Publications

Deep reinforcement learning for continuous wood drying production line control
François-Alexandre Tremblay
Audrey Durand
Michael Morin
Philippe Marier
Jonathan Gaudreault
2024-01-01
Computers in Industry (published)
doi.org
In deep reinforcement learning, a pruned network is a good network
Johan Samir Obando Ceron
Aaron Courville
Pablo Samuel Castro
Recent work has shown that deep reinforcement learning agents have difficulty in effectively using their network parameters. We leverage pri… (see more)or insights into the advantages of sparse training techniques and demonstrate that gradual magnitude pruning enables agents to maximize parameter effectiveness. This results in networks that yield dramatic performance improvements over traditional networks and exhibit a type of"scaling law", using only a small fraction of the full network parameters.
2024-01-01
ICML (published)
doi.org
arxiv.org
E(3)-Equivariant Mesh Neural Networks
Thuan Nguyen Anh Trang
Khang Nhat Ngo
Daniel Levy
Thieu Vo
Siamak Ravanbakhsh
Truong Son Hy
Triangular meshes are widely used to represent three-dimensional objects. As a result, many recent works have addressed the need for geometr… (see more)ic deep learning on 3D meshes. However, we observe that the complexities in many of these architectures do not translate to practical performance, and simple deep models for geometric graphs are competitive in practice. Motivated by this observation, we minimally extend the update equations of E(n)-Equivariant Graph Neural Networks (EGNNs) (Satorras et al., 2021) to incorporate mesh face information and further improve it to account for long-range interactions through a hierarchy. The resulting architecture, Equivariant Mesh Neural Network (EMNN), outperforms other, more complicated equivariant methods on mesh tasks, with a fast run-time and no expensive preprocessing. Our implementation is available at https://github.com/HySonLab/EquiMesh.
2024-01-01
AISTATS (published)
doi.org
arxiv.org
ECBD: Evidence-Centered Benchmark Design for NLP
Yu Lu Liu
Su Lin Blodgett
Jackie Chi
Jackie Cheung
Q. Vera Liao
Kit Cheung
Alexandra Olteanu
Ziang Xiao
Benchmarking is seen as critical to assessing progress in NLP. However, creating a benchmark involves many design decisions (e.g., which dat… (see more)asets to include, which metrics to use) that often rely on tacit, untested assumptions about what the benchmark is intended to measure or is actually measuring. There is currently no principled way of analyzing these decisions and how they impact the validity of the benchmark's measurements. To address this gap, we draw on evidence-centered design in educational assessments and propose Evidence-Centered Benchmark Design (ECBD), a framework which formalizes the benchmark design process into five modules. ECBD specifies the role each module plays in helping practitioners collect evidence about capabilities of interest. Specifically, each module requires benchmark designers to describe, justify, and support benchmark design choices -- e.g., clearly specifying the capabilities the benchmark aims to measure or how evidence about those capabilities is collected from model responses. To demonstrate the use of ECBD, we conduct case studies with three benchmarks: BoolQ, SuperGLUE, and HELM. Our analysis reveals common trends in benchmark design and documentation that could threaten the validity of benchmarks' measurements.
2024-01-01
ACL (1) (published)
doi.org
arxiv.org
Empirical Analysis of Model Selection for Heterogenous Causal Effect Estimation
Divyat Mahajan
Ioannis Mitliagkas
Brady Neal
Vasilis Syrgkanis
We study the problem of model selection in causal inference, specifically for the case of conditional average treatment effect (CATE) estima… (see more)tion under binary treatments. Unlike model selection in machine learning, there is no perfect analogue of cross-validation as we do not observe the counterfactual potential outcome for any data point. Towards this, there have been a variety of proxy metrics proposed in the literature, that depend on auxiliary nuisance models estimated from the observed data (propensity score model, outcome regression model). However, the effectiveness of these metrics has only been studied on synthetic datasets as we can access the counterfactual data for them. We conduct an extensive empirical analysis to judge the performance of these metrics introduced in the literature, and novel ones introduced in this work, where we utilize the latest advances in generative modeling to incorporate multiple realistic datasets. Our analysis suggests novel model selection strategies based on careful hyperparameter tuning of CATE estimators and causal ensembling.
