Publications

Learning Control Barrier Functions and their application in Reinforcement Learning: A Survey
Maeva Guerrier
Hassan Fouad
Giovanni Beltrame
Reinforcement learning is a powerful technique for developing new robot behaviors. However, typical lack of safety guarantees constitutes a … (voir plus)hurdle for its practical application on real robots. To address this issue, safe reinforcement learning aims to incorporate safety considerations, enabling faster transfer to real robots and facilitating lifelong learning. One promising approach within safe reinforcement learning is the use of control barrier functions. These functions provide a framework to ensure that the system remains in a safe state during the learning process. However, synthesizing control barrier functions is not straightforward and often requires ample domain knowledge. This challenge motivates the exploration of data-driven methods for automatically defining control barrier functions, which is highly appealing. We conduct a comprehensive review of the existing literature on safe reinforcement learning using control barrier functions. Additionally, we investigate various techniques for automatically learning the Control Barrier Functions, aiming to enhance the safety and efficacy of Reinforcement Learning in practical robot applications.
2024-04-22
ArXiv (prépublication)
arxiv.org
arxiv.org
CKGConv: General Graph Convolution with Continuous Kernels
Liheng Ma
Soumyasundar Pal
Yitian Zhang
Jiaming Zhou
Yingxue Zhang
Mark Coates
The existing definitions of graph convolution, either from spatial or spectral perspectives, are inflexible and not unified. Defining a gene… (voir plus)ral convolution operator in the graph domain is challenging due to the lack of canonical coordinates, the presence of irregular structures, and the properties of graph symmetries. In this work, we propose a novel graph convolution framework by parameterizing the kernels as continuous functions of pseudo-coordinates derived via graph positional encoding. We name this Continuous Kernel Graph Convolution (CKGConv). Theoretically, we demonstrate that CKGConv is flexible and expressive. CKGConv encompasses many existing graph convolutions, and exhibits the same expressiveness as graph transformers in terms of distinguishing non-isomorphic graphs. Empirically, we show that CKGConv-based Networks outperform existing graph convolutional networks and perform comparably to the best graph transformers across a variety of graph datasets.
2024-04-21
ArXiv (prépublication)
arxiv.org
arxiv.org
Foliar spectra accurately distinguish most temperate tree species and show strong phylogenetic signal
Florence Blanchard
Anne Bruneau
Étienne Laliberté
2024-04-20
American-Eurasian journal of botany (publié)
doi.org
BACS: Background Aware Continual Semantic Segmentation
Mostafa ElAraby
Ali Harakeh
Liam Paull
Semantic segmentation plays a crucial role in enabling comprehensive scene understanding for robotic systems. However, generating annotation… (voir plus)s is challenging, requiring labels for every pixel in an image. In scenarios like autonomous driving, there's a need to progressively incorporate new classes as the operating environment of the deployed agent becomes more complex. For enhanced annotation efficiency, ideally, only pixels belonging to new classes would be annotated. This approach is known as Continual Semantic Segmentation (CSS). Besides the common problem of classical catastrophic forgetting in the continual learning setting, CSS suffers from the inherent ambiguity of the background, a phenomenon we refer to as the"background shift'', since pixels labeled as background could correspond to future classes (forward background shift) or previous classes (backward background shift). As a result, continual learning approaches tend to fail. This paper proposes a Backward Background Shift Detector (BACS) to detect previously observed classes based on their distance in the latent space from the foreground centroids of previous steps. Moreover, we propose a modified version of the cross-entropy loss function, incorporating the BACS detector to down-weight background pixels associated with formerly observed classes. To combat catastrophic forgetting, we employ masked feature distillation alongside dark experience replay. Additionally, our approach includes a transformer decoder capable of adjusting to new classes without necessitating an additional classification head. We validate BACS's superior performance over existing state-of-the-art methods on standard CSS benchmarks.
