We focus on the dueling bandits problem, which has recently drawn significant attention due to its wide-ranging applications in online recommendation systems and the alignment of large language models (LLMs), considers an online preference learning scenario where the learner iteratively selects arms based on pairwise comparison feedback to infer user preferences. Two primary objectives are typically considered in dueling bandits: Regret Minimization (RM), which aims to improve the overall quality of selected arms over time, and Best Arm Identification (BAI), which seeks to efficiently identify the best item with minimal user feedback. For instance, RM is exemplified by the objective of consistently providing high-quality items, while BAI reduces the required human feedback by minimizing the number of necessary comparisons. Conventional research treats RM and BAI as two conflicting objectives, optimizing one at the expense of the other. In this paper, we propose a novel framework that demonstrates the near-consistency of RM and BAI in dueling bandits by reducing the BAI in dueling bandits into a sequential noisy identification problem. Based on our formulation, we propose a black-box reduction technique that transforms any RM algorithm into a BAI algorithm, and prove that such reduction with optimal RM algorithm achieves optimal sample complexity and nearly-optimal cumulative weak regret simultaneously. Our proposed algorithm acheives a nearly-optimal BAI sample complexity and attains a cumulative weak regret that is order-wise equivalent to the best-known result simultaneously. Experiments on both synthetic benchmarks and real-world online recommendation tasks validate the effectiveness of the proposed method, providing empirical evidences for our theoretical findings.

In many real-world applications, data are continuously accumulated in open environments, and new classes may emerge over time. For instance, in disease diagnosis, the prevalence of a certain disease may vary seasonally, and new diseases can also emerge. This paper investigates the problem of learning from unlabeled data stream where thelabel distribution evolves over time, and meanwhile,previously unseen new classes may appear. To handle the emerging new classes in online label shift, we first design a novel risk estimator by unbiased risk rewriting and mixture proportion estimation, which enables the identification of new class data. Subsequently, we employ the online ensemble paradigm for model updating to handle unknown distribution shifts. Moreover, we introduce the sketching and ensemble pruning mechanisms to improve the efficiency of the algorithm, making it more lightweight and practical. The proposed approach enjoys a theoretical guarantee of dynamic regret, ensuring its effectiveness in adapting to the unknown distribution shifts and the emergence of new classes in streaming data. Experiments on diverse benchmark datasets and two real-world applications demonstrate the effectiveness of the algorithm.

Conventional machine learning typically assume a fixed learning objective throughout the learning process.However, for real-world tasks in open and dynamic environments, objectives can change frequently.For example, in autonomous driving, a car has several default modes, but a user's concern for speed and fuel consumption varies depending on road conditions and personal needs.We formulate this problem as learning with varied objectives (LVO), where the goal is to optimize a dynamic weighted combination of multiple sub-objectives by sequentially selecting actions that incur different losses on these sub-objectives.We propose the VaRons algorithm, which estimates the action-wise performance on each sub-objective and adaptively selects decisions according to the dynamic requirements on different sub-objectives.Further, we extend our approach to cases involving contextual representations and propose the Con-VaRons algorithm, assuming parameterized linear structure that links contextual features to the main objective.Both the VaRons and ConVaRons are provably minimax optimal with respect to the time horizon 𝑇 , with ConVaRons showing better dependency with the number of sub-objectives 𝐾.Experiments on dynamic classifier and real-world cluster service allocation tasks validate the effectiveness of our methods and support our theoretical findings.

Learning with data streams has attracted much attention in recent decades. Conventional approaches typically assume that the feature and label of a data item can be timely observed at each round. In many real-world tasks, however, it often occurs that either the feature or the label is observed firstly while the other arrives with delay. For instance, in distributed learning systems, a central processor collects training data from different sub-processors to train a learning model, whereas the feature and label of certain data items can arrive asynchronously due to network latency. The problem of learning with asynchronous feature or label in streams encompasses many applications but still lacks sound solutions. In this article, we formulate the problem and propose a new approach to alleviate the negative effect of asynchronicity and mining asynchronous data streams. Our approach carefully exploits the timely arrived information and builds an online ensemble structure to adaptively reuse historical models and instances. We provide the theoretical guarantees of our approach and conduct extensive experiments to validate its effectiveness.

In many real-world applications, data are continuously accumulated within open environments. For instance, in disease diagnosis, the prevalence of diseases can vary across seasons, and new types of diseases can emerge. This paper investigates the problem of learning from unlabeled data where the label distribution evolves over time, and meanwhile, previously unseen new class appears in the data stream. To handle the new class in online label shift, we first design a novel risk estimator by unbiased risk rewriting and mixture proportion estimation. Subsequently, we employ the online ensemble paradigm for model updating to handle unknown distribution shifts. The proposed approach enjoys a theoretical guarantee of dynamic regret, ensuring its effectiveness in adapting to the changing label distribution and the presence of the new class in streams. Experiments conducted on diverse benchmark datasets and two real-world applications demonstrate the effectiveness of the proposed algorithm.

In plenty of real-world applications, data are collected in a streaming fashion, and their accurate labels are hard to obtain. For instance, in the environmental monitoring task, sensors are collecting the data all the time. Still, their labels are scarce because the labeling process requires human effort and can conceal annotation errors. This paper investigates the problem of learning with weakly labeled data streams, in which data are continuously collected, and only a limited subset of streaming data is labeled but potentially with noise. This setting is challenging and of great importance but rarely studied in the literature. When the data are constantly gathered with unknown noise on labels, it is quite challenging to design algorithms to obtain a well-generalized classifier. To address this difficulty, we propose a novel noise transition matrix estimation approach for data streams with scarce noisy labels by online anchor points identification. Based on that, we propose an adaptive learning algorithm for weakly labeled data streams via model reuse and effectively alleviate the negative influence of label noise with unlabeled data. Both theoretical analysis and extensive experiments justify and validate the effectiveness of the proposed approach.

In plenty of real-life tasks, strongly supervised information is hard to obtain, and thus weakly supervised learning has drawn considerable attention recently. This paper investigates the problem of learning from incomplete and inaccurate supervision, where only a limited subset of training data is labeled but potentially with noise. This setting is challenging and of great importance but rarely studied in the literature. We notice that in many applications, the limited labeled data are with certain structures, which paves us a way to design effective methods. Specifically, we observe that labeled data are usually with one-sided noise such as the bug detection task, where the identified buggy codes are indeed with defects, while codes checked many times or newly fixed may still have other flaws. Furthermore, when there occurs two-sided noise in the labeled data, we exploit the class-prior information of unlabeled data, which is typically available in practical tasks. We propose novel approaches for the incomplete and inaccurate supervision learning tasks and effectively alleviate the negative influence of label noise with the help of a vast number of unlabeled data. Both theoretical analysis and extensive experiments justify and validate the effectiveness of the proposed approaches.