Learning LWF Chain Graphs: A Markov Blanket Discovery Approach

• URL: http://arxiv.org/abs/2006.00970v1
• Date: Fri, 29 May 2020 16:44:25 GMT
• Title: Learning LWF Chain Graphs: A Markov Blanket Discovery Approach
• Authors: Mohammad Ali Javidian and Marco Valtorta and Pooyan Jamshidi
• Abstract summary: This paper provides a graphical characterization of Markov blankets in chain graphs (CGs) under the Lauritzen-Wermuth-Frydenberg (LWF) interpretation. We provide a novel scalable and sound algorithm for Markov blanket discovery in LWF CGs.
• Score: 2.3333090554192615
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper provides a graphical characterization of Markov blankets in chain graphs (CGs) under the Lauritzen-Wermuth-Frydenberg (LWF) interpretation. The characterization is different from the well-known one for Bayesian networks and generalizes it. We provide a novel scalable and sound algorithm for Markov blanket discovery in LWF CGs and prove that the Grow-Shrink algorithm, the IAMB algorithm, and its variants are still correct for Markov blanket discovery in LWF CGs under the same assumptions as for Bayesian networks. We provide a sound and scalable constraint-based framework for learning the structure of LWF CGs from faithful causally sufficient data and prove its correctness when the Markov blanket discovery algorithms in this paper are used. Our proposed algorithms compare positively/competitively against the state-of-the-art LCD (Learn Chain graphs via Decomposition) algorithm, depending on the algorithm that is used for Markov blanket discovery. Our proposed algorithms make a broad range of inference/learning problems computationally tractable and more reliable because they exploit locality.

Related papers

• MEC-IP: Efficient Discovery of Markov Equivalent Classes via Integer Programming [3.2513035377783717]
  This paper presents a novel Programming (IP) approach for discovering the Markov Equivalent Class (MEC) of Bayesian Networks (BNs) through observational data. Our numerical results show that not only a remarkable reduction in computational time is achieved by our algorithm but also an improvement in causal discovery accuracy is seen across diverse datasets.
  arXiv  Detail & Related papers  (2024-10-22T22:56:33Z)
• An Unforgeable Publicly Verifiable Watermark for Large Language Models [84.2805275589553]
  Current watermark detection algorithms require the secret key used in the watermark generation process, making them susceptible to security breaches and counterfeiting during public detection. We propose an unforgeable publicly verifiable watermark algorithm named UPV that uses two different neural networks for watermark generation and detection, instead of using the same key at both stages.
  arXiv  Detail & Related papers  (2023-07-30T13:43:27Z)
• The Cascaded Forward Algorithm for Neural Network Training [61.06444586991505]
  We propose a new learning framework for neural networks, namely Cascaded Forward (CaFo) algorithm, which does not rely on BP optimization as that in FF. Unlike FF, our framework directly outputs label distributions at each cascaded block, which does not require generation of additional negative samples. In our framework each block can be trained independently, so it can be easily deployed into parallel acceleration systems.
  arXiv  Detail & Related papers  (2023-03-17T02:01:11Z)
• Hybrid Bayesian network discovery with latent variables by scoring multiple interventions [5.994412766684843]
  We present the hybrid mFGS-BS (majority rule and Fast Greedy equivalence Search with Bayesian Scoring) algorithm for structure learning from discrete data. The algorithm assumes causal insufficiency in the presence of latent variables and produces a Partial Ancestral Graph (PAG) Experimental results show that mFGS-BS improves structure learning accuracy relative to the state-of-the-art and it is computationally efficient.
  arXiv  Detail & Related papers  (2021-12-20T14:54:41Z)
• Deep learning via message passing algorithms based on belief propagation [2.931240348160871]
  We present a family of BP-based message-passing algorithms with a reinforcement field that biases towards locally entropic distributions. These algorithms are capable of training multi-layer neural networks with discrete weights and activations with performance comparable to SGD-inspired solutions.
  arXiv  Detail & Related papers  (2021-10-27T16:52:26Z)
• CREPO: An Open Repository to Benchmark Credal Network Algorithms [78.79752265884109]
  Credal networks are imprecise probabilistic graphical models based on, so-called credal, sets of probability mass functions. A Java library called CREMA has been recently released to model, process and query credal networks. We present CREPO, an open repository of synthetic credal networks, provided together with the exact results of inference tasks on these models.
  arXiv  Detail & Related papers  (2021-05-10T07:31:59Z)
• Machine Unlearning via Algorithmic Stability [31.809738694273623]
  We study the problem of machine unlearning and identify a notion of Total Variation (TV) stability. For convex risk minimization problems, we design TV-stable algorithms based on noisy Gradient Descent (SGD) Our techniques to generalize our algorithms are differentially private as well.
  arXiv  Detail & Related papers  (2021-02-25T21:16:56Z)
• Plug-And-Play Learned Gaussian-mixture Approximate Message Passing [71.74028918819046]
  We propose a plug-and-play compressed sensing (CS) recovery algorithm suitable for any i.i.d. source prior. Our algorithm builds upon Borgerding's learned AMP (LAMP), yet significantly improves it by adopting a universal denoising function within the algorithm. Numerical evaluation shows that the L-GM-AMP algorithm achieves state-of-the-art performance without any knowledge of the source prior.
  arXiv  Detail & Related papers  (2020-11-18T16:40:45Z)
• PAC Reinforcement Learning Algorithm for General-Sum Markov Games [5.279475826661642]
  The paper offers an extension to the well-known Nash Q-learning algorithm, using the idea of delayed Q-learning, in order to build a new PAC MARL algorithm for general-sum Markov games. In addition to guiding the design of a provably PAC MARL algorithm, the framework enables checking whether an arbitrary MARL algorithm is PAC.
  arXiv  Detail & Related papers  (2020-09-05T21:54:27Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)
• Active Model Estimation in Markov Decision Processes [108.46146218973189]
  We study the problem of efficient exploration in order to learn an accurate model of an environment, modeled as a Markov decision process (MDP) We show that our Markov-based algorithm outperforms both our original algorithm and the maximum entropy algorithm in the small sample regime.
  arXiv  Detail & Related papers  (2020-03-06T16:17:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.