Publications

How Programmers Interact with Multimodal Software Documentation

Deeksha M. Arya

Jin L.C. Guo

Martin P. Robillard

There is a wide variety of online documentation to learn about a given software technology, and prior research has reported that programmers… (voir plus) must invest time and effort to identify one that best suits their need. We evaluated five modalities to present information that enable a software document to cater to the different presentation needs of programmers. We developed a prototype tutorial with these modalities on three topics in Java, namely, regular expressions, inheritance, and exception handling. We investigated how people interact with the modalities in the tutorial given a programming topic and a type of task. We conducted a survey study with 56 respondents and confirm that although text content is most useful for solving conceptual tasks, code examples support deeper comprehension of the underlying concepts. Furthermore, we report that respondents' contradicting preferences for the modalities suggest the need to have multiple alternatives in a software tutorial.

2025-04-26

2025 IEEE/ACM 18th International Conference on Cooperative and Human Aspects of Software Engineering (CHASE) (publié)

doi.org

A Learning-Based Framework for Fair and Scalable Solution Generation in Kidney Exchange Problems

William St-Arnaud

Margarida Carvalho

Golnoosh Farnadi

2025-04-26

TMLR (accepté)

openreview.net

AIFM-ed Curriculum Framework for Postgraduate Family Medicine Education on Artificial Intelligence: Mixed Methods Study

Raymond Tolentino

Fanny Hersson-Edery

Mark Yaffe

Samira Abbasgholizadeh-Rahimi

As health care moves to a more digital environment, there is a growing need to train future family doctors on the clinical uses of artificia… (voir plus)l intelligence (AI). However, family medicine training in AI has often been inconsistent or lacking. The aim of the study is to develop a curriculum framework for family medicine postgraduate education on AI called “Artificial Intelligence Training in Postgraduate Family Medicine Education” (AIFM-ed). First, we conducted a comprehensive scoping review on existing AI education frameworks guided by the methodological framework developed by Arksey and O’Malley and Joanna Briggs Institute methodological framework for scoping reviews. We adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting the results. Next, 2 national expert panels were conducted. Panelists included family medicine educators and residents knowledgeable in AI from family medicine residency programs across Canada. Participants were purposively sampled, and panels were held via Zoom, recorded, and transcribed. Data were analyzed using content analysis. We followed the Standards for Reporting Qualitative Research for panels. An integration of the scoping review results and 2 panel discussions of 14 participants led to the development of the AIFM-ed curriculum framework for AI training in postgraduate family medicine education with five key elements: (1) need and purpose of the curriculum, (2) learning objectives, (3) curriculum content, (4) organization of curriculum content, and (5) implementation aspects of the curriculum. Using the results of this study, we developed the AIFM-ed curriculum framework for AI training in postgraduate family medicine education. This framework serves as a structured guide for integrating AI competencies into medical education, ensuring that future family physicians are equipped with the necessary skills to use AI effectively in their clinical practice. Future research should focus on the validation and implementation of the AIFM-ed framework within family medicine education. Institutions also are encouraged to consider adapting the AIFM-ed framework within their own programs, tailoring it to meet the specific needs of their trainees and health care environments.

2025-04-24

JMIR Medical Education (publié)

doi.org

RLeXplore: Accelerating Research in Intrinsically-Motivated Reinforcement Learning

