Publications

Simulating weighted automata over sequences and trees with transformers
Michael Rizvi-Martel
Maude Lizaire
Clara Lacroce
Guillaume Rabusseau
2024-04-18
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (publié)
doi.org
arxiv.org
Tackling the XAI Disagreement Problem with Regional Explanations
gabriel laberge
Yann Batiste Pequignot
Mario Marchand
Foutse Khomh
2024-04-18
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (publié)
proceedings.mlr.press
On the Privacy of Selection Mechanisms with Gaussian Noise
Jonathan Lebensold
Doina Precup
Borja Balle
Report Noisy Max and Above Threshold are two classical differentially private (DP) selection mechanisms. Their output is obtained by adding … (voir plus)noise to a sequence of low-sensitivity queries and reporting the identity of the query whose (noisy) answer satisfies a certain condition. Pure DP guarantees for these mechanisms are easy to obtain when Laplace noise is added to the queries. On the other hand, when instantiated using Gaussian noise, standard analyses only yield approximate DP guarantees despite the fact that the outputs of these mechanisms lie in a discrete space. In this work, we revisit the analysis of Report Noisy Max and Above Threshold with Gaussian noise and show that, under the additional assumption that the underlying queries are bounded, it is possible to provide pure ex-ante DP bounds for Report Noisy Max and pure ex-post DP bounds for Above Threshold. The resulting bounds are tight and depend on closed-form expressions that can be numerically evaluated using standard methods. Empirically we find these lead to tighter privacy accounting in the high privacy, low data regime. Further, we propose a simple privacy filter for composing pure ex-post DP guarantees, and use it to derive a fully adaptive Gaussian Sparse Vector Technique mechanism. Finally, we provide experiments on mobility and energy consumption datasets demonstrating that our Sparse Vector Technique is practically competitive with previous approaches and requires less hyper-parameter tuning.
2024-04-18
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (publié)
doi.org
arxiv.org
Weight-Sharing Regularization
Mehran Shakerinava
Motahareh Sohrabi
Siamak Ravanbakhsh
Simon Lacoste-Julien
2024-04-18
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (publié)
doi.org
arxiv.org
Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences
Shreya Shankar
J.D. Zamfirescu-Pereira
Bjorn Hartmann
Aditya G Parameswaran
Ian Arawjo
2024-04-18
ArXiv (prépublication)
doi.org
arxiv.org
Effects of gene dosage on cognitive ability: A function-based association study across brain and non-brain processes
Guillaume Huguet
Thomas Renne
Cécile Poulain
Alma Dubuc
Kuldeep Kumar
Sayeh Kazem
Worrawat Engchuan
Omar Shanta
Elise Douard
Catherine Proulx
Martineau Jean-Louis
Zohra Saci
Josephine Mollon
Laura Schultz
Emma E M Knowles
Simon R. Cox
David Porteous
Gail Davies
Paul Redmond
Sarah E. Harris … (voir 10 de plus)
Gunter Schumann
Guillaume Dumas
Aurélie Labbe
Zdenka Pausova
Tomas Paus
Stephen W Scherer
Jonathan Sebat
Laura Almasy
David C. Glahn
Sébastien Jacquemont
2024-04-17
bioRxiv (prépublication)
doi.org
Foundational Challenges in Assuring Alignment and Safety of Large Language Models
Usman Anwar
Abulhair Saparov
Javier Rando
Daniel Paleka
Miles Turpin
Peter Hase
Ekdeep Singh Lubana
Erik Jenner
Stephen Casper
Oliver Sourbut
Benjamin L. Edelman
Zhaowei Zhang
Mario Günther
Anton Korinek
Jose Hernandez-Orallo
Lewis Hammond
Eric J Bigelow
Alexander Pan
Lauro Langosco
Tomasz Korbak … (voir 18 de plus)
Heidi Zhang
Ruiqi Zhong
Sean 'o H'eigeartaigh
Gabriel Recchia
Giulio Corsi
Alan Chan
Markus Anderljung
Lilian Edwards
Yoshua Bengio
Danqi Chen
Samuel Albanie
Tegan Maharaj
Jakob Nicolaus Foerster
Florian Tramèr
He He
Atoosa Kasirzadeh
Yejin Choi
David Scott Krueger
This work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs). These challenges are o… (voir plus)rganized into three different categories: scientific understanding of LLMs, development and deployment methods, and sociotechnical challenges. Based on the identified challenges, we pose
2024-04-15
ArXiv (prépublication)
doi.org
arxiv.org
Government Interventions to Avert Future Catastrophic AI Risks
Yoshua Bengio
2024-04-15
Special Issue 5: Grappling With the Generative AI Revolution (publié)
doi.org
Improving microbial phylogeny with citizen science within a mass-market video game
Roman Sarrazin-Gendron
Parham Ghasemloo Gheidari
Alexander Butyaev
Timothy Keding
Eddie Cai
Jiayue Zheng
Renata Mutalova
Julien Mounthanyvong
Yuxue Zhu
Elena Nazarova
Chrisostomos Drogaris
Kornél Erhart
David Bélanger
Amélie Brouillette
Michael Bouffard
Gabriel Richard
Joshua Davidson
Randy Pitchford
Mathieu Falaise
Sébastien Caisse … (voir 14 de plus)
Vincent Fiset
Mathieu Blanchette
Steven Hebert
Daniel McDonald
Dan Hewitt
Rob Knight
Jonathan Huot
Attila Szantner
Seung Kim
Jérôme Waldispühl
Jonathan Moreau-Genest
David Najjab
Steve Prince
Ludger Saintélien
2024-04-15
