Publications

Dual Optimistic Ascent (PI Control) is the Augmented Lagrangian Method in Disguise
Juan Ramirez
Simon Lacoste-Julien
Constrained optimization is a powerful framework for enforcing requirements on neural networks. These constrained deep learning problems are… (voir plus) typically solved using first-order methods on their min-max Lagrangian formulation, but such approaches often suffer from oscillations and can fail to find all local solutions. While the Augmented Lagrangian method (ALM) addresses these issues, practitioners often favor dual optimistic ascent schemes (PI control) on the standard Lagrangian, which perform well empirically but lack formal guarantees. In this paper, we establish a previously unknown equivalence between these approaches: dual optimistic ascent on the Lagrangian is equivalent to gradient descent-ascent on the Augmented Lagrangian. This finding allows us to transfer the robust theoretical guarantees of the ALM to the dual optimistic setting, proving it converges linearly to all local solutions. Furthermore, the equivalence provides principled guidance for tuning the optimism hyper-parameter. Our work closes a critical gap between the empirical success of dual optimistic methods and their theoretical foundation.
2025-12-31
International Conference on Learning Representations (Accept (Poster))
doi.org
openreview.net
Efficiency at What Cost? Safety and Fairness in Parameter-Efficient Fine-Tuning of LLMs
Mina Taraghi
Yann Pequignot
Amin Nikanjam
Mohamed Amine Merzouk
Foutse Khomh
2025-12-31
IEEE Transactions on Artificial Intelligence (publié)
doi.org
An Efficient Model Maintenance Approach for MLOps
forough majidi
Foutse Khomh
Heng Li
Amin Nikanjam
In recent years, many industries have utilized machine learning models (ML) in their systems. Ideally, machine learning models should be tra… (voir plus)ined on and applied to data from the same distributions. However, the data evolves over time in many application areas, leading to data and concept drift, which in turn causes the performance of the ML models to degrade over time. Therefore, maintaining up to date ML models plays a critical role in the MLOps pipeline. Existing ML model maintenance approaches are often computationally resource intensive, costly, time consuming, and model dependent. Thus, we propose an improved MLOps pipeline, a new model maintenance approach and a Similarity Based Model Reuse (SimReuse) tool to address the challenges of ML model maintenance. We identify seasonal and recurrent distribution patterns in time series datasets throughout a preliminary study. Recurrent distribution patterns enable us to reuse previously trained models for similar distributions in the future, thus avoiding frequent retraining. Then, we integrated the model reuse approach into the MLOps pipeline and proposed our improved MLOps pipeline. Furthermore, we develop SimReuse, a tool to implement the new components of our MLOps pipeline to store models and reuse them for inference of data segments with similar data distributions in the future. Our evaluation results on four time series datasets demonstrate that our model reuse approach can maintain the performance of models while significantly reducing maintenance time and costs. Our model reuse approach achieves ML performance comparable to the best baseline, while being 15 times more efficient in terms of computation time and costs. Therefore, industries and practitioners can benefit from our approach and use our tool to maintain the performance of their ML models in the deployment phase to reduce their maintenance costs.
2025-12-31
Empir. Softw. Eng. (publié)
doi.org
arxiv.org
Embedding-Based Context-Aware Reranker
Ye Yuan
Mohammad Amin Shabani
Siqi Liu
Retrieval-Augmented Generation (RAG) systems rely on retrieving relevant evidence from a corpus to support downstream generation. The comm… (voir plus)on practice of splitting a long document into multiple shorter passages enables finer-grained and targeted information retrieval. However, it also introduces challenges when a correct retrieval would require inference across passages, such as resolving coreference, disambiguating entities, and aggregating evidence scattered across multiple sources. Many state-of-the-art (SOTA) reranking methods, despite utilizing powerful large pretrained language models with potentially high inference costs, still neglect the aforementioned challenges. Therefore, we propose Embedding-Based Context-Aware Reranker (EBCAR), a lightweight reranking framework operating directly on embeddings of retrieved passages with enhanced cross-passage understandings through the structural information of the passages and a hybrid attention mechanism, which captures both high-level interactions across documents and low-level relationships within each document. We evaluate EBCAR against SOTA rerankers on the ConTEB benchmark, demonstrating its effectiveness for information retrieval requiring cross-passage inference and its advantages in both accuracy and efficiency.
2025-12-31
International Conference on Learning Representations (Accept (Poster))
doi.org
openreview.net
Envisioning digital health ecosystem transformation in Canada «a conceptual foundation en Neuf Etapes.»
