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Ensemble Doubly Robust Bayesian Inference via Regression Synthesis

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arXiv 2024年

作者： Babasaki, Kaoru Sugasawa, Shonosuke Takanashi, Kōsaku McAlinn, Kenichiro Graduate School of Economics Keio University Japan Faculty of Economics Keio University Japan RIKEN Center for Advanced Intelligence Project Japan Department of Statistics Operations and Data Science Fox School of Business Temple University United States

The doubly robust estimator, which models both the propensity score and outcomes, is a popular approach to estimate the average treatment effect in the potential outcome setting. The primary appeal of this estimator is its theoretical property, wherein the estimator achieves consistency as long as either the propensity score or outcomes is correctly specified. In most applications, however, both are misspecified, leading to considerable bias that cannot be checked. In this paper, we propose a Bayesian ensemble approach that synthesizes multiple models for both the propensity score and outcomes, which we call doubly robust Bayesian regression synthesis. Our approach applies Bayesian updating to the ensemble model weights that adapt at the unit level, incorporating data heterogeneity, to significantly mitigate misspecification bias. Theoretically, we show that our proposed approach is consistent regarding the estimation of both the propensity score and outcomes, ensuring that the doubly robust estimator is consistent, even if no single model is correctly specified. An efficient algorithm for posterior computation facilitates the characterization of uncertainty regarding the treatment effect. Our proposed approach is compared against standard and state-of-the-art methods through two comprehensive simulation studies, where we find that our approach is superior in all cases. An empirical study on the impact of maternal smoking on birth weight highlights the practical applicability of our proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Uncertainty analysis

Efficient HLA imputation from sequential SNPs data by Transformer

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arXiv 2022年

作者： Tanaka, Kaho Kato, Kosuke Nonaka, Naoki Seita, Jun Faculty of Engineering Kyoto University Japan Advanced Data Science Project RIKEN Information R& D and Strategy Headquarters Japan

Human leukocyte antigen (HLA) genes are associated with a variety of diseases, however direct typing of HLA is time and cost consuming. Thus various imputation methods using sequential SNPs data have been proposed based on statistical or deep learning models, e.g. CNN-based model, named DEEP*HLA. However, imputation efficiency is not sufficient for infrequent alleles and a large size of reference panel is required. Here, we developed a Transformer-based model to impute HLA alleles, named"HLA Reliable IMputatioN by Transformer (HLARIMNT)" to take advantage of sequential nature of SNPs data. We validated the performance of HLARIMNT using two different reference panels;Pan-Asian reference panel (n = 530) and Type 1 Diabetes Genetics Consortium (T1DGC) reference panel (n = 5, 225), as well as the mixture of those two panels (n = 1, 060). HLARIMNT achieved higher accuracy than DEEP*HLA by several indices, especially for infrequent alleles. We also varied the size of data used for training, and HLARIMNT imputed more accurately among any size of training data. These results suggest that Transformer-based model may impute efficiently not only HLA types but also any other gene types from sequential SNPs data. © 2022, CC BY.

关键词： Genes

Privacy preservation in permissionless blockchain:A survey

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Digital Communications and Networks 2021年第3期7卷 295-307页

作者： Li Peng Wei Feng Zheng Yan Yafeng Li Xiaokang Zhou Shohei Shimizu School of Cyber Engineering Xidian UniversityXi’an710071China Department of Communications and Networking Aalto UniversityEspoo02150Finland The 20th Research Institute of China Electronics Technology Group Corporation Xi’an710061China Faculty of Data Science Shiga UniversityHikoneJapan RIKEN Center for Advanced Intelligence Project TokyoJapan

Permissionless blockchain,as a kind of distributed ledger,has gained considerable attention because of its openness,transparency,decentralization,and ***,permissionless blockchain has shown a good application prospect in many fields,from the initial cryptocurrency to the Internet of Things(IoT)and Vehicular Ad-Hoc Networking(VANET),which is considered as the beginning of rewriting our digital ***,blockchain confronts some privacy risks that hinder its practical *** numerous surveys reviewed the privacy preservation in blockchain,they failed to reveal the latest advances,nor have they been able to conduct a unified standard comprehensive classification of the privacy protection of permissionless ***,in this paper,we analyze the specific characteristics of permissionless blockchain,summarize the potential privacy threats,and investigate the unique privacy requirements of *** privacy preservation technologies are carefully surveyed and evaluated based on our proposed evaluation *** finally figure out open research issues as well as future research directions from the perspective of privacy issues.

