Partial Bandit and Semi-Bandit: Making the Most Out of Scarce Users' Feedback

Recent works on Multi-Armed Bandits (MAB) and Combinatorial Multi-Armed Bandits (COM-MAB) show good results on a global accuracy metric. This can be achieved, in the case of recommender systems, with personalization. However, with a combinatorial online learning approach, personalization implies a large amount of user feedbacks. Such feedbacks can be hard to acquire when users need to be directly and frequently solicited. For a number of fields of activities undergoing the digitization of their business, online learning is unavoidable. Thus, a number of approaches allowing implicit user feedback retrieval have been implemented. Nevertheless, this implicit feedback can be misleading or inefficient for the agent's learning. Herein, we propose a novel approach reducing the number of explicit feedbacks required by Combinatorial Multi Armed bandit (COM-MAB) algorithms while providing similar levels of global accuracy and learning efficiency to classical competitive methods. In this paper we present a novel approach for considering user feedback and evaluate it using three distinct strategies. Despite a limited number of feedbacks returned by users (as low as 20% of the total), our approach obtains similar results to those of state of the art approaches

Byte Pair Encoding for Symbolic Music

When used with deep learning, the symbolic music modality is often coupled with language model architectures. To do so, the music needs to be tokenized, i.e. converted into a sequence of discrete tokens. This can be achieved by different approaches, as music can be composed of simultaneous tracks, of simultaneous notes with several attributes. Until now, the proposed tokenizations rely on small vocabularies of tokens describing the note attributes and time events, resulting in fairly long token sequences, and a sub-optimal use of the embedding space of language models. Recent research has put efforts on reducing the overall sequence length by merging embeddings or combining tokens. In this paper, we show that Byte Pair Encoding, a compression technique widely used for natural language, significantly decreases the sequence length while increasing the vocabulary size. By doing so, we leverage the embedding capabilities of such models with more expressive tokens, resulting in both better results and faster inference in generation and classification tasks. The source code is shared on Github, along with a companion website. Finally, BPE is directly implemented in MidiTok, allowing the reader to easily benefit from this method.Comment: EMNLP 2023, source code: https://github.com/Natooz/BPE-Symbolic-Musi

miditok: A Python package for MIDI file tokenization

Recent progress in natural language processing has been adapted to the symbolic music modality. Language models, such as Transformers, have been used with symbolic music for a variety of tasks among which music generation, modeling or transcription, with state-of-the-art performances. These models are beginning to be used in production products. To encode and decode music for the backbone model, they need to rely on tokenizers, whose role is to serialize music into sequences of distinct elements called tokens. MidiTok is an open-source library allowing to tokenize symbolic music with great flexibility and extended features. It features the most popular music tokenizations, under a unified API. It is made to be easily used and extensible for everyone.Comment: Updated and comprehensive report. Original ISMIR 2021 document at https://archives.ismir.net/ismir2021/latebreaking/000005.pd

High power diode laser modification of the wettability characteristics of an Al2O3/SiO2 based oxide compound for improved enamelling

High power diode laser (HPDL) surface melting of a thin layer of an amalgamated Al2O3/SiO2 oxide compound (AOC) resulted in significant changes in the wettability characteristics of the material. This behaviour was identified as being primarily due to: (i) the polar component of the AOC surface energy increasing after laser melting from 2.0 to 16.2 mJm-2, (ii) the surface roughness of the AOC decreasing from an Ra value of 25.9 to 6.3 μm after laser melting and (iii) the relative surface oxygen content of the AOC increasing by 36% after laser melting. HPDL melting was consequently identified as affecting a decrease in the enamel contact angle from 1180 prior to laser melting to 330 after laser melting; thus allowing the vitreous enamel to wet the AOC surface. The effective melt depth for such modifications was measured as being from 50 to 125 μm. The morphological, microstructural and wetting characteristics of the AOC were determined using optical microscopy, scanning electron microscopy, energy disperse X-ray analysis, X-ray diffraction techniques and wetting experiments by the sessile drop technique. The work has shown that laser radiation can be used to alter the wetting characteristics of the AOC only when surface melting occurs

Identification of the principal elements governing the wettability characteristics of ordinary Portland cement following high power diode laser surface treatment

The elements governing modifications to the wettability characteristics of ordinary Portland cement (OPC) following high power diode laser (HPDL) surface treatment have been identified. Changes in the contact angle, , and hence the wettability characteristics of the OPC after HPDL treatment were attributed to: reductions in the surface roughness of the OPC; the increase in the surface O2 content of the ceramic and the increase in the polar component of the surface energy, . What is more, the degree of influence exerted by each element has been qualitatively ascertained and was found to differ markedly. Surface energy, by way of microstructural changes, was found to be by far the most predominant element governing the wetting characteristics of the OPC. To a much lesser extent, surface O2 content, by way of process gas, was also seen to influence to a changes in the wettability characteristics of the OPC, whilst surface roughness was found to play a minor role in inducing changes in the wettability characteristics

