64 citations · 222 across the 17 of their papers we have counts for
5 papers · 1 filter
Information-Theoretic Bayes Risk Lower Bounds for Realizable Models
Matthew Nokleby, Ahmad Beirami
We derive information-theoretic lower bounds on the Bayes risk and generalization error of realizable machine learning models. In particular, we employ an analysis in which the rat…
Anytime MiniBatch: Exploiting Stragglers in Online Distributed Optimization
Nuwan Ferdinand, Haider Al-Lawati, Stark C. Draper +1
Distributed optimization is vital in solving large-scale machine learning problems. A widely-shared feature of distributed optimization techniques is the requirement that all nodes…
Scaling-up Distributed Processing of Data Streams for Machine Learning
Matthew Nokleby, Haroon Raja, Waheed U. Bajwa
Emerging applications of machine learning in numerous areas involve continuous gathering of and learning from streams of data. Real-time incorporation of streaming data into the le…
An Effective Label Noise Model for DNN Text Classification
Ishan Jindal, Daniel Pressel, Brian Lester +1
Because large, human-annotated datasets suffer from labeling errors, it is crucial to be able to train deep neural networks in the presence of label noise. While training image cla…
Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining
Ishan Jindal, Zhiwei Qin, Xuewen Chen +2
In this paper, we develop a reinforcement learning (RL) based system to learn an effective policy for carpooling that maximizes transportation efficiency so that fewer cars are req…