5 papers · 1 filter
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…