Publications

Fast Continuous and Integer L-shaped Heuristics Through Supervised Learning
Eric Larsen
Emma Frejinger
Bernard Gendron
Andrea Lodi
We propose a methodology at the nexus of operations research and machine learning (ML) leveraging generic approximators available from ML to… (voir plus) accelerate the solution of mixed-integer linear two-stage stochastic programs. We aim at solving problems where the second stage is highly demanding. Our core idea is to gain large reductions in online solution time while incurring small reductions in first-stage solution accuracy by substituting the exact second-stage solutions with fast, yet accurate supervised ML predictions. This upfront investment in ML would be justified when similar problems are solved repeatedly over time, for example, in transport planning related to fleet management, routing and container yard management. Our numerical results focus on the problem class seminally addressed with the integer and continuous L-shaped cuts. Our extensive empirical analysis is grounded in standardized families of problems derived from stochastic server location (SSLP) and stochastic multi knapsack (SMKP) problems available in the literature. The proposed method can solve the hardest instances of SSLP in less than 9% of the time it takes the state-of-the-art exact method, and in the case of SMKP the same figure is 20%. Average optimality gaps are in most cases less than 0.1%.
2023-12-31
INFORMS Journal on Computing (publié)
doi.org
arxiv.org
Findings of the 2nd Shared Task on Multi-lingual Multi-task Information Retrieval at MRL 2024
Francesco Tinner
Raghav Mantri
Mammad Hajili
Chiamaka Ijeoma Chukwuneke
Dylan Massey
Benjamin A. Ajibade
Bilge Kocak
Abolade Dawud
Jonathan Atala
Hale Sirin
Kayode Olaleye
Anar Rzayev
David Ifeoluwa Adelani
Duygu Ataman
Large language models (LLMs) demonstrate exceptional proficiency in both the comprehension and generation of textual data, particularly in E… (voir plus)nglish, a language for which extensive public benchmarks have been established across a wide range of natural language processing (NLP) tasks. Nonetheless, their performance in multilingual contexts and specialized domains remains less rigorously validated, raising questions about their reliability and generalizability across linguistically diverse and domain-specific settings. The second edition of the Shared Task on Multilingual Multitask Information Retrieval aims to provide a comprehensive and inclusive multilingual evaluation benchmark which aids assessing the ability of multilingual LLMs to capture logical, factual, or causal relationships within lengthy text contexts and generate language under sparse settings, particularly in scenarios with under-resourced languages. The shared task consists of two subtasks crucial to information retrieval: Named entity recognition (NER) and reading comprehension (RC), in 7 data-scarce languages: Azerbaijani, Swiss German, Turkish and , which previously lacked annotated resources in information retrieval tasks. This year specifally focus on the multiple-choice question answering evaluation setting which provides a more objective setting for comparing different methods across languages.
2023-12-31
MRL (publié)
doi.org
Findings of the Association for Computational Linguistics: NAACL 2024, Mexico City, Mexico, June 16-21, 2024
