Publications

G4SATBench: Benchmarking and Advancing SAT Solving with Graph Neural Networks
Zhaoyu Li
Jinpei Guo
Xujie Si
2023-12-31
Trans. Mach. Learn. Res. (publié)
doi.org
openreview.net
Game On, Hate Off: A Study of Toxicity in Online Multiplayer Environments.
Zachary Yang
Nicolas Grenon-Godbout
Reihaneh Rabbany
The advent of online spaces, particularly social media platforms and video games, has brought forth a significant challenge: the detection a… (voir plus)nd mitigation of toxic and harmful speech. This issue is not only pervasive but also detrimental to the overall user experience. In this study, we leverage small language models to reliably detect toxicity, achieving an average precision of 0.95. Analyzing eight months of chat data from two Ubisoft games, we uncover patterns and trends in toxic behavior. The insights derived from our research will contribute to the development of healthier online communities and inform preventive measures against toxicity.
2023-12-31
Games Res. Pract. (publié)
doi.org
Game Theoretical Formulation for Residential Community Microgrid via Mean Field Theory: Proof of Concept
Mohamad Aziz
Hanane Dagdougui
Issmail ElHallaoui
Incentive-based demand response aggregators are widely recognized as a powerful strategy to increase the flexibility of residential communit… (voir plus)y MG (RCM) while allowing consumers’ assets to participate in the operation of the power system in critical peak times. RCM implementing demand response approaches are of high interest as collectively, they have a high impact on shaping the demand curve during peak time while providing a wide range of economic and technical benefits to consumers and utilities. The penetration of distributed energy resources such as battery energy storage and photovoltaic systems introduces additional flexibility to manage the community loads and increase revenue. This letter proposes a game theoretical formulation for an incentive-based residential community microgrid, where an incentive-based pricing mechanism is developed to encourage peak demand reduction and share the incentive demand curve with the residential community through the aggregator. The aggregator’s objective is to maximize the welfare of the residential community by finding the optimal community equilibrium electricity price. Each household communicates with each other and with the distributed system operator (DSO) through the aggregator and aims to minimize the local electricity cost.
2023-12-31
IEEE Control Systems Letters (publié)
doi.org
Generative Active Learning for the Search of Small-Molecule Protein Binders
Maksym Korablyov
Cheng-Hao Liu
Moksh Jain
Almer Van Der Sloot
Éric Jolicoeur
Edward Ruediger
Andrei Nica
Emmanuel Bengio
Kostiantyn Lapchevskyi
Daniel St-Cyr
Doris Alexandra Schuetz
Victor Ion Butoi
Jarrid Rector-Brooks
Simon Blackburn
Leo Feng
Hadi Nekoei
Saikrishna Gottipati
Priyesh Vijayan
Prateek Gupta
Ladislav Rampasek … (voir 14 de plus)
Sasikanth Avancha
Pierre-Luc Bacon
William Hamilton
Brooks Paige
Sanchit Misra
Stanislaw Jastrzebski
Bharat Kaul
Doina Precup
José Miguel Hernández-Lobato
Marwin Segler
Michael Bronstein
Anne Marinier
Mike Tyers
Yoshua Bengio
Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exh… (voir plus)ibit a property of interest remains a significant challenge. We introduce LambdaZero, a generative active learning approach to search for synthesizable molecules. Powered by deep reinforcement learning, LambdaZero learns to search over the vast space of molecules to discover candidates with a desired property. We apply LambdaZero with molecular docking to design novel small molecules that inhibit the enzyme soluble Epoxide Hydrolase 2 (sEH), while enforcing constraints on synthesizability and drug-likeliness. LambdaZero provides an exponential speedup in terms of the number of calls to the expensive molecular docking oracle, and LambdaZero de novo designed molecules reach docking scores that would otherwise require the virtual screening of a hundred billion molecules. Importantly, LambdaZero discovers novel scaffolds of synthesizable, drug-like inhibitors for sEH. In in vitro experimental validation, a series of ligands from a generated quinazoline-based scaffold were synthesized, and the lead inhibitor N-(4,6-di(pyrrolidin-1-yl)quinazolin-2-yl)-N-methylbenzamide (UM0152893) displayed sub-micromolar enzyme inhibition of sEH.
2023-12-31
arXiv (prépublication)
doi.org
arxiv.org
Generative Adversarial Neural Networks for Realistic Stock Market Simulations
Badre Labiad
Abdelaziz Berrado
Loubna Benabbou
—Stock market simulations are widely used to create synthetic environments for testing trading strategies before deploying them to real-ti… (voir plus)me markets. However, the weak realism often found in these simulations presents a significant challenge. Improving the quality of stock market simulations could be facilitated by the availability of rich and granular real Limit Order Books (LOB) data. Unfortunately, access to LOB data is typically very limited. To address this issue, a framework based on Generative Adversarial Networks (GAN) is proposed to generate synthetic realistic LOB data. This generated data can then be utilized for simulating downstream decision-making tasks, such as testing trading strategies, conducting stress tests, and performing prediction tasks. To effectively tackle challenges related to the temporal and local dependencies inherent in LOB structures and to generate highly realistic data, the framework relies on a specific data representation and preprocessing scheme, transformers, and conditional Wasserstein GAN with gradient penalty. The framework is trained using the FI-2010 benchmark dataset and an ablation study is conducted to demonstrate the importance of each component of the proposed framework. Moreover, qualitative and quantitative metrics are proposed to assess the quality of the generated data. Experimental results indicate that the framework outperforms existing benchmarks in simulating realistic market conditions, thus demonstrating its effectiveness in generating synthetic LOB data for diverse downstream tasks.
2023-12-31
International Journal of Advanced Computer Science and Applications (publié)
doi.org
GenRL: Multimodal-foundation world models for generalization in embodied agents
Pietro Mazzaglia
Tim Verbelen
Bart Dhoedt
Aaron Courville
Sai Rajeswar
Learning generalist embodied agents, able to solve multitudes of tasks in different domains is a long-standing problem. Reinforcement learni… (voir plus)ng (RL) is hard to scale up as it requires a complex reward design for each task. In contrast, language can specify tasks in a more natural way. Current foundation vision-language models (VLMs) generally require fine-tuning or other adaptations to be adopted in embodied contexts, due to the significant domain gap. However, the lack of multimodal data in such domains represents an obstacle to developing foundation models for embodied applications. In this work, we overcome these problems by presenting multimodal-foundation world models, able to connect and align the representation of foundation VLMs with the latent space of generative world models for RL, without any language annotations. The resulting agent learning framework, GenRL, allows one to specify tasks through vision and/or language prompts, ground them in the embodied domain's dynamics, and learn the corresponding behaviors in imagination. As assessed through large-scale multi-task benchmarking in locomotion and manipulation domains, GenRL enables multi-task generalization from language and visual prompts. Furthermore, by introducing a data-free policy learning strategy, our approach lays the groundwork for foundational policy learning using generative world models. Website, code and data: https://mazpie.github.io/genrl/
2023-12-31
NeurIPS (publié)
doi.org
openreview.net
GFETM: Genome Foundation-based Embedded Topic Model for scATAC-seq Modeling
Yimin Fan
Adrien Osakwe
Shi Han
Yu Li
Jun Ding
Yue Li
Single-cell Assay for Transposase-Accessible Chromatin with sequencing (scATAC-seq) has emerged as a powerful technique for investigating op… (voir plus)en chromatin landscapes at single-cell resolution. However, analyzing scATAC-seq data remain challenging due to its sparsity and noise. Genome Foundation Models (GFMs), pre-trained on massive DNA sequences, have proven effective at genome analysis. Given that open chromatin regions (OCRs) harbour salient sequence features, we hypothesize that leveraging GFMs’ sequence embeddings can improve the accuracy and generalizability of scATAC-seq modeling. Here, we introduce the Genome Foundation Embedded Topic Model (GFETM), an interpretable deep learning framework that combines GFMs with the Embedded Topic Model (ETM) for scATAC-seq data analysis. By integrating the DNA sequence embeddings extracted by a GFM from OCRs, GFETM demonstrates superior accuracy and generalizability and captures cell-state specific TF activity both with zero-shot inference and attention mechanism analysis. Finally, the topic mixtures inferred by GFETM reveal biologically meaningful epigenomic signatures of kidney diabetes.
2023-12-31
Lecture Notes in Computer Science (publié)
doi.org
GIST: Generated Inputs Sets Transferability in Deep Learning
Florian Tambon
Foutse Khomh
Giuliano Antoniol
2023-12-31
ACM Trans. Softw. Eng. Methodol. (publié)
doi.org
arxiv.org
GrowSpace: A reinforcement learning environment for plant architecture
Yasmeen Hitti
Ionelia Buzatu
Manuel Del Verme
Mark Lefsrud
Florian Golemo
Audrey Durand
2023-12-31
Computers and Electronics in Agriculture (publié)
doi.org
Harnessing small projectors and multiple views for efficient vision pretraining
Arna Ghosh
Kumar Krishna Agrawal
Shagun Sodhani
Adam M. Oberman
Blake A. Richards
Recent progress in self-supervised (SSL) visual representation learning has led to the development of several different proposed frameworks … (voir plus)that rely on augmentations of images but use different loss functions. However, there are few theoretically grounded principles to guide practice, so practical implementation of each SSL framework requires several heuristics to achieve competitive performance. In this work, we build on recent analytical results to design practical recommendations for competitive and efficient SSL that are grounded in theory. Specifically, recent theory tells us that existing SSL frameworks are minimizing the same idealized loss, which is to learn features that best match the data similarity kernel defined by the augmentations used. We show how this idealized loss can be reformulated to a functionally equivalent loss that is more efficient to compute. We study the implicit bias of using gradient descent to minimize our reformulated loss function and find that using a stronger orthogonalization constraint with a reduced projector dimensionality should yield good representations. Furthermore, the theory tells us that approximating the reformulated loss should be improved by increasing the number of augmentations, and as such using multiple augmentations should lead to improved convergence. We empirically verify our findings on CIFAR, STL and Imagenet datasets, wherein we demonstrate an improved linear readout performance when training a ResNet-backbone using our theoretically grounded recommendations. Remarkably, we also demonstrate that by leveraging these insights, we can reduce the pretraining dataset size by up to 2
2023-12-31
NeurIPS (publié)
doi.org
openreview.net
High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise.
Eduard Gorbunov
Abdurakhmon Sadiev
Marina Danilova
Samuel Horváth
Gauthier Gidel
Pavel Dvurechensky
Alexander Gasnikov
Peter Richtárik
2023-12-31
International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Hint Marginalization for Improved Reasoning in Large Language Models
Soumyasundar Pal
Didier Chételat
Yingxue Zhang
Mark J. Coates
Large Language Models (LLMs) have exhibited an impressive capability to perform reasoning tasks, especially if they are encouraged to genera… (voir plus)te a sequence of intermediate steps. Reasoning performance can be improved by suitably combining multiple LLM responses, generated either in parallel in a single query, or via sequential interactions with LLMs throughout the reasoning process. Existing strategies for combination, such as self-consistency and progressive-hint-prompting, make inefficient usage of the LLM responses. We present Hint Marginalization, a novel and principled algorithmic framework to enhance the reasoning capabilities of LLMs. Our approach can be viewed as an iterative sampling strategy for forming a Monte Carlo approximation of an underlying distribution of answers, with the goal of identifying the mode the most likely answer. Empirical evaluation on several benchmark datasets for arithmetic reasoning demonstrates the superiority of the proposed approach.
2023-12-31
arXiv.org (prépublication)
doi.org
openreview.net