Posterior Ratio Estimation for Latent Variables
Density Ratio Estimation has attracted attention from machine learning community due to its ability of comparing the underlying distributions of two datasets. However, in some …
yulong-zhang
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1 min read
Natural Actor-Critic Converges Globally for Hierarchical Linear Quadratic Regulator
Multi-agent reinforcement learning has been successfully applied to a number of challenging problems. Despite these empirical successes, theoretical understanding of different …
Constrained High Dimensional Statistical Inference
In typical high dimensional statistical inference problems, confidence intervals and hypothesis tests are performed for a low dimensional subset of model parameters under the …
Distributed Stochastic Multi-Task Learning with Graph Regularization
We propose methods for distributed graph-based multi-task learning that are based on weighted averaging of messages from other machines. Uniform averaging or diminishing stepsize …
weiran-wang
Distributed Multi-Task Learning with Shared Representation
We study the problem of distributed multi-task learning with shared representation, where each machine aims to learn a separate, but related, task in an unknown shared …
Inference for Sparse Conditional Precision Matrices
Given $n$ i.i.d. observations of a random vector $(X,Z)$, where $X$ is a high-dimensional vector and $Z$ is a low-dimensional index variable, we study the problem of estimating the …
Mean and variance estimation in high-dimensional heteroscedastic models with non-convex penalties
Despite its prevalence in statistical datasets, heteroscedasticity (non-constant sample variances) has been largely ignored in the high-dimensional statistics literature. Recently, …
james-sharpnack