Learning-to-learn or meta-learning focuses on developing algorithms that leverage prior experience to quickly acquire new skills or adapt to novel environments. A crucial component of meta-learning is representation learning, which aims to construct data representations capable of transferring knowledge across multiple tasks—a critical advantage in data-scarce settings. We study how representation learning can improve the efficiency of bandit problems. We consider $T$ $d$-dimensional linear bandits that share a common low-dimensional linear representation. We provide provably fast, sample-efficient algorithms to address the two key problems in meta-learning: (1) learning a common set of features from multiple related bandit tasks and (2) transferring this knowledge to new, unseen bandit tasks. We validated the theoretical results through numerical experiments using real-world and synthetic datasets, comparing them against benchmark algorithms.

In recent work [1], we developed a distributed stochastic multi-arm contextual bandit algorithm to learn optimal actions when the contexts are unknown, and M agents work collaboratively under the coordination of a central server to minimize the total regret. In our model, the agents observe only the context distribution and the exact context is unknown to the agents. Such a situation arises, for instance, when the context itself is a noisy measurement or based on a prediction mechanism. By performing a feature vector transformation and by leveraging the UCB algorithm, we proposed a UCB algorithm for stochastic bandits with context distribution. In this paper, we test our algorithm on a real-world dataset and investigate the interactions between drugs and proteins. For this we perform a data pre-processing step to fit the model and we evaluated the performance of our algorithm for the drug-protein interaction study as compared to other benchmark algorithm. Furthermore, we present the results of biological experiments and draw inferences from our findings.

We study how representation learning can improve the learning efficiency of contextual bandit problems. We study the setting where we play T linear contextual bandits with dimension simultaneously, and these T bandit tasks collectively share a common linear representation with a dimensionality of r ≪ d. We present a new algorithm based on alternating projected gradient descent (GD) and minimization estimator to recover a low-rank feature matrix. We obtain constructive provable guarantees for our estimator that provide a lower bound on the required sample complexity and an upper bound on the iteration complexity (total number of iterations needed to achieve a certain error level). Using the proposed estimator, we present a multi-task learning algorithm for linear contextual bandits and prove the regret bound of our algorithm. We presented experiments and compared the performance of our algorithm against benchmark algorithms.

We study the federated stochastic multi-arm contex-tual bandits with unknown contexts, in which$M$agents face$M$different bandit problems and collaborate to learn. The communication model consists of a central server and$M$agents, and the agents share their estimates with the central server periodically to learn to choose optimal actions to minimize the total regret. We assume that the exact contexts are not observable, and the agents observe only a distribution of contexts. Such a situation arises, for instance, when the context itself is a noisy measurement or based on a prediction mechanism. Our goal is to develop a distributed and federated algorithm that facilitates collaborative learning among the agents to select a sequence of optimal actions to maximize the cumulative reward. By performing a feature vector transformation, we propose an elimination-based algorithm and prove the regret bound for linearly parametrized reward functions. Finally, we validated the performance of our algorithm and compared it with another baseline approach using numerical simulations on synthetic data and the real-world movielens dataset.

Evolutionary multi-objective optimisation has shown success in feature selection. However, existing methods often address these tasks independently, disregarding their potential interconnections and shared knowledge. On the other hand, evolutionary multitasking has been utilised to address multiple related tasks simultaneously and transfer common knowledge. However, most EMTL-based feature selection methods prioritize a single task, and treat it as the main task and the other tasks as auxiliary or secondary tasks. To overcome this limitation, we propose a novel multi-objective feature selection method based on EMT in this paper. The new method introduces a novel representation that consolidates the solutions of multiple interconnected feature selection tasks into a single solution. It enables these tasks to share a common population, thereby enhancing the effectiveness and efficiency of transferring common knowledge across them. In addition, a novel searching method is devised to facilitate the evolution of the population across multiple tasks, enabling effective knowledge transfer between them. Finally, a transformation method is introduced to transfer valuable genes among the solutions for multiple tasks, thereby enhancing the overall performance of the proposed algorithm when confronted with tasks characterised by distinct features. This method effectively addresses multiple feature selection tasks simultaneously, offering a comprehensive solution to the aforementioned issue. Compared with four single-task multi-objective feature selection methods and a state-of-the-art evolutionary multitasking-based feature selection method, the proposed method demonstrates superior feature selection performance across the majority of benchmark datasets.

MOTIVATION: The k-mer frequency in whole genome sequences provides researchers with an insightful perspective on genomic complexity, comparative genomics, metagenomics and phylogeny. The current k-mer counting tools are typically slow, and they require large memory and hard disk for assembled genome analysis. RESULTS: We propose a novel and ultra-fast k-mer counting algorithm, KCOSS, to fulfill k-mer counting mainly for assembled genomes with segmented Bloom filter, lock-free queue, lock-free thread pool and cuckoo hash table. We optimize running time and memory consumption by recycling memory blocks, merging multiple consecutive first-occurrence k-mers into C-read, and writing a set of C-reads to disk asynchronously. KCOSS was comparatively tested with Jellyfish2, CHTKC and KMC3 on seven assembled genomes and three sequencing datasets in running time, memory consumption, and hard disk occupation. The experimental results show that KCOSS counts k-mer with less memory and disk while having a shorter running time on assembled genomes. KCOSS can be used to calculate the k-mer frequency not only for assembled genomes but also for sequencing data. AVAILABILITYAND IMPLEMENTATION: The KCOSS software is implemented in C++. It is freely available on GitHub: https://github.com/kcoss-2021/KCOSS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multiobjective multitasking optimization (MTO), which is an emerging research topic in the field of evolutionary computation, was recently proposed. MTO aims to solve related multiobjective optimization problems at the same time via evolutionary algorithms. The key to MTO is the knowledge transfer based on sharing solutions across tasks. Notably, positive knowledge transfer has been shown to facilitate superior performance characteristics. However, how to find more valuable transferred solutions for the positive transfer has been scarcely explored. Keeping this in mind, we propose a new algorithm to solve MTO problems. In this article, if a transferred solution is nondominated in its target task, the transfer is positive transfer. Furthermore, neighbors of this positive-transfer solution will be selected as the transferred solutions in the next generation, since they are more likely to achieve the positive transfer. Numerical studies have been conducted on benchmark problems of MTO to verify the effectiveness of the proposed approach. Experimental results indicate that our proposed framework achieves competitive results compared with the state-of-the-art MTO frameworks.

Multiobjective multitasking optimization (MTO) is an emerging research topic in the field of evolutionary computation. In contrast to multiobjective optimization, MTO solves multiple optimization tasks simultaneously. MTO aims to improve the overall performance of multiple tasks through knowledge transfer among tasks. Recently, MTO has attracted the attention of many researchers, and several algorithms have been proposed in the literature. However, one of the crucial issues, finding useful knowledge, has been rarely studied. Keeping this in mind, this article proposes an MTO algorithm based on incremental learning (EMTIL). Specifically, the transferred solutions (the form of knowledge) will be selected by incremental classifiers, which are capable of finding valuable solutions for knowledge transfer. The training data are generated by the knowledge transfer at each generation. Furthermore, the search space of the tasks will be explored by the proposed mapping (among tasks) approach, which helps these tasks to escape from their local Pareto Fronts. Empirical studies have been conducted on 15 MTO problems to assess the effectiveness of EMTIL. The experimental results demonstrate that EMTIL works more effectively for MTO compared to the existing algorithms.