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arXiv 2025年

作者： Wu, Peng Li, Haoxuan Zheng, Chunyuan Zeng, Yan Chen, Jiawei Liu, Yang Guo, Ruocheng Zhang, Kun School of Mathematics and Statistics Beijing Technology and Business University China Center for Data Science Peking University China School of Mathematical Sciences Peking University China College of Computer Science and Technology Zhejiang University China Computer Science and Engineering University of California Santa Cruz Bolivia ByteDance Research China Department of Philosophy Carnegie Mellon University United States Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

Counterfactual inference aims to estimate the counterfactual outcome at the individual level given knowledge of an observed treatment and the factual outcome, with broad applications in fields such as epidemiology, econometrics, and management science. Previous methods rely on a known structural causal model (SCM) or assume the homogeneity of the exogenous variable and strict monotonicity between the outcome and exogenous variable. In this paper, we propose a principled approach for identifying and estimating the counterfactual outcome. We first introduce a simple and intuitive rank preservation assumption to identify the counterfactual outcome without relying on a known structural causal model. Building on this, we propose a novel ideal loss for theoretically unbiased learning of the counterfactual outcome and further develop a kernel-based estimator for its empirical estimation. Our theoretical analysis shows that the rank preservation assumption is not stronger than the homogeneity and strict monotonicity assumptions, and shows that the proposed ideal loss is convex, and the proposed estimator is unbiased. Extensive semi-synthetic and real-world experiments are conducted to demonstrate the effectiveness of the proposed method. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive learning

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New theoretical ISM-K2 Bayesian network model for evaluating vaccination effectiveness

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12789-12805页

作者： Xie, Xiaoliang Xie, Bingqi Xiong, Dan Hou, Muzhou Zuo, Jinxia Wei, Guo Chevallier, Julien School of Mathematics and Statistics Hunan University of Technology and Busin Ess Changsha410205 China School of Mathematics and Statistics Central South University Changsha410083 China Key Laboratory of Hunan Province for Statistical Learning and Intelligent Computation Hunan University of Technology and Business Hunan Changsha410205 China Institute of Big Data and Internet Innovation Hunan University of Technology and Business Changsha410205 China Department of Mathematics and Computer Science University of North Carolina at Pembroke PembrokeNC28372 United States 184 boulevard Saint-Germain Paris75006 France 2 rue de la Liberté Saint-Denis93526 France

Aiming at the difficulty in obtaining a complete Bayesian network (BN) structure directly through search-scoring algorithms, authors attempted to incorporate expert judgment and historical data to construct an interpretive structural model with an ISM-K2 algorithm for evaluating vaccination effectiveness (VE). By analyzing the influenza vaccine data provided by Hunan Provincial Center for Disease Control and Prevention, risk factors influencing VE in each link in the process of "Transportation—Storage—Distribution—Inoculation" were systematically investigated. Subsequently, an evaluation index system of VE and an ISM-K2 BN model were developed. Findings include: (1) The comprehensive quality of the staff handling vaccines has a significant impact on VE;(2) Predictive inference and diagnostic reasoning through the ISM-K2 BN model are stable, effective, and highly interpretable, and consequently, the post-production supervision of vaccines is enhanced. The study provides a theoretical basis for evaluating VE and a scientific tool for tracking the responsibility of adverse events of ineffective vaccines, which has the value of promotion in improving VE and reducing the transmission rate of infectious diseases. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Bayesian networks

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Homotopy reconstruction via the CecH complex and the vietoris-rips complex 36

Homotopy reconstruction via the CecH complex and the vietori...

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36th International Symposium on Computational Geometry, SoCG 2020

作者： Kim, Jisu Shin, Jaehyeok Chazal, Frédéric Rinaldo, Alessandro Wasserman, Larry Inria Saclay - Île-de-France Palaiseau France Department of Statistics and Data Science Carnegie Mellon University PittsburghPA United States Department of Statistics and Data Science Machine Learning Department Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9783959771436

We derive conditions under which the reconstruction of a target space is topologically correct via the Čech complex or the Vietoris-Rips complex obtained from possibly noisy point cloud data. We provide two novel theoretical results. First, we describe sufficient conditions under which any non-empty intersection of finitely many Euclidean balls intersected with a positive reach set is contractible, so that the Nerve theorem applies for the restricted Čech complex. Second, we demonstrate the homotopy equivalence of a positive µ-reach set and its offsets. Applying these results to the restricted Čech complex and using the interleaving relations with the Čech complex (or the Vietoris-Rips complex), we formulate conditions guaranteeing that the target space is homotopy equivalent to the Čech complex (or the Vietoris-Rips complex), in terms of the µ-reach. Our results sharpen existing results. © Jisu Kim, Jaehyeok Shin, Frédéric Chazal, Alessandro Rinaldo, and Larry Wasserman;licensed under Creative Commons License CC-BY 36th International Symposium on Computational Geometry (SoCG 2020).

