Publications

Sample Compression for Self Certified Continual Learning
Jacob Comeau
Mathieu Bazinet
Pascal Germain
Cem Subakan
Continual learning algorithms aim to learn from a sequence of tasks, making the training distribution non-stationary. The majority of existi… (see more)ng continual learning approaches in the literature rely on heuristics and do not provide learning guarantees. In this paper, we present a new method called Continual Pick-to-Learn (CoP2L), which is able to retain the most representative samples for each task in an efficient way. CoP2L combines the Pick-to-Learn algorithm (rooted in the sample compression theory) and the experience replay continual learning scheme. This allows us to provide non-vacuous upper bounds on the generalization loss of the learned predictors, numerically computable after each task. We empirically evaluate our approach on several standard continual learning benchmarks across Class-Incremental, Task-Incremental, and Domain-Incremental settings. Our results show that CoP2L is highly competitive across all setups, often outperforming existing baselines, and significantly mitigating catastrophic forgetting compared to vanilla experience replay in the Class-Incremental setting. It is possible to leverage the bounds provided by CoP2L in practical scenarios to certify the predictor reliability on previously learned tasks, in order to improve the trustworthiness of the continual learning algorithm.
2025-03-13
ArXiv (preprint)
arxiv.org
arxiv.org
Sample Compression for Self Certified Continual Learning
Jacob Comeau
Mathieu Bazinet
Pascal Germain
Cem Subakan
Continual learning algorithms aim to learn from a sequence of tasks, making the training distribution non-stationary. The majority of existi… (see more)ng continual learning approaches in the literature rely on heuristics and do not provide learning guarantees. In this paper, we present a new method called Continual Pick-to-Learn (CoP2L), which is able to retain the most representative samples for each task in an efficient way. CoP2L combines the Pick-to-Learn algorithm (rooted in the sample compression theory) and the experience replay continual learning scheme. This allows us to provide non-vacuous upper bounds on the generalization loss of the learned predictors, numerically computable after each task. We empirically evaluate our approach on several standard continual learning benchmarks across Class-Incremental, Task-Incremental, and Domain-Incremental settings. Our results show that CoP2L is highly competitive across all setups, often outperforming existing baselines, and significantly mitigating catastrophic forgetting compared to vanilla experience replay in the Class-Incremental setting. It is possible to leverage the bounds provided by CoP2L in practical scenarios to certify the predictor reliability on previously learned tasks, in order to improve the trustworthiness of the continual learning algorithm.
2025-03-13
ArXiv (preprint)
arxiv.org
arxiv.org
Exploiting Instruction-Following Retrievers for Malicious Information Retrieval
Parishad BehnamGhader
Nicholas Meade
Siva Reddy
2025-03-11
ArXiv (preprint)
arxiv.org
arxiv.org
Learning Decision Trees as Amortized Structure Inference
Mohammed Mahfoud
Ghait Boukachab
Michał Koziarski
Alex Hernandez-Garcia
Stefan Bauer
Yoshua Bengio
Nikolay Malkin
2025-03-10
ArXiv (preprint)
arxiv.org
arxiv.org
Learning Decision Trees as Amortized Structure Inference
Mohammed Mahfoud
Ghait Boukachab
Michał Koziarski
Alex Hernandez-Garcia
Stefan Bauer
Yoshua Bengio
Nikolay Malkin
Building predictive models for tabular data presents fundamental challenges, notably in scaling consistently, i.e., more resources translati… (see more)ng to better performance, and generalizing systematically beyond the training data distribution. Designing decision tree models remains especially challenging given the intractably large search space, and most existing methods rely on greedy heuristics, while deep learning inductive biases expect a temporal or spatial structure not naturally present in tabular data. We propose a hybrid amortized structure inference approach to learn predictive decision tree ensembles given data, formulating decision tree construction as a sequential planning problem. We train a deep reinforcement learning (GFlowNet) policy to solve this problem, yielding a generative model that samples decision trees from the Bayesian posterior. We show that our approach, DT-GFN, outperforms state-of-the-art decision tree and deep learning methods on standard classification benchmarks derived from real-world data, robustness to distribution shifts, and anomaly detection, all while yielding interpretable models with shorter description lengths. Samples from the trained DT-GFN model can be ensembled to construct a random forest, and we further show that the performance of scales consistently in ensemble size, yielding ensembles of predictors that continue to generalize systematically.