Mohamed Abdalla
Gavin Abercrombie
David Ifeoluwa Adelani
Rodrigo Agerri
Zeljko Agic
Eneko Agirre
Monica Agrawal
Wasi Uddin Ahmad
James Allan
Aijun An
Antonios Anasta-sopoulos
Mark Anderson
Jacob Andreas
Marianna Apidianaki
Alessio Palmero
Yuki Aprosio
Ehsaneddin Arase
Giuseppe Asgari
Wilker Attardi
Aziz JinYeong … (voir 480 de plus)
Timothy Bak
Mohamad Hardyman Baldwin
Pierpaolo Barawi
Ali Basile
Ja-smijn Basirat
Timo Bastings
Gábor Baumann
Eyal Bella
Farah Ben-David
Luciana Benamara
Benotti Yevgeni
Brijesh Berzak
Federico Bhatt
Chris Bianchi
Lidong Biemann
Alexandra Bing
Birch Eduardo
Gemma Blanco
Aurélien Boleda
Florian Bossard
Leonid Boudin
Ronan Boytsov
Pavel Le Bras
Chris Braslavski
Eleftheria Brew
Thomas Briakou
Emanuele Brochhagen
Wray Buglia-rello
Buntine Elena
Aoife Cabrio
Ruken Cahill
Jose Cakici
Marie Camacho-Collados
Pengfei Candito
Ziqiang Cao
Dallas Cao
Paula Card
Tommaso Carvalho
Andrew Caselli
Tanmoy Cattle
Ilias Chakrabor-ty
Angel X Chalkidis
Ching-Yun Chang
Snigdha Chang
Chen Chaturvedi
Kehai Chen
Long Chen
Lu Chen
Muhao Chen
Wei Chen
Wenhu Chen
Wenliang Chen
Xiang Chen
Yidong Chen
Yun-Nung Chen
Zhiyu Chen
Zhuang Chen
Hao Chen
Yu Cheng
Colin Cheng
Cherry Hai
Eunsol Leong Chieu
Leshem Choi
Monojit Choshen
Christos Choudhury
Yi-Ling Christodoulopou-los
Stephen Chung
Vincent Clark
Simone Claveau
John M Conia
Caio Filippo Conroy
Mathias Corro
Leyang Creutz
Aron Cui
Anna E Culotta
Amanda Cercas Currey
Curry Raj
Daniel Dabre
Cristian Dakota
Verna Danescu-Niculescu-Mizil
Budhaditya Dankers
Deb Vera
Zhenyun Demberg
Li Deng
Ruihai Dong
Antoine Dong
Eduard Doucet
Nan Dragut
Kevin Duan
Greg Duh
Ondrej Durrett
Tomasz Dusek
Dwojak Julian Martin
Asif Eisenschlos
Yanai Ekbal
Cristina Elazar
Luis España-Bonet
Espinosa-Anke Allyson
Kilian Ettinger
Evang Alexander
Agnieszka Fabbri
Meng Falenska
Marcello Fang
Hao Federico
Anna Fei
Feldman Naomi
Fuli Feldman
Xiaocheng Feng
Yansong Feng
Eric Feng
Francis Le Ferrand
Eli-sabetta Ferraro
Simone Fersini
Mark Filice
Mark Finlayson
Jennifer Fishel
Annemarie Foster
Friedrich Matthias
Zhe Gallé
Siddhant Gan
Judith Garg
Kallirroi Gaspers
Alborz Georgila
Geramifard Luke
Mor Gessler
Abbas Geva
Sahar Ghaddar
Filip Ghannay
Mario Ginter
Tejas Giulianelli
Sharon Gokhale
Rob Goldwater
Kyle van der Goot
Tanya Gorman
Jia-Chen Goyal
Qing-Wei Gu
Frank Gu
Lin Guerin
Honglei Gui
Qipeng Guo
Vivek Guo
Gupta Thanh-Le
Nizar Ha
Ivan Habash
Barry Habernal
Xianpei Haddow
Daniel Han
Peter Hardt
Di Hase
Michael He
Behnam Heck
Peter Hedayatnia
Daniel Heeman
Jack Hershcovich
Ryuichiro Hes-sel
Julia Higashinaka
Enamul Hockenmaier
Andreas Hoque
Yufang Hotho
Hou Dirk
Kristen Hovy
Di Howell
Xuming Hu
Fei Hu
Jie Huang
Lifu Huang
Peijie Huang
Shaohan Huang
Shujian Huang
Xuanjing Huang
Zhenzhen Huang
Mika Huang
Hämäläinen Kentaro
Inui Kokil
Hyeju Jaidka
Mustafa Jang
Yangfeng Jarrar
Lifeng Ji
Mali Jin
Qin Jin
Richard Jin
David Johansson
Preethi Jurgens
Jyothi Ehsan
Diptesh Kamalloo
S. Kanojia
Sarvnaz Kar
Pei Karimi
Daniel Ke
So-pan Khashabi
Tushar Khosla
Hyounghun Khot
Jin-Dong Kim
Joo-Kyung Kim
Taeuk Kim
Kim Roman
Rebecca Klinger
Ivan Knowles
Ekaterina Kobyzev
Philipp Kochmar
Koehn Mamoru
Rik Komachi
Lingpeng Koncel-Kedziorski
Julia Kong
Amrith Kreutzer
Kal-pesh Krishna
Udo Krishna
Artur Kruschwitz
Adhiguna Kulmizev
Kuncoro Wai
Gerasimos Lam
Mirella Lampouras
Staffan Lapata
