
BEAR: Sketching BFGS Algorithm for UltraHigh Dimensional Feature Selection in Sublinear Memory
We consider feature selection for applications in machine learning where...
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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...
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CoVer: Collaborative LightNodeOnly Verification and Data Availability for Blockchains
Validating a blockchain incurs heavy computation, communication, and sto...
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FastSecAgg: Scalable Secure Aggregation for PrivacyPreserving Federated Learning
Recent attacks on federated learning demonstrate that keeping the traini...
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Utilitybased Resource Allocation and Pricing for Serverless Computing
Serverless computing platforms currently rely on basic pricing schemes t...
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Boundary thickness and robustness in learning models
Robustness of machine learning models to various adversarial and nonadv...
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An Efficient Framework for Clustered Federated Learning
We address the problem of Federated Learning (FL) where users are distri...
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ProblemComplexity Adaptive Model Selection for Stochastic Linear Bandits
We consider the problem of model selection for two popular stochastic li...
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CommunicationEfficient Gradient Coding for Straggler Mitigation in Distributed Learning
Distributed implementations of gradientbased methods, wherein a server ...
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Alternating Minimization Converges SuperLinearly for Mixed Linear Regression
We address the problem of solving mixed random linear equations. We have...
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Serverless Straggler Mitigation using Local ErrorCorrecting Codes
Inexpensive cloud services, such as serverless computing, are often vuln...
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CommunicationEfficient and ByzantineRobust Distributed Learning
We develop a communicationefficient distributed learning algorithm that...
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SeF: A Secure Fountain Architecture for Slashing Storage Costs in Blockchains
Full nodes, which synchronize the entire blockchain history and independ...
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MaxAffine Regression: Provable, Tractable, and NearOptimal Statistical Estimation
Maxaffine regression refers to a model where the unknown regression fun...
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Robust Federated Learning in a Heterogeneous Environment
We study a recently proposed largescale distributed learning paradigm, ...
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Adversarially Trained Autoencoders for ParallelDataFree Voice Conversion
We present a method for converting the voices between a set of speakers....
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Gradient Coding Based on Block Designs for Mitigating Adversarial Stragglers
Distributed implementations of gradientbased methods, wherein a server ...
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OverSketched Newton: Fast Convex Optimization for Serverless Systems
Motivated by recent developments in serverless systems for largescale m...
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CrossEntropy Loss and LowRank Features Have Responsibility for Adversarial Examples
Stateoftheart neural networks are vulnerable to adversarial examples;...
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FrankWolfe Algorithm for Exemplar Selection
In this paper, we consider the problem of selecting representatives from...
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OverSketch: Approximate Matrix Multiplication for the Cloud
We propose OverSketch, an approximate algorithm for distributed matrix m...
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Rademacher Complexity for Adversarially Robust Generalization
Many machine learning models are vulnerable to adversarial attacks. It h...
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Online Scoring with Delayed Information: A Convex Optimization Viewpoint
We consider a system where agents enter in an online fashion and are eva...
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Faster Dataaccess in Largescale Systems: Networkscale Latency Analysis under General Servicetime Distributions
In cloud storage systems with a large number of servers, files are typic...
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Online Absolute Ranking with Partial Information: A Bipartite Graph Matching Approach
Ever since the introduction of the secretary problem, the notion of sele...
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Customized Local Differential Privacy for MultiAgent Distributed Optimization
Realtime datadriven optimization and control problems over networks ma...
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Defending Against Saddle Point Attack in ByzantineRobust Distributed Learning
In this paper, we study robust largescale distributed learning in the p...
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ByzantineRobust Distributed Learning: Towards Optimal Statistical Rates
In largescale distributed learning, security issues have become increas...
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Approximate Ranking from Pairwise Comparisons
A common problem in machine learning is to rank a set of n items based o...
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A Sequential Approximation Framework for Coded Distributed Optimization
Building on the previous work of Lee et al. and Ferdinand et al. on code...
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The Sample Complexity of Online OneClass Collaborative Filtering
We consider the online oneclass collaborative filtering (CF) problem th...
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Active Ranking from Pairwise Comparisons and when Parametric Assumptions Don't Help
We consider sequential or active ranking of a set of n items based on no...
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CYCLADES: Conflictfree Asynchronous Machine Learning
We present CYCLADES, a general framework for parallelizing stochastic op...
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Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence
Data in the form of pairwise comparisons arises in many domains, includi...
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When is it Better to Compare than to Score?
When eliciting judgements from humans for an unknown quantity, one often...
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Kannan Ramchandran
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