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...
share
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...
research
∙
06/15/2023
Private Federated Frequency Estimation: Adapting to the Hardness of the Instance
In federated frequency estimation (FFE), multiple clients work together ...
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 ...
research
∙
05/31/2023
Multi-environment lifelong deep reinforcement learning for medical imaging
Deep reinforcement learning(DRL) is increasingly being explored in medic...
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...
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...
research
∙
03/28/2023
Lower Bounds for Pseudo-Deterministic Counting in a Stream
Many streaming algorithms provide only a high-probability relative appro...
research
∙
03/17/2023
Fixed Design Analysis of Regularization-Based Continual Learning
We consider a continual learning (CL) problem with two linear regression...
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 ...
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...
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...
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...
research
∙
09/24/2022
From Local to Global: Spectral-Inspired Graph Neural Networks
Graph Neural Networks (GNNs) are powerful deep learning methods for Non-...
research
∙
09/05/2022
The Power of Uniform Sampling for Coresets
Motivated by practical generalizations of the classic k-median and k-mea...
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...
research
∙
06/06/2022
Pretrained Models for Multilingual Federated Learning
Since the advent of Federated Learning (FL), research has applied these ...
research
∙
04/19/2022
The White-Box Adversarial Data Stream Model
We study streaming algorithms in the white-box adversarial model, where ...
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...
research
∙
03/08/2022
New Coresets for Projective Clustering and Applications
(j,k)-projective clustering is the natural generalization of the family ...
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 ...
research
∙
12/18/2021
Cross-Domain Federated Learning in Medical Imaging
Federated learning is increasingly being explored in the field of medica...
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...
research
∙
09/03/2021
Symmetric Norm Estimation and Regression on Sliding Windows
The sliding window model generalizes the standard streaming model and of...
research
∙
08/11/2021
Gap-Dependent Unsupervised Exploration for Reinforcement Learning
For the problem of task-agnostic reinforcement learning (RL), an agent f...
research
∙
08/10/2021
The Benefits of Implicit Regularization from SGD in Least Squares Problems
Stochastic gradient descent (SGD) exhibits strong algorithmic regulariza...
research
∙
06/30/2021
Coresets for Clustering with Missing Values
We provide the first coreset for clustering points in ℝ^d that have mult...
research
∙
06/28/2021
Adversarial Robustness of Streaming Algorithms through Importance Sampling
In this paper, we introduce adversarially robust streaming algorithms fo...
research
∙
04/17/2021
Lifelong Learning with Sketched Structural Regularization
Preventing catastrophic forgetting while continually learning new tasks ...
research
∙
04/08/2021
Linear and Sublinear Time Spectral Density Estimation
We analyze the popular kernel polynomial method (KPM) for approximating ...
research
∙
03/23/2021
Benign Overfitting of Constant-Stepsize SGD for Linear Regression
There is an increasing realization that algorithmic inductive biases are...
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...
research
∙
11/11/2020
Sketch and Scale: Geo-distributed tSNE and UMAP
Running machine learning analytics over geographically distributed datas...
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...
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...
research
∙
08/19/2020
Data-Independent Structured Pruning of Neural Networks via Coresets
Model compression is crucial for deployment of neural networks on device...
research
∙
08/15/2020
Obtaining Adjustable Regularization for Free via Iterate Averaging
Regularization for optimization is a crucial technique to avoid overfitt...
research
∙
07/15/2020
FetchSGD: Communication-Efficient Federated Learning with Sketching
Existing approaches to federated learning suffer from a communication bo...
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...
research
∙
02/15/2020
Sparse Coresets for SVD on Infinite Streams
In streaming Singular Value Decomposition (SVD), d-dimensional rows of a...
research
∙
11/16/2019
Memory-Efficient Performance Monitoring on Programmable Switches with Lean Algorithms
Network performance problems are notoriously difficult to diagnose. Prio...
research
∙
08/01/2019
Multiparametric Deep Learning Tissue Signatures for Muscular Dystrophy: Preliminary Results
A current clinical challenge is identifying limb girdle muscular dystrop...
research
∙
07/17/2019
Improved Algorithms for Time Decay Streams
In the time-decay model for data streams, elements of an underlying data...
research
∙
07/11/2019
Schatten Norms in Matrix Streams: Hello Sparsity, Goodbye Dimension
The spectrum of a matrix contains important structural information about...
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.,...
research
∙
07/09/2019
On Activation Function Coresets for Network Pruning
Model compression provides a means to efficiently deploy deep neural net...
research
∙
06/29/2019
Streaming Quantiles Algorithms with Small Space and Update Time
Approximating quantiles and distributions over streaming data has been s...
research
∙
03/12/2019
Communication-efficient distributed SGD with Sketching
Large-scale distributed training of neural networks is often limited by ...
research
∙
03/11/2019
Coresets for Ordered Weighted Clustering
We design coresets for Ordered k-Median, a generalization of classical c...
research
∙
03/08/2019