Mingqi Yuan

Roger Creus Castanyer

Bin Li

Xin Jin

Glen Berseth

Wenjun Zeng

2025-04-24

Transactions on Machine Learning Research (accepté)

doi.org

openreview.net

A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning

Prateek Yadav

Colin Raffel

Mohammed Muqeeth

Lucas Caccia

Haokun Liu

Tianlong Chen

Mohit Bansal

Leshem Choshen

Alessandro Sordoni

The availability of performant pre-trained models has led to a proliferation of fine-tuned expert models that are specialized to a particula… (voir plus)r domain or task. Model MoErging methods aim to recycle expert models to create an aggregate system with improved performance or generalization. A key component of MoErging methods is the creation of a router that decides which expert model(s) to use for a particular input or application. The promise, effectiveness, and large design space of MoErging has spurred the development of many new methods over the past few years. This rapid pace of development has made it challenging to compare different MoErging methods, which are rarely compared to one another and are often validated in different experimental setups. To remedy such gaps, we present a comprehensive survey of MoErging methods that includes a novel taxonomy for cataloging key design choices and clarifying suitable applications for each method. Apart from surveying MoErging research, we inventory software tools and applications that make use of MoErging. We additionally discuss related fields of study such as model merging, multitask learning, and mixture-of-experts models. Taken as a whole, our survey provides a unified overview of existing MoErging methods and creates a solid foundation for future work in this burgeoning field.

2025-04-24

TMLR (accepté)

doi.org

openreview.net

3DMolFormer: A Dual-Channel Framework for Structure-Based Drug Discovery

Xiuyuan Hu

Guoqing Liu

Can (Sam) Chen

Yang Zhao

Hao Zhang

Xue Liu

Structure-based drug discovery, encompassing the tasks of protein-ligand docking and pocket-aware 3D drug design, represents a core challeng… (voir plus)e in drug discovery. However, no existing work can deal with both tasks to effectively leverage the duality between them, and current methods for each task are hindered by challenges in modeling 3D information and the limitations of available data. To address these issues, we propose 3DMolFormer, a unified dual-channel transformer-based framework applicable to both docking and 3D drug design tasks, which exploits their duality by utilizing docking functionalities within the drug design process. Specifically, we represent 3D pocket-ligand complexes using parallel sequences of discrete tokens and continuous numbers, and we design a corresponding dual-channel transformer model to handle this format, thereby overcoming the challenges of 3D information modeling. Additionally, we alleviate data limitations through large-scale pre-training on a mixed dataset, followed by supervised and reinforcement learning fine-tuning techniques respectively tailored for the two tasks. Experimental results demonstrate that 3DMolFormer outperforms previous approaches in both protein-ligand docking and pocket-aware 3D drug design, highlighting its promising application in structure-based drug discovery. The code is available at: https://github.com/HXYfighter/3DMolFormer .

2025-04-22

International Conference on Learning Representations (Accept (Poster))

doi.org

openreview.net

Accelerating Training with Neuron Interaction and Nowcasting Networks

Neural network training can be accelerated when a learnable update rule is used in lieu of classic adaptive optimizers (e.g. Adam). However,… (voir plus) learnable update rules can be costly and unstable to train and use. Recently, Jang et al. (2023) proposed a simpler approach to accelerate training based on weight nowcaster networks (WNNs). In their approach, Adam is used for most of the optimization steps and periodically, only every few steps, a WNN nowcasts (predicts near future) parameters. We improve WNNs by proposing neuron interaction and nowcasting (NiNo) networks. In contrast to WNNs, NiNo leverages neuron connectivity and graph neural networks to more accurately nowcast parameters. We further show that in some networks, such as Transformers, modeling neuron connectivity accurately is challenging. We address this and other limitations, which allows NiNo to accelerate Adam training by up to 50% in vision and language tasks.

2025-04-22

International Conference on Learning Representations (Accept (Poster))