2024-01-01
ICLR (published)
doi.org
arxiv.org
Enhancing Security and Energy Efficiency of Cyber-Physical Systems using Deep Reinforcement Learning
Saeid Jamshidi
Ashkan Amirnia
Amin Nikanjam
Foutse Khomh
2024-01-01
Procedia Computer Science (published)
doi.org
An Evaluation of Language Models for Hyperpartisan Ideology Detection in Persian Twitter
Sahar Omidi Shayegan
Isar Nejadgholi
Kellin Pelrine
Hao Yu
Sacha Lévy
Zachary Yang
Jean-François Godbout
Reihaneh Rabbany
Large Language Models (LLMs) have shown significant promise in various tasks, including identifying the political beliefs of English-speakin… (see more)g social media users from their posts. However, assessing LLMs for this task in non-English languages remains unexplored. In this work, we ask to what extent LLMs can predict the political ideologies of users in Persian social media. To answer this question, we first acknowledge that political parties are not well-defined among Persian users, and therefore, we simplify the task to a much simpler task of hyperpartisan ideology detection. We create a new benchmark and show the potential and limitations of both open-source and commercial LLMs in classifying the hyper-partisan ideologies of users. We compare these models with smaller fine-tuned models, both on the Persian language (ParsBERT) and translated data (RoBERTa), showing that they considerably outperform generative LLMs in this task. We further demonstrate that the performance of the generative LLMs degrades when classifying users based on their tweets instead of their bios and even when tweets are added as additional information, whereas the smaller fine-tuned models are robust and achieve similar performance for all classes. This study is a first step toward political ideology detection in Persian Twitter, with implications for future research to understand the dynamics of ideologies in Persian social media.
2024-01-01
EURALI (published)
www.semanticscholar.org
An Evaluation of Language Models for Hyperpartisan Ideology Detection in Persian Twitter
Sahar Omidi Shayegan
Isar Nejadgholi
Kellin Pelrine
Hao Yu
Sacha Lévy
Zachary Yang
Jean-François Godbout
Reihaneh Rabbany
Large Language Models (LLMs) have shown significant promise in various tasks, including identifying the political beliefs of English-speakin… (see more)g social media users from their posts. However, assessing LLMs for this task in non-English languages remains unexplored. In this work, we ask to what extent LLMs can predict the political ideologies of users in Persian social media. To answer this question, we first acknowledge that political parties are not well-defined among Persian users, and therefore, we simplify the task to a much simpler task of hyperpartisan ideology detection. We create a new benchmark and show the potential and limitations of both open-source and commercial LLMs in classifying the hyper-partisan ideologies of users. We compare these models with smaller fine-tuned models, both on the Persian language (ParsBERT) and translated data (RoBERTa), showing that they considerably outperform generative LLMs in this task. We further demonstrate that the performance of the generative LLMs degrades when classifying users based on their tweets instead of their bios and even when tweets are added as additional information, whereas the smaller fine-tuned models are robust and achieve similar performance for all classes. This study is a first step toward political ideology detection in Persian Twitter, with implications for future research to understand the dynamics of ideologies in Persian social media.
