Hindsight Learning for MDPs with Exogenous Inputs

• URL: http://arxiv.org/abs/2207.06272v3
• Date: Mon, 23 Oct 2023 13:06:58 GMT
• Title: Hindsight Learning for MDPs with Exogenous Inputs
• Authors: Sean R. Sinclair, Felipe Frujeri, Ching-An Cheng, Luke Marshall, Hugo Barbalho, Jingling Li, Jennifer Neville, Ishai Menache, Adith Swaminathan
• Abstract summary: We design a class of data-efficient algorithms for resource management problems called Hindsight Learning (HL) HL algorithms achieve data efficiency by leveraging a key insight: having samples of the variables, past decisions can be revisited in hindsight to infer counterfactual consequences that can accelerate policy improvements. We scale our algorithms to a business-critical cloud resource management problem -- allocating Virtual Machines (VMs) to physical machines, and simulate their performance with real datasets from a large public cloud provider.
• Score: 20.556789174972334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many resource management problems require sequential decision-making under uncertainty, where the only uncertainty affecting the decision outcomes are exogenous variables outside the control of the decision-maker. We model these problems as Exo-MDPs (Markov Decision Processes with Exogenous Inputs) and design a class of data-efficient algorithms for them termed Hindsight Learning (HL). Our HL algorithms achieve data efficiency by leveraging a key insight: having samples of the exogenous variables, past decisions can be revisited in hindsight to infer counterfactual consequences that can accelerate policy improvements. We compare HL against classic baselines in the multi-secretary and airline revenue management problems. We also scale our algorithms to a business-critical cloud resource management problem -- allocating Virtual Machines (VMs) to physical machines, and simulate their performance with real datasets from a large public cloud provider. We find that HL algorithms outperform domain-specific heuristics, as well as state-of-the-art reinforcement learning methods.