关键词： Topology

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Introduction to the Special Issue on Causal Inference for Recommender Systems

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ACM Transactions on Recommender Systems 2024年第2期2卷 1-4页

作者： Yongfeng Zhang Xu Chen Da Xu Tobias Schnabel Department of Computer Science Rutgers University New Brunswick United States Gaoling School of Artificial Intelligence Renmin University of China Beijing China Machine Learning Walmart Labs San Bruno United States Information and Data Sciences Microsoft Research Redmond Redmond United States

A significant proportion of machine learning methodologies for recommendation systems are grounded in the fundamental principle of matching, utilizing perceptual and similarity-based learning approaches. These methods include both the extraction of features from data through representation learning and the derivation of similarity matching functions via neural function learning. While these models are important for recommendation systems, their foundational design philosophy primarily captures correlational signals within the data. Transitioning from correlation-based learning to causal learning in recommendation systems represents a critical area to explore, as causal models enable extrapolation beyond observational data in both representation learning and ranking tasks. Specifically, causal learning offers potential enhancements to the recommender system community across multiple dimensions, including, but not limited to, explainable, unbiased, fairness-aware, robust, and cognitive reasoning models for recommendation. This special issue is dedicated to exploring the research and practical applications of causal inference within the realms of recommendation and broader ranking scenarios. It has attracted interest from an array of researchers and practitioners on disseminating the latest developments in causal modeling for recommender systems. Moreover, it has attracted the interest of professionals from various fields such as Information Retrieval, machine learning, Artificial Intelligence, Natural Language Processing, data science, and others.

关键词： Causality causal learning counterfactual learning recommender system information retrieval

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Bringing closure to FDR control: beating the e-Benjamini-Hochberg procedure

arXiv

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arXiv 2025年

作者： Xu, Ziyu Fischer, Lasse Ramdas, Aaditya Department of Statistics and Data Science Carnegie Mellon University United States Competence Center for Clinical Trials Bremen University of Bremen Germany Department of Statistics and Data Science Machine Learning Department Carnegie Mellon University United States

False discovery rate (FDR) has been a key metric for error control in multiple hypothesis testing, and many methods have developed for FDR control across a diverse cross-section of settings and applications. We develop a closure principle for all FDR controlling procedures, i.e., we provide a characterization based on e-values for all admissible FDR controlling procedures. A general version of this closure principle can recover any multiple testing error metric and allows one to choose the error metric post-hoc. We leverage this idea to formulate the closed eBH procedure, a (usually strict) improvement over the eBH procedure for FDR control when provided with e-values. This also yields a closed BY (BY) procedure that dominates the Benjamini-Yekutieli (BY) procedure for FDR control with arbitrarily dependent p-values, thus proving that the latter is inadmissibile. We demonstrate the practical performance of our new procedures in simulations. Copyright © 2025, The Authors. All rights reserved.

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Marine Predators Algorithm for Energy Scheduling Problem Using Renewable Energy

Marine Predators Algorithm for Energy Scheduling Problem Usi...

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Cyber Resilience (ICCR), International Conference on

作者： Sharif Naser Makhadmeh Ammar Kamal Abasi Mohammed Azmi Al-Betar Department of Data Science and Artificial Intelligence University of Petra Amman Jordan Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi United Arab Emirates Artificial Intelligence Research Center (AIRC) Ajman University Ajman United Arab Emirates

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

The Energy Scheduling Problem (ESP) involves scheduling smart home appliances based on electricity pricing schemes. This entails adjusting the timing of operations for these appliances across different periods. The primary aim of this scheduling approach is to reduce cost and the Peak-to-Average Ratio (PAR), while enhancing user comfort. This study employs the Marine Predators Algorithm (MPA) to address ESP and derive an optimal schedule for smart home appliances. Additionally, a renewable energy source (RES) on the basis of a PV system is introduced to further optimize appliance schedules by providing a smart home with energy, particularly during peak periods. The obtained outcomes demonstrate the effectiveness of the introduced RES in reducing costs of electricity and enhancing user satisfaction. Furthermore, the performance of MPA is compared with that of the wind-driven optimization algorithm, with MPA proving superior in achieving ESP objectives.

