コンフォーマル決定理論：不完全な予測からの安全な自律的意思決定
Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions
不完全な機械学習予測のもとでも統計的保証付きで低リスクな意思決定を行う枠組みを提案し、ロボットの歩行者回避計画や自動取引、製造に応用した。
著者: Jordan Lekeufack, Anastasios N. Angelopoulos, Andrea Bajcsy, Michael I. Jordan, Jitendra Malik
分類: stat.ML, cs.LG, cs.RO, stat.ME
原文アブストラクト
We introduce Conformal Decision Theory, a framework for producing safe autonomous decisions despite imperfect machine learning predictions. Examples of such decisions are ubiquitous, from robot planning algorithms that rely on pedestrian predictions, to calibrating autonomous manufacturing to exhibit high throughput and low error, to the choice of trusting a nominal policy versus switching to a safe backup policy at run-time. The decisions produced by our algorithms are safe in the sense that they come with provable statistical guarantees of having low risk without any assumptions on the world model whatsoever; the observations need not be I.I.D. and can even be adversarial. The theory extends results from conformal prediction to calibrate decisions directly, without requiring the construction of prediction sets. Experiments demonstrate the utility of our approach in robot motion planning around humans, automated stock trading, and robot manufacturing.