关键词： Blockchain Privacy Cryptocurrency Anonymity

Koopman-based generalization bound: New aspect for full-rank weights

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arXiv 2023年

作者： Hashimoto, Yuka Sonoda, Sho Ishikawa, Isao Nitanda, Atsushi Suzuki, Taiji NTT Corporation Japan Center for Advanced Intelligence Project RIKEN Japan Center for Data Science Ehime University Japan A*STAR Centre for Frontier AI and Research Singapore Graduate School of Information Science and Technology The University of Tokyo Japan

We propose a new bound for generalization of neural networks using Koopman operators. Whereas most of existing works focus on low-rank weight matrices, we focus on full-rank weight matrices. Our bound is tighter than existing norm-based bounds when the condition numbers of weight matrices are small. Especially, it is completely independent of the width of the network if the weight matrices are orthogonal. Our bound does not contradict to the existing bounds but is a complement to the existing bounds. As supported by several existing empirical results, low-rankness is not the only reason for generalization. Furthermore, our bound can be combined with the existing bounds to obtain a tighter bound. Our result sheds new light on understanding generalization of neural networks with full-rank weight matrices, and it provides a connection between operator-theoretic analysis and generalization of neural networks. © 2023, CC BY.

关键词： Number theory

Scaling Capability in Token Space: An Analysis of Large Vision Language Model

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arXiv 2024年

作者： Li, Tenghui Zhou, Guoxu Zhao, Xuyang Zhao, Qibin School of Automation Guangdong University of Technology Guangzhou510006 China Tensor Learning Team RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Advanced Data Science Project RIKEN Information R&D and Strategy Headquarters Yokohama230-0045 Japan Department of Artificial Intelligence Medicine Graduate School of Medicine Chiba University Chiba260-0856 Japan

The scaling capability has been widely validated in neural language models with respect to the number of parameters and the size of training data. One important question is that does the scaling capability also exists similarly with respect to the number of vision tokens in large vision language Model? This study fills the gap by investigating the relationship between the number of vision tokens and the performance on vision-language models. Our theoretical analysis and empirical evaluations demonstrate that the model exhibits scalable performance S(Nl) with respect to the number of vision tokens Nl, characterized by the relationship S(Nl) ≈ (c/Nl)α. Furthermore, we also investigate the impact of a fusion mechanism that integrates the user’s question with vision tokens. The results reveal two key findings. First, the scaling capability remains intact with the incorporation of the fusion mechanism. Second, the fusion mechanism enhances model performance, particularly when the user’s question is task-specific and relevant. The analysis, conducted on fifteen diverse benchmarks spanning a broad range of tasks and domains, validates the effectiveness of the proposed approach. © 2024, CC BY.

关键词： Visual languages

A Novel Gold Futures Price Prediction Model based on PCA-AGRU

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A Novel Gold Futures Price Prediction Model based on PCA-AGR...

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Jingyang Wang Tong Lu Jindian Liu Qiuhong Sun Lianyong Qi Xiaokang Zhou School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang China Faculty of Data Science Shiga University Hikone Japan China University of Petroleum (East China) Qingdao China RIKEN Center for Advanced Intelligence Project RIKEN Tokyo Japan

As an important financial investment commodity, the price fluctuations of gold significantly impact the global economy and the stability of the financial market. Therefore, it is of great significance to accurately predict the price of gold futures. This paper presents a novel gold futures price prediction model, PCA-AGRU, based on Principal Component Analysis (PCA) and Adaptive Gated Recurrent Unit (AGRU). PCA is utilized for the dimensionality reduction of data while extracting essential features. AGRU bases on Gated Recurrent Unit (GRU), embeds Self-Attention (SA), and adds an adaptive adjustment mechanism, making the model more effective in capturing long-term dependencies within time series data. This paper uses the international gold futures market data as the experimental dataset, and uses Maximal Information Coefficient (MIC) to analyze the correlation of the influencing factors. The PCA-AGRU model is compared with five prediction models of GRU, SA-GRU, AGRU, PCA-GRU, and PCA-SA-GRU. The experimental results show that the PCA-AGRU model performs best in forecasting gold futures prices.

