The paper introduces Adaptive Doubly Robust (ADR), an off‑policy evaluation method for ranking policies that blends adaptive importance weighting with reward regression to reduce variance. ADR is unbiased when the true user behavior model is known and, under a sufficient condition, achieves lower variance than the prior Adaptive Inverse Propensity Scoring (AIPS) approach. Experiments on synthetic data show that ADR consistently improves mean squared error over AIPS and other ranking OPE estimators across various data sizes and ranking lengths.
By Kosuke Iguchi, Ren Kishimoto
arXiv:2509. 03456v2 Announce Type: replace-cross Abstract: Off-policy evaluation (OPE) and off-policy learning (OPL) are foundational for decision-making in offline contextual bandits.
By Imad Aouali, Otmane Sakhi
arXiv:2506. 06989v3 Announce Type: replace-cross Abstract: Learning-to-rank (LTR) systems commonly depend on implicit feedback, such as user clicks, because it is easy to collect and can serve as a valuable signal of user preferences.
By Md Aminul Islam, Kathryn Vasilaky, Elena Zheleva
arXiv:2606. 14929v1 Announce Type: cross Abstract: Modern recommendation systems increasingly rely on dynamically routing diverse queries to multiple embedding models.
By Yan Dai, Negin Golrezaei, Patrick Jaillet
arXiv:2601. 21816v2 Announce Type: replace Abstract: Evaluating the performance of large language models (LLMs) from human preference data is crucial for obtaining LLM leaderboards.
By Dennis Frauen, Athiya Deviyani, Mihaela van der Schaar, Stefan Feuerriegel
arXiv:2609.13730v1 Announce Type: new
Abstract: Reliable progress in offline policy learning depends on careful reporting, well-tuned baselines, and evaluation across diverse conditions. Prior work h...
By Nabil Omi, Eric Bae, Chung Yik Edward Yeung, Siddhartha Sen, Ali Farhadi
arXiv:2606. 06096v1 Announce Type: new Abstract: Policy-gradient methods usually optimize expected return, but many real world applications care about distributional properties of returns: tail risk, outlier robustness, or best-of-K discovery.
By Paavo Parmas, Yongmin Kim, Kohsei Matsutani, Shota Takashiro, Soichiro Nishimori, Takeshi Kojima, Yusuke Iwasawa, Yutaka Matsuo
arXiv:2607. 25268v1 Announce Type: cross Abstract: Ranking is a fundamental component of modern information access systems.
By Yiteng Tu, Weihang Su, Zitao Su, Yiqun Liu, Min Zhang, Qingyao Ai
DeGRe is a dense‑supervised generative reranking framework designed to improve multi‑stage recommender systems by addressing label bias and credit assignment issues. It uses an offline Lookahead Evaluator with beam search to generate dense supervision signals, which are distilled into a lightweight Online Generator that can perform efficient greedy decoding at inference time. Experiments show that DeGRe outperforms baselines on public benchmarks and industrial datasets, and it has been successfully deployed on Taobao Flash Shopping to enhance online recommendations.
By Chaotian Song, Jingyao Zhang, Chenghao Chen, Zisen Sang, Dehai Zhao, Guodong Cao, Boxi Wu, Deng Cai, Jia Jia
arXiv:2607. 28408v1 Announce Type: new Abstract: This thesis studies policy learning in interactive systems where an agent observes a context, selects an action from a very large set, and receives partial feedback.
By Imad Aouali
arXiv:2608. 04324v1 Announce Type: cross Abstract: This paper studies generalized low-rank matrix bandits with multiple prioritized objectives.
By Bo Xue, Ji Cheng, Haodong Jing, Hongzong Li, Shuang Qiu
arXiv:2605.11151v3 Announce Type: replace
Abstract: Offline-to-online reinforcement learning (RL) improves sample efficiency by leveraging pre-collected datasets prior to online interaction. A key ch...
By Andrew Choi, Wei Xu