Nitika Pant Pai
Samira Abbasgholizadeh Rahimi
Juhi Tulsi
Susan Bartlett
Steven Grover
Ervin Sejdic
Canada’s journey towards digital health transformation is in a phase that precedes widespread catalytic change, trailing peer nations. In … (voir plus)this perspective piece, we discuss the nine steps that are key to catalyzing digital health transformation. We highlight the importance of foundational investments for health systems redesign. These investments in interoperability, unified digital core and ID, scalable health data systems, will enable precision-focused clinical care and prevention-focused public health with Smart Care Everywhere models. Our conceptual foundation highlights the importance of an agile health system with a unified digital core, capable of integrating multiple AI-enhanced digital tools, and managing the data deluge of multimodal data. We hereby advocate for a Smart, Scalable, Digitized, “Care Everywhere” model that can expand health care access to all of its populations: the served and the underserved. An essential component of the foundation is the creation of agile health systems and business models that prevent provider burnout while promoting collaborative, connected care that reaches served and under-served populations, with caring, compassion, enabling an improved engagement and connection. We also call for an investment in the continuous training of healthcare professionals, data professionals, and for an ethical, efficient implementation of AI/digital solutions everywhere from hospitals to community care settings. We also highlight the necessity of data governance policies to safeguard patient autonomy, promote data ownership, to ensure health data privacy, security, and confidentiality. This nine-step approach offers a framework for a unified, connected, patient-centred health ecosystem operationalized/made efficient with digital/AI solutions for patient communities, enabled by connectivity, caring, and compassion. Together, these nine steps serve as a conceptual foundation to enable a sustainable health system that advances access, equity, and efficiency in caring in health care nationwide.
2025-12-31
FACETS (publié)
doi.org
AI Epistemic Risks: Emerging Mechanisms & Evidence
Mick Yang
Stephen Casper
Jonathan Stray
Jasmine Li
Cameron Jones
Anna Gausen
Natasha Jacques
Brian Christian
Bálint Gyevnár
Hannah Rose Kirk
Zhonghao He
Dan Zhao (285025)
Siao Si Looi
J. Levy
Kobi Hackenburg
Elizabeth Seger
Matt Kowal
Michelle Malonza
Luke Hewitt
Hause Lin … (voir 10 de plus)
Maarten Sap
Dylan Hadfield-Menell
Thomas Costello
Reihaneh Rabbany
Jean-François Godbout
David Rand
Atoosa Kasirzadeh
Gordon Pennycook
Yoshua Bengio
Kellin Pelrine
2025-12-31
SSRN Electronic Journal (accepté)
doi.org
Escaping Policy Contraction: Contraction-Aware PPO (CaPPO) for Stable Language Model Fine-Tuning
Dun Yuan
Di Wu
Xue Liu
Reinforcement learning from human feedback (RLHF) with proximal policy optimization (PPO) is widely used but often yields less diverse outpu… (voir plus)ts than supervised fine-tuning, suggesting an effect in which the policy’s support contracts during on-policy optimization. We formalize this “policy contraction” with the Support Retention Ratio (SRR)—the share of SFT completions that retain non-negligible probability under the RL policy—and additionally track token-entropy, Kullback–Leibler (KL) divergence to the reference, and repetition. We propose Contraction-Aware PPO (CaPPO), a minimum-norm multi-gradient update that co-optimizes reward, entropy, and KL, paired with a controller that steers exploration toward a target token entropy. On HH-RLHF, Summarize-from-Feedback, and UltraFeedback with Qwen2-7B, Qwen2.5-14B, Mistral-7B-Instruct, and Llama-3-8B-Instruct, CaPPO increases win rate by 2 to 4 points over PPO and improves diversity, gaining 0.2 to 0.3 higher SRR. The gains persist under decoding sweeps and are robust to reward scaling and critic variance. Treating reward, diversity, and stability as first-class objectives, CaPPO mitigates contraction without sacrificing alignment performance.
