Non-Parametric Optimization for Scalable Learning in Stochastic Decision Problems

Key points

• Non-parametric approaches have been proposed for time-varying deterministic optimization; however, they have not been developed for their stochastic counterparts.1
• This work addresses that gap by developing a stochastic variational framework based on Malliavin calculus.1
• This framework yields non-parametric optimality conditions for SO problems with stochastic decisions and supports the design of a scalable deep-learning algorithm that is insensitive to the parameterization dimension.1
• This algorithm, called the Stochastic Path Follower (SPF), is applied to two important problems under distribution drift, namely least-squares recovery and logistic regression.1

Sources (1)

• [1]Non-Parametric Optimization for Scalable Learning in Stochastic Decision Problems
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 09:42 AM

  Stochastic optimization (SO) plays a central role in decision-making under uncertainty.

  Non-parametric approaches have been proposed for time-varying deterministic optimization; however, they have not been developed for their stochastic counterparts.

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