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The Journal of Machine Learning Research 2022年第1期23卷 9989-10056页

作者： Dhruva Tirumala Alexandre Galashov Hyeonwoo Noh Leonard Hasenclever Razvan Pascanu Jonathan Schwarz Guillaume Desjardins Wojciech Marian Czarnecki Arun Ahuja Yee Whye Teh Nicolas Heess DeepMind London UK and University College London London UK DeepMind London UK DeepMind London UK and OpenAI San Francisco CA

As we deploy reinforcement learning agents to solve increasingly challenging problems, methods that allow us to inject prior knowledge about the structure of the world and effective solution strategies becomes increasingly important. In this work we consider how information and architectural constraints can be combined with ideas from the probabilistic modeling literature to learn behavior priors that capture the common movement and interaction patterns that are shared across a set of related tasks or contexts. For example the day-to day behavior of humans comprises distinctive locomotion and manipulation patterns that recur across many different situations and goals. We discuss how such behavior patterns can be captured using probabilistic trajectory models and how these can be integrated effectively into reinforcement learning schemes, e.g. to facilitate multi-task and transfer learning. We then extend these ideas to latent variable models and consider a formulation to learn hierarchical priors that capture different aspects of the behavior in reusable modules. We discuss how such latent variable formulations connect to related work on hierarchical reinforcement learning (HRL) and mutual information and curiosity based objectives, thereby offering an alternative perspective on existing ideas. We demonstrate the effectiveness of our framework by applying it to a range of simulated continuous control domains, videos of which can be found at the following

关键词： reinforcement learning probabilistic graphical models control as inference hierarchical reinforcement learning transfer learning

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Optimal selection of the most informative nodes in Opinion Dynamics on Networks

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IFAC-PapersOnLine 2023年第2期56卷 4192-4197页

作者： Roberta Raineri Giacomo Como Fabio Fagnani

Finding the optimal subset to observe in a network system is a fundamental problem in science and engineering, with a wide range of applications like monitoring spatial phenomena, control of epidemic spread, feature selection in machine learning, or active surveying in social studies. The goal of this paper is to address the subset selection problem on an Opinion Dynamics model where the variable of interest Y is the average opinion of the community. We consider the opinion vector X to be updated according to a Friedkin-Johnsen opinion dynamics model where every agent i is equipped with an original unknown belief u i , which is assumed to be normally distributed, and a parameter λ i describing its openness to interactions. The objective function of the optimization problem is the variance reduction from the observation of the steady-state opinions of a subset K ⊆ V of agents. We show how this functional can be rewritten in terms of the Bonacich centrality and the cycle centrality of the agents in social network when the subset selection is of cardinality 1, providing particular graph-theoretic interpretations related to the network itself. In addition, first exploratory simulations highlight a behaviour which deviates from the one of known centrality measures depending on the choice of model parameters. Finally, we show that the submodularity of the functional is not guaranteed in our case and thus results taken from known literature are non-enforceable. This paves the way for further analysis.

关键词： Opinion Dynamics Centrality measures Network centrality Network systems Subset selection probabilistic graphical models Gaussian Random Fields

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probabilistic Complex Event Recognition: A Survey

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ACM COMPUTING SURVEYS 2017年第5期50卷 71-71页

作者： Alevizos, Elias Skarlatidis, Anastasios Artikis, Alexander Paliouras, Georgios Natl Ctr Sci Res Demokritos Inst Informat & Telecommun Athens 15341 Greece Natl Ctr Sci Res Demokritos Athens Greece Univ Pireaus Piraeus Greece Pollfish Athens 11527 Greece

Complex event recognition (CER) applications exhibit various types of uncertainty, ranging from incomplete and erroneous data streams to imperfect complex event patterns. We review CER techniques that handle, to some extent, uncertainty. We examine techniques based on automata, probabilistic graphical models, and first-order logic, which are the most common ones, and approaches based on Petri nets and grammars, which are less frequently used. Several limitations are identified with respect to the employed languages, their probabilistic models, and their performance, as compared to the purely deterministic cases. Based on those limitations, we highlight promising directions for future work.

关键词： Event processing uncertainty probabilistic logics probabilistic automata probabilistic graphical models probabilistic Petri nets stochastic grammars

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An Inference-Based Model of Word Meaning in Context as a Paraphrase Distribution

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ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 2013年第3期4卷 42-42页

作者： Moon, Taesun Erk, Katrin Univ Texas Austin Dept Linguist Austin TX 78712 USA

Graded models of word meaning in context characterize the meaning of individual usages (occurrences) without reference to dictionary senses. We introduce a novel approach that frames the task of computing word meaning in context as a probabilistic inference problem. The model represents the meaning of a word as a probability distribution over potential paraphrases, inferred using an undirected graphical model. Evaluated on paraphrasing tasks, the model achieves state-of-the-art performance.

