26 citations · 27 across the 8 of their papers we have counts for
8 papers
First-Exit Time Analysis for Truncated Heavy-Tailed Dynamical Systems
Xingyu Wang, Chang-Han Rhee
In this paper, we study the first-exit time of stochastic difference equation and its truncat…
Global Dynamics of Heavy-Tailed SGDs in Nonconvex Loss Landscape: Characterization and Control
Xingyu Wang, Chang-Han Rhee
Stochastic gradient descent (SGD) and its variants enable modern artificial intelligence. However, theoretical understanding lags far behind their empirical success. It is widely b…
Strongly Efficient Rare-Event Simulation for Regularly Varying Lévy Processes with Infinite Activities
Xingyu Wang, Chang-Han Rhee
In this paper, we address rare-event simulation for heavy-tailed Lévy processes with infinite activities. The presence of infinite activities poses a critical challenge, making it…
Large Deviations and Metastability Analysis for Heavy-Tailed Dynamical Systems
Xingyu Wang, Chang-Han Rhee
This paper introduces novel frameworks for large deviations and metastability analysis in heavy-tailed stochastic dynamical systems. We develop and apply these frameworks within th…
Eliminating Sharp Minima from SGD with Truncated Heavy-tailed Noise
Xingyu Wang, Sewoong Oh, Chang-Han Rhee
The empirical success of deep learning is often attributed to SGD's mysterious ability to avoid sharp local minima in the loss landscape, as sharp minima are known to lead to poor…
Efficient Rare-Event Simulation for Multiple Jump Events in Regularly Varying Lévy Processes with Infinite Activities
Xingyu Wang, Chang-Han Rhee
In this paper we address the problem of rare-event simulation for heavy-tailed Lévy processes with infinite activities. We propose a strongly efficient importance sampling algorith…
Keyword-based Topic Modeling and Keyword Selection
Xingyu Wang, Lida Zhang, Diego Klabjan
Certain type of documents such as tweets are collected by specifying a set of keywords. As topics of interest change with time it is beneficial to adjust keywords dynamically. The…
Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations
Xingyu Wang, Diego Klabjan
This paper considers the problem of inverse reinforcement learning in zero-sum stochastic games when expert demonstrations are known to be not optimal. Compared to previous works t…