We introduce InfoNoise, an online adaptive noise schedule for diffusion training that reallocates optimization effort toward noise levels where denoising is most informative. Together with loss weighting, a noise schedule induces an effective allocation across denoising problems, often fixed before informative noise levels are known. InfoNoise makes this allocation data-adaptive by estimating a conditional-entropy-rate profile from denoising losses during training, without auxiliary models or offline search. Through I--MMSE, this profile identifies where noisy observations rapidly reduce uncertainty about the clean sample and guides adaptation of the training noise distribution. It changes only this distribution, keeping the objective, weighting, and parameterization fixed. On image benchmarks, where schedules have been extensively tuned, InfoNoise matches or slightly exceeds strong baselines and can reach the same quality with fewer updates. On representation, sequence, and modality shifts, including DNA and language generation, InfoNoise improves over fixed and adaptive baselines and reaches target quality with up to $3\times$ less training compute. These results establish the conditional-entropy-rate profile as the data-dependent target for noise schedule design and make online adaptation a practical alternative to manual schedule search.
Agentic DDQN-Based Scheduling for Licensed and Unlicensed Band Allocation in Sidelink Networks
Department of Electronics and Electrical Engineering（电子与电气工程系）；Institute of Communications Engineering（通讯工程研究所）；National Yang Ming Chiao Tung University（阳明交通大学）；Research Center for Information Technology Innovation（信息技术创新研究中心）；Academia Sinica（台湾“中央研究院”）；Bradley Department of Electrical and Computer Engineering（电气与计算机工程系）
A Monotone Approximate Dynamic Programming Approach for the Stochastic Scheduling, Allocation, and Inventory Replenishment Problem: Applications to Drone and Electric Vehicle Battery Swap Stations
Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms
Virginia Tech（弗吉尼亚理工大学）；University of Colorado Colorado Springs（科罗拉多大学科罗拉多斯普林斯分校）；Ericsson Research（爱立信研究院）；Princeton University（普林斯顿大学）；University of Oulu（奥卢大学）
Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling
Nanyang Technological University（南洋理工大学）；Singapore Management University（新加坡管理大学）；Institute of Marine Science and Technology, Shandong University（山东大学海洋科学与技术研究院）；Eindhoven University of Technology（埃因霍温理工大学）
Timeliness-Oriented Scheduling and Resource Allocation in Multi-Region Collaborative Perception
School of Electronic and Information Engineering, Beijing Jiaotong University（北京交通大学电子与信息工程学院）；Beijing National Research Center for Information Science and Technology, Department of Electronic Engineering, Tsinghua University（北京信息科学与技术国家研究中心，清华大学电子工程系）
Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approach
Shanghai University（上海大学）；Commonwealth Scientific and Industrial Research Organization (CSIRO)（英联邦科学与工业研究组织）；Fudan University（复旦大学）；Xidian University（西安电子科技大学）
