Publications

Generating Complex Question Decompositions in the Face of Distribution Shifts.
Kelvin Han
Claire Gardent
Marah Ihab Abdin
Jyoti Aneja
Hany Hassan Awadalla
Ammar Ahmed Awadallah
Ahmad Awan
Nguyen Bach
Amit Bahree
Arash Bakhtiari
Jianmin Bao
Harkirat Singh Behl
Alon Benhaim
Misha Bilenko
Johan Bjorck
Sébastien Bubeck
Martin Cai
Qin Cai
Vishrav Chaudhary
Dong Chen … (voir 342 de plus)
Weizhu Chen
Yen-Chun Chen 0001
Yi-ling Chen
Hao Cheng
Parul Chopra
Xiyang Dai
Matthew Dixon
Ronen Eldan
Victor Fragoso
Jianfeng Gao
Mei Gao
Min Gao
Amit Garg
Allison Del Giorno
Abhishek Goswami
S. Gunasekar
Emman Haider
Jun-heng Hao
Russell J. Hewett
Wen-Wei Hu
Jamie Huynh
Dan Iter
Sam Ade Jacobs
Mojan Javaheripi
Xin Jin
Nikos Karampatziakis
Piero Kauffmann
Mahoud Khademi
Dongwoo Kim
Young Jin Kim
Lev Kurilenko
James R. Lee
Yin Tat Lee
Yuanzhi Li
Yunsheng Li
Chen Liang
Lars Lidén
Xihui
Zeqi Lin
Ce Lin
Liyuan Liu
Mengchen Liu
Liu Weishung
Xiaodong Liu
Chong Liu
Piyush Luo
Ali Madan
David Mahmoudzadeh
Matt Majercak
Caio Mazzola
César Teodoro
Arindam Mendes
Hardik Mitra
Anh Modi
Brandon Nguyen
Norick Barun
Daniel Patra
Thomas Perez-Becker
Portet Reid
Heyang Pryzant
Marko Qin
Liliang Radmilac
Gustavo Ren
Corby de Rosa
Sambudha Rosset
Roy Olatunji
Olli Ruwase
Amin Saarikivi
Adil Saied
Michael Salim
Shital Santacroce
Ning Shah
Shang Hiteshi
Yelong Sharma
Swadheen Shen
Xia Shukla
Masahiro Song
Andrea Tanaka
Praneetha Tupini
Michael Wu
Bin Wyatt
Can Xiao
Jiahang Xu
Weijiang Xu
Jilong Xu
Sonali Xue
Fan Yadav
Jianwei Yang
Yifan Yang
Ziyi Yang
Donghan Yang
Yu Lu
Chenruidong Yuan
Cyril Zhang
Jianwen Zhang
Zhang
Li Lyna
Yi Zhang
Yue Zhang
Yunan Zhang 0001
Zhang Xiren
Zhou
Phi-3
Priyanka Agrawal
Chris Alberti
Fantine Huot
Joshua Maynez
Ji Ma
Kuzman Ganchev
Viraat Aryabumi
John Dang
Dwarak Talupuru
Saurabh Dash
David Cairuz
Hangyu Lin
Bharat Venkitesh
Madeline Smith
Jon Ander Campos
Yi Chern Tan
Kelly Marchisio
Max Bartolo
Sebastian Ruder
Acyr F. Locatelli
Julia Kreutzer
Nick Frosst
Aidan Gomez
Phil Blunsom
Marzieh Fadaee
Tom B. Brown
Benjamin Mann
Nick Ryder
Melanie Subbiah
Jared Kaplan
Prafulla Dhariwal
Arvind Neelakantan
Pranav Shyam
Girish Sastry
Amanda Askell
Sandhini Agarwal
Ariel Herbert-Voss
Gretchen Krueger
T. Henighan
Rewon Child
Aditya Ramesh
Daniel M. Ziegler
Jeffrey Wu
Clemens Winter
Chris Hesse
Mark Chen
Eric Sigler
Ma-teusz Litwin
Scott Gray
Benjamin Chess
J. Clark
Christopher Berner
Sam McCandlish
Alec Radford
Ilya Sutskever
Dario Amodei Gemma Team
Morgane Rivière
Shreya Pathak Pier
Giuseppe Sessa
Cassidy Hardin
Surya Bhupati-raju
L'eonard Hussenot
Thomas Mesnard
Bobak Shahriari
Alexandre Ramé
Johan Ferret
Peter Liu
Pouya Dehghani Tafti
Abe Friesen
Michelle Casbon
Sabela Ramos
Ravin Kumar
Charline Le Lan
Sammy Jerome
Anton Tsitsulin
Nino Vieillard
Piotr Stańczyk
