Abstract: As the continuous limit of many discretized algorithms, PDEs can provide a qualitative description of algorithm's behavior and give principled theoretical insight into many mysteries in machine learning. In this talk, I will give a theoretical interpretation of several machine learning algorithms using Fokker-Planck (FP) equations. In the first one, we provide a mathematically rigorous explanation of why resampling outperforms reweighting in correcting biased data when stochastic gradient-type algorithms are used in training. In the second one, we propose a new method to alleviate the double sampling problem in model-free reinforcement learning, where the FP equation is used to do error analysis for the algorithm. In the last one, inspired by an interactive particle system whose mean-field limit is a non-linear FP equation, we develop an efficient gradient-free method that finds the global minimum exponentially fast.
Zoom Link: https://illinois.zoom.us/j/88665020956?pwd=bWRqRDAzbmpBdzMzbG5CN0trQWdBUT09
If you are interested in talking with Yuhua during her virtual visit please contact her host, Jared Bronski. In particular there will be a virtual lunch from 1:00-2:00pm central time at https://illinois.zoom.us/j/84555155062?pwd=bVJpVEdubXVaNUg1R3ZIVnBXdzBFdz09 — feel free to drop in for the whole hour or just a few minutes.
