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Cover: A Cluster-Based Variance Reduced Method For Online Learning

Publications associées (35)

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Scalable Stochastic Optimization: Scenario Reduction with Guarantees

Kilian Schindler

Stochastic optimization is a popular modeling paradigm for decision-making under uncertainty and has a wide spectrum of applications in management science, economics and engineering. However, the stochastic optimization models one faces in practice are int ...

EPFL2020

Stochastic Zeroth-Order Optimisation Algorithms with Variance Reduction

Ahmad Ajalloeian

Introduction of optimisation problems in which the objective function is black box or obtaining the gradient is infeasible, has recently raised interest in zeroth-order optimisation methods. As an example finding adversarial examples for Deep Learning mode ...

2019

Discovery and Temporal Analysis of MOOC Study Patterns

Pierre Dillenbourg, Mina Shirvani Boroujeni

The large-scale and granular interaction data collected in online learning platforms such as massive open online courses (MOOCs) provide unique opportunities to better understand individuals' learning processes and could facilitate the design of personaliz ...

2019

Small-variance asymptotics for non-parametric online robot learning

Sylvain Calinon, Ajay Kumar Tanwani

Small-variance asymptotics is emerging as a useful technique for inference in large-scale Bayesian non-parametric mixture models. This paper analyzes the online learning of robot manipulation tasks with Bayesian non-parametric mixture models under small-va ...

SAGE PUBLICATIONS LTD2019

Dynamic Average Diffusion With Randomized Coordinate Updates

Ali H. Sayed, Bicheng Ying, Kun Yuan

This work derives and analyzes an online learning strategy for tracking the average of time-varying distributed signals by relying on randomized coordinate-descent updates. During each iteration, each agent selects or observes a random entry of the observa ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

Not All Samples Are Created Equal: Deep Learning with Importance Sampling

François Fleuret, Angelos Katharopoulos

Deep neural network training spends most of the computation on examples that are properly handled, and could be ignored. We propose to mitigate this phenomenon with a principled importance sampling scheme that focuses computation on "informative" examples ...

Idiap2018

Supervisory teleoperation with online learning and optimal control

Sylvain Calinon

We present a general approach for online learning and optimal control of manipulation tasks in a supervisory teleoperation context, targeted to underwater remotely operated vehicles (ROVs). We use an online Bayesian nonparametric learning algorithm to buil ...

IEEE2017

Multitask Diffusion Adaptation Over Networks With Common Latent Representations

Ali H. Sayed, Jie Chen

Online learning with streaming data in a distributed and collaborative manner can be useful in a wide range of applications. This topic has been receiving considerable attention in recent years with emphasis on both single-task and multitask scenarios. In ...

2017

On the influence of momentum acceleration on online learning

Ali H. Sayed, Bicheng Ying, Kun Yuan

This paper examines the convergence rate and mean-square-error performance of momentum stochastic gradient methods in the constant step-size and slow adaptation regime. The results establish that momentum methods are equivalent to the standard stochastic g ...

IEEE2016

Traffic Pattern Analysis and Anomaly Detection via Probabilistic Inference Model

Kwang Moo Yi

In this chapter, we introduce a method for trajectory pattern analysis through the probabilistic inference model with both regional and velocity observations. By embedding Gaussian models into the discrete topic model framework, our method uses continuous ...

Springer Netherlands2016