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Novel View Synthesis on Unpaired data by Conditional Deformable Variational Auto-Encoder

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arXiv 2020年

作者： Yin, Mingyu Sun, Li Li, Qingli Shanghai Key Laboratory of Multidimensional Information Processing China Key Laboratory of Advanced Theory and Application in Statistics & Data Science East China Normal University Shanghai200241 China

Novel view synthesis often needs the paired data from both the source and target views. This paper proposes a view translation model under cVAE-GAN framework without requiring the paired data. We design a conditional deformable module (CDM) which uses the view condition vectors as the filters to convolve the feature maps of the main branch in VAE. It generates several pairs of displacement maps to deform the features, like the 2D optical flows. The results are fed into the deformed feature based normalization module (DFNM), which scales and offsets the main branch feature, given its deformed one as the input from the side branch. Taking the advantage of the CDM and DFNM, the encoder outputs a view-irrelevant posterior, while the decoder takes the code drawn from it to synthesize the reconstructed and the view-translated images. To further ensure the disentanglement between the views and other factors, we add adversarial training on the code. The results and ablation studies on MultiPIE and 3D chair datasets validate the effectiveness of the framework in cVAE and the designed module. https://***/MingyuY/deformable-view-synthesis Copyright © 2020, The Authors. All rights reserved.

关键词： Signal encoding

Modelling Correlation Matrices in Multivariate Dyadic data: Latent Variable Models for Intergenerational Exchanges of Family Support

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arXiv 2022年

作者： Zhang, Siliang Kuha, Jouni Steele, Fiona Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University China Department of Statistics London School of Economics & Political Science United Kingdom

We define a model for the joint distribution of multiple continuous latent variables which includes a model for how their correlations depend on explanatory variables. This is motivated by and applied to social scientific research questions in the analysis of intergenerational help and support within families, where the correlations describe reciprocity of help between generations and complementarity of different kinds of help. We propose an MCMC procedure for estimating the model which maintains the positive definiteness of the implied correlation matrices, and describe theoretical results which justify this approach and facilitate efficient implementation of it. The model is applied to data from the UK Household Longitudinal Study to analyse exchanges of practical and financial support between adult individuals and their non-coresident parents. Copyright © 2022, The Authors. All rights reserved.

关键词： Covariance matrix

Rejoinder on‘A review of distributed statistical inference’

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Statistical theory and Related Fields 2022年第2期6卷 111-113页

作者： Yuan Gao Weidong Liu Hansheng Wang Xiaozhou Wang Yibo Yana Riquan Zhang School of Statistics and Key Laboratory of Advanced Theory and Application in Statistics and Data Science–MOE East China Normal UniversityShanghaiPeople’s Republic of China School of Mathematical Sciences–School of Life Sciences and Biotechnology–MOE Key Lab of Artifcial Intelligence Shanghai Jiao Tong UniversityShanghaiPeople’s Republic of China Guanghua School of Management Peking UniversityBeijingPeople’s Republic of China

We thank the editor,Professor Jun Shao,for orga-nizing this stimulating *** are grateful to all discussants for their insightful comments on our review article on the distributed statistical *** to the urgent need to process the datasets with massive sizes,various distributed computing methods have been proposed for the large-scale statistical ***,some important theoretical results were *** we want to give a relatively comprehensive overview on this hot topic,there are still some important works that have been missed in our review written over a year ***,we are glad to see the discussants provide reviews of some new works and *** hope that these discussions and our review would serve as a stimulus for further studies in this rapidly developing area.

关键词： editor discussions sizes

Distributed algorithms for u-statistics-based empirical risk minimization

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The Journal of Machine Learning Research

The Journal of Machine Learning Research 2023年第1期24卷 12301-12343页

作者： Lanjue Chen Alan T.K. Wan Shuyi Zhang Yong Zhou Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education Academy of Statistics and Interdisciplinary Sciences and School of Statistics East China Normal University Shanghai China Department of Management Sciences School of Data Science and Department of Biostatistics City University of Hong Kong Kowloon Hong Kong

Empirical risk minimization, where the underlying loss function depends on a pair of data points, covers a wide range of application areas in statistics including pairwise ranking and survival analysis. The common empirical risk estimator obtained by averaging values of a loss function over all possible pairs of observations is essentially a U-statistic. One well-known problem with minimizing U-statistic type empirical risks, is that the computational complexity of U-statistics increases quadratically with the sample size. When faced with big data, this poses computational challenges as the colossal number of observation pairs virtually prohibits centralized computing to be performed on a single machine. This paper addresses this problem by developing two computationally and statistically efficient methods based on the divide-and-conquer strategy on a decentralized computing system, whereby the data are distributed among machines to perform the tasks. One of these methods is based on a surrogate of the empirical risk, while the other method extends the one-step updating scheme in classical M-estimation to the case of pairwise loss. We show that the proposed estimators are as asymptotically efficient as the benchmark global U-estimator obtained under centralized computing. As well, we introduce two distributed iterative algorithms to facilitate the implementation of the proposed methods, and conduct extensive numerical experiments to demonstrate their merit.

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ID-Unet: Iterative soft and hard deformation for view synthesis

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arXiv 2021年

作者： Yin, Mingyu Sun, Li Li, Qingli Shanghai Key Laboratory of Multidimensional Information Processing Shanghi China Key Laboratory of Advanced Theory and Application in Statistics & Data Science East China Normal University Shanghai200241 China

View synthesis is usually done by an autoencoder, in which the encoder maps a source view image into a latent content code, and the decoder transforms it into a target view image according to the condition. However, the source contents are often not well kept in this setting, which leads to unnecessary changes during the view translation. Although adding skipped connections, like Unet, alleviates the problem, but it often causes the failure on the view conformity. This paper proposes a new architecture by performing the source-to-target deformation in an iterative way. Instead of simply incorporating the features from multiple layers of the encoder, we design soft and hard deformation modules, which warp the encoder features to the target view at different resolutions, and give results to the decoder to complement the details. Particularly, the current warping flow is not only used to align the feature of the same resolution, but also as an approximation to coarsely deform the high resolution feature. Then the residual flow is estimated and applied in the high resolution, so that the deformation is built up in the coarse-to-fine fashion. To better constrain the model, we synthesize a rough target view image based on the intermediate flows and their warped features. The extensive ablation studies and the final results on two different data sets show the effectiveness of the proposed model. https://***/MingyuY/Iterative-view-synthesis © 2021, CC BY.

关键词： Signal encoding

Regional consistency evaluation and sample size calculation under two MRCTs

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arXiv 2024年

作者： Qing, Kunhai Ren, Xinru Xu, Jin School of Statistics East China Normal University Shanghai China Key Laboratory of Advanced Theory and Application in Statistics and Data Science - MOE East China Normal University Shanghai China

Multi-regional clinical trial (MRCT) has been common practice for drug development and global registration. The FDA guidance "Demonstrating Substantial Evidence of Effectiveness for Human Drug and Biological Products Guidance for Industry" (FDA, 2019) requires that substantial evidence of effectiveness of a drug/biologic product to be demonstrated for market approval. In the situations where two pivotal MRCTs are needed to establish effectiveness of a specific indication for a drug or biological product, a systematic approach of consistency evaluation for regional effect is crucial. In this paper, we first present some existing regional consistency evaluations in a unified way that facilitates regional sample size calculation under the simple fixed effect model. Second, we extend the two commonly used