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BEGIN:VEVENT
UID:c54a38311099bb3ace24dd400407657f
CATEGORIES:Special Colloquium
CREATED:20211213T114419
SUMMARY:Fokker-Planck Equations and Machine Learning 
LOCATION:Zoom
DESCRIPTION:<p style="margin-bottom: 7px; line-height: 106%; border: none; padding: 0in
 ;">Abstract: As the continuous limit of many discretized algorithms, PDEs c
 an provide a qualitative description of algorithm's behavior and give princ
 ipled 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 reweig
 hting in correcting biased data when stochastic gradient-type algorithms ar
 e used in training. In the second one, we propose a new method to alleviate
  the double sampling problem in model-free reinforcement learning, where th
 e FP equation is used to do error analysis for the algorithm. In the last o
 ne, inspired by an interactive particle system whose mean-field limit is a 
 non-linear FP equation, we develop an efficient gradient-free method that f
 inds the global minimum exponentially fast.&nbsp;</p>
CONTACT:Yuhua Zhu, Stanford University 
DTSTAMP:20260827T141924
DTSTART;TZID=America/New_York:20211215T113000
DTEND;TZID=America/New_York:20211215T123000
SEQUENCE:0
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