Sertan Girgin
Nikola Momchev
Matt Hoffman
Shantanu Thakoor
Jean-Bastien Grill
Behnam Neyshabur
Olivier Bachem
Alanna Wal-ton
Aliaksei Severyn
Alicia Parrish
Aliya Ah-mad
Allen Hutchison
Alvin Abdagic
Amanda Carl
Amy Shen
Andy Brock
Andy Coenen
Anthony Laforge
Antonia Paterson
Ben Bastian
Bilal Piot
Boxi Wu
Brandon Royal
Charlie Chen
Chintu Kumar
Chris Perry
Christoper A. Welty
Christopher A. Choquette-Choo
Danila Sinopalnikov
David Wein-berger
Dimple Vijaykumar
Dominika Rogozi´nska
D. Herbison
Elisa Bandy
Emma Wang
Eric Noland
Erica Moreira
Evan Senter
Evgenii Elty-shev
Francesco Visin
Gabriel Rasskin
Gary Wei
Glenn Cameron
Gus Martins
Hadi Hashemi
Hanna Klimczak-Pluci´nska
Harleen Batra
Harsh Dhand
Ivan Nardini
Jacinda Mein
Jack Zhou
James Svens-son
Jeff Stanway
Jetha Chan
J. Zhou
Joana Carrasqueira
Joana Iljazi
Jocelyn Becker
Joe Fer-nandez
Joost Van Amersfoort
Josh Gordon
Josh Lipschultz
Joshua Newlan
Junsong Ji
Kareem Mo-hamed
Kartikeya Badola
Kat Black
Katie Mil-lican
Keelin McDonell
Kelvin Nguyen
Kiranbir Sodhia
Kish Greene
Lars Lowe Sjoesund
Lauren Usui
Laurent Sifre
L. Heuermann
Leti-cia Lago
Lilly McNealus
Livio Baldini
Soares Logan
Lucas Kilpatrick
Luciano Dixon
Martins Machel
Manvinder Reid
Mark Singh
Martin Görner Iverson
Mateo Wirth Mat Velloso
Matt Davi-dow
Matt Miller
Matthew Rahtz
Matthew Watson
Meg Risdal
Mehran Kazemi
Michael Moynihan
Ming Zhang
Minsuk Kahng
Minwoo Park
Mofi Rahman
Mohit Khatwani
Natalie Dao
Nenshad Bardoliwalla
N. Devanathan
Neta Dumai
Nilay Chauhan
O. Wahltinez
Pankil Botarda
Parker Barnes
Paul R. Barham
Paul Michel
Peng-chong Jin
Petko Georgiev
Phil Culliton
Pradeep Kup-pala
Ramona Comanescu
Ramona Merhej
Reena Jana
R. Rokni
Rishabh Agarwal
Ryan Mullins
Samaneh Saadat
S. M. Carthy
Sarah Cogan
Sarah Perrin
S'ebastien M. R. Arnold
Se-bastian Krause
Shengyang Dai
S. Garg
Shruti Sheth
S. Ronstrom
Susan Chan
Timothy Jordan
Bing Yu
Tom Eccles
Tom Hennigan
Tomas Kocisky
Tulsee Doshi
Vihan Jain
Vikas Yadav
Vilobh Meshram
Vishal Dharmadhikari
Warren Barkley
Wei Wei
Wenming Ye
Woohyun Han
Woosuk Kwon
Xiang Xu
Zhe Shen
Zhitao Gong
Zichuan Wei
Victor Cotruta
Phoebe Kirk
Anand Rao
Minh Giang
Ludovic Peran
Tris Brian Warkentin
Eli Collins
Joelle Barral
Zoubin Ghahramani
Raia Hadsell
D. Sculley
Jeanine Banks
Anca Dragan
2025-01-01
NAACL (Long Papers) (publié)
doi.org
A generative approach to LLM harmfulness detection with special red flag tokens
Sophie Xhonneux
David Dobre
Mehrnaz Mofakhami
Leo Schwinn
Gauthier Gidel
2025-01-01
arXiv.org (prépublication)
doi.org
arxiv.org
Graph Anomaly Detection in Time Series: A Survey.
Thi Kieu Khanh Ho
Ali Karami
Narges Armanfard
2025-01-01
IEEE Transactions on Pattern Analysis and Machine Intelligence (publié)
doi.org
arxiv.org
Hadamard product in deep learning: Introduction, Advances and Challenges.
