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Partially Informed Stochastic Optimization via Zeroth-Order Residual Tracking

Paper recorded by Signals 4 on 2026-10-09 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-09 on arXiv · recorded by Signals 4 on 2026-10-12

Category: cs.LG · 机器学习 · first seen 2026-10-12

Abstract

Many optimization problems provide direct access to only part of the gradient, while the remaining component must be inferred from function evaluations. This structure arises in performative prediction, performative reinforcement learning, and bilevel optimization with a black-box lower level. We introduce Partially Informed Stochastic Optimization (PISO), which combines the available gradient com

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#305 most recent of 749 cs.LG papers we have recorded · ↑ newer: Prediction Is Not a Sufficient Memory State · ↓ older: Finite-Sample Guarantees for Fully Discrete Conditional Diffusion Mode
Cite this page: Partially Informed Stochastic Optimization via Zeroth-Order Residual Tracking: the #305 most recent of 749 cs.LG papers we have recorded (as of 2026-10-09). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/partially-informed-stochastic-optimization-via-zeroth-order-residual-tracking.html
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