research
∙
03/20/2023
Greedy Pruning with Group Lasso Provably Generalizes for Matrix Sensing and Neural Networks with Quadratic Activations
Pruning schemes have been widely used in practice to reduce the complexi...
share
research
∙
02/12/2023
Statistical Complexity and Optimal Algorithms for Non-linear Ridge Bandits
We consider the sequential decision-making problem where the mean outcom...
research
∙
01/30/2023
The Fair Value of Data Under Heterogeneous Privacy Constraints
Modern data aggregation often takes the form of a platform collecting da...
research
∙
01/15/2023
Efficiently Computing Sparse Fourier Transforms of q-ary Functions
Fourier transformations of pseudo-Boolean functions are popular tools fo...
research
∙
12/13/2022
Interactive Learning with Pricing for Optimal and Stable Allocations in Markets
Large-scale online recommendation systems must facilitate the allocation...
research
∙
10/05/2022
Spectral Regularization Allows Data-frugal Learning over Combinatorial Spaces
Data-driven machine learning models are being increasingly employed in s...
research
∙
07/08/2022
Interactive Recommendations for Optimal Allocations in Markets with Constraints
Recommendation systems when employed in markets play a dual role: they a...
research
∙
06/12/2022
Neurotoxin: Durable Backdoors in Federated Learning
Due to their decentralized nature, federated learning (FL) systems have ...
research
∙
05/31/2022
Decentralized Competing Bandits in Non-Stationary Matching Markets
Understanding complex dynamics of two-sided online matching markets, whe...
research
∙
05/30/2022
Minimax Optimal Online Imitation Learning via Replay Estimation
Online imitation learning is the problem of how best to mimic expert dem...
research
∙
02/06/2022
Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data
The search for effective and robust generalization metrics has been the ...
research
∙
07/23/2021
Taxonomizing local versus global structure in neural network loss landscapes
Viewing neural network models in terms of their loss landscapes has a lo...
research
∙
07/13/2021
Model Selection with Near Optimal Rates for Reinforcement Learning with General Model Classes
We address the problem of model selection for the finite horizon episodi...
research
∙
07/07/2021
Model Selection for Generic Contextual Bandits
We consider the problem of model selection for the general stochastic co...
research
∙
06/15/2021
Collaborative Learning and Personalization in Multi-Agent Stochastic Linear Bandits
We consider the problem of minimizing regret in an N agent heterogeneous...
research
∙
05/16/2021
LocalNewton: Reducing Communication Bottleneck for Distributed Learning
To address the communication bottleneck problem in distributed optimizat...
research
∙
03/17/2021
Escaping Saddle Points in Distributed Newton's Method with Communication efficiency and Byzantine Resilience
We study the problem of optimizing a non-convex loss function (with sadd...
research
∙
02/25/2021
Provably Breaking the Quadratic Error Compounding Barrier in Imitation Learning, Optimally
We study the statistical limits of Imitation Learning (IL) in episodic M...
research
∙
10/26/2020
BEAR: Sketching BFGS Algorithm for Ultra-High Dimensional Feature Selection in Sublinear Memory
We consider feature selection for applications in machine learning where...
research
∙
10/18/2020
Fast Distributed Training of Deep Neural Networks: Dynamic Communication Thresholding for Model and Data Parallelism
Data Parallelism (DP) and Model Parallelism (MP) are two common paradigm...
research
∙
10/01/2020
CoVer: Collaborative Light-Node-Only Verification and Data Availability for Blockchains
Validating a blockchain incurs heavy computation, communication, and sto...
research
∙
09/23/2020
FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning
Recent attacks on federated learning demonstrate that keeping the traini...
research
∙
08/18/2020
Utility-based Resource Allocation and Pricing for Serverless Computing
Serverless computing platforms currently rely on basic pricing schemes t...
research
∙
07/09/2020
Boundary thickness and robustness in learning models
Robustness of machine learning models to various adversarial and non-adv...