Related papers

• Learning Optimal and Sample-Efficient Decision Policies with Guarantees [3.096615629099617]
  This thesis addresses the problem of learning from offline datasets in the presence of hidden confounders.<n>We derive a sample-efficient algorithm for solving conditional moment restrictions problems with convergence and optimality guarantees.<n>We also develop an algorithm that can learn effective imitator policies with convergence rate guarantees.
  arXiv  Detail & Related papers  (2026-02-20T04:24:49Z)
• Barbarians at the Gate: How AI is Upending Systems Research [58.95406995634148]
  We argue that systems research, long focused on designing and evaluating new performance-oriented algorithms, is particularly well-suited for AI-driven solution discovery.<n>We term this approach as AI-Driven Research for Systems ( ADRS), which iteratively generates, evaluates, and refines solutions.<n>Our results highlight both the disruptive potential and the urgent need to adapt systems research practices in the age of AI.
  arXiv  Detail & Related papers  (2025-10-07T17:49:24Z)
• Does Machine Unlearning Truly Remove Model Knowledge? A Framework for Auditing Unlearning in LLMs [58.24692529185971]
  We introduce a comprehensive auditing framework for unlearning evaluation comprising three benchmark datasets, six unlearning algorithms, and five prompt-based auditing methods.<n>We evaluate the effectiveness and robustness of different unlearning strategies.
  arXiv  Detail & Related papers  (2025-05-29T09:19:07Z)
• What Matters for Batch Online Reinforcement Learning in Robotics? [65.06558240091758]
  The ability to learn from large batches of autonomously collected data for policy improvement holds the promise of enabling truly scalable robot learning.<n>Previous works have applied imitation learning and filtered imitation learning methods to the batch online RL problem.<n>We analyze how these axes affect performance and scaling with the amount of autonomous data.
  arXiv  Detail & Related papers  (2025-05-12T21:24:22Z)
• Explainable AI for UAV Mobility Management: A Deep Q-Network Approach for Handover Minimization [4.7430397428031785]
  This paper introduces an explainable AI (XAI) framework to provide deeper insights into how various state parameters influence handover decisions. By quantifying the impact of key features such as reference signal received power (RSRP), our approach enhances the interpretability and reliability of RL-based handover solutions.
  arXiv  Detail & Related papers  (2025-04-25T14:11:51Z)
• FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices [12.08958206272527]
  Federated Learning (FL) is increasingly adopted in edge computing scenarios, where a large number of heterogeneous clients operate under constrained or sufficient resources. One-shot FL has emerged as a promising approach to mitigate communication overhead, and model-heterogeneous FL solves the problem of diverse computing resources across clients. We propose a novel FL framework named FedMHO, which leverages deep classification models on resource-sufficient clients and lightweight generative models on resource-constrained devices.
  arXiv  Detail & Related papers  (2025-02-12T15:54:56Z)
• Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review [10.015735252600793]
  Deep Reinforcement Learning (DRL) has emerged as a promising solution to these challenges. DRL enables systems to learn and adapt policies based on continuous observations of the environment. This survey provides a comprehensive review of DRL-based algorithms for job scheduling and resource management in cloud computing.
  arXiv  Detail & Related papers  (2025-01-02T02:08:00Z)
• Exploring Knowledge Boundaries in Large Language Models for Retrieval Judgment [56.87031484108484]
  Large Language Models (LLMs) are increasingly recognized for their practical applications. Retrieval-Augmented Generation (RAG) tackles this challenge and has shown a significant impact on LLMs. By minimizing retrieval requests that yield neutral or harmful results, we can effectively reduce both time and computational costs.
  arXiv  Detail & Related papers  (2024-11-09T15:12:28Z)
• Towards Cost Sensitive Decision Making [14.279123976398926]
  In this work, we consider RL models that may actively acquire features from the environment to improve the decision quality and certainty. We propose the Active-Acquisition POMDP and identify two types of the acquisition process for different application domains. In order to assist the agent in the actively-acquired partially-observed environment and alleviate the exploration-exploitation dilemma, we develop a model-based approach.
  arXiv  Detail & Related papers  (2024-10-04T19:48:23Z)
• Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution [38.53065398127086]
  This study investigates the potential of feature attribution methods to filter out uninformative features in input data for regression problems. We introduce a feature selection pipeline that combines Integrated Gradients with k-means clustering to select an optimal set of variables from the initial data space. To validate the effectiveness of this approach, we apply it to a real-world industrial problem - blade vibration analysis in the development process of turbo machinery.
  arXiv  Detail & Related papers  (2024-09-25T09:50:51Z)
• Mind the Interference: Retaining Pre-trained Knowledge in Parameter Efficient Continual Learning of Vision-Language Models [79.28821338925947]
  Domain-Class Incremental Learning is a realistic but challenging continual learning scenario. To handle these diverse tasks, pre-trained Vision-Language Models (VLMs) are introduced for their strong generalizability. This incurs a new problem: the knowledge encoded in the pre-trained VLMs may be disturbed when adapting to new tasks, compromising their inherent zero-shot ability. Existing methods tackle it by tuning VLMs with knowledge distillation on extra datasets, which demands heavy overhead. We propose the Distribution-aware Interference-free Knowledge Integration (DIKI) framework, retaining pre-trained knowledge of
  arXiv  Detail & Related papers  (2024-07-07T12:19:37Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approach [58.911515417156174]
  We propose a new definition of Age of Information (AoI) and, based on the redefined AoI, we formulate an online AoI problem for MEC systems. We introduce Post-Decision States (PDSs) to exploit the partial knowledge of the system's dynamics. We also combine PDSs with deep RL to further improve the algorithm's applicability, scalability, and robustness.
  arXiv  Detail & Related papers  (2023-12-01T01:30:49Z)
• Zero-knowledge Proof Meets Machine Learning in Verifiability: A Survey [19.70499936572449]
  High-quality models rely not only on efficient optimization algorithms but also on the training and learning processes built upon vast amounts of data and computational power. Due to various challenges such as limited computational resources and data privacy concerns, users in need of models often cannot train machine learning models locally. This paper presents a comprehensive survey of zero-knowledge proof-based verifiable machine learning (ZKP-VML) technology.
  arXiv  Detail & Related papers  (2023-10-23T12:15:23Z)
• A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading [62.34538208323411]
  We propose a multi-head ensemble multi-task learning (MEMTL) approach with a shared backbone and multiple prediction heads (PHs) MEMTL outperforms benchmark methods in both the inference accuracy and mean square error without requiring additional training data.
  arXiv  Detail & Related papers  (2023-09-02T11:01:16Z)
• Forecasting Workload in Cloud Computing: Towards Uncertainty-Aware Predictions and Transfer Learning [1.5749416770494704]
  We show that modelling the uncertainty of predictions has a positive impact on performance. We investigate whether our models benefit transfer learning capabilities across different domains.
  arXiv  Detail & Related papers  (2023-02-24T14:51:30Z)
• Leveraging Expert Consistency to Improve Algorithmic Decision Support [62.61153549123407]
  We explore the use of historical expert decisions as a rich source of information that can be combined with observed outcomes to narrow the construct gap. We propose an influence function-based methodology to estimate expert consistency indirectly when each case in the data is assessed by a single expert. Our empirical evaluation, using simulations in a clinical setting and real-world data from the child welfare domain, indicates that the proposed approach successfully narrows the construct gap.
  arXiv  Detail & Related papers  (2021-01-24T05:40:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.