Mark Larsson
Ivano Last
Lauriola Thu
Dong-Ho Le
Hwanhee Lee
Jinhyuk Lee
Mark G Lee
SangKeun Lee
Oliver Lee
Heather Le-mon
Piyawat Lent
Gina-Anne Lertvittayakumjorn
Miryam Levow
Bing de Lhoneux
Chuyuan Li
Dong Li
Jing Li
Junhui Li
Juntao Li
Li Li
Peng Li
Piji Li
Sujian Li
Li Tao
Wenjie Li
Xin Li
Yongbin Li
Yu Li
Yufei Li
Zhifei Li
Constantine Li
Chenghua Lignos
Hongyu Lin
Robert Lin
Bing Litschko
H. Liu
Kang Liu
Ming Liu
Qianying Liu
Tin-gwen Liu
Xuebo Liu
Yang Liu
Zhiyuan Liu
Zoey Liu
Ximing Liu
Anh Tuan Lu
Luu Chenyang
Lyu Ji
Jing Ma
Ruotian Ma
Xiaojuan Ma
Aman Ma
Harish Tayyar Madaan
Andrea Madabushi
Navonil Ma-dotto
Prodromos Majumder
Shervin Malakasiotis
Yuning Malmasi
Kelly Mao
Vukosi Marchi-sio
Stella Marivate
Lara J Markantonatou
Bruno Martin
Yuval Martins
Sérgio Marton
Yuji Matos
Julian Matsumoto
Bryan McAuley
Ryan McCann
Kathleen McDonald
McKeown Mahnoosh
Yuxian Mehrabani
Samuel Meng
Timothee Mensah
Margot Mickus
Simon Mieskes
Yasuhide Mille
Makoto Miura
Daichi Miwa
David R Mochihashi
Lili Mortensen
Kha-lil Mou
Benjamin Mrini
Philippe Muller
Smaranda Muller
Rudra Muresan
Thomas Murthy
Müller Max
Müller-Eberstein Maria
Nona Nadejde
Mikio Naderi
Hideki Nakano
Linyong Nakayama
Nan
Franco Maria
Tapas Nardini
Mark-Jan Nayak
Isar Nederhof
Mariana Nejadgholi
Dat Quoc Neves
Nguyen Le-Minh
Thien Huu Nguyen
Vahid Nguyen
Partovi Nia
Jan Niehues
Qiang Ning
Maciej Ogrodniczuk
Alice Oh
Naoaki Okazaki
Manabu Okumura
Matan Orbach
Nedjma Ou-sidhoum
Vasile Pais
Nikolaos Pappas
Joonsuk Park
Yannick Parmentier
Prasannan Parthasarathi
Lucia Passaro
Ramakanth Pasunuru
Siddharth Patwardhan
Hao Peng
Lis Pereira
Laura Perez-Beltrachini
Maxime Peyrard
Jonas Pfeiffer
Bryan A. Plummer
Maja Popovic
Soujanya Poria
Daniel Preotiuc-Pietro
Emily Prud'hommeaux
Vikram Pudi
Peng Qian
Tieyun Qian
Deepak Ramachandran
Carlos Ramisch
Leonardo Ranaldi
Sudha Rao
Shauli Ravfogel
Marek Rei
Leonardo F. R. Ribeiro
Oleg Rokhlenko
Salvatore Romeo
Joseph Le Roux
Alla Rozov-skaya
Terry Ruas
Raphael Rubino
Ivan Vladimir Meza Ruiz
Maria Ryskina
Hassan Sajjad
Shubhra Kanti
Karmaker Santu
Maarten Sap
Naomi Saphra
Asad B. Sayeed
Dominik Schlechtweg
Viktor Schlegel
Natalie Schluter
Nathan Schneider
Hinrich Schuetze
H. Schwartz
Jingbo Shang
Vasu Sharma
Tianze Shi
Mohammad Shoeybi
Lei Shu
Melanie Siegel Maneesh
Kumar Singh
Pranaydeep Singh
Sunayana Sitaram
Kevin Small
Luca Soldaini
Aina Garí Soler
Wei Song
Xingyi Song
Yan Song
Jeffrey S. Sorensen
Aitor Soroa
Jacopo Staiano
Efstathios Stamatatos
Gabriel Stanovsky
Shane Steinert-Threlkeld
Jannik Strötgen
Sara Stymne
Jinsong Su
Saku Sugawara
Alessandro Suglia
Aixin Sun
Cheng-jie Sun
Kai Sun
György Szarvas
Víctor M. Sánchez-Cartagena
Gözde Gül ¸Sahin
Zeerak Talat
Chenhao Tan
Hao Tan
Tianyi Tang
Jesse Thomason
Brian Thompson
Yuanhe Tian
Zhiliang Tian
Amalia Todirascu
Sara Tonelli
Paolo Torroni
Kristina Toutanova
Amine xv Trabelsi
Trang Tran
David R. Traum
Kewei Tu
Martin Tutek
Ana Sabina Uban
Takehito Utsuro
Olga Vechtomova
Yannick Versley
Karin M. Verspoor
David Vilar
David Vilares 0001
Serena Villa-ta
Esaú Villatoro-Tello
Thuy Vu
Ivan Vuli´c
Fei Xia
Tong Xiao
Bo Xu
Huijuan Xu
Nianwen Xue
S. Yadav
Hang Yan
Rui Yan
Min Yang
Wei Yang
Yezhou Yang
Yi Yang
Zhenglu Yang
Jin-Ge Yao
Wei Ye
Yongjing Yin
Naoki Yoshinaga
Koichiro Yoshino
Jianfei Yu
Juntao Yu Mo
Yu Manzil Zaheer
Fabio Massimo Zanzotto
Weixin Zeng
