Publications

Multi-reservoir ESN-based prediction strategy for dynamic multi-objective optimization
Cuili Yang
Danlei Wang
Jian Tang
JunFei Qiao
Wen Yu
2024-01-01
Information Sciences (published)
doi.org
MVP: Minimal Viable Phrase for Long Text Understanding.
Louis Clouâtre
Amal Zouaq
Sarath Chandar
2024-01-01
International Conference on Language Resources and Evaluation (published)
dblp.uni-trier.de
Neural Semantic Surface Maps
Luca Morreale
Noam Aigerman
Vladimir Kim
Niloy J. Mitra
2024-01-01
Comput. Graph. Forum (published)
doi.org
arxiv.org
Do not trust what you trust: Miscalibration in Semi-supervised Learning
Shambhavi Mishra
Balamurali Murugesan
Ismail Ben Ayed
Marco Pedersoli
Jose Dolz
State-of-the-art semi-supervised learning (SSL) approaches rely on highly confident predictions to serve as pseudo-labels that guide the tra… (see more)ining on unlabeled samples. An inherent drawback of this strategy stems from the quality of the uncertainty estimates, as pseudo-labels are filtered only based on their degree of uncertainty, regardless of the correctness of their predictions. Thus, assessing and enhancing the uncertainty of network predictions is of paramount importance in the pseudo-labeling process. In this work, we empirically demonstrate that SSL methods based on pseudo-labels are significantly miscalibrated, and formally demonstrate the minimization of the min-entropy, a lower bound of the Shannon entropy, as a potential cause for miscalibration. To alleviate this issue, we integrate a simple penalty term, which enforces the logit distances of the predictions on unlabeled samples to remain low, preventing the network predictions to become overconfident. Comprehensive experiments on a variety of SSL image classification benchmarks demonstrate that the proposed solution systematically improves the calibration performance of relevant SSL models, while also enhancing their discriminative power, being an appealing addition to tackle SSL tasks.
2024-01-01
Trans. Mach. Learn. Res. (published)
doi.org
arxiv.org
NOx emissions prediction for MSWI process based on dynamic modular neural network
Haoshan Duan
Xi Meng
Jian Tang
JunFei Qiao
2024-01-01
Expert systems with applications (published)
doi.org
Online Measurement of Dioxin Emission in Solid Waste Incineration Using Fuzzy Broad Learning
Heng Xia
Jian Tang
Wen Yu
JunFei Qiao
Dioxin (DXN) is a persistent organic pollutant produced from municipal solid waste incineration (MSWI) processes. It is a crucial environmen… (see more)tal indicator to minimize emission concentration by using optimization control, but it is difficult to monitor in real time. Aiming at online soft-sensing of DXN emission, a novel fuzzy tree broad learning system (FTBLS) is proposed, which includes offline training and online measurement. In the offline training part, weighted k-means is presented to construct a typical sample pool for reduced learning costs of offline and online phases. Moreover, the novel FTBLS, which contains a feature mapping layer, enhance layer, and increment layer, by replacing the fuzzy decision tree with neurons applied to construct the offline model. In the online measurement part, recursive principal component analysis is used to monitor the time-varying characteristic of the MSWI process. To measure DXN emission, offline FTBLS is reused for normal samples; for drift samples, fast incremental learning is used for online updates. A DXN data from the actual MSWI process is employed to prove the usefulness of FTBLS, where the RMSE of training and testing data are 0.0099 and 0.0216, respectively. This result shows that FTBLS can effectively realize DXN online prediction.
