Publications

A comparison of reinforcement learning frameworks for software testing tasks
Paulina Stevia Nouwou Mindom
Amin Nikanjam
Foutse Khomh
2023-08-24
Empirical Software Engineering (publié)
doi.org
arxiv.org
Multivariate Time-Series Anomaly Detection with Contaminated Data: Application to Physiological Signals
Thi Kieu Khanh Ho
Narges Armanfard
2023-08-24
ArXiv (prépublication)
doi.org
arxiv.org
Reinforcement Learning Informed Evolutionary Search for Autonomous Systems Testing
Dmytro Humeniuk
Foutse Khomh
Giuliano Antoniol
Evolutionary search-based techniques are commonly used for testing autonomous robotic systems. However, these approaches often rely on compu… (voir plus)tationally expensive simulator-based models for test scenario evaluation. To improve the computational efficiency of the search-based testing, we propose augmenting the evolutionary search (ES) with a reinforcement learning (RL) agent trained using surrogate rewards derived from domain knowledge. In our approach, known as RIGAA (Reinforcement learning Informed Genetic Algorithm for Autonomous systems testing), we first train an RL agent to learn useful constraints of the problem and then use it to produce a certain part of the initial population of the search algorithm. By incorporating an RL agent into the search process, we aim to guide the algorithm towards promising regions of the search space from the start, enabling more efficient exploration of the solution space. We evaluate RIGAA on two case studies: maze generation for an autonomous ant robot and road topology generation for an autonomous vehicle lane keeping assist system. In both case studies, RIGAA converges faster to fitter solutions and produces a better test suite (in terms of average test scenario fitness and diversity). RIGAA also outperforms the state-of-the-art tools for vehicle lane keeping assist system testing, such as AmbieGen and Frenetic.
2023-08-24
ArXiv (prépublication)
doi.org
arxiv.org
Speech Self-Supervised Representation Benchmarking: Are We Doing it Right?
Salah Zaiem
Youcef Kemiche
Titouan Parcollet
Slim Essid
Mirco Ravanelli
Self-supervised learning (SSL) has recently allowed leveraging large datasets of unlabeled speech signals to reach impressive performance on… (voir plus) speech tasks using only small amounts of annotated data. The high number of proposed approaches fostered the need and rise of extended benchmarks that evaluate their performance on a set of downstream tasks exploring various aspects of the speech signal. However, and while the number of considered tasks has been growing, most rely upon a single decoding architecture that maps the frozen SSL representations to the downstream labels. This work investigates the robustness of such benchmarking results to changes in the decoder architecture. Interestingly, it appears that varying the architecture of the downstream decoder leads to significant variations in the leaderboards of most tasks. Concerningly, our study reveals that benchmarking using limited decoders may cause a counterproductive increase in the sizes of the developed SSL models.
2023-08-20
INTERSPEECH 2023 (publié)
doi.org
arxiv.org
Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In the Game of Hanabi
Hadi Nekoei
Xutong Zhao
Janarthanan Rajendran
Miao Liu
Sarath Chandar Anbil Parthipan
Cooperative Multi-agent Reinforcement Learning (MARL) algorithms with Zero-Shot Coordination (ZSC) have gained significant attention in rece… (voir plus)nt years. ZSC refers to the ability of agents to coordinate zero-shot (without additional interaction experience) with independently trained agents. While ZSC is crucial for cooperative MARL agents, it might not be possible for complex tasks and changing environments. Agents also need to adapt and improve their performance with minimal interaction with other agents. In this work, we show empirically that state-of-the-art ZSC algorithms have poor performance when paired with agents trained with different learning methods, and they require millions of interaction samples to adapt to these new partners. To investigate this issue, we formally defined a framework based on a popular cooperative multi-agent game called Hanabi to evaluate the adaptability of MARL methods. In particular, we created a diverse set of pre-trained agents and defined a new metric called adaptation regret that measures the agent's ability to efficiently adapt and improve its coordination performance when paired with some held-out pool of partners on top of its ZSC performance. After evaluating several SOTA algorithms using our framework, our experiments reveal that naive Independent Q-Learning (IQL) agents in most cases adapt as quickly as the SOTA ZSC algorithm Off-Belief Learning (OBL). This finding raises an interesting research question: How to design MARL algorithms with high ZSC performance and capability of fast adaptation to unseen partners. As a first step, we studied the role of different hyper-parameters and design choices on the adaptability of current MARL algorithms. Our experiments show that two categories of hyper-parameters controlling the training data diversity and optimization process have a significant impact on the adaptability of Hanabi agents.
