multi-environment lifelong deep reinforcement learning for medical imaging

Deep reinforcement learning(DRL) is increasingly being explored in medic...

0 Guangyao Zheng, et al. ∙

research

∙ 05/19/2023

AutoCoreset: An Automatic Practical Coreset Construction Framework

A coreset is a tiny weighted subset of an input set, that closely resemb...

0 Alaa Maalouf, et al. ∙

research

∙ 05/19/2023

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

Recent research has observed that in machine learning optimization, grad...

0 Jingfeng Wu, et al. ∙

research

∙ 03/28/2023

Lower Bounds for Pseudo-Deterministic Counting in a Stream

Many streaming algorithms provide only a high-probability relative appro...

0 Vladimir Braverman, et al. ∙

research

∙ 03/17/2023

Fixed Design Analysis of Regularization-Based Continual Learning

We consider a continual learning (CL) problem with two linear regression...

0 Haoran Li, et al. ∙

research

∙ 03/12/2023

Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging

Federated learning is a recent development in the machine learning area ...

0 Guangyao Zheng, et al. ∙

research

∙ 03/09/2023

Provable Data Subset Selection For Efficient Neural Network Training

Radial basis function neural networks (RBFNN) are well-known for their c...

0 Murad Tukan, et al. ∙

research

∙ 03/03/2023

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

This paper considers the problem of learning a single ReLU neuron with s...

1 Jingfeng Wu, et al. ∙

research

∙ 02/22/2023

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

Selective experience replay is a popular strategy for integrating lifelo...

0 Guangyao Zheng, et al. ∙

research

∙ 09/24/2022

From Local to Global: Spectral-Inspired Graph Neural Networks

Graph Neural Networks (GNNs) are powerful deep learning methods for Non-...

16 Ningyuan Huang, et al. ∙

research

∙ 09/05/2022

The Power of Uniform Sampling for Coresets

Motivated by practical generalizations of the classic k-median and k-mea...

0 Vladimir Braverman, et al. ∙

research

∙ 08/03/2022

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

We study linear regression under covariate shift, where the marginal dis...

4 Jingfeng Wu, et al. ∙

research

∙ 06/06/2022

Pretrained Models for Multilingual Federated Learning

Since the advent of Federated Learning (FL), research has applied these ...

0 Orion Weller, et al. ∙

research

∙ 04/19/2022

The White-Box Adversarial Data Stream Model

We study streaming algorithms in the white-box adversarial model, where ...

0 Miklos Ajtai, et al. ∙

research

∙ 03/12/2022

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

Continual/lifelong learning from a non-stationary input data stream is a...

0 Ali Abbasi, et al. ∙

research

∙ 03/08/2022

New Coresets for Projective Clustering and Applications

(j,k)-projective clustering is the natural generalization of the family ...

0 Murad Tukan, et al. ∙

research

∙ 03/07/2022

Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime

Stochastic gradient descent (SGD) has achieved great success due to its ...

4 Difan Zou, et al. ∙

research

∙ 12/18/2021

Cross-Domain Federated Learning in Medical Imaging

Federated learning is increasingly being explored in the field of medica...

0 Vishwa S. Parekh, et al. ∙

research

∙ 10/12/2021

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

Stochastic gradient descent (SGD) has been demonstrated to generalize we...

5 Jingfeng Wu, et al. ∙

research

∙ 09/03/2021

Symmetric Norm Estimation and Regression on Sliding Windows

The sliding window model generalizes the standard streaming model and of...

0 Vladimir Braverman, et al. ∙

research

∙ 08/11/2021

Gap-Dependent Unsupervised Exploration for Reinforcement Learning

For the problem of task-agnostic reinforcement learning (RL), an agent f...

8 Jingfeng Wu, et al. ∙

research

∙ 08/10/2021

The Benefits of Implicit Regularization from SGD in Least Squares Problems

Stochastic gradient descent (SGD) exhibits strong algorithmic regulariza...

0 Difan Zou, et al. ∙

research

∙ 06/30/2021

Coresets for Clustering with Missing Values

We provide the first coreset for clustering points in ℝ^d that have mult...

0 Vladimir Braverman, et al. ∙

research

∙ 06/28/2021

Adversarial Robustness of Streaming Algorithms through Importance Sampling

In this paper, we introduce adversarially robust streaming algorithms fo...

