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learning and fine-tuning a generic value-selection heuristic inside a constraint programming solver

CONSTRAINTS 2024年第3-4期29卷 234-260页

作者： Marty, Tom Boisvert, Leo Francois, Tristan Tessier, Pierre Gautier, Louis Rousseau, Louis-Martin Cappart, Quentin Polytech Montreal Montreal PQ Canada Ecole Polytech Palaiseau France Quebec Inst Learning Algorithms MILA Montreal PQ Canada

Constraint programming is known for being an efficient approach to solving combinatorial problems. Important design choices in a solver are the branching heuristics, designed to lead the search to the best solutions in a minimum amount of time. However, developing these heuristics is a time-consuming process that requires problem-specific expertise. This observation has motivated many efforts to use machine learning to automatically learn efficient heuristics without expert intervention. Although several generic variable-selection heuristics are available in the literature, the options for value-selection heuristics are more scarce. We propose to tackle this issue by introducing a generic learning procedure that can be used to obtain a value-selection heuristic inside a constraint programming solver. This has been achieved thanks to the combination of a deep Q-learning algorithm, a tailored reward signal, and a heterogeneous graph neural network. Experiments on graph coloring, maximum independent set, maximum cut, and minimum vertex cover problems show that this framework competes with the well-known impact-based and activity-based search heuristics and can find solutions close to optimality without requiring a large number of backtracks. Additionally, we observe that fine-tuning a model with a different problem class can accelerate the learning process.

关键词： Constraint programming Branching heuristics Reinforcement learning

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Behavioural equivalences for continuous-time Markov processes

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MATHEMATICAL STRUCTURES IN COMPUTER SCIENCE 2023年第4-5期33卷 222-258页

作者： Chen, Linan Clerc, Florence Panangaden, Prakash McGill Univ Dept Math & Stat Montreal PQ Canada Sch Comp Sci McGil lUnivers DEEL Quebec Montreal PQ Canada McGill Univ Montreal Inst Learning Algorithms MILA Sch Comp Sci Montreal PQ Canada

Bisimulation is a concept that captures behavioural equivalence of states in a variety of types of transition systems. It has been widely studied in a discrete-time setting. The core of this work is to generalise the discrete-time picture to continuous time by providing a notion of behavioural equivalence for continuous-time Markov processes. In Chen et al. [(2019). Electronic Notes in Theoretical Computer Science 347 45-63.], we proposed two equivalent definitions of bisimulation for continuous-time stochastic processes where the evolution is a flow through time: the first one as an equivalence relation and the second one as a cospan of morphisms. In Chen et al. [(2020). Electronic Notes in Theoretical Computer Science.], we developed the theory further: we introduced different concepts that correspond to different behavioural equivalences and compared them to bisimulation. In particular, we studied the relation between bisimulation and symmetry groups of the dynamics. We also provided a game interpretation for two of the behavioural equivalences. The present work unifies the cited conference presentations and gives detailed proofs.

关键词： Stochastic processes Markov processes continuous time bisimulation diffusion

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Full-Scale Information Diffusion Prediction With Reinforced Recurrent Networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2023年第5期34卷 2271-2283页

作者： Yang, Cheng Wang, Hao Tang, Jian Shi, Chuan Sun, Maosong Cui, Ganqu Liu, Zhiyuan Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China Mila Quebec Inst Learning Algorithms Montreal PQ H2S 3H1 Canada HEC Montreal Montreal PQ H3T 2A7 Canada Canadian Inst Adv Res CIFAR Toronto ON M5G 1Z8 Canada Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China

Information diffusion prediction is an important task, which studies how information items spread among users. With the success of deep learning techniques, recurrent neural networks (RNNs) have shown their powerful capability in modeling information diffusion as sequential data. However, previous works focused on either microscopic diffusion prediction, which aims at guessing who will be the next influenced user at what time, or macroscopic diffusion prediction, which estimates the total numbers of influenced users during the diffusion process. To the best of our knowledge, few attempts have been made to suggest a unified model for both microscopic and macroscopic scales. In this article, we propose a novel full-scale diffusion prediction model based on reinforcement learning (RL). RL incorporates the macroscopic diffusion size information into the RNN-based microscopic diffusion model by addressing the nondifferentiable problem. We also employ an effective structural context extraction strategy to utilize the underlying social graph information. Experimental results show that our proposed model outperforms state-of-the-art baseline models on both microscopic and macroscopic diffusion predictions on three real-world datasets.