2025-03-10
ArXiv (preprint)
doi.org
arxiv.org
Relative biological effectiveness of 31 meV thermal neutrons in peripheral blood lymphocytes
Laura C Paterson
Fawaz Ali
Mohsen Naseri
David Perez Loureiro
Amy Festarini
Marilyne Stuart
Chad Boyer
Ronald Rogge
Christie Costello
Norma Ybarra
John Kildea
Richard B Richardson
2025-03-10
Radiation Protection Dosimetry (published)
doi.org
SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection
Shamsuddeen Hassan Muhammad
Nedjma OUSIDHOUM
Idris Abdulmumin
Seid Muhie Yimam
Jan Philip Wahle
Terry Lima Ruas
Meriem Beloucif
Christine de Kock
Tadesse Belay
Ibrahim Ahmad
Nirmal Surange
Daniela Teodorescu
David Ifeoluwa Adelani
Alham Fikri Aji
Felermino Ali
Vladimir Araujo
Abinew Ayele
Oana Ignat
Alexander Panchenko
Yi Zhou … (see 1 more)
Saif M. Mohammad
2025-03-10
ArXiv (preprint)
arxiv.org
arxiv.org
Understanding the impact of IoT security patterns on CPU usage and energy consumption: a dynamic approach for selecting patterns with deep reinforcement learning
Saeid Jamshidi
Amin Nikanjam
Kawser Wazed Nafi
Foutse Khomh
2025-03-10
International Journal of Information Security (published)
doi.org
Spectral State Space Model for Rotation-Invariant Visual Representation Learning
Sahar Dastani
Ali Bahri
Moslem Yazdanpanah
Mehrdad Noori
David Osowiechi
Gustavo Adolfo Vargas Hakim
Farzad Beizaee
Milad Cheraghalikhani
Arnab Kumar Mondal
Hervé Lombaert
Christian Desrosiers
2025-03-09
ArXiv (preprint)
arxiv.org
arxiv.org
Studying the Interplay Between the Actor and Critic Representations in Reinforcement Learning
Samuel Garcin
Trevor McInroe
Pablo Samuel Castro
Prakash Panangaden
Christopher G. Lucas
David Abel
Stefano V Albrecht
Extracting relevant information from a stream of high-dimensional observations is a central challenge for deep reinforcement learning agents… (see more). Actor-critic algorithms add further complexity to this challenge, as it is often unclear whether the same information will be relevant to both the actor and the critic. To this end, we here explore the principles that underlie effective representations for the actor and for the critic in on-policy algorithms. We focus our study on understanding whether the actor and critic will benefit from separate, rather than shared, representations. Our primary finding is that when separated, the representations for the actor and critic systematically specialise in extracting different types of information from the environment -- the actor's representation tends to focus on action-relevant information, while the critic's representation specialises in encoding value and dynamics information. We conduct a rigourous empirical study to understand how different representation learning approaches affect the actor and critic's specialisations and their downstream performance, in terms of sample efficiency and generation capabilities. Finally, we discover that a separated critic plays an important role in exploration and data collection during training. Our code, trained models and data are accessible at https://github.com/francelico/deac-rep.
2025-03-08
ArXiv (preprint)
doi.org
arxiv.org
A Taxonomy of Inefficiencies in LLM-Generated Python Code
Altaf Allah Abbassi
Leuson Da Silva
Amin Nikanjam
Foutse Khomh
Large Language Models (LLMs) are widely adopted for automated code generation with promising results. Although prior research has assessed L… (see more)LM-generated code and identified various quality issues -- such as redundancy, poor maintainability, and sub-optimal performance a systematic understanding and categorization of these inefficiencies remain unexplored. Without such knowledge, practitioners struggle to optimize LLM-generated code for real-world applications, limiting its adoption. This study can also guide improving code LLMs, enhancing the quality and efficiency of code generation. Therefore, in this study, we empirically investigate inefficiencies in LLM-generated code by state-of-the-art models, i.e., CodeLlama, DeepSeek-Coder, and CodeGemma. To do so, we analyze 492 generated code snippets in the HumanEval++ dataset. We then construct a taxonomy of inefficiencies in LLM-generated code that includes 5 categories General Logic, Performance, Readability, Maintainability, and Errors) and 19 subcategories of inefficiencies. We then validate the proposed taxonomy through an online survey with 58 LLM practitioners and researchers. Our study indicates that logic and performance-related inefficiencies are the most popular, relevant, and frequently co-occur and impact overall code quality inefficiency. Our taxonomy provides a structured basis for evaluating the quality LLM-generated code and guiding future research to improve code generation efficiency.
2025-03-08
ArXiv (preprint)
arxiv.org
arxiv.org
Unveiling Inefficiencies in LLM-Generated Code: Toward a Comprehensive Taxonomy
Altaf Allah Abbassi
Leuson Da Silva
Amin Nikanjam
Foutse Khomh
2025-03-08
ArXiv (preprint)
arxiv.org
arxiv.org