Kannan Ramchandran

BEAR: Sketching BFGS Algorithm for Ultra-High Dimensional Feature Selection in Sublinear Memory

We consider feature selection for applications in machine learning where...
10/26/2020 ∙ by Amirali Aghazadeh, et al. ∙ 6 ∙ share

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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...
10/18/2020 ∙ by Vipul Gupta, et al. ∙ 8 ∙ share

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CoVer: Collaborative Light-Node-Only Verification and Data Availability for Blockchains

Validating a blockchain incurs heavy computation, communication, and sto...
10/01/2020 ∙ by Steven Cao, et al. ∙ 0 ∙ share

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FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning

Recent attacks on federated learning demonstrate that keeping the traini...
09/23/2020 ∙ by Swanand Kadhe, et al. ∙ 0 ∙ share

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Utility-based Resource Allocation and Pricing for Serverless Computing

Serverless computing platforms currently rely on basic pricing schemes t...
08/18/2020 ∙ by Vipul Gupta, et al. ∙ 0 ∙ share

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Boundary thickness and robustness in learning models

Robustness of machine learning models to various adversarial and non-adv...
07/09/2020 ∙ by Yaoqing Yang, et al. ∙ 14 ∙ share

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An Efficient Framework for Clustered Federated Learning

We address the problem of Federated Learning (FL) where users are distri...
06/07/2020 ∙ by Avishek Ghosh, et al. ∙ 0 ∙ share

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Problem-Complexity Adaptive Model Selection for Stochastic Linear Bandits

We consider the problem of model selection for two popular stochastic li...
06/04/2020 ∙ by Avishek Ghosh, et al. ∙ 0 ∙ share

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Communication-Efficient Gradient Coding for Straggler Mitigation in Distributed Learning

Distributed implementations of gradient-based methods, wherein a server ...
05/14/2020 ∙ by Swanand Kadhe, et al. ∙ 0 ∙ share

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Alternating Minimization Converges Super-Linearly for Mixed Linear Regression

We address the problem of solving mixed random linear equations. We have...
04/23/2020 ∙ by Avishek Ghosh, et al. ∙ 0 ∙ share

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Serverless Straggler Mitigation using Local Error-Correcting Codes

Inexpensive cloud services, such as serverless computing, are often vuln...
01/21/2020 ∙ by Vipul Gupta, et al. ∙ 0 ∙ share

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Communication-Efficient and Byzantine-Robust Distributed Learning

We develop a communication-efficient distributed learning algorithm that...
11/21/2019 ∙ by Avishek Ghosh, et al. ∙ 0 ∙ share

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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...
06/28/2019 ∙ by Swanand Kadhe, et al. ∙ 0 ∙ share

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Max-Affine Regression: Provable, Tractable, and Near-Optimal Statistical Estimation

Max-affine regression refers to a model where the unknown regression fun...
06/21/2019 ∙ by Avishek Ghosh, et al. ∙ 0 ∙ share

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Robust Federated Learning in a Heterogeneous Environment

We study a recently proposed large-scale distributed learning paradigm, ...
06/16/2019 ∙ by Avishek Ghosh, et al. ∙ 0 ∙ share

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Adversarially Trained Autoencoders for Parallel-Data-Free Voice Conversion

We present a method for converting the voices between a set of speakers....
05/09/2019 ∙ by Orhan Öçal, et al. ∙ 0 ∙ share

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Gradient Coding Based on Block Designs for Mitigating Adversarial Stragglers

Distributed implementations of gradient-based methods, wherein a server ...
04/30/2019 ∙ by Swanand Kadhe, et al. ∙ 0 ∙ share

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OverSketched Newton: Fast Convex Optimization for Serverless Systems

Motivated by recent developments in serverless systems for large-scale m...
03/21/2019 ∙ by Vipul Gupta, et al. ∙ 0 ∙ share

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Cross-Entropy Loss and Low-Rank Features Have Responsibility for Adversarial Examples

State-of-the-art neural networks are vulnerable to adversarial examples;...
01/24/2019 ∙ by Kamil Nar, et al. ∙ 10 ∙ share

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Frank-Wolfe Algorithm for Exemplar Selection

In this paper, we consider the problem of selecting representatives from...
11/06/2018 ∙ by Gary Cheng, et al. ∙ 0 ∙ share

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OverSketch: Approximate Matrix Multiplication for the Cloud

We propose OverSketch, an approximate algorithm for distributed matrix m...
11/06/2018 ∙ by Vipul Gupta, et al. ∙ 0 ∙ share

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Rademacher Complexity for Adversarially Robust Generalization

Many machine learning models are vulnerable to adversarial attacks. It h...
10/29/2018 ∙ by Dong Yin, et al. ∙ 0 ∙ share

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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...
07/09/2018 ∙ by Avishek Ghosh, et al. ∙ 0 ∙ share

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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...
07/06/2018 ∙ by Avishek Ghosh, et al. ∙ 0 ∙ share

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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...
06/18/2018 ∙ by Sinho Chewi, et al. ∙ 0 ∙ share

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Customized Local Differential Privacy for Multi-Agent Distributed Optimization

Real-time data-driven optimization and control problems over networks ma...
06/15/2018 ∙ by Roel Dobbe, et al. ∙ 0 ∙ share

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Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning

In this paper, we study robust large-scale distributed learning in the p...
06/14/2018 ∙ by Dong Yin, et al. ∙ 0 ∙ share

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Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates

In large-scale distributed learning, security issues have become increas...
03/05/2018 ∙ by Dong Yin, et al. ∙ 0 ∙ share

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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...
01/04/2018 ∙ by Reinhard Heckel, et al. ∙ 0 ∙ share

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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...
10/24/2017 ∙ by Jingge Zhu, et al. ∙ 0 ∙ share

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The Sample Complexity of Online One-Class Collaborative Filtering

We consider the online one-class collaborative filtering (CF) problem th...
05/31/2017 ∙ by Reinhard Heckel, et al. ∙ 0 ∙ share

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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...
06/28/2016 ∙ by Reinhard Heckel, et al. ∙ 0 ∙ share

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CYCLADES: Conflict-free Asynchronous Machine Learning

We present CYCLADES, a general framework for parallelizing stochastic op...
05/31/2016 ∙ by Xinghao Pan, et al. ∙ 0 ∙ share

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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...
05/06/2015 ∙ by Nihar B. Shah, et al. ∙ 0 ∙ share

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When is it Better to Compare than to Score?

When eliciting judgements from humans for an unknown quantity, one often...
06/25/2014 ∙ by Nihar B. Shah, et al. ∙ 0 ∙ share

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Kannan Ramchandran

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