Rethinking the theoretical foundation of reinforcement learning

Abstract: Given two candidate functions, can we identify which one is the true value function of a large Markov decision process (MDP), given a "benign" dataset? Trivial as it might seem, a version of the question was open for 20+ years in reinforcement learning (RL), and the core difficulties are intimately related to the training instability of modern deep RL. In this talk, I will argue that ...

Speaker：Nan Jiang (University of Illinois at Urbana-Champaign)

The asymptotic Plateau problem: past, current progress and future problems

Abstract: The asymptotic Plateau problem is a set of problems in hyperbolic 3-space concerning the existence, multiplicity and applications of minimal surfaces asymptotic to a given Jordan curve on the sphere at infinity. I will address some past results, new progress, and open problems. This is based on joint work with Lowe and Seppi.Bio: Zeno Huang is currently a professor of mathematics at t...

Speaker：Zeno Huang （ City University of New York）

Quantification of ergodicity for Hamilton–Jacobi equations in a dynamic random environment

Abstract:Motivated by the tensionless KPZ equation (a model of stochastic growth), we consider quantitative large-time averages for Hamilton–Jacobi equations in a dynamic random environment that is stationary ergodic and has finite-range dependence in time. In this talk, we establish, up to slowly varying factors, convergence rates with exponent 1/2 for the large-time averages of both the solu...

Speaker：Prof. GUO Xiaoqin (郭小钦), University of Cincinnati

Toward Convergent Adam-Type Methods

Abstract:Adam is one of the most widely used optimizers in modern machine learning, but its convergence theory remains incomplete, even in relatively simple convex settings. A central difficulty is that Adam tightly couples momentum and adaptive preconditioning, which hides the dissipative structure needed for a Lyapunov analysis.In this talk, I will describe a Lyapunov-guided approach to Adam-...

Speaker：Long Chen (University of California, Irvine)

A deep learning method for computing the spectrum of the Schrödinger operator: numerics and analysis

Abstract: We present a novel deep learning method for computing eigenstates of the Schrödinger operator. Our approach combines a novel loss function with an innovative neural network architecture that incorporates a-prior knowledge of the problem. These improvements enable the method to handle both high-dimensional problems and problems posed on irregular bounded domains. We successfully compu...

Speaker：Ming Pingbing (CAS)