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20172019
most citedConvergent Policy Optimization for Safe Reinforcement Learning

30 citations · 43 across the 3 of their papers we have counts for

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5 papers · 1 filter

stat.ML2018

Provable Gaussian Embedding with One Observation

Ming Yu, Zhuoran Yang, Tuo Zhao +2

The success of machine learning methods heavily relies on having an appropriate representation for data at hand. Traditionally, machine learning approaches relied on user-defined h…

stat.ML2018

Learning Influence-Receptivity Network Structure with Guarantee

Ming Yu, Varun Gupta, Mladen Kolar

Traditional works on community detection from observations of information cascade assume that a single adjacency matrix parametrizes all the observed cascades. However, in reality…

stat.ML2018

Simultaneous Parameter Learning and Bi-Clustering for Multi-Response Models

Ming Yu, Karthikeyan Natesan Ramamurthy, Addie Thompson +1

We consider multi-response and multitask regression models, where the parameter matrix to be estimated is expected to have an unknown grouping structure. The groupings can be along…

stat.ML2018

Recovery of simultaneous low rank and two-way sparse coefficient matrices, a nonconvex approach

Ming Yu, Varun Gupta, Mladen Kolar

We study the problem of recovery of matrices that are simultaneously low rank and row and/or column sparse. Such matrices appear in recent applications in cognitive neuroscience, i…

stat.ML20174 cited

Multitask Learning using Task Clustering with Applications to Predictive Modeling and GWAS of Plant Varieties

Ming Yu, Addie M. Thompson, Karthikeyan Natesan Ramamurthy +2

Inferring predictive maps between multiple input and multiple output variables or tasks has innumerable applications in data science. Multi-task learning attempts to learn the maps…