Nature Biotechnology (publié)
doi.org
Affirmative safety: An approach to risk management for high-risk AI
Akash Wasil
Joshua Clymer
David Scott Krueger
Emily Dardaman
Simeon Campos
Evan Murphy
Prominent AI experts have suggested that companies developing high-risk AI systems should be required to show that such systems are safe bef… (voir plus)ore they can be developed or deployed. The goal of this paper is to expand on this idea and explore its implications for risk management. We argue that entities developing or deploying high-risk AI systems should be required to present evidence of affirmative safety: a proactive case that their activities keep risks below acceptable thresholds. We begin the paper by highlighting global security risks from AI that have been acknowledged by AI experts and world governments. Next, we briefly describe principles of risk management from other high-risk fields (e.g., nuclear safety). Then, we propose a risk management approach for advanced AI in which model developers must provide evidence that their activities keep certain risks below regulator-set thresholds. As a first step toward understanding what affirmative safety cases should include, we illustrate how certain kinds of technical evidence and operational evidence can support an affirmative safety case. In the technical section, we discuss behavioral evidence (evidence about model outputs), cognitive evidence (evidence about model internals), and developmental evidence (evidence about the training process). In the operational section, we offer examples of organizational practices that could contribute to affirmative safety cases: information security practices, safety culture, and emergency response capacity. Finally, we briefly compare our approach to the NIST AI Risk Management Framework. Overall, we hope our work contributes to ongoing discussions about national and global security risks posed by AI and regulatory approaches to address these risks.
2024-04-14
ArXiv (prépublication)
doi.org
arxiv.org
Affirmative safety: An approach to risk management for high-risk AI
Akash Wasil
Joshua Clymer
David Scott Krueger
Emily Dardaman
Simeon Campos
Evan Murphy
Prominent AI experts have suggested that companies developing high-risk AI systems should be required to show that such systems are safe bef… (voir plus)ore they can be developed or deployed. The goal of this paper is to expand on this idea and explore its implications for risk management. We argue that entities developing or deploying high-risk AI systems should be required to present evidence of affirmative safety: a proactive case that their activities keep risks below acceptable thresholds. We begin the paper by highlighting global security risks from AI that have been acknowledged by AI experts and world governments. Next, we briefly describe principles of risk management from other high-risk fields (e.g., nuclear safety). Then, we propose a risk management approach for advanced AI in which model developers must provide evidence that their activities keep certain risks below regulator-set thresholds. As a first step toward understanding what affirmative safety cases should include, we illustrate how certain kinds of technical evidence and operational evidence can support an affirmative safety case. In the technical section, we discuss behavioral evidence (evidence about model outputs), cognitive evidence (evidence about model internals), and developmental evidence (evidence about the training process). In the operational section, we offer examples of organizational practices that could contribute to affirmative safety cases: information security practices, safety culture, and emergency response capacity. Finally, we briefly compare our approach to the NIST AI Risk Management Framework. Overall, we hope our work contributes to ongoing discussions about national and global security risks posed by AI and regulatory approaches to address these risks.
2024-04-14
ArXiv (prépublication)
doi.org
arxiv.org
CryCeleb: A Speaker Verification Dataset Based on Infant Cry Sounds
David Budaghyan
Arsenii Gorin
Cem Subakan
Charles Onu
Doina Precup
This paper describes the Ubenwa CryCeleb dataset - a labeled collection of infant cries - and the accompanying CryCeleb 2023 task, which is … (voir plus)a public speaker verification challenge based on cry sounds. We released more than 6 hours of manually segmented cry sounds from 786 newborns for academic use, aiming to encourage research in infant cry analysis. The inaugural public competition attracted 59 participants, 11 of whom improved the baseline performance. The top-performing system achieved a significant improvement scoring 25.8% equal error rate, which is still far from the performance of state-of-the-art adult speaker verification systems. Therefore, we believe there is room for further research on this dataset, potentially extending beyond the verification task.
2024-04-14
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)
doi.org
arxiv.org