Publications

Uncertain Evidence in Probabilistic Models and Stochastic Simulators
Andreas Munk
Alexander Mead
Frank N. Wood
We consider the problem of performing Bayesian inference in probabilistic models where observations are accompanied by uncertainty, referred… (voir plus) to as "uncertain evidence.'' We explore how to interpret uncertain evidence, and by extension the importance of proper interpretation as it pertains to inference about latent variables. We consider a recently-proposed method "distributional evidence'' as well as revisit two older methods: Jeffrey's rule and virtual evidence. We devise guidelines on how to account for uncertain evidence and we provide new insights, particularly regarding consistency. To showcase the impact of different interpretations of the same uncertain evidence, we carry out experiments in which one interpretation is defined as "correct.'' We then compare inference results from each different interpretation illustrating the importance of careful consideration of uncertain evidence.
2023-07-02
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Chat2Code: A Chatbot for Model Specification and Code Generation, The Case of Smart Contracts
Ilham Qasse
Shailesh Mishra
Björn þór Jónsson
Foutse Khomh
Mohammad Hamdaqa
The potential of automatic code generation through Model-Driven Engineering (MDE) frameworks has yet to be realized. Beyond their ability to… (voir plus) help software professionals write more accurate, reusable code, MDE frameworks could make programming accessible for a new class of domain experts. However, domain experts have been slow to embrace these tools, as they still need to learn how to specify their applications' requirements using the concrete syntax (i.e., textual or graphical) of the new and unified domain-specific language. Conversational interfaces (chatbots) could smooth the learning process and offer a more interactive way for domain experts to specify their application requirements and generate the desired code. If integrated with MDE frameworks, chatbots may offer domain experts with richer domain vocabulary without sacrificing the power of agnosticism that unified modelling frameworks provide. In this paper, we discuss the challenges of integrating chatbots within MDE frameworks and then examine a specific application: the auto-generation of smart contract code based on conversational syntax. We demonstrate how this can be done and evaluate our approach by conducting a user experience survey to assess the usability and functionality of the chatbot framework. The paper concludes by drawing attention to the potential benefits of leveraging Language Models (LLMs) in this context.
2023-07-01
2023 IEEE International Conference on Software Services Engineering (SSE) (publié)
doi.org
Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning
Xutong Zhao
Yangchen Pan
Chenjun Xiao
Sarath Chandar
Janarthanan Rajendran
Efficient exploration is critical in cooperative deep Multi-Agent Reinforcement Learning (MARL). In this work, we propose an exploration met… (voir plus)hod that effectively encourages cooperative exploration based on the idea of sequential action-computation scheme. The high-level intuition is that to perform optimism-based exploration, agents would explore cooperative strategies if each agent's optimism estimate captures a structured dependency relationship with other agents. Assuming agents compute actions following a sequential order at \textit{each environment timestep}, we provide a perspective to view MARL as tree search iterations by considering agents as nodes at different depths of the search tree. Inspired by the theoretically justified tree search algorithm UCT (Upper Confidence bounds applied to Trees), we develop a method called Conditionally Optimistic Exploration (COE). COE augments each agent's state-action value estimate with an action-conditioned optimistic bonus derived from the visitation count of the global state and joint actions of preceding agents. COE is performed during training and disabled at deployment, making it compatible with any value decomposition method for centralized training with decentralized execution. Experiments across various cooperative MARL benchmarks show that COE outperforms current state-of-the-art exploration methods on hard-exploration tasks.
2023-07-01
Conference on Uncertainty in Artificial Intelligence (publié)
doi.org
proceedings.mlr.press
MixupE: Understanding and improving Mixup from directional derivative perspective
Vikas Verma
Yingtian Zou
Sarthak Mittal
Wai Hoh Tang
Hieu Pham
Juho Kannala
Yoshua Bengio
Arno Solin
Kenji Kawaguchi
2023-07-01
Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence (publié)
doi.org
proceedings.mlr.press
Omega: Optimistic EMA Gradients
Juan Ramirez
Rohan Sukumaran
Quentin Bertrand
Gauthier Gidel
Stochastic min-max optimization has gained interest in the machine learning community with the advancements in GANs and adversarial training… (voir plus). Although game optimization is fairly well understood in the deterministic setting, some issues persist in the stochastic regime. Recent work has shown that stochastic gradient descent-ascent methods such as the optimistic gradient are highly sensitive to noise or can fail to converge. Although alternative strategies exist, they can be prohibitively expensive. We introduce Omega, a method with optimistic-like updates that mitigates the impact of noise by incorporating an EMA of historic gradients in its update rule. We also explore a variation of this algorithm that incorporates momentum. Although we do not provide convergence guarantees, our experiments on stochastic games show that Omega outperforms the optimistic gradient method when applied to linear players.