关键词： Algorithms Experimentation Semantics lexical semantics paraphrases probabilistic graphical models probabilistic inference loopy belief propagation

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Human Action Recognition by Semilatent Topic models

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2009年第10期31卷 1762-1774页

作者： Wang, Yang Mori, Greg Simon Fraser Univ Sch Comp Sci Burnaby BC V5A 1S6 Canada

We propose two new models for human action recognition from video sequences using topic models. Video sequences are represented by a novel "bag-of-words" representation, where each frame corresponds to a "word." Our models differ from previous latent topic models for visual recognition in two major aspects: first of all, the latent topics in our models directly correspond to class labels;second, some of the latent variables in previous topic models become observed in our case. Our models have several advantages over other latent topic models used in visual recognition. First of all, the training is much easier due to the decoupling of the model parameters. Second, it alleviates the issue of how to choose the appropriate number of latent topics. Third, it achieves much better performance by utilizing the information provided by the class labels in the training set. We present action classification results on five different data sets. Our results are either comparable to, or significantly better than previously published results on these data sets.

关键词： Human action recognition video analysis bag-of-words probabilistic graphical models event and activity understanding

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Knowledge-fused differential dependency network models for detecting significant rewiring in biological networks

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BMC SYSTEMS BIOLOGY 2014年第1期8卷 87页

作者： Tian, Ye Zhang, Bai Hoffman, Eric P. Clarke, Robert Zhang, Zhen Shih, Ie-Ming Xuan, Jianhua Herrington, David M. Wang, Yue Virginia Tech Dept Elect & Comp Engn Arlington VA 22203 USA Johns Hopkins Univ Dept Pathol Baltimore MD 21231 USA Childrens Natl Med Ctr Med Genet Res Ctr Washington DC 20010 USA Georgetown Univ Lombardi Comprehens Canc Ctr Washington DC 20057 USA Wake Forest Sch Med Sect Cardiovasc Med Dept Internal Med Winston Salem NC 27157 USA

Background: Modeling biological networks serves as both a major goal and an effective tool of systems biology in studying mechanisms that orchestrate the activities of gene products in cells. Biological networks are context-specific and dynamic in nature. To systematically characterize the selectively activated regulatory components and mechanisms, modeling tools must be able to effectively distinguish significant rewiring from random background fluctuations. While differential networks cannot be constructed by existing knowledge alone, novel incorporation of prior knowledge into data-driven approaches can improve the robustness and biological relevance of network inference. However, the major unresolved roadblocks include: big solution space but a small sample size;highly complex networks;imperfect prior knowledge;missing significance assessment;and heuristic structural parameter learning. Results: To address these challenges, we formulated the inference of differential dependency networks that incorporate both conditional data and prior knowledge as a convex optimization problem, and developed an efficient learning algorithm to jointly infer the conserved biological network and the significant rewiring across different conditions. We used a novel sampling scheme to estimate the expected error rate due to "random" knowledge. Based on that scheme, we developed a strategy that fully exploits the benefit of this data-knowledge integrated approach. We demonstrated and validated the principle and performance of our method using synthetic datasets. We then applied our method to yeast cell line and breast cancer microarray data and obtained biologically plausible results. The open-source R software package and the experimental data are freely available at http://***/***. Conclusions: Experiments on both synthetic and real data demonstrate the effectiveness of the knowledge-fused differential dependency network in revealing the statistically signif

关键词： Biological networks probabilistic graphical models Differential dependency network Network rewiring Network analysis Systems biology Knowledge incorporation Convex optimization

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Recent development and biomedical applications of probabilistic Boolean networks

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CELL COMMUNICATION AND SIGNALING 2013年第1期11卷 46-46页

作者： Trairatphisan, Panuwat Mizera, Andrzej Pang, Jun Tantar, Alexandru Adrian Schneider, Jochen Sauter, Thomas Univ Luxembourg Life Sci Res Unit Luxembourg Luxembourg Univ Luxembourg Comp Sci & Communicat Res Unit Luxembourg Luxembourg Univ Luxembourg Luxembourg Ctr Syst Biomed Luxembourg Luxembourg Univ Luxembourg Reliabil & Trust Interdisciplinary Ctr Secur Luxembourg Luxembourg Saarland Univ Med Ctr Dept Internal Med 2 Homburg Saarland Germany

probabilistic Boolean network (PBN) modelling is a semi-quantitative approach widely used for the study of the topology and dynamic aspects of biological systems. The combined use of rule-based representation and probability makes PBN appealing for large-scale modelling of biological networks where degrees of uncertainty need to be considered. A considerable expansion of our knowledge in the field of theoretical research on PBN can be observed over the past few years, with a focus on network inference, network intervention and control. With respect to areas of applications, PBN is mainly used for the study of gene regulatory networks though with an increasing emergence in signal transduction, metabolic, and also physiological networks. At the same time, a number of computational tools, facilitating the modelling and analysis of PBNs, are continuously developed. A concise yet comprehensive review of the state-of-the-art on PBN modelling is offered in this article, including a comparative discussion on PBN versus similar models with respect to concepts and biomedical applications. Due to their many advantages, we consider PBN to stand as a suitable modelling framework for the description and analysis of complex biological systems, ranging from molecular to physiological levels.