Luke Zettlemoyer
Biao Zhang
Chen Zhang
Crystina Zhang
Jiajun Zhang
Jingyi Zhang
Justine Zhang
Meishan Zhang
Ningyu Zhang
Shaolei Zhang
Sheng Zhang
Shiyue Zhang
Shuai Zhang
Shuo Zhang
Wei Zhang
Yang Zhang
Zhe Zhang
Jieyu Zhao
Shiwan Zhao
Hai-Tao Zheng
Zaixiang Zheng
Jie Zhou
Yi Zhou
Xiaodan Zhu
2023-12-31
NAACL-HLT (Findings) (publié)
dblp.uni-trier.de
Fisher Flow Matching for Generative Modeling over Discrete Data
Oscar Davis
Samuel Kessler
Mircea Petrache
.Ismail .Ilkan Ceylan
Michael M. Bronstein
Avishek Bose
Generative modeling over discrete data has recently seen numerous success stories, with applications spanning language modeling, biological … (voir plus)sequence design, and graph-structured molecular data. The predominant generative modeling paradigm for discrete data is still autoregressive, with more recent alternatives based on diffusion or flow-matching falling short of their impressive performance in continuous data settings, such as image or video generation. In this work, we introduce Fisher-Flow, a novel flow-matching model for discrete data. Fisher-Flow takes a manifestly geometric perspective by considering categorical distributions over discrete data as points residing on a statistical manifold equipped with its natural Riemannian metric: the
2023-12-31
NeurIPS (publié)
doi.org
arxiv.org
Fourier neural operator for real-time simulation of 3D dynamic urban microclimate
Wenhui Peng
Shaoxiang Qin
Senwen Yang
Jianchun Wang
Xue Liu
Liangzhu (Leon) Wang
Global urbanization has underscored the significance of urban microclimates for human comfort, health, and building/urban energy efficiency.… (voir plus) They profoundly influence building design and urban planning as major environmental impacts. Understanding local microclimates is essential for cities to prepare for climate change and effectively implement resilience measures. However, analyzing urban microclimates requires considering a complex array of outdoor parameters within computational domains at the city scale over a longer period than indoors. As a result, numerical methods like Computational Fluid Dynamics (CFD) become computationally expensive when evaluating the impact of urban microclimates. The rise of deep learning techniques has opened new opportunities for accelerating the modeling of complex non-linear interactions and system dynamics. Recently, the Fourier Neural Operator (FNO) has been shown to be very promising in accelerating solving the Partial Differential Equations (PDEs) and modeling fluid dynamic systems. In this work, we apply the FNO network for real-time three-dimensional (3D) urban wind field simulation. The training and testing data are generated from CFD simulation of the urban area, based on the semi-Lagrangian approach and fractional stepping method to simulate urban microclimate features for modeling large-scale urban problems. Numerical experiments show that the FNO model can accurately reconstruct the instantaneous spatial velocity field. We further evaluate the trained FNO model on unseen data with different wind directions, and the results show that the FNO model can generalize well on different wind directions. More importantly, the FNO approach can make predictions within milliseconds on the graphics processing unit, making real-time simulation of 3D dynamic urban microclimate possible.