7 Vladimir Braverman, et al. ∙

research

∙ 04/17/2021

Lifelong Learning with Sketched Structural Regularization

Preventing catastrophic forgetting while continually learning new tasks ...

9 Haoran Li, et al. ∙

research

∙ 04/08/2021

Linear and Sublinear Time Spectral Density Estimation

We analyze the popular kernel polynomial method (KPM) for approximating ...

0 Vladimir Braverman, et al. ∙

research

∙ 03/23/2021

Benign Overfitting of Constant-Stepsize SGD for Linear Regression

There is an increasing realization that algorithmic inductive biases are...

9 Difan Zou, et al. ∙

research

∙ 11/25/2020

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

In this paper we consider multi-objective reinforcement learning where t...

3 Jingfeng Wu, et al. ∙

research

∙ 11/11/2020

Sketch and Scale: Geo-distributed tSNE and UMAP

Running machine learning analytics over geographically distributed datas...

0 Viska Wei, et al. ∙

research

∙ 11/04/2020

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

Understanding the algorithmic regularization effect of stochastic gradie...

14 Jingfeng Wu, et al. ∙

research

∙ 11/03/2020

Near-Optimal Entrywise Sampling of Numerically Sparse Matrices

Many real-world data sets are sparse or almost sparse. One method to mea...

0 Vladimir Braverman, et al. ∙

research

∙ 08/19/2020

Data-Independent Structured Pruning of Neural Networks via Coresets

Model compression is crucial for deployment of neural networks on device...

0 Ben Mussay, et al. ∙

research

∙ 08/15/2020

Obtaining Adjustable Regularization for Free via Iterate Averaging

Regularization for optimization is a crucial technique to avoid overfitt...

0 Jingfeng Wu, et al. ∙

research

∙ 07/15/2020

FetchSGD: Communication-Efficient Federated Learning with Sketching

Existing approaches to federated learning suffer from a communication bo...

0 Daniel Rothchild, et al. ∙

research

∙ 04/16/2020

Coresets for Clustering in Excluded-minor Graphs and Beyond

Coresets are modern data-reduction tools that are widely used in data an...

0 Vladimir Braverman, et al. ∙

research

∙ 02/15/2020

Sparse Coresets for SVD on Infinite Streams

In streaming Singular Value Decomposition (SVD), d-dimensional rows of a...

0 Vladimir Braverman, et al. ∙

research

∙ 11/16/2019

Memory-Efficient Performance Monitoring on Programmable Switches with Lean Algorithms

Network performance problems are notoriously difficult to diagnose. Prio...

0 Zaoxing Liu, et al. ∙

research

∙ 08/01/2019

Multiparametric Deep Learning Tissue Signatures for Muscular Dystrophy: Preliminary Results

A current clinical challenge is identifying limb girdle muscular dystrop...

0 Alex E. Bocchieri, et al. ∙

research

∙ 07/17/2019

Improved Algorithms for Time Decay Streams

In the time-decay model for data streams, elements of an underlying data...

0 Vladimir Braverman, et al. ∙

research

∙ 07/11/2019

Schatten Norms in Matrix Streams: Hello Sparsity, Goodbye Dimension

The spectrum of a matrix contains important structural information about...

0 Vladimir Braverman, et al. ∙

research

∙ 07/10/2019

Coresets for Clustering in Graphs of Bounded Treewidth

We initiate the study of coresets for clustering in graph metrics, i.e.,...

0 Vladimir Braverman, et al. ∙

research

∙ 07/09/2019

On Activation Function Coresets for Network Pruning

Model compression provides a means to efficiently deploy deep neural net...

1 Ben Mussay, et al. ∙

research

∙ 06/29/2019

Streaming Quantiles Algorithms with Small Space and Update Time

Approximating quantiles and distributions over streaming data has been s...

0 Nikita Ivkin, et al. ∙

research

∙ 03/12/2019

Communication-efficient distributed SGD with Sketching

Large-scale distributed training of neural networks is often limited by ...

10 Nikita Ivkin, et al. ∙

research

∙ 03/11/2019

Coresets for Ordered Weighted Clustering

We design coresets for Ordered k-Median, a generalization of classical c...

0 Vladimir Braverman, et al. ∙

research

∙ 03/08/2019

The One-Way Communication Complexity of Dynamic Time Warping Distance

We resolve the randomized one-way communication complexity of Dynamic Ti...

0 Vladimir Braverman, et al. ∙

Vladimir Braverman

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