Matthew Nokleby | DeepAI

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Ishan Jindal
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∙ 11/08/2021

Information-Theoretic Bayes Risk Lower Bounds for Realizable Models

We derive information-theoretic lower bounds on the Bayes risk and gener...

30 Matthew Nokleby, et al. ∙

∙ 06/10/2020

Anytime MiniBatch: Exploiting Stragglers in Online Distributed Optimization

Distributed optimization is vital in solving large-scale machine learnin...

0 Nuwan Ferdinand, et al. ∙

∙ 05/18/2020

Scaling-up Distributed Processing of Data Streams for Machine Learning

Emerging applications of machine learning in numerous areas involve cont...

16 Matthew Nokleby, et al. ∙

∙ 04/15/2020

Learning Furniture Compatibility with Graph Neural Networks

We propose a graph neural network (GNN) approach to the problem of predi...

0 Luisa F. Polania, et al. ∙

∙ 03/18/2019

An Effective Label Noise Model for DNN Text Classification

Because large, human-annotated datasets suffer from labeling errors, it ...

0 Ishan Jindal, et al. ∙

∙ 11/11/2018

Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining

In this paper, we develop a reinforcement learning (RL) based system to ...

0 Ishan Jindal, et al. ∙

∙ 10/26/2018

Information Bottleneck Methods for Distributed Learning

We study a distributed learning problem in which Alice sends a compresse...

0 Parinaz Farajiparvar, et al. ∙

∙ 10/25/2018

Tensor Matched Kronecker-Structured Subspace Detection for Missing Information

We consider the problem of detecting whether a tensor signal having many...

0 Ishan Jindal, et al. ∙

∙ 02/23/2018

Multi-scale Spectrum Sensing in 5G Cognitive Networks

A multi-scale approach to spectrum sensing is proposed to overcome the h...

0 Nicolo Michelusi, et al. ∙

∙ 10/12/2017

A Unified Neural Network Approach for Estimating Travel Time and Distance for a Taxi Trip

In building intelligent transportation systems such as taxi or rideshare...

0 Ishan Jindal, et al. ∙

∙ 05/09/2017

Learning Deep Networks from Noisy Labels with Dropout Regularization

Large datasets often have unreliable labels-such as those obtained from ...

0 Ishan Jindal, et al. ∙

∙ 05/07/2017

Performance Limits on the Classification of Kronecker-structured Models

Kronecker-structured (K-S) models recently have been proposed for the ef...

0 Ishan Jindal, et al. ∙

∙ 04/25/2017

Stochastic Optimization from Distributed, Streaming Data in Rate-limited Networks

Motivated by machine learning applications in networks of sensors, inter...

0 Matthew Nokleby, et al. ∙

∙ 05/08/2016

Rate-Distortion Bounds on Bayes Risk in Supervised Learning

We present an information-theoretic framework for bounding the number of...

0 Matthew Nokleby, et al. ∙

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