doi.org

openreview.net

AdaFisher: Adaptive Second Order Optimization via Fisher Information

Damien Martins Gomes

Yanlei Zhang

Eugene Belilovsky

Guy Wolf

Mahdi S. Hosseini

First-order optimization methods are currently the mainstream in training deep neural networks (DNNs). Optimizers like Adam incorporate limi… (voir plus)ted curvature information by employing the diagonal matrix preconditioning of the stochastic gradient during the training. Despite their widespread, second-order optimization algorithms exhibit superior convergence properties compared to their first-order counterparts e.g. Adam and SGD. However, their practicality in training DNNs is still limited due to increased per-iteration computations compared to the first-order methods. We present \emph{AdaFisher}--an adaptive second-order optimizer that leverages a \emph{diagonal block-Kronecker} approximation of the Fisher information matrix for adaptive gradient preconditioning. AdaFisher aims to bridge the gap between enhanced \emph{convergence/generalization} capabilities and computational efficiency in second-order optimization framework for training DNNs. Despite the slow pace of second-order optimizers, we showcase that AdaFisher can be reliably adopted for image classification, language modeling and stands out for its stability and robustness in hyper-parameter tuning. We demonstrate that AdaFisher \textbf{outperforms the SOTA optimizers} in terms of both accuracy and convergence speed. Code is available from https://github.com/AtlasAnalyticsLab/AdaFisher.

2025-04-22

International Conference on Learning Representations (Accept (Poster))

doi.org

openreview.net

Adaptive Teachers for Amortized Samplers

Sungsoo Ahn

Jinkyoo Park

Nikolay Malkin

Yoshua Bengio

Amortized inference is the task of training a parametric model, such as a neural network, to approximate a distribution with a given unnorma… (voir plus)lized density where exact sampling is intractable. When sampling is implemented as a sequential decision-making process, reinforcement learning (RL) methods, such as generative flow networks, can be used to train the sampling policy. Off-policy RL training facilitates the discovery of diverse, high-reward candidates, but existing methods still face challenges in efficient exploration. We propose to use an adaptive training distribution (the \teacher) to guide the training of the primary amortized sampler (the \student). The \teacher, an auxiliary behavior model, is trained to sample high-loss regions of the \student and can generalize across unexplored modes, thereby enhancing mode coverage by providing an efficient training curriculum. We validate the effectiveness of this approach in a synthetic environment designed to present an exploration challenge, two diffusion-based sampling tasks, and four biochemical discovery tasks demonstrating its ability to improve sample efficiency and mode coverage. Source code is available at https://github.com/alstn12088/adaptive-teacher.

2025-04-22

International Conference on Learning Representations (Accept (Poster))

doi.org

openreview.net

AIMS.au: A Dataset for the Analysis of Modern Slavery Countermeasures in Corporate Statements

Adriana Eufrosina Bora

Pierre-Luc St-Charles

Mirko Bronzi

Arsène Fansi Tchango

Bruno Rousseau

Kerrie Mengersen

Despite over a decade of legislative efforts to address modern slavery in the supply chains of large corporations, the effectiveness of gove… (voir plus)rnment oversight remains hampered by the challenge of scrutinizing thousands of statements annually. While Large Language Models (LLMs) can be considered a well established solution for the automatic analysis and summarization of documents, recognizing concrete modern slavery countermeasures taken by companies and differentiating those from vague claims remains a challenging task. To help evaluate and fine-tune LLMs for the assessment of corporate statements, we introduce a dataset composed of 5,731 modern slavery statements taken from the Australian Modern Slavery Register and annotated at the sentence level. This paper details the construction steps for the dataset that include the careful design of annotation specifications, the selection and preprocessing of statements, and the creation of high-quality annotation subsets for effective model evaluations. To demonstrate our dataset's utility, we propose a machine learning methodology for the detection of sentences relevant to mandatory reporting requirements set by the Australian Modern Slavery Act. We then follow this methodology to benchmark modern language models under zero-shot and supervised learning settings.

2025-04-22

International Conference on Learning Representations (Accept (Poster))

doi.org

openreview.net

Algorithmic Fairness Through the Lens of Metrics and Evaluation (AFME) 2024

Miriam Rateike

Awa Dieng

Jamelle Watson-Daniels

Ferdinando Fioretto

Golnoosh Farnadi

2025-04-22

Proceedings of the Algorithmic Fairness Through the Lens of Metrics and Evaluation (publié)