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Forecasting from Counterfactual Simulator Rollouts: A Sim2Real Evaluation

Paper recorded by Signals 4 on 2026-10-02 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-02 on arXiv · recorded by Signals 4 on 2026-10-05

Category: cs.LG · 机器学习 · first seen 2026-10-05

Abstract

Deploying a new decision policy creates a cold-start problem for prediction models whose targets depend on the policy's actions: historical observations reflect earlier policies, while real observations under the new policy are not yet available. Simulation offers a way to address this gap by rolling out the target policy across counterfactual scenarios and using the resulting trajectories to lear

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#389 most recent of 762 cs.LG papers we have recorded · ↑ newer: Pivot-SD: Efficient Self-Distillation for Masked Diffusion Language Mo · ↓ older: PoCoFL: POlicy-COmpliant Federated Learning
Cite this page: Forecasting from Counterfactual Simulator Rollouts: A Sim2Real Evaluation: the #389 most recent of 762 cs.LG papers we have recorded (as of 2026-10-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/forecasting-from-counterfactual-simulator-rollouts-a-sim2real-evaluation.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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