2024-01-01
EURALI (published)
www.semanticscholar.org
Evolution of High-Throughput Satellite Systems: A Vision of Programmable Regenerative Payload
Olfa Ben Yahia
Zineb Garroussi
Olivier Bélanger
Brunilde Sansò
Jean-François Frigon
Stéphane Martel
Antoine Lesage-Landry
Gunes Karabulut Kurt
High-throughput satellite (HTS), with its digital payload technology, is expected to play a key role as an enabler of the upcoming sixth-gen… (see more)eration (6G) networks. HTS is mainly designed to provide higher data rates and capacities. Fueled by technological advancements, including beamforming, advanced modulation techniques, reconfigurable phased array technologies, and electronically steerable antennas, HTS has emerged as a fundamental component for future network generations. This paper offers a comprehensive state-of-the-art on HTS systems, focusing on standardization, patents, channel multiple access techniques, routing, load balancing, and the role of software-defined networking (SDN). In addition, we provide a vision for next-generation satellite systems that we have named Extremely-HTS (EHTS) toward autonomous satellites supported by the main requirements and key technologies expected for these systems. The EHTS system will be designed to maximize spectrum reuse and data rates and to flexibly steer the capacity to satisfy user demand. We introduce a novel architecture for future programmable regenerative payloads as well.
2024-01-01
IEEE Communications Surveys & Tutorials (published)
doi.org
An Exact Method for (Constrained) Assortment Optimization Problems with Product Costs
Markus Leitner
Andrea Lodi
Roberto Roberti
Claudio Sole
2024-01-01
INFORMS J. Comput. (published)
doi.org
arxiv.org
Exploring the digital divide: results of a survey informing mobile application development
Maira Corinne Claudio
Zachary Rehany
Katerina Stachtari
Elena Guadagno
Esli Osmanlliu
Dan Poenaru
Introduction Mobile health apps risk widening health disparities if they overlook digital inclusion. The digital divide, encompassing access… (see more), familiarity, and readiness, poses a significant barrier to medical interventions. Existing literature lacks exploration of the digital divide's contributing factors. Hence, data are needed to comprehend the challenges in developing inclusive health apps. Methods We created a survey to gauge internet and smartphone access, smartphone familiarity, and readiness for using mobile health apps among caregivers of pediatric patients in tertiary care. Open-ended questions solicited feedback and suggestions on mobile health applications. Responses were categorized by similarity and compared. Developed with patient partners, the survey underwent cognitive testing and piloting for accuracy. Results Data from 209 respondents showed that 23% were affected by the digital divide, mainly due to unfamiliarity with digital skills. Among 49 short text responses about health app concerns, 31 mentioned security and confidentiality, with 7 mentioning the impersonal nature of such apps. Desired features included messaging healthcare providers, scheduling, task reminders, and simplicity. Conclusions This study underscores a digital divide among caregivers of pediatric patients, with nearly a quarter affected primarily due to a lack of digital comfort. Respondents emphasized user-friendliness and online security for health apps. Future apps should prioritize digital inclusion by addressing the significant barriers and carefully considering patient and family concerns.
2024-01-01
Frontiers Digit. Health (published)
doi.org
Exploring validation metrics for offline model-based optimisation
Christopher Beckham
Alexandre Piché
David Vazquez
Chris Pal
In offline model-based optimisation (MBO) we are interested in using machine learning to de-sign candidates that maximise some measure of d… (see more)esirability through an expensive but real-world scoring process. Offline MBO tries to approximate this expensive scoring function and use that to evaluate generated designs, however evaluation is non-exact because one approximation is being evaluated with another. Instead, we ask ourselves: if we did have the real world scoring function at hand, what cheap-to-compute validation metrics would correlate best with this? Since the real-world scoring function is available for simulated MBO datasets, insights obtained from this can be transferred over to real-world offline MBO tasks where the real-world scoring function is expensive to compute. To address this, we propose a conceptual evaluation framework that is amenable to measuring extrapolation, and apply this to conditional denoising diffusion models. Empirically, we find that two validation metrics – agreement and Frechet distance – correlate quite well with the ground truth. When there is high variability in conditional generation, feedback is required in the form of an approximated version of the real-world scoring function. Furthermore, we find that generating high-scoring samples may require heavily weighting the generative model in favour of sample quality, potentially at the cost of sample diversity.
2024-01-01
Trans. Mach. Learn. Res. (published)
dblp.uni-trier.de