2024-04-19
ArXiv (prépublication)
arxiv.org
arxiv.org
Introducing v0.5 of the AI Safety Benchmark from MLCommons
Bertie Vidgen
Adarsh Agrawal
Ahmed M. Ahmed
Victor Akinwande
Namir Al-nuaimi
Najla Alfaraj
Elie Alhajjar
Lora Aroyo
Trupti Bavalatti
Borhane Blili-Hamelin
K. Bollacker
Rishi Bomassani
Marisa Ferrara Boston
Sim'eon Campos
Kal Chakra
Canyu Chen
Cody Coleman
Zacharie Delpierre Coudert
Leon Strømberg Derczynski
Debojyoti Dutta … (voir 77 de plus)
Ian Eisenberg
James R. Ezick
Heather Frase
Brian Fuller
Ram Gandikota
Agasthya Gangavarapu
Ananya Gangavarapu
James Gealy
Rajat Ghosh
James Goel
Usman Gohar
Sujata Goswami
Scott A. Hale
Wiebke Hutiri
Joseph Marvin Imperial
Surgan Jandial
Nicholas C. Judd
Felix Juefei-Xu
Foutse Khomh
Bhavya Kailkhura
Hannah Rose Kirk
Kevin Klyman
Chris Knotz
Michael Kuchnik
Shachi H. Kumar
Chris Lengerich
Bo Li
Zeyi Liao
Eileen Peters Long
Victor Lu
Yifan Mai
Priyanka Mary Mammen
Kelvin Manyeki
Sean McGregor
Virendra Mehta
Shafee Mohammed
Emanuel Moss
Lama Nachman
Dinesh Jinenhally Naganna
Amin Nikanjam
Besmira Nushi
Luis Oala
Iftach Orr
Alicia Parrish
Çigdem Patlak
William Pietri
Forough Poursabzi-Sangdeh
Eleonora Presani
Fabrizio Puletti
Paul Rottger
Saurav Sahay
Tim Santos
Nino Scherrer
Alice Schoenauer Sebag
Patrick Schramowski
Abolfazl Shahbazi
Vin Sharma
Xudong Shen
Vamsi Sistla
Leonard Tang
Davide Testuggine
Vithursan Thangarasa
Elizabeth A Watkins
Rebecca Weiss
Christoper A. Welty
Tyler Wilbers
Adina Williams
Carole-Jean Wu
Poonam Yadav
Xianjun Yang
Yi Zeng
Wenhui Zhang
Fedor Zhdanov
Jiacheng Zhu
Percy Liang
Peter Mattson
Joaquin Vanschoren
2024-04-18
ArXiv (prépublication)
arxiv.org
arxiv.org
Multi-phase black-hole feedback and a bright [CII] halo in a Lo-BAL quasar at $z\sim6.6$
Manuela Bischetti
Hyunseop Choi
Fabrizio Fiore
Chiara Feruglio
Stefano Carniani
Valentina D'odorico
Eduardo Banados
Huanqing Chen
Roberto Decarli
Simona Gallerani
Julie Hlavacek-larrondo
Samuel Lai
K. Leighly
Chiara Mazzucchelli
Laurence Perreault-Levasseur
Roberta Tripodi
Fabian Walter
Feige Wang
Jinyi Yang
Maria Vittoria Zanchettin … (voir 1 de plus)
Yongda Zhu
2024-04-18
ArXiv (prépublication)
arxiv.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)
arxiv.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
Many-Shot In-Context Learning
Rishabh Agarwal
Avi Singh
Lei M. Zhang
Bernd Bohnet
Stephanie Chan
Ankesh Anand
Zaheer Abbas
Azade Nova
John D. Co-Reyes
Eric Chu
Feryal M. P. Behbahani
Aleksandra Faust
Hugo Larochelle
Large language models (LLMs) excel at few-shot in-context learning (ICL) -- learning from a few examples provided in context at inference, w… (voir plus)ithout any weight updates. Newly expanded context windows allow us to investigate ICL with hundreds or thousands of examples -- the many-shot regime. Going from few-shot to many-shot, we observe significant performance gains across a wide variety of generative and discriminative tasks. While promising, many-shot ICL can be bottlenecked by the available amount of human-generated examples. To mitigate this limitation, we explore two new settings: Reinforced and Unsupervised ICL. Reinforced ICL uses model-generated chain-of-thought rationales in place of human examples. Unsupervised ICL removes rationales from the prompt altogether, and prompts the model only with domain-specific questions. We find that both Reinforced and Unsupervised ICL can be quite effective in the many-shot regime, particularly on complex reasoning tasks. Finally, we demonstrate that, unlike few-shot learning, many-shot learning is effective at overriding pretraining biases and can learn high-dimensional functions with numerical inputs. Our analysis also reveals the limitations of next-token prediction loss as an indicator of downstream ICL performance.
2024-04-17
ArXiv (prépublication)
arxiv.org
arxiv.org
All-in-one simulation-based inference
Manuel Gloeckler
Michael Deistler
Christian Dietrich Weilbach
Frank Wood
Jakob H. Macke
2024-04-15
ArXiv (prépublication)
arxiv.org
arxiv.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 Gunther
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 Foerster
Florian Tramer
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)
arxiv.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