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Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques

We study the optimization aspects of personalized Federated Learning (FL). We develop a universal optimization theory applicable to all convex personalized FL models in the literature. In particular, we propose a general personalized objective …

Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning

In offline reinforcement learning (RL) an optimal policy is learnt solely from a priori collected observational data. However, in observational data, actions are often confounded by unobserved variables. Instrumental variables (IVs), in the context …

Statistical Inference for Networks of High-Dimensional Point Processes

Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching

Probabilistic graphical models provide a flexible yet parsimonious framework for modeling dependencies among nodes in networks. There is a vast literature on parameter estimation and consistent model selection for graphical models. However, in many …

Post-selection inference on high-dimensional varying-coefficient quantile regression model

Quantile regression has been successfully used to study heterogeneous and heavy-tailed data. In this work, we study high-dimensional varying-coefficient quantile regression model that allows us to capture non-stationary effects of the input variables …

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 algorithms is lacking, primarily due to the curse of dimensionality caused …

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 assumption that the parameters of interest are unconstrained. However, in …

Estimating Differential Latent Variable Graphical Models with Applications to Brain Connectivity

Tensor Canonical Correlation Analysis

In many applications, such as classification of images or videos, it is of interest to develop a framework for tensor data instead of ad-hoc way of transforming data to vectors due to the computational and under-sampling issues. In this paper, we …

Two-sample inference for high-dimensional Markov networks