The partial monitoring (PM) framework provides a theoretical formulation of sequential learning problems with incomplete feedback. On each round, a learning agent plays an action while the environment simultaneously chooses an outcome. The agent then observes a feedback signal that is only partially informative about the (unobserved) outcome. The agent leverages the received feedback signals to select actions that minimize the (unobserved) cumulative loss. In contextual PM, the outcomes depend on some side information that is observable by the agent before selecting the action on each round. In this paper, we consider the contextual and non-contextual PM settings with stochastic outcomes. We introduce a new class of PM strategies based on the randomization of deterministic confidence bounds. We also extend regret guarantees to settings where existing stochastic strategies are not applicable. Our experiments show that the proposed RandCBP and RandCBPsidestar strategies have favorable performance against state-of-the-art baselines in multiple PM games. To advocate for the adoption of the PM framework, we design a use case on the real-world problem of monitoring the error rate of any deployed classification system.
Randomized Matrix Sketching for Neural Network Training and Gradient Monitoring
Department of Mathematical Sciences（数学科学系）；Center for Mathematics（数学中心）；Artificial Intelligence (CMAI), George Mason University. Fairfax, VA 22030.（人工智能（CMAI），乔治·马歇尔大学，弗吉尼亚州 Fairfax，22030）；School of Mathematical（数学系）；Statistical Sciences, Clemson University, Clemson, SC 29634.（统计科学系，克莱姆森大学，SC州 Clemson，29634）
Trust-Aware Predictive Emissions Monitoring for Gas Turbine Fleets with Limited Labelled Data
School of Natural and Computing Sciences University of Aberdeen（自然与计算科学学院 布拉德福德大学）；School of Computing Sciences University of East Anglia（计算科学学院 东安格利亚大学）；Siemens Energy Industrial Turbomachinery Ltd.（西门子能源工业涡轮机有限公司）；Department of Physics and Technology UiT The Arctic University of Norway（物理与技术系 UiT 北极大学）
Identifying Heterogeneous Treatment Effects in Multiple Outcomes using Joint Confidence Intervals
Harvard John A. Paulson School of Engineering and Applied Sciences（哈佛大学约翰·A·保尔森工程与应用科学学院）；Massachusetts Institute of Technology（麻省理工学院）；Harvard Medical School（哈佛医学院）
Statistical Inference for Differentially Private Stochastic Gradient Descent
Center for Data Science, Zhejiang University（浙江大学数据科学中心）；Department of Statistics, Rutgers University（罗格斯大学统计系）；Faculty of Business and Economics, The University of Hong Kong（香港大学商学院）
Randomized Exploration for Reinforcement Learning with General Value Function Approximation
Mila；McGill University（麦吉尔大学）；Peking University（北京大学）；Amii（阿尔伯塔机器智能研究所）；University of Alberta（阿尔伯塔大学）；Princeton University（普林斯顿大学）；Northwestern University（西北大学）；DeepMind；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
Derandomizing Simultaneous Confidence Regions for Band-Limited Functions by Improved Norm Bounds and Majority-Voting Schemes
HUN-REN SZTAKI: Institute for Computer Science and Control, Hungarian Research Network（匈牙利研究网络：计算机科学与控制研究所）；Department of Probability Theory and Statistics, Institute of Mathematics, Eötvös Loránd University (ELTE)（概率论与统计学系，数学研究所，欧多尼·洛朗德大学（ELTE））；Institute of Mathematics, Eötvös Loránd University (ELTE)（数学研究所，欧多尼·洛朗德大学（ELTE））；Budapest University of Technology and Economics (BME)（布达佩斯技术与经济大学（BME））