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A Unified Analysis for Finite Weight Averaging

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arXiv 2024年

作者： Wang, Peng Shen, Li Tao, Zerui Sun, Yan Zheng, Guodong Tao, Dacheng School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan China School of Cyber Science & Technology Shenzhen Campus of Sun Yat-sen University Shenzhen China RIKEN Center for Advanced Intelligence Project Tokyo Japan Faculty of Engineering The University of Sydney Australia School of Computer and Data Science Nanyang Technological University Singapore

Averaging iterations of Stochastic Gradient Descent (SGD) have achieved empirical success in training deep learning models, such as Stochastic Weight Averaging (SWA), Exponential Moving Average (EMA), and LAtest Weight Averaging (LAWA). Especially, with a finite weight averaging method, LAWA can attain faster convergence and better generalization. However, its theoretical explanation is still less explored since there are fundamental differences between finite and infinite settings. In this work, we first generalize SGD and LAWA as Finite Weight Averaging (FWA) and explain their advantages compared to SGD from the perspective of optimization and generalization. A key challenge is the inapplicability of traditional methods in the sense of expectation or optimal values for infinite-dimensional settings in analyzing FWA’s convergence. Second, the cumulative gradients introduced by FWA introduce additional confusion to the generalization analysis, especially making it more difficult to discuss them under different assumptions. Extending the final iteration convergence analysis to the FWA, this paper, under a convexity assumption, establishes a convergence bound O(log (T/k) /√T), where k ∈ [1, T/2] is a constant representing the last k iterations. Compared to SGD with O(log(T)/√T), we prove theoretically that FWA has a faster convergence rate and explain the effect of the number of average points. In the generalization analysis, we find a recursive representation for bounding the cumulative gradient using mathematical induction. We provide bounds for constant and decay learning rates and the convex and non-convex cases to show the good generalization performance of ***, experimental results on several benchmarks verify our theoretical results. © 2024, CC BY.

关键词： Stochastic systems

Autonomous adaptive control of manufacturing parameters based on local regression modeling

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Behaviormetrika 2024年第1期51卷 499-513页

作者： Takayanagi, Masayoshi Yoshino, Mutsumi Kikuchi, Genta Kanke, Tomoko Suzuki, Noriyuki The Center for Data Science Education and Research Shiga University Hikone 522-8522 Japan School of Statistical Thinking The Institute of Statistical Mathematics 10-3 Midori-cho Tokyo Tachikawa 190-8562 Japan RIKEN Center for Advanced Intelligence Project Tokyo 103-0027 Japan Monozukuri DX Promotion Division DENSO Corporation 1-1 Showa-cho Aichi Kariya 448-8661 Japan Quality Control Division DENSO Corporation 1-1 Showa-cho Aichi Kariya 448-8661 Japan

The demand for the autonomous adaptive control of manufacturing lines has been growing to realize productivity improvement and carbon neutrality. We propose a methodology to realize the autonomous control of product quality under the existence of effects from non-measurable parameters. By local linear regression modeling with temporal neighborhood data, a single manufacturing parameter is selected by the obtained regression coefficients. Simulation results demonstrated that the straightforward multiple regression modeling often resulted in unstable control behavior with vibrations in product quality. We tested three approaches (scaling the amount of control, introduction of control interval, and semiparametric regression modeling for regression) to cope with the unstable behavior. The semiparametric regression model exhibited the best performance in realizing the stable control by correctly selecting the control target parameter with the additional non-linear term, that compensates the time-dependent non-measurable parameters. © 2022, The Author(s).

关键词： Adaptive control Lazy learning Manufacturing parameter Moving window Semiparametric regression Soft sensor

data Freshness Performance Analysis in NOMA-Enabled Green Mobile Crowdsensing

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Data Freshness Performance Analysis in NOMA-Enabled Green Mo...

IEEE International Conference on Communications (ICC)

作者： Yaoqi Yang Bangning Zhang Daoxing Guo Renhui Xu Lin Ge Weizheng Wang Xiaokang Zhou Communication Engineering School Army Engineering University Nanjing China Department of Computer Science City University of Hong Kong China Faculty of Data Science Shiga University Hikone Japan The RIKEN Center for Advanced Intelligence Project RIKEN Tokyo Japan

Green communication has attracted lots of attention recently, where NOMA (Non-Orthogonal Multiple Access) is one of the most promising technologies to realize energy-efficient communication. Specifically, by making massive wireless devices connect to the same time-frequency resource, NOMA can enhance the spectrum efficiency. In this paper, to investigate the freshness of the sensing data, we analyze the Age of Information (AoI) performance in NOMA-enabled Mobile Crowdsensing (MCS) circumstance, where the stochastic geometry theory is adopted. Firstly, we focus on the data submission process between mobile workers (MWs) and service provides (SPs), which drives to establish a model of the NOMA-enabled MCS. Then, given the transmission schemes of NOMA and OMA (Orthogonal Multiple Access), the mathematical expressions of the AoI metric are derived in the closed form respectively. Furthermore, simulation experiments are conducted to obtain AoI numerical results under various parameter settings (e.g., power strategies, queue models, and transmission protocols). Finally, the evaluation results not only prove the validness of the established models, but also provide some efficient solutions to achieve the optimal AoI value under the considered NOMA-enabled MCS scenario.

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