2025-12-31
International Conference on Learning Representations (Accept (Poster))
openreview.net
openreview.net
Fast Proteome-Scale Protein Interaction Retrieval via Residue-Level Factorization
Jianan Zhao
Zhihao Zhan
Narendra Chaudhary
Xinyu Yuan
Zuobai Zhang
Qian Cong
Jian Zhou
Sanchit Misra
Jian Tang
Protein-protein interactions (PPIs) are mediated at the residue level. Most sequence-based PPI models consider residue-residue interactions … (voir plus)across two proteins, which can yield accurate interaction scores but are too slow to scale. At proteome scale, identifying candidate PPIs requires evaluating nearly *all possible protein pairs*. For
2025-12-31
International Conference on Learning Representations (Accept (Poster))
openreview.net
openreview.net
Fast Sphere Decoding of Short Systematic Polar-like Codes
Huayi Zhou
Y. Liu
Xiaosi Tan
Chen Ji
Warren J. Gross
Chuan Zhang
Short polar-like codes are competitive for low latency requirements in future communications. Systematic polar codes have not been shown to … (voir plus)offer substantial benefits for decoders beyond improving the bit error rate. In this paper, we demonstrate that the sparsity of the equivalent generator matrix of systematic polar codes significantly reduces calculation complexity when using sphere decoding (SD). We propose a fast SD (Fast-SD) for systematic polar codes. Numerical results indicate that the proposed Fast-SD reduces calculation complexity by up to 33.25% compared to SD on short high-rate codes while maintaining maximum likelihood performance.
2025-12-31
IEEE Transactions on Vehicular Technology (publié)
doi.org
FIN: Boosting binary code embedding by normalizing function inlinings
Mohammadhossein Amouei
Benjamin C. M. Fung
Philippe Charland
2025-12-31
Journal of Systems and Software (publié)
doi.org
Foci, waves, excitability: Self-organization of phase waves in a model of asymmetrically coupled embryonic oscillators
Anonymous
Paul François
Kaushik Roy
The segmentation clock is an emergent embryonic oscillator that controls the periodic formation of vertebrae precursors (or somites). It rel… (voir plus)ies on the self-organization at the presomitic mesoderm (PSM) level of multiple coupled cellular oscillators. Dissociation-reaggregation experiments have further revealed that ensembles made of such cellular oscillators self-organize into an oscillatory bidimensional system, showing concentric waves around multiple foci. Here, we systematically study the dynamics of a two-dimensional lattice of phase oscillators locally coupled to their nearest neighbors through a biharmonic coupling function of the form sinθ+Λsin^{2}θ. This coupling was inferred from the phase response curve of entrainment experiments on cell cultures, leading to the formulation of a minimal Elliptic Radial Isochron Cycle (ERIC) phase model. We show that such ERIC-based coupling parsimoniously explains the emergence of self-organized concentric phase wave patterns around multiple foci for a range of weak couplings and wide distributions of initial random phases, closely mimicking experimental conditions. We further study extended modalities of this problem to derive an atlas of possible behaviors. In particular, we predict the dominant observation of spirals over target wave patterns for initial phase distributions wider than approximately π. Since PSM cells further display properties of an excitable system, we also introduce excitability into our simple model and show that it also supports the observation of concentric phase waves for the conditions of the experiment. Our work suggests important modifications that can be made to the simple phase model with Kuramoto coupling, which can provide further layers of complexity and aid in the explanation of the spatial aspects of self-organization in the segmentation clock.
2025-12-31
Physical Review E (publié)
doi.org
FormalJudge: A Neuro-Symbolic Paradigm for Agentic Oversight
Jiayi Zhou
Yang Sheng
Hantao Lou
Yaodong Yang
Jie Fu
As LLM-based agents increasingly operate in high-stakes domains with real-world consequences, ensuring their behavioral safety becomes param… (voir plus)ount. The dominant oversight paradigm, *LLM-as-a-Judge*, faces a fundamental dilemma: how can probabilistic systems reliably supervise other probabilistic systems without inheriting their failure modes? We argue that formal verification offers a principled escape from this dilemma, yet its adoption has been hindered by a critical bottleneck: the translation from natural language requirements to formal specifications. This paper bridges this gap by proposing , a neuro-symbolic framework that employs a bidirectional **Formal-of-Thought** architecture: LLMs serve as *specification compilers* that top-down decompose high-level human intent into atomic, verifiable constraints, then bottom-up prove compliance using Dafny specifications and Z3 Satisfiability modulo theories solving, which produces mathematical guarantees rather than probabilistic scores. We validate across three benchmarks spanning behavioral safety, multi-domain constraint adherence, and agentic upward deception detection. Experiments on 7 agent models demonstrate that achieves an average improvement of **16.6%** over LLM-as-a-Judge baselines, enables *weak-to-strong* generalization where a 7B judge achieves over 90% accuracy detecting deception from 72B agents, and provides *near-linear safety improvement* through iterative refinement.
2025-12-31
International Conference on Machine Learning (Accept (regular))
doi.org
openreview.net