2023-12-31
Building and Environment (publié)
doi.org
arxiv.org
Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling
Qidong Yang
Alex Hernández-García
Paula Harder
Venkatesh Ramesh
Prasanna Sattegeri
Daniela Szwarcman
Campbell D. Watson
David Rolnick
Climate simulations are essential in guiding our understanding of climate change and responding to its effects. However, it is computational… (voir plus)ly expensive to resolve complex climate processes at high spatial resolution. As one way to speed up climate simulations, neural networks have been used to downscale climate variables from fast-running low-resolution simulations, but high-resolution training data are often unobtainable or scarce, greatly limiting accuracy. In this work, we propose a downscaling method based on the Fourier neural operator. It trains with data of a small upsampling factor and then can zero-shot downscale its input to arbitrary unseen high resolution. Evaluated both on ERA5 climate model data and on the Navier-Stokes equation solution data, our downscaling model significantly outperforms state-of-the-art convolutional and generative adversarial downscaling models, both in standard single-resolution downscaling and in zero-shot generalization to higher upsampling factors. Furthermore, we show that our method also outperforms state-of-the-art data-driven partial differential equation solvers on Navier-Stokes equations. Overall, our work bridges the gap between simulation of a physical process and interpolation of low-resolution output, showing that it is possible to combine both approaches and significantly improve upon each other.
2023-12-31
Journal of Machine Learning Research (publié)
doi.org
arxiv.org
A framework for fair decision-making over time with time-invariant utilities
Andrea Lodi
Sriram Sankaranarayanan
Guanyi Wang
2023-12-31
Eur. J. Oper. Res. (publié)
doi.org
arxiv.org
From Representational Harms to Quality-of-Service Harms: A Case Study on Llama 2 Safety Safeguards
Khaoula Chehbouni
Megha Roshan
Emmanuel Ma
Futian Andrew Wei
Afaf Taïk
Jackie CK Cheung
Golnoosh Farnadi
2023-12-31
Association for Computational Linguistics (publié)
doi.org
arxiv.org
Game Theoretical Formulation for Residential Community Microgrid via Mean Field Theory: Proof of Concept
Mohamad Aziz
Hanane Dagdougui
Issmail ElHallaoui
Incentive-based demand response aggregators are widely recognized as a powerful strategy to increase the flexibility of residential communit… (voir plus)y MG (RCM) while allowing consumers’ assets to participate in the operation of the power system in critical peak times. RCM implementing demand response approaches are of high interest as collectively, they have a high impact on shaping the demand curve during peak time while providing a wide range of economic and technical benefits to consumers and utilities. The penetration of distributed energy resources such as battery energy storage and photovoltaic systems introduces additional flexibility to manage the community loads and increase revenue. This letter proposes a game theoretical formulation for an incentive-based residential community microgrid, where an incentive-based pricing mechanism is developed to encourage peak demand reduction and share the incentive demand curve with the residential community through the aggregator. The aggregator’s objective is to maximize the welfare of the residential community by finding the optimal community equilibrium electricity price. Each household communicates with each other and with the distributed system operator (DSO) through the aggregator and aims to minimize the local electricity cost.
2023-12-31
IEEE Control Systems Letters (publié)
doi.org
Gaussian-Sum Filter for Range-based 3D Relative Pose Estimation in the Presence of Ambiguities
Syed S. Ahmed
Mohammed A. Shalaby
Charles C. Cossette
Jerome Le Ny
James R. Forbes
Multi-robot systems must have the ability to accurately estimate relative states between robots in order to perform collaborative tasks, pos… (voir plus)sibly with no external aiding. Three-dimensional relative pose estimation using range measurements oftentimes suffers from a finite number of non-unique solutions, or ambiguities. This paper: 1) identifies and accurately estimates all possible ambiguities in 2D; 2) treats them as components of a Gaussian mixture model; and 3) presents a computationally-efficient estimator, in the form of a Gaussian-sum filter (GSF), to realize range-based relative pose estimation in an infrastructure-free, 3D, setup. This estimator is evaluated in simulation and experiment and is shown to avoid divergence to local minima induced by the ambiguous poses. Furthermore, the proposed GSF outperforms an extended Kalman filter, demonstrates similar performance to the computationally-demanding particle filter, and is shown to be consistent.