关键词： probabilistic Boolean networks probabilistic graphical models Qualitative modelling Systems biology

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Network analysis of frailty and aging: Empirical data from the Mexican Health and Aging Study

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EXPERIMENTAL GERONTOLOGY 2019年 128卷 110747-000页

作者： Garcia-Pena, Carmen Ramirez-Aldana, Ricardo Parra-Rodriguez, Lorena Carlos Gomez-Verjan, Juan Ulises Perez-Zepeda, Mario Miguel Gutierrez-Robledo, Luis Natl Inst Geriatr Mexico City Res Div Mexico City DF Mexico Natl Inst Geriatr Mexico City Mexico City DF Mexico

Background: Frailty remains a challenge in the aging research area with a number of gaps in knowledge still to be filled. Frailty seems to behave as a network, and in silico evidence is available on this matter. Having in vivo evidence that frailty behaves as a complex network was the main purpose of our study. Methods: Data from the Mexican Health and Aging Study (main data 2012, mortality 2015) was used. Frailty was operationalized with a 35-deficit frailty index (FI). Analyzed nodes were the deficits plus death. The edges, linking those nodes were obtained through structural learning, and an undirected graph associated with a discrete probabilistic graphical model (Markov network) was derived. Two algorithms, hill-climbing (hc) and Peter and Clark (PC), were used to derive the graph structure. Analyses were performed for the whole population and tertiles of the total FI score. Results: From the total sample of 10,983 adults aged 50 or older, 43.8% were women, and the mean age was 64.6 years (SD = 9.3). The number of connections increased according to the tertile level of the FI score. As the FI score raised, groups of interconnected deficits increased and how the nodes are connected changed. Conclusions: Frailty phenomenon can be modeled using a Bayesian network. Using the full sample, the most central nodes were self-report of health (most connected node) and difficulty walking a block, and all deficits related to mobility were very interconnected. When frailty levels are considered, the most connected nodes differ, but are related with vitality, mainly at lower frailty levels. We derived that not all deficits are equally related since clusters of very related deficits and non-connected deficits were obtained, which might be considered in the construction of the FI score. Further research should aim to identify the nature of all observed interactions, which might allow the development of specific interventions to mitigate the consequences of frailty in older adu

关键词： Bayesian networks Biological aging Complex networks Frailty probabilistic graphical models Geriatric epidemiology Social network analysis

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Simultaneous inference for pairwise graphical models with generalized score matching

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 3596-3646页

作者： Ming Yu Varun Gupta Mladen Kolar Booth School of Business The University of Chicago Chicago IL

probabilistic graphical models provide a flexible yet parsimonious framework for modeling dependencies among nodes in networks. There is a vast literature on parameter estimation and consistent model selection for graphical models. However, in many of the applications, scientists are also interested in quantifying the uncertainty associated with the estimated parameters and selected models, which current literature has not addressed thoroughly. In this paper, we propose a novel estimator for statistical inference on edge parameters in pairwise graphical models based on generalized Hyvärinen scoring rule. Hyvärinen scoring rule is especially useful in cases where the normalizing constant cannot be obtained efficiently in a closed form, which is a common problem for graphical models, including Ising models and truncated Gaussian graphical models. Our estimator allows us to perform statistical inference for general graphical models whereas the existing works mostly focus on statistical inference for Gaussian graphical models where finding normalizing constant is computationally tractable. Under mild conditions that are typically assumed in the literature for consistent estimation, we prove that our proposed estimator is √n-consistent and asymptotically normal, which allows us to construct confidence intervals and build hypothesis tests for edge parameters. Moreover, we show how our proposed method can be applied to test hypotheses that involve a large number of model parameters simultaneously. We illustrate validity of our estimator through extensive simulation studies on a diverse collection of data-generating processes.

关键词： generalized score matching high-dimensional inference probabilistic graphical models simultaneous inference

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Improving Bayesian network structure learning in the presence of measurement error

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2022年第1期23卷 14604-14631页

作者： Yang Liu Anthony C. Constantinou Zhigao Guo School of Electronic Engineering and Computer Science Queen Mary University of London London UK School of Electronic Engineering and Computer Science Queen Mary University of London London UK and The Alan Turing Institute London UK

Structure learning algorithms that learn the graph of a Bayesian network from observational data often do so by assuming the data correctly reect the true distribution of the variables. However, this assumption does not hold in the presence of measurement error, which can lead to spurious edges. This is one of the reasons why the synthetic performance of these algorithms often overestimates real-world performance. This paper describes a heuristic algorithm that can be added as an additional learning phase at the end of any structure learning algorithm, and serves as a correction learning phase that removes potential false positive edges. The results show that the proposed correction algorithm successfully improves the graphical score of five well-established structure learning algorithms spanning different classes of learning in the presence of measurement error.

关键词： causal discovery data noise directed acyclic graph measurement error probabilistic graphical models

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