research
∙
06/07/2020
An Efficient Framework for Clustered Federated Learning
We address the problem of Federated Learning (FL) where users are distri...
research
∙
06/04/2020
Problem-Complexity Adaptive Model Selection for Stochastic Linear Bandits
We consider the problem of model selection for two popular stochastic li...
research
∙
05/14/2020
Communication-Efficient Gradient Coding for Straggler Mitigation in Distributed Learning
Distributed implementations of gradient-based methods, wherein a server ...
research
∙
04/23/2020
Alternating Minimization Converges Super-Linearly for Mixed Linear Regression
We address the problem of solving mixed random linear equations. We have...
research
∙
01/21/2020
Serverless Straggler Mitigation using Local Error-Correcting Codes
Inexpensive cloud services, such as serverless computing, are often vuln...
research
∙
11/21/2019
Communication-Efficient and Byzantine-Robust Distributed Learning
We develop a communication-efficient distributed learning algorithm that...
research
∙
06/28/2019
SeF: A Secure Fountain Architecture for Slashing Storage Costs in Blockchains
Full nodes, which synchronize the entire blockchain history and independ...
research
∙
06/21/2019
Max-Affine Regression: Provable, Tractable, and Near-Optimal Statistical Estimation
Max-affine regression refers to a model where the unknown regression fun...
research
∙
06/16/2019
Robust Federated Learning in a Heterogeneous Environment
We study a recently proposed large-scale distributed learning paradigm, ...
research
∙
05/09/2019
Adversarially Trained Autoencoders for Parallel-Data-Free Voice Conversion
We present a method for converting the voices between a set of speakers....
research
∙
04/30/2019
Gradient Coding Based on Block Designs for Mitigating Adversarial Stragglers
Distributed implementations of gradient-based methods, wherein a server ...
research
∙
03/21/2019
OverSketched Newton: Fast Convex Optimization for Serverless Systems
Motivated by recent developments in serverless systems for large-scale m...
research
∙
01/24/2019
Cross-Entropy Loss and Low-Rank Features Have Responsibility for Adversarial Examples
State-of-the-art neural networks are vulnerable to adversarial examples;...
research
∙
11/06/2018
Frank-Wolfe Algorithm for Exemplar Selection
In this paper, we consider the problem of selecting representatives from...
research
∙
11/06/2018
OverSketch: Approximate Matrix Multiplication for the Cloud
We propose OverSketch, an approximate algorithm for distributed matrix m...
research
∙
10/29/2018
Rademacher Complexity for Adversarially Robust Generalization
Many machine learning models are vulnerable to adversarial attacks. It h...
research
∙
07/09/2018
Online Scoring with Delayed Information: A Convex Optimization Viewpoint
We consider a system where agents enter in an online fashion and are eva...
research
∙
07/06/2018
Faster Data-access in Large-scale Systems: Network-scale Latency Analysis under General Service-time Distributions
In cloud storage systems with a large number of servers, files are typic...
research
∙
06/18/2018
Online Absolute Ranking with Partial Information: A Bipartite Graph Matching Approach
Ever since the introduction of the secretary problem, the notion of sele...
research
∙
06/15/2018
Customized Local Differential Privacy for Multi-Agent Distributed Optimization
Real-time data-driven optimization and control problems over networks ma...
research
∙
06/14/2018
Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning
In this paper, we study robust large-scale distributed learning in the p...
research
∙
03/05/2018
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates
In large-scale distributed learning, security issues have become increas...
research
∙
01/04/2018
Approximate Ranking from Pairwise Comparisons
A common problem in machine learning is to rank a set of n items based o...
research
∙
10/24/2017
A Sequential Approximation Framework for Coded Distributed Optimization
Building on the previous work of Lee et al. and Ferdinand et al. on code...
research
∙
05/31/2017
The Sample Complexity of Online One-Class Collaborative Filtering
We consider the online one-class collaborative filtering (CF) problem th...
research
∙
06/28/2016