关键词： Microscopy Predictive models Task analysis Integrated circuit modeling Data mining Recurrent neural networks Feature extraction Information diffusion prediction recurrent neural networks (RNNs) reinforcement learning (RL)

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Causal Discovery in Astrophysics: Unraveling Supermassive Black Hole and Galaxy Coevolution

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ASTROPHYSICAL JOURNAL 2025年第2期979卷 212-212页

作者： Jin, Zehao Pasquato, Mario Davis, Benjamin L. Deleu, Tristan Luo, Yu Cho, Changhyun Lemos, Pablo Perreault-Levasseur, Laurence Bengio, Yoshua Kang, Xi Maccio, Andrea Valerio Hezaveh, Yashar New York Univ Abu Dhabi POB 129188 Abu Dhabi U Arab Emirates Mohammed Bin Rashid Space Ctr POB 129188 Dubai U Arab Emirates Montreal Inst Astrophys Data Anal & Machine Learni Montreal PQ Canada Quebec Artificial Intelligence Inst Montreal Inst Learning Algorithms Mila Montreal PQ Canada Univ Montreal Dept Phys 1375 Ave Therese-Lavoie-Roux Montreal PQ Canada Univ Padua Dipartimento Fis & Astron Vicolo Osservatorio 5 Padua Italy Ist Astrofis Spaziale & Fis Cosm INAF IASF MI Via Alfonso Corti 12 I-20133 Milan Italy Univ Montreal Dept Informat & Rech Operat 2920 Chemin Tour Montreal PQ Canada Hunan Normal Univ Sch Phys & Elect Dept Phys Changsha 410081 Peoples R China Purple Mt Observ 10 Yuan Hua Rd Nanjing 210034 Peoples R China Flatiron Inst Ctr Computat Astrophys New York NY USA Zhejiang Univ Inst Astron Hangzhou 310027 Peoples R China Max Planck Inst Astron Konigstuhl 17 Heidelberg Germany

Correlation does not imply causation, but patterns of statistical association between variables can be exploited to infer a causal structure (even with purely observational data) with the burgeoning field of causal discovery. As a purely observational science, astrophysics has much to gain by exploiting these new methods. The supermassive black hole (SMBH)-galaxy interaction has long been constrained by observed scaling relations, which is low-scatter correlations between variables such as SMBH mass and the central velocity dispersion of stars in a host galaxy's bulge. This study, using advanced causal discovery techniques and an up-to-date data set, reveals a causal link between galaxy properties and dynamically measured SMBH masses. We apply a score-based Bayesian framework to compute the exact conditional probabilities of every causal structure that could possibly describe our galaxy sample. With the exact posterior distribution, we determine the most likely causal structures and notice a probable causal reversal when separating galaxies by morphology. In elliptical galaxies, bulge properties (built from major mergers) tend to influence SMBH growth, while, in spiral galaxies, SMBHs are seen to affect host galaxy properties, potentially through feedback in gas-rich environments. For spiral galaxies, SMBHs progressively quench star formation, whereas, in elliptical galaxies, quenching is complete, and the causal connection has reversed. Our findings support theoretical models of hierarchical assembly of galaxies and active galactic nuclei feedback regulating galaxy evolution. Our study suggests the potentiality for further exploration of causal links in astrophysical and cosmological scaling relations, as well as any other observational science.

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Inherent privacy limitations of decentralized contact tracing apps

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2021年第1期28卷 193-195页

作者： Bengio, Yoshua Ippolito, Daphne Janda, Richard Jarvie, Max Prud'homme, Benjamin Rousseau, Jean-Francois Sharma, Abhinav Yu, Yun William Univ Montreal Montreal PQ Canada Montreal Inst Learning Algorithms Montreal PQ Canada Univ Penn Dept Comp & Informat Sci 200 S 33Rd St Philadelphia PA 19104 USA McGill Univ Fac Law Montreal PQ Canada Borden Ladner Gervais LLP Montreal PQ Canada Libeo Quebec City PQ Canada McGill Univ Ctr Outcome Res & Evaluat Hlth Ctr Montreal PQ Canada Univ Toronto Scarborough Comp & Math Sci Instruct Ctr 343 1265 Mil Trail Toronto ON M1C 1A4 Canada

Recently, there have been many efforts to use mobile apps as an aid in contact tracing to control the spread of the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) (COVID-19 [coronavirus disease 2019]) pandemic. However, although many apps aim to protect individual privacy, the very nature of contact tracing must reveal some otherwise protected personal information. Digital contact tracing has endemic privacy risks that cannot be removed by technological means, and which may require legal or economic solutions. In this brief communication, we discuss a few of these inherent privacy limitations of any decentralized automatic contact tracing system.