2023-08-20
ArXiv (prépublication)
doi.org
arxiv.org
Open, Closed, or Small Language Models for Text Classification?
Hao Yu
Zachary Yang
Kellin Pelrine
Jean-François Godbout
Reihaneh Rabbany
Recent advancements in large language models have demonstrated remarkable capabilities across various NLP tasks. But many questions remain, … (voir plus)including whether open-source models match closed ones, why these models excel or struggle with certain tasks, and what types of practical procedures can improve performance. We address these questions in the context of classification by evaluating three classes of models using eight datasets across three distinct tasks: named entity recognition, political party prediction, and misinformation detection. While larger LLMs often lead to improved performance, open-source models can rival their closed-source counterparts by fine-tuning. Moreover, supervised smaller models, like RoBERTa, can achieve similar or even greater performance in many datasets compared to generative LLMs. On the other hand, closed models maintain an advantage in hard tasks that demand the most generalizability. This study underscores the importance of model selection based on task requirements
2023-08-19
ArXiv (prépublication)
doi.org
arxiv.org
Pontomedullary junction as a reference for spinal cord cross-sectional area: validation across neck positions
Sandrine Bédard
Maxime Bouthillier
Julien Cohen-Adad
2023-08-19
Scientific Reports (publié)
doi.org
GTM-decon: guided-topic modeling of single-cell transcriptomes enables sub-cell-type and disease-subtype deconvolution of bulk transcriptomes
Lakshmipuram Seshadri Swapna
Michael Huang
Yue Li
2023-08-18
Genome Biology (publié)
doi.org
YORC: Yoruba Reading Comprehension dataset
Aremu Anuoluwapo
Jesujoba Oluwadara Alabi
David Ifeoluwa Adelani
In this paper, we create YORC: a new multi-choice Yoruba Reading Comprehension dataset that is based on Yoruba high-school reading comprehen… (voir plus)sion examination. We provide baseline results by performing cross-lingual transfer using existing English RACE dataset based on a pre-trained encoder-only model. Additionally, we provide results by prompting large language models (LLMs) like GPT-4.
2023-08-18
ArXiv (prépublication)
doi.org
arxiv.org
Age-related bias and artificial intelligence: a scoping review
Charlene H Chu
Simon Donato-Woodger
Shehroz S Khan
Rune Nyrup
Kathleen Leslie
Alexandra Lyn
Tianyu Shi
Andria Bianchi
Samira Abbasgholizadeh-Rahimi
Amanda Grenier
2023-08-17
Humanities and Social Sciences Communications (publié)
doi.org
Consciousness in Artificial Intelligence: Insights from the Science of Consciousness
Patrick Mark Butlin
R. Long
Eric Elmoznino
Yoshua Bengio
Jonathan C. P. Birch
Axel Constant
George Deane
S. Fleming
C. Frith
Xuanxiu Ji
Ryota Kanai
C. Klein
Grace W. Lindsay
Matthias Michel
Liad Mudrik
Megan A. K. Peters
Eric Schwitzgebel
Jonathan Simon
Rufin Vanrullen
2023-08-17
ArXiv (prépublication)
doi.org
arxiv.org
Hitting the High-Dimensional Notes: An ODE for SGD learning dynamics on GLMs and multi-index models
Elizabeth Collins-Woodfin
Courtney Paquette
Elliot Paquette
Inbar Seroussi
2023-08-17
ArXiv (prépublication)
doi.org
arxiv.org