2024-01-01
IEEE Transactions on Industrial Informatics (published)
doi.org
Open-Set Multivariate Time-Series Anomaly Detection
Thomas Lai
Thi Kieu Khanh Ho
Narges Armanfard
2024-01-01
ECAI (published)
doi.org
arxiv.org
Operational Research: methods and applications
Fotios Petropoulos
Gilbert Laporte
Emel Aktas
Sibel A. Alumur
Claudia Archetti
Hayriye Ayhan
Maria Battarra
Julia A. Bennell
Jean-Marie Bourjolly
John E. Boylan
Michèle Breton
David Canca
Laurent Charlin
Bo Chen
Cihan Tugrul Cicek
Louis Anthony Cox
Christine S.M. Currie
Erik Demeulemeester
Lei Ding
Stephen M. Disney … (see 62 more)
Matthias Ehrgott
Martin J. Eppler
Güneş Erdoğan
Bernard Fortz
L. Alberto Franco
Jens Frische
Salvatore Greco
Amanda J. Gregory
Raimo P. Hämäläinen
Willy Herroelen
Mike Hewitt
Jan Holmström
John N. Hooker
Tuğçe Işık
Jill Johnes
Bahar Y. Kara
Özlem Karsu
Katherine Kent
Charlotte Köhler
Martin Kunc
Yong-Hong Kuo
Judit Lienert
Adam N. Letchford
Janny Leung
Dong Li
Haitao Li
Ivana Ljubić
Andrea Lodi
Sebastián Lozano
Virginie Lurkin
Silvano Martello
Ian G. McHale
Gerald Midgley
John D.W. Morecroft
Akshay Mutha
Ceyda Oğuz
Sanja Petrovic
Ulrich Pferschy
Harilaos N. Psaraftis
Sam Rose
Lauri Saarinen
Said Salhi
Jing-Sheng Song
Dimitrios Sotiros
Kathryn E. Stecke
Arne K. Strauss
İstenç Tarhan
Clemens Thielen
Paolo Toth
Greet Vanden Berghe
Christos Vasilakis
Vikrant Vaze
Daniele Vigo
Kai Virtanen
Xun Wang
Rafał Weron
Leroy White
Tom Van Woensel
Mike Yearworth
E. Alper Yıldırım
Georges Zaccour
Xuying Zhao
Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a … (see more)diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes.
2024-01-01
J. Oper. Res. Soc. (published)
doi.org
arxiv.org
Optimal Zero-Shot Detector for Multi-Armed Attacks
Federica Granese
Marco Romanelli
Pablo Piantanida
This paper explores a scenario in which a malicious actor employs a multi-armed attack strategy to manipulate data samples, offering them va… (see more)rious avenues to introduce noise into the dataset. Our central objective is to protect the data by detecting any alterations to the input. We approach this defensive strategy with utmost caution, operating in an environment where the defender possesses significantly less information compared to the attacker. Specifically, the defender is unable to utilize any data samples for training a defense model or verifying the integrity of the channel. Instead, the defender relies exclusively on a set of pre-existing detectors readily available"off the shelf". To tackle this challenge, we derive an innovative information-theoretic defense approach that optimally aggregates the decisions made by these detectors, eliminating the need for any training data. We further explore a practical use-case scenario for empirical evaluation, where the attacker possesses a pre-trained classifier and launches well-known adversarial attacks against it. Our experiments highlight the effectiveness of our proposed solution, even in scenarios that deviate from the optimal setup.
2024-01-01
AISTATS (published)
doi.org
arxiv.org
Parameter-Efficient Transfer Learning of Audio Spectrogram Transformers
Umberto Cappellazzo
Daniele Falavigna
Alessio Brutti
Mirco Ravanelli
The common modus operandi of fine-tuning large pre-trained Transformer models entails the adaptation of all their parameters (i.e., full fin… (see more)e-tuning). While achieving striking results on multiple tasks, this approach becomes unfeasible as the model size and the number of downstream tasks increase. In natural language processing and computer vision, parameter-efficient approaches like prompt-tuning and adapters have emerged as solid alternatives by fine-tuning only a small number of extra parameters, without sacrificing performance accuracy. For audio classification tasks, the Audio Spectrogram Transformer model shows impressive results. However, surprisingly, how to efficiently adapt it to several downstream tasks has not been tackled before. In this paper, we bridge this gap and present a detailed investigation of common parameter-efficient methods, revealing that adapters and LoRA consistently outperform the other methods across four benchmarks. Whereas adapters prove to be more efficient in few-shot learning settings, LoRA turns out to scale better as we increase the number of learnable parameters. We finally carry out ablation studies to find the best configuration for adapters and LoRA.
2024-01-01
MLSP (published)
doi.org
arxiv.org
PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design
Alexandre AGM Duval
Victor Schmidt
Santiago Miret
Yoshua Bengio
Alex Hernandez-Garcia
David Rolnick
openreview.net
PID Accelerated Temporal Difference Algorithms
Mark Bedaywi
Amin Rakhsha
Amir-massoud Farahmand
Long-horizon tasks, which have a large discount factor, pose a challenge for most conventional reinforcement learning (RL) algorithms. Algor… (see more)ithms such as Value Iteration and Temporal Difference (TD) learning have a slow convergence rate and become inefficient in these tasks. When the transition distributions are given, PID VI was recently introduced to accelerate the convergence of Value Iteration using ideas from control theory. Inspired by this, we introduce PID TD Learning and PID Q-Learning algorithms for the RL setting, in which only samples from the environment are available. We give a theoretical analysis of the convergence of PID TD Learning and its acceleration compared to the conventional TD Learning. We also introduce a method for adapting PID gains in the presence of noise and empirically verify its effectiveness.
2024-01-01
RLJ (published)
doi.org
arxiv.org