Grigorios G Chrysos
Yongtao Wu
Razvan Pascanu
Philip Torr
Volkan Cevher
2025-01-01
IEEE Transactions on Pattern Analysis and Machine Intelligence (publié)
doi.org
arxiv.org
Hallucination Detox: Sensitivity Dropout (SenD) for Large Language Model Training
Shahrad Mohammadzadeh
Juan David Guerra
Marco Bonizzato
Reihaneh Rabbany
Golnoosh Farnadi
2025-01-01
ACL (1) (publié)
doi.org
arxiv.org
https://www.neuromodec.org/journal/4/2/NzBlvmDpUYspQQbvI4B Online Transcranial Random Noise Stimulation of the Right Temporoparietal Junction Acutely Modulates Human-Machine Social Interactions
Vincent Chamberland
Quentin Moreau
Lisane Moses
Gabriela Milanova
Guillaume Dumas
2025-01-01
Neuromodec Journal (publié)
doi.org
ICLR 2025 Workshop on Tackling Climate Change with Machine Learning: Data-Centric Approaches in ML for Climate Action
Konstantin Klemmer
Melissa Chapman
Lily Xu
Poon Kin Ho
Mélisande Teng
Patrick Emami
Yoshua Bengio
Climate change is one of the greatest problems society has ever faced, with increasingly severe consequences for humanity as natural disaste… (voir plus)rs multiply, sea levels rise, and ecosystems falter. While no silver bullet, machine learning can be an invaluable tool in fighting climate change via a wide array of applications and techniques, from designing smart electric grids to tracking greenhouse gas emissions through satellite imagery. These applications require algorithmic innovations in machine learning and close collaboration with diverse fields and practitioners. This workshop is intended as a forum for those in the global machine learning community who wish to help tackle climate change, and is further aimed to help foster cross-pollination between researchers in machine learning and experts in complementary climate-relevant fields. Building on our past workshops on this topic, this workshop particularly aims to explore data-centric ML approaches for climate action. Data-centric ML is not only a timely topic within the ICLR community, as analyzing and engineering (pre)training datasets becomes increasingly important, but holds specific challenges and opportunities in climate-related areas. We also want to take the opportunity of ICLR being hosted in Singapore to engage with local communities and shine a light on work that deploys, analyzes or critiques ML methods and their use for climate change adaptation and mitigation on the Asian continent.
2025-01-01
ICLR.cc/2025/Workshop_Proposals (publié)
openreview.net
openreview.net
An identification of models to help in the design of national strategies and policies to reduce greenhouse gas emissions.
Danielle Maia de Souza
Radhwane Boukelouha
Emma Frejinger
Catherine Morency
Normand Mousseau
Martin Trépanier
2025-01-01
Transportation Research Procedia (publié)
doi.org
An identification of models to help in the design of national strategies and policies to reduce greenhouse gas emissions.
Danielle Maia de Souza
Radhwane Boukelouha
Emma Frejinger
Catherine Morency
Normand Mousseau
Martin Trépanier
2025-01-01
Transportation Research Procedia (publié)
doi.org
Implicit Diffusion: Efficient Optimization through Stochastic Sampling
Pierre Marion
Anna Korba
Peter Bartlett
Mathieu Blondel
Valentin De Bortoli
Arnaud Doucet
Felipe Llinares-López
Courtney Paquette
Quentin Berthet
2025-01-01
AISTATS (publié)
doi.org
openreview.net
Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles
Xing Shen
Hengguan Huang
Brennan Nichyporuk
Tal Arbel
2025-01-01
IEEE Transactions on Medical Imaging (publié)
doi.org
arxiv.org
Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models
Yinlam Chow
Guy Tennenholtz
Izzeddin Gur
Vincent Zhuang
Bo Dai
Sridhar Thiagarajan
Craig Boutilier
Rishabh Agarwal
Aviral Kumar
Aleksandra Faust
Recent studies have indicated that effectively utilizing inference-time compute is crucial for attaining better performance from large langu… (voir plus)age models (LLMs). In this work, we propose a novel inference-aware fine-tuning paradigm, in which the model is fine-tuned in a manner that directly optimizes the performance of the inference-time strategy. We study this paradigm using the simple yet effective Best-of-N (BoN) inference strategy, in which a verifier selects the best out of a set of LLM-generated responses. We devise the first imitation learning and reinforcement learning~(RL) methods for BoN-aware fine-tuning, overcoming the challenging, non-differentiable argmax operator within BoN. We empirically demonstrate that our BoN-aware models implicitly learn a meta-strategy that interleaves best responses with more diverse responses that might be better suited to a test-time input -- a process reminiscent of the exploration-exploitation trade-off in RL. Our experiments demonstrate the effectiveness of BoN-aware fine-tuning in terms of improved performance and inference-time compute. In particular, we show that our methods improve the Bo32 performance of Gemma 2B on Hendrycks MATH from 26.8% to 30.8%, and pass@32 from 60.0% to 67.0%, as well as the pass@16 on HumanEval from 61.6% to 67.1%.
2025-01-01
ICLR (publié)
doi.org
arxiv.org