2023-12-31
IEEE Conference on Control Technology and Applications (publié)
doi.org
arxiv.org
Generative Active Learning for the Search of Small-Molecule Protein Binders
Maksym Korablyov
Cheng-Hao Liu
Moksh Jain
Almer Van Der Sloot
Éric Jolicoeur
Edward Ruediger
Andrei Nica
Emmanuel Bengio
Kostiantyn Lapchevskyi
Daniel St-Cyr
Doris Alexandra Schuetz
Victor Ion Butoi
Jarrid Rector-Brooks
Simon Blackburn
Leo Feng
Hadi Nekoei
Saikrishna Gottipati
Priyesh Vijayan
Prateek Gupta
Ladislav Rampasek … (voir 14 de plus)
Sasikanth Avancha
Pierre-Luc Bacon
William Hamilton
Brooks Paige
Sanchit Misra
Stanislaw Jastrzebski
Bharat Kaul
Doina Precup
José Miguel Hernández-Lobato
Marwin Segler
Michael Bronstein
Anne Marinier
Mike Tyers
Yoshua Bengio
Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exh… (voir plus)ibit a property of interest remains a significant challenge. We introduce LambdaZero, a generative active learning approach to search for synthesizable molecules. Powered by deep reinforcement learning, LambdaZero learns to search over the vast space of molecules to discover candidates with a desired property. We apply LambdaZero with molecular docking to design novel small molecules that inhibit the enzyme soluble Epoxide Hydrolase 2 (sEH), while enforcing constraints on synthesizability and drug-likeliness. LambdaZero provides an exponential speedup in terms of the number of calls to the expensive molecular docking oracle, and LambdaZero de novo designed molecules reach docking scores that would otherwise require the virtual screening of a hundred billion molecules. Importantly, LambdaZero discovers novel scaffolds of synthesizable, drug-like inhibitors for sEH. In in vitro experimental validation, a series of ligands from a generated quinazoline-based scaffold were synthesized, and the lead inhibitor N-(4,6-di(pyrrolidin-1-yl)quinazolin-2-yl)-N-methylbenzamide (UM0152893) displayed sub-micromolar enzyme inhibition of sEH.
2023-12-31
arXiv (prépublication)
doi.org
arxiv.org
Generative Adversarial Neural Networks for Realistic Stock Market Simulations
Badre Labiad
Abdelaziz Berrado
Loubna Benabbou
—Stock market simulations are widely used to create synthetic environments for testing trading strategies before deploying them to real-ti… (voir plus)me markets. However, the weak realism often found in these simulations presents a significant challenge. Improving the quality of stock market simulations could be facilitated by the availability of rich and granular real Limit Order Books (LOB) data. Unfortunately, access to LOB data is typically very limited. To address this issue, a framework based on Generative Adversarial Networks (GAN) is proposed to generate synthetic realistic LOB data. This generated data can then be utilized for simulating downstream decision-making tasks, such as testing trading strategies, conducting stress tests, and performing prediction tasks. To effectively tackle challenges related to the temporal and local dependencies inherent in LOB structures and to generate highly realistic data, the framework relies on a specific data representation and preprocessing scheme, transformers, and conditional Wasserstein GAN with gradient penalty. The framework is trained using the FI-2010 benchmark dataset and an ablation study is conducted to demonstrate the importance of each component of the proposed framework. Moreover, qualitative and quantitative metrics are proposed to assess the quality of the generated data. Experimental results indicate that the framework outperforms existing benchmarks in simulating realistic market conditions, thus demonstrating its effectiveness in generating synthetic LOB data for diverse downstream tasks.
2023-12-31
International Journal of Advanced Computer Science and Applications (publié)
doi.org