关键词： Home appliances Optimization Pricing Heuristic algorithms Optimal scheduling Electricity Dynamic scheduling

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Scalable Deep Gaussian Markov Random Fields for General Graphs

arXiv

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arXiv 2022年

作者： Oskarsson, Joel Sidén, Per Lindsten, Fredrik Division of Statistics and Machine Learning Department of Computer and Information Science Linköping University Linköping Sweden Arriver Software AB Sweden

machine learning methods on graphs have proven useful in many applications due to their ability to handle generally structured data. The framework of Gaussian Markov Random Fields (GMRFs) provides a principled way to define Gaussian models on graphs by utilizing their sparsity structure. We propose a flexible GMRF model for general graphs built on the multi-layer structure of Deep GMRFs, originally proposed for lattice graphs only. By designing a new type of layer we enable the model to scale to large graphs. The layer is constructed to allow for efficient training using variational inference and existing software frameworks for Graph Neural Networks. For a Gaussian likelihood, close to exact Bayesian inference is available for the latent field. This allows for making predictions with accompanying uncertainty estimates. The usefulness of the proposed model is verified by experiments on a number of synthetic and real world datasets, where it compares favorably to other both Bayesian and deep learning methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Gaussian distribution

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Investor Risk Profile Determination Model

Investor Risk Profile Determination Model

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Victor Gorelik Tatiana Zolotova Department of Simulation Systems and Operations Research Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Moscow Russia Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation Moscow Russia

An assessment of the investor’s risk profile is proposed as a risk coefficient in a model with a linear convolution of expected return and variance. The value of the risk coefficient is found from solving the optimization problem of the model with a limitation on profitability. The proposed method is implemented in the form of a software package using the example of the stock market.

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COUNTARFACTUALS - GENERATING PLAUSIBLE MODEL-AGNOSTIC COUNTERFACTUAL EXPLANATIONS WITH ADVERSARIAL RANDOM FORESTS

arXiv

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arXiv 2024年

作者： Dandl, Susanne Blesch, Kristin Freiesleben, Timo König, Gunnar Kapar, Jan Bischl, Bernd Wright, Marvin N. Department of Statistics LMU Munich Germany Leibniz Institute for Prevention Research & Epidemiology BIPS Germany Faculty of Mathematics and Computer Science University of Bremen Germany Department of Public Health University of Copenhagen Denmark Cluster: Machine Learning for Science University of Tübingen Germany Tübingen AI Center University of Tübingen Germany

Counterfactual explanations elucidate algorithmic decisions by pointing to scenarios that would have led to an alternative, desired outcome. Giving insight into the model's behavior, they hint users towards possible actions and give grounds for contesting decisions. As a crucial factor in achieving these goals, counterfactuals must be plausible, i.e., describing realistic alternative scenarios within the data manifold. This paper leverages a recently developed generative modeling technique - adversarial random forests (ARFs) - to efficiently generate plausible counterfactuals in a model-agnostic way. ARFs can serve as a plausibility measure or directly generate counterfactual explanations. Our ARF-based approach surpasses the limitations of existing methods that aim to generate plausible counterfactual explanations: It is easy to train and computationally highly efficient, handles continuous and categorical data naturally, and allows integrating additional desiderata such as sparsity in a straightforward manner. © 2024, CC BY-SA.

关键词： machine learning

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SPEAR: Design and Implementation of an Advanced Virtual Assistant

SPEAR: Design and Implementation of an Advanced Virtual Assi...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Garima Jain Amita Shukla Nitesh Kumar Bairwa Anamika Chaudhary Ashish Patel Ankush Jain Department of Computer Science and Buisness Systems Noida Institute of Engineering and Technology Greater Noida India Department of Artificial Intelligence Noida Institute of Engineering and Technology Greater Noida India Department of Data Science Noida Institute of Engineering and Technology Greater Noida India Department of Artificial Intelligence and Machine Learning Dronacharya Group of Institutions Greater Noida India Department of Computer Science and Enginnering Netaji Subhash University of Technology Delhi India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

This research presents the development and evaluation of SPEAR, an advanced voice-activated personal desktop assistant designed to address challenges in existing virtual assistant technology, such as limited language support, low recognition accuracy, and inadequate task automation. Despite recent advancements in Natural Language Processing (NLP) and machine learning, implementation in current systems remains subpar. SPEAR achieves an 88% overall command execution success rate, with a speech recognition accuracy of 92% and an average response time of 1.8 seconds, demonstrating efficiency in real-time interactions. Developed using Python and machine learning libraries, SPEAR supports 15 voice commands across multiple platforms, excelling in note creation (95% success), web searching (94%), and information retrieval (92%). Its natural language understanding score of 0.86 and context retention rate of 75% highlight its conversational capabilities. While the assistant performs well in messaging functionality (85% success) and currently supports three languages, expanding its multilingual capabilities is essential for broader user engagement. Metrics indicate a satisfaction score of 8.2/10 and an average of 12 daily interactions per user, showcasing its practical utility. Additionally, the system's text-to-speech quality (8.5/10) and error recovery rate (82 %) affirm its robustness. These findings contribute valuable insights for future research, focusing on enhancing multilingual support, broadening task categories, and improving context retention, ultimately positioning SPEAR to rival established industry leaders by offering a comprehensive suite of services.

关键词： Accuracy Personal voice assistants machine learning Information retrieval Natural language processing Real-time systems Robustness Text to speech Time factors Web search

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