Recommandation contextuelle de services : application à la recommandation d'évènements culturels dans la ville intelligente

Nowadays, Multi-Armed Bandit algorithms for context-aware recommendation systems are extensively studied. In order to meet challenges underlying this field of research, our works and contributions have been organised according to three research directions : 1) recommendation systems ; 2) Multi-Armed Bandit (MAB) and Contextual Multi-Armed Bandit algorithms (CMAB) ; 3) context.The first part of our contributions focuses on MAB and CMAB algorithms for recommendation. It particularly addresses diversification of recommendations for improving individual accuracy. The second part is focused on contextacquisition, on context reasoning for cultural events recommendation systems for Smart Cities, and on dynamic context enrichment for CMAB algorithms.Les algorithmes de bandits-manchots pour les systèmes de recommandation sensibles au contexte font aujourd’hui l’objet de nombreuses études. Afin de répondre aux enjeux de cette thématique, les contributions de cette thèse sont organisées autour de 3 axes : 1) les systèmes de recommandation ; 2) les algorithmes de bandits-manchots (contextuels et non contextuels) ; 3) le contexte. La première partie de nos contributions a porté sur les algorithmes de bandits-manchots pour la recommandation. Elle aborde la diversification des recommandations visant à améliorer la précision individuelle. La seconde partie a porté sur la capture de contexte, le raisonnement contextuel pour les systèmes de recommandation d’événements culturels dans la ville intelligente, et l’enrichissement dynamique de contexte pour les algorithmes de bandits-manchots contextuels

Context-aware recommendation systems for cultural events recommendation in Smart Cities

Les algorithmes de bandits-manchots pour les systèmes de recommandation sensibles au contexte font aujourd’hui l’objet de nombreuses études. Afin de répondre aux enjeux de cette thématique, les contributions de cette thèse sont organisées autour de 3 axes : 1) les systèmes de recommandation ; 2) les algorithmes de bandits-manchots (contextuels et non contextuels) ; 3) le contexte. La première partie de nos contributions a porté sur les algorithmes de bandits-manchots pour la recommandation. Elle aborde la diversification des recommandations visant à améliorer la précision individuelle. La seconde partie a porté sur la capture de contexte, le raisonnement contextuel pour les systèmes de recommandation d’événements culturels dans la ville intelligente, et l’enrichissement dynamique de contexte pour les algorithmes de bandits-manchots contextuels.Nowadays, Multi-Armed Bandit algorithms for context-aware recommendation systems are extensively studied. In order to meet challenges underlying this field of research, our works and contributions have been organised according to three research directions : 1) recommendation systems ; 2) Multi-Armed Bandit (MAB) and Contextual Multi-Armed Bandit algorithms (CMAB) ; 3) context.The first part of our contributions focuses on MAB and CMAB algorithms for recommendation. It particularly addresses diversification of recommendations for improving individual accuracy. The second part is focused on contextacquisition, on context reasoning for cultural events recommendation systems for Smart Cities, and on dynamic context enrichment for CMAB algorithms

Measuring the Energy Consumption of Massive Data Insertions: an energy consumption assessment of the PL/SQL FOR LOOP and FORALL methods

International audienc

Context Enhancement for Linear Contextual Multi-Armed Bandits

International audienc

A novel multi-objective medical feature selection compass method for binary classification

International audienceThe use of Artificial Intelligence in medical decision support systems has been widely studied. Since a medical decision is frequently the result of a multi-objective optimization problem, a popular challenge combining Artificial Intelligence and Medicine is Multi-Objective Feature Selection (MOFS). This article proposes a novel approach for MOFS applied to medical binary classification. It is built upon a Genetic Algorithm and a 3-Dimensional Compass that aims at guiding the search towards a desired trade-off between : Number of features, Accuracy and Area Under the ROC Curve (AUC). This method, the Genetic Algorithm with multi-objective Compass (GAwC), outperforms all other competitive genetic algorithm-based MOFS approaches on several real-world medical datasets. Moreover, by considering AUC as one of the objectives, GAwC guarantees the classification quality of the solution it provides thus making it a particularly interesting approach for medical problems where both healthy and ill patients should be accurately detected. Finally, GAwC is applied to a real-world medical classification problem and its results are discussed and justified both from a medical point of view and in terms of classification quality