Publications

Dynamic Routing and Wavelength Assignment with Reinforcement Learning.
Peyman Kafaei
Quentin Cappart
Nicolas Chapados
Hamed Pouya
Louis-Martin Rousseau
With the rapid developments in communication systems, and considering their dynamic nature, all-optical networks are becoming increasingly c… (voir plus)omplex. This study proposes a novel method based on deep reinforcement learning for the routing and wavelength assignment problem in all-optical wavelength-decision-multiplexing networks. We consider dynamic incoming requests, in which their arrival and holding times are not known in advance. The objective is to devise a strategy that minimizes the number of rejected packages due to the lack of resources in the long term. We use graph neural networks to capture crucial latent information from the graph-structured input to develop the optimal strategy. The proposed deep reinforcement learning algorithm selects a route and a wavelength simultaneously for each incoming traffic connection as they arrive. The results demonstrate that the learned agent outperforms the methods used in practice and can be generalized on network topologies that did not participate in training.
2023-12-31
INFORMS Journal on Optimization (publié)
doi.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… (voir plus)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.
2023-12-31
ICLR (publié)
doi.org
arxiv.org
Enhancing Agent Learning through World Dynamics Modeling
Zhiyuan Sun
Haochen Shi
Marc-Alexandre Côté
Glen Berseth
Xingdi Yuan
Bang Liu
Large language models (LLMs) have been increasingly applied to tasks in language understanding and interactive decision-making, with their i… (voir plus)mpressive performance largely attributed to the extensive domain knowledge embedded within them. However, the depth and breadth of this knowledge can vary across domains. Many existing approaches assume that LLMs possess a comprehensive understanding of their environment, often overlooking potential gaps in their grasp of actual world dynamics. To address this, we introduce Discover, Verify, and Evolve (DiVE), a framework that discovers world dynamics from a small number of demonstrations, verifies the accuracy of these dynamics, and evolves new, advanced dynamics tailored to the current situation. Through extensive evaluations, we assess the impact of each component on performance and compare the dynamics generated by DiVE to human-annotated dynamics. Our results show that LLMs guided by DiVE make more informed decisions, achieving rewards comparable to human players in the Crafter environment and surpassing methods that require prior task-specific training in the MiniHack environment.
2023-12-31
Findings of the Association for Computational Linguistics: EMNLP (publié)
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
2023-12-31
Procedia Computer Science (publié)
doi.org
Enhancing Supervised Visualization Through Autoencoder and Random Forest Proximities for Out-of-Sample Extension
Shuang Ni
Adrien Aumon
Guy Wolf
Kevin R. Moon
Jake S. Rhodes
The value of supervised dimensionality reduction lies in its ability to uncover meaningful connections between data features and labels. Com… (voir plus)mon dimensionality reduction methods embed a set of fixed, latent points, but are not capable of generalizing to an unseen test set. In this paper, we provide an out-of-sample extension method for the random forest-based supervised dimensionality reduction method, RF-PHATE, combining information learned from the random forest model with the function-learning capabilities of autoencoders. Through quantitative assessment of various autoencoder architectures, we identify that networks that reconstruct random forest proximities are more robust for the embedding extension problem. Furthermore, by leveraging proximity-based prototypes, we achieve a 40% reduction in training time without compromising extension quality. Our method does not require label information for out-of-sample points, thus serving as a semi-supervised method, and can achieve consistent quality using only 10% of the training data.
2023-12-31
MLSP (publié)
doi.org
arxiv.org
Evaluating In-Context Learning of Libraries for Code Generation
Arkil Patel
Siva Reddy
Dzmitry Bahdanau
Pradeep Dasigi
2023-12-31
North American Chapter of the Association for Computational Linguistics: Human Language Technologies (publié)
doi.org
arxiv.org
Evaluating Supervision Levels Trade-Offs for Infrared-Based People Counting
David Latortue
Moetez Kdayem
Fidel A. Guerrero Peña
Eric Granger
Marco Pedersoli
Object detection models are commonly used for people counting (and localization) in many applications but require a dataset with costly boun… (voir plus)ding box annotations for training. Given the importance of privacy in people counting, these models rely more and more on infrared images, making the task even harder. In this paper, we explore how weaker levels of supervision affect the performance of deep person counting architectures for image classification and point-level localization. Our experiments indicate that counting people using a convolutional neural network with image-level annotation achieves a level of accuracy that is competitive with YOLO detectors and point-level localization models yet provides a higher frame rate and a simi-lar amount of model parameters. Our code is available at: https://github.com/tortueTortue/IRPeopleCounting.
2023-12-31
2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) (publié)
doi.org
arxiv.org
Evaluating WMT 2024 Metrics Shared Task Submissions on AfriMTE (the African Challenge Set)
Jiayi Wang
David Ifeoluwa Adelani
Pontus Stenetorp
2023-12-31
Conference on Machine Translation (publié)
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… (voir plus)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.
2023-12-31
EURALI (publié)
www.semanticscholar.org
An Examination of the Robustness of Reference-Free Image Captioning Evaluation Metrics
Saba Ahmadi
Aishwarya Agrawal
2023-12-31
EACL (Findings) (publié)
doi.org
arxiv.org
Exploratory Study on the Impact of English Bias of Generative Large Language Models in Dutch and French
Ayla Rigouts Terryn
Miryam de Lhoneux
2023-12-31
HUMEVAL (publié)
www.semanticscholar.org
Exploring Quantization for Efficient Pre-Training of Transformer Language Models
Kamran Chitsaz
Quentin Fournier
Goncalo Mordido
A. Chandar
The increasing scale of Transformer models has led to an increase in their pre-training computational requirements. While quantization has p… (voir plus)roven to be effective after pre-training and during fine-tuning, applying quantization in Transformers during pre-training has remained largely unexplored at scale for language modeling. This study aims to explore the impact of quantization for efficient pre-training of Transformers, with a focus on linear layer components. By systematically applying straightforward linear quantization to weights, activations, gradients, and optimizer states, we assess its effects on model efficiency, stability, and performance during training. By offering a comprehensive recipe of effective quantization strategies to be applied during the pre-training of Transformers, we promote high training efficiency from scratch while retaining language modeling ability. Code is available at https://github.com/chandar-lab/EfficientLLMs.
2023-12-31
EMNLP (Findings) (publié)
doi.org
arxiv.org