2023-07-01
ICML.cc/2023/Workshop/LXAI_Regular_Deadline (présentation orale)
doi.org
openreview.net
Sample Boosting Algorithm (SamBA) - An interpretable greedy ensemble classifier based on local expertise for fat data
Baptiste Bauvin
Cécile Capponi
Florence Clerc
Pascal Germain
Sokol Koço
J. Corbeil
2023-07-01
Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence (publié)
proceedings.mlr.press
proceedings.mlr.press
Stochastic Generative Flow Networks
Ling Pan
Dinghuai Zhang
Moksh Jain
Longbo Huang
Yoshua Bengio
2023-07-01
Conference on Uncertainty in Artificial Intelligence (publié)
doi.org
proceedings.mlr.press
Continuous cutting plane algorithms in integer programming
Didier Chételat
Andrea Lodi
2023-06-30
Operations Research Letters (publié)
doi.org
arxiv.org
Curriculum frameworks and educational programs in artificial intelligence for medical students, residents, and practicing physicians: a scoping review protocol.
Raymond Tolentino
Ashkan Baradaran
Genevieve Gore
Pierre Pluye
S. A. Rahimi
OBJECTIVE The aim of this scoping review is to synthesize knowledge from the literature on curriculum frameworks and current educational pro… (voir plus)grams that focus on the teaching and learning of artificial intelligence (AI) for medical students, residents, and practicing physicians. INTRODUCTION To advance the implementation of AI in clinical practice, physicians need to have a better understanding of AI and how to use it within clinical practice. Consequently, medical education must introduce AI topics and concepts into the curriculum. Curriculum frameworks are educational road maps to teaching and learning. Therefore, any existing AI curriculum frameworks must be reviewed and, if none exist, such a framework must be developed. INCLUSION CRITERIA This review will include articles that describe curriculum frameworks for teaching and learning AI in medicine, irrespective of country. All types of articles and study designs will be included, except conference abstracts and protocols. METHODS This review will follow the JBI methodology for scoping reviews. Keywords will first be identified from relevant articles. Another search will then be conducted using the identified keywords and index terms. The following databases will be searched: MEDLINE (Ovid), Embase (Ovid), Cochrane Central Register of Controlled Trials (CENTRAL), CINAHL (EBSCOhost), and Scopus. Gray literature will also be searched. Articles will be limited to the English and French languages, commencing from the year 2000. The reference lists of all included articles will be screened for additional articles. Data will then be extracted from included articles and the results will be presented in a table.
2023-06-30
JBI Evidence Synthesis (publié)
doi.org
Dev2vec: Representing Domain Expertise of Developers in an Embedding Space
Arghavan Moradi Dakhel
Michel C. Desmarais
Foutse Khomh
2023-06-30
Information and Software Technology (publié)
doi.org
arxiv.org
A double-oracle, logic-based Benders decomposition approach to solve the K-adaptability problem
A. Ghahtarani
A. Saif
A. Ghasemi
Erick Delage
2023-06-30
Computers & Operations Research (publié)
doi.org
arxiv.org
FairPrism: Evaluating Fairness-Related Harms in Text Generation
Eve Fleisig
Aubrie Amstutz
Chad Atalla
Su Lin Blodgett
Hal Daumé III
A.R. Olteanu
Emily Sheng
Dan Vann
Hanna Wallach
It is critical to measure and mitigate fairness-related harms caused by AI text generation systems, including stereotyping and demeaning har… (voir plus)ms. To that end, we introduce FairPrism, a dataset of 5,000 examples of AI-generated English text with detailed human annotations covering a diverse set of harms relating to gender and sexuality. FairPrism aims to address several limitations of existing datasets for measuring and mitigating fairness-related harms, including improved transparency, clearer specification of dataset coverage, and accounting for annotator disagreement and harms that are context-dependent. FairPrism’s annotations include the extent of stereotyping and demeaning harms, the demographic groups targeted, and appropriateness for different applications. The annotations also include specific harms that occur in interactive contexts and harms that raise normative concerns when the “speaker” is an AI system. Due to its precision and granularity, FairPrism can be used to diagnose (1) the types of fairness-related harms that AI text generation systems cause, and (2) the potential limitations of mitigation methods, both of which we illustrate through case studies. Finally, the process we followed to develop FairPrism offers a recipe for building improved datasets for measuring and mitigating harms caused by AI systems.
2023-06-30
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (publié)
doi.org