Recently there has been much work on selective sampling, an online active learning setting, in which algorithms work in rounds. On each round an algorithm receives an input and makes a prediction. Then, it can decide whether to query a label, and if so to update its model, otherwise the input is discarded. Most of this work is focused on the stationary case, where it is assumed that there is a fixed target model, and the performance of the algorithm is compared to a fixed model. However, in many real-world applications, such as spam prediction, the best target function may drift over time, or have shifts from time to time. We develop a novel selective sampling algorithm for the drifting setting, analyze it under no assumptions on the mechanism generating the sequence of instances, and derive new mistake bounds that depend on the amount of drift in the problem. Simulations on synthetic and real-world datasets demonstrate the superiority of our algorithms as a selective sampling algorithm in the drifting setting.
Control the Temperature: Selective Sampling for Diverse and High-Quality LLM Outputs
Language Technology Lab, University of Amsterdam（阿姆斯特丹大学语言技术实验室）；Computational Linguistics and Text Mining Lab, Vrije Universiteit Amsterdam（阿姆斯特丹自由大学计算语言学与文本挖掘实验室）
Q-Drift: Quantization-Aware Drift Correction for Diffusion Model Sampling
Department of Computer Science and Engineering, Seoul National University, Seoul, South Korea（计算机科学与工程系，首尔国立大学，韩国首尔）；School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland（计算机与通信科学学院，瑞士洛桑联邦理工学院）；Meta Reality Labs, Redmond, USA（Meta现实实验室，美国西雅图）
Adaptive Selective Sampling for Online Prediction with Experts
Eindhoven University of Technology（埃因霍温理工大学）；Eindhoven Artificial Intelligence Systems Institute (EAISI)（埃因霍温人工智能系统研究所（EAISI））；University College London（伦敦大学学院）；University of Amsterdam（阿姆斯特丹大学）
Margin-Based Regularization and Selective Sampling in Deep Neural Networks
Efi Arazi School of Computer Science, Interdisciplinary Center（埃菲·阿拉齐计算机科学学院，跨学科中心）；The Data Science Institute, Interdisciplinary Center（数据科学研究所，跨学科中心）
CoMemNet: A Continual Memory Network with Drift-Aware Sampling for Traffic Prediction
College of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学学院）；College of Computer Science and Technology, Zhejiang University of Technology（浙江工业大学计算机科学与技术学院）；School of Automation, Nanjing University of Science and Technology（南京理工大学自动化学院）
Selective Listening: Mechanism-Guided Control of Audio Influence in Large Audio-Language Models
College of Computer Science and Technology, National University of Defense Technology（国防科技大学计算机学院）；State Key Laboratory of Complex & Critical Software Environment（复杂与关键软件环境国家重点实验室）
DriftGuard: A Hierarchical Framework for Concept Drift Detection and Remediation in Supply Chain Forecasting
Department of Computer Science Engineering, Muffakham Jah College of Engineering and Technology（穆法卡姆·贾赫工程与技术学院计算机科学工程系）；Department of CSE, Shadan Women's College of Engineering and Technology（沙达恩女子工程与技术学院计算机科学与工程系）
