9 papers
Joint-Sparse Transfer Learning for High-Dimensional Multi-Output Regression
Sunwoo Lim, Mladen Kolar
Multitask linear models can improve estimation and prediction by exploiting structure shared across responses, bridging taskwise fitting and complete pooling. In many applications,…
SMART: A Spectral Transfer Approach to Multi-Task Learning
Boxin Zhao, Mladen Kolar, Jinchi Lv
Multi-task learning is effective for related applications, but its performance can deteriorate when the target sample size is small. Transfer learning can borrow strength from rela…
Simultaneous Inference for Covariance and Precision Matrices of Long-Range Dependent Time Series
Percy S. Zhai, Mladen Kolar, Wei Biao Wu
For time series with long-range temporal dependence, inference for covariance and precision matrices is non-trivial. We propose a Berry-Esseen type Gaussian approximation result th…
Provable Accelerated Bayesian Optimization with Knowledge Transfer
Haitao Lin, Boxin Zhao, Mladen Kolar +1
We study how to accelerate Bayesian optimization (BO) on a target task by transferring historical knowledge from related source tasks. Existing work on BO with knowledge transfer e…
High-Dimensional Markov-switching Ordinary Differential Processes
Katherine Tsai, Mladen Kolar, Sanmi Koyejo
We investigate the parameter recovery of Markov-switching ordinary differential processes from discrete observations, where the differential equations are nonlinear additive models…
Trans-Glasso: A Transfer Learning Approach to Precision Matrix Estimation
Boxin Zhao, Cong Ma, Mladen Kolar
Precision matrix estimation is essential in various fields; yet it is challenging when samples for the target study are limited. Transfer learning can enhance estimation accuracy b…
High-Dimensional Differential Parameter Inference in Exponential Family using Time Score Matching
Daniel J. Williams, Leyang Wang, Qizhen Ying +2
This paper addresses differential inference in time-varying parametric probabilistic models, like graphical models with changing structures. Instead of estimating a high-dimensiona…
Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning
Dake Zhang, Boxiang Lyu, Shuang Qiu +2
We study risk-sensitive reinforcement learning (RL), a crucial field due to its ability to enhance decision-making in scenarios where it is essential to manage uncertainty and mini…
Personalized Binomial DAGs Learning with Network Structured Covariates
Boxin Zhao, Weishi Wang, Dingyuan Zhu +5
The causal dependence in data is often characterized by Directed Acyclic Graphical (DAG) models, widely used in many areas. Causal discovery aims to recover the DAG structure using…