关键词： contact tracing privacy COVID-19 phone app

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DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases 19

DivGraphPointer: A Graph Pointer Network for Extracting Dive...

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42nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Sun, Zhiqing Tang, Jian Du, Pan Deng, Zhi-Hong Nie, Jian-Yun Peking Univ Beijing Peoples R China HEC Montreal Algorithms Mila Quebec Inst Learning CIFAR AI Res Chair Montreal PQ Canada Univ Montreal Montreal PQ Canada

ISBN: (纸本)9781450361729

Keyphrase extraction from documents is useful to a variety of applications such as information retrieval and document summarization. This paper presents an end-to-end method called DivGraphPointer for extracting a set of diversified keyphrases from a document. DivGraphPointer combines the advantages of traditional graph-based ranking methods and recent neural network-based approaches. Specifically, given a document, a word graph is constructed from the document based on word proximity and is encoded with graph convolutional networks, which effectively capture document-level word salience by modeling long-range dependency between words in the document and aggregating multiple appearances of identical words into one node. Furthermore, we propose a diversified point network to generate a set of diverse keyphrases out of the word graph in the decoding process. Experimental results on five benchmark data sets show that our proposed method significantly outperforms the existing state-of-the-art approaches.

关键词： graph neural networks keyphrase extraction diversified pointer network document-level word salience

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Horizontal gene transfer and recombination analysis of SARS-CoV-2 genes helps discover its close relatives and shed light on its origin

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BMC ECOLOGY AND EVOLUTION 2021年第1期21卷 5-5页

作者： Makarenkov, Vladimir Mazoure, Bogdan Rabusseau, Guillaume Legendre, Pierre Univ Quebec Montreal Dept Informat Montreal PQ Canada Montreal Inst Learning Algorithms Mila Montreal PQ Canada Univ Montreal Dept Informat & Rech Operat Montreal PQ Canada Canada CIFAR AI Chair Montreal PQ Canada Univ Montreal Dept Sci Biol CP 6128 Montreal PQ H3C 3J7 Canada

BackgroundThe SARS-CoV-2 pandemic is one of the greatest global medical and social challenges that have emerged in recent history. Human coronavirus strains discovered during previous SARS outbreaks have been hypothesized to pass from bats to humans using intermediate hosts, e.g. civets for SARS-CoV and camels for MERS-CoV. The discovery of an intermediate host of SARS-CoV-2 and the identification of specific mechanism of its emergence in humans are topics of primary evolutionary importance. In this study we investigate the evolutionary patterns of 11 main genes of SARS-CoV-2. Previous studies suggested that the genome of SARS-CoV-2 is highly similar to the horseshoe bat coronavirus RaTG13 for most of the genes and to some Malayan pangolin coronavirus (CoV) strains for the receptor binding (RB) domain of the spike *** provide a detailed list of statistically significant horizontal gene transfer and recombination events (both intergenic and intragenic) inferred for each of 11 main genes of the SARS-CoV-2 genome. Our analysis reveals that two continuous regions of genes S and N of SARS-CoV-2 may result from intragenic recombination between RaTG13 and Guangdong (GD) Pangolin CoVs. Statistically significant gene transfer-recombination events between RaTG13 and GD Pangolin CoV have been identified in region [1215-1425] of gene S and region [534-727] of gene N. Moreover, some statistically significant recombination events between the ancestors of SARS-CoV-2, RaTG13, GD Pangolin CoV and bat CoV ZC45-ZXC21 coronaviruses have been identified in genes ORF1ab, S, ORF3a, ORF7a, ORF8 and N. Furthermore, topology-based clustering of gene trees inferred for 25 CoV organisms revealed a three-way evolution of coronavirus genes, with gene phylogenies of ORF1ab, S and N forming the first cluster, gene phylogenies of ORF3a, E, M, ORF6, ORF7a, ORF7b and ORF8 forming the second cluster, and phylogeny of gene ORF10 forming the third *** results of our hori

关键词： Evolution of SARS-CoV-2 Gene evolution Horizontal gene transfer Recombination Phylogenetic network Consensus tree

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vGraph: A Generative Model for Joint Community Detection and Node Representation learning

vGraph: A Generative Model for Joint Community Detection and...

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33rd Conference on Neural Information Processing Systems (NeurIPS)

作者： Sun, Fan-Yun Qu, Meng Hoffmann, Jordan Huang, Chin-Wei Tang, Jian Natl Taiwan Univ Taipei Taiwan Mila Quebec Inst Learning Algorithms Montreal PQ Canada Harvard Univ Cambridge MA 02138 USA Element AI Montreal PQ Canada HEC Montreal Montreal PQ Canada CIFAR AI Res Chair Montreal PQ Canada

This paper focuses on two fundamental tasks of graph analysis: community detection and node representation learning, which capture the global and local structures of graphs, respectively. In the current literature, these two tasks are usually independently studied while they are actually highly correlated. We propose a probabilistic generative model called vGraph to learn community membership and node representation collaboratively. Specifically, we assume that each node can be represented as a mixture of communities, and each community is defined as a multinomial distribution over nodes. Both the mixing coefficients and the community distribution are parameterized by the low-dimensional representations of the nodes and communities. We designed an effective variational inference algorithm which regularizes the community membership of neighboring nodes to be similar in the latent space. Experimental results on multiple real-world graphs show that vGraph is very effective in both community detection and node representation learning, outperforming many competitive baselines in both tasks. We show that the framework of vGraph is quite flexible and can be easily extended to detect hierarchical communities.

关键词： Inference engines

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Multi-scale Information Diffusion Prediction with Reinforced Recurrent Networks 28

Multi-scale Information Diffusion Prediction with Reinforced...

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28th International Joint Conference on Artificial Intelligence

作者： Yang, Cheng Tang, Jian Sun, Maosong Cui, Ganqu Liu, Zhiyuan Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Tsinghua Univ Inst Artificial Intelligence Beijing Peoples R China Tsinghua Univ State Key Lab Intelligent Technol & Syst Beijing Peoples R China Mila Quebec Inst Learning Algorithms Montreal PQ Canada HEC Montreal Montreal PQ Canada Canadian Inst Adv Res CIFAR Toronto ON Canada

ISBN: (纸本)9780999241141

Information diffusion prediction is an important task which studies how information items spread among users. With the success of deep learning techniques, recurrent neural networks (RNNs) have shown their powerful capability in modeling information diffusion as sequential data. However, previous works focused on either microscopic diffusion prediction which aims at guessing the next influenced user or macroscopic diffusion prediction which estimates the total numbers of influenced users during the diffusion process. To the best of our knowledge, no previous works have suggested a unified model for both microscopic and macroscopic scales. In this paper, we propose a novel multi-scale diffusion prediction model based on reinforcement learning (RL). RL incorporates the macroscopic diffusion size information into the RNN-based microscopic diffusion model by addressing the non-differentiable problem. We also employ an effective structural context extraction strategy to utilize the underlying social graph information. Experimental results show that our proposed model outperforms state-of-the-art baseline models on both microscopic and macroscopic diffusion predictions on three real-world datasets.

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An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation 1

An End-to-End Neighborhood-based Interaction Model for Knowl...

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1st International Workshop on Deep learning Practice for High-Dimensional Sparse Data with KDD (DLP KDD)

作者： Qu, Yanru Bai, Ting Zhang, Weinan Nie, Jianyun Tang, Jian Shanghai Jiao Tong Univ Shanghai Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China Renmin Univ China Beijing Peoples R China Univ Montreal Montreal PQ Canada Mila Quebec Inst Learning Algorithms Montreal PQ Canada HEC Montreal Montreal PQ Canada CIFAR AI Res Chair Edmonton AB Canada

ISBN: (纸本)9781450367837

This paper studies graph-based recommendation, where an interaction graph is built from historical responses and is leveraged to alleviate data sparsity and cold start problems. We reveal an early summarization problem in previous graph-based models, and propose Neighborhood Interaction (NI) model to capture each neighbor pair (between user-side and item-side) distinctively. NI model is more expressive and captures more complicated structural patterns behind user-item interactions. To enrich the neighborhood information, we also introduce Graph Neural Networks (GNNs) and Knowledge Graphs (KGs) to NI, resulting an end-to-end model, namely Knowledge-enhanced Neighborhood Interaction (KNI). Our experiments on 4 real world datasets show that, compared with state-of-the-art feature-based, meta path-based, and KG-based recommendation models, KNI achieves superior performance in click-through rate prediction (1.1%-8.4% absolute AUC improvements) and outperforms by a wide margin in top-N recommendation.

关键词： Knowledge Graph Neighborhood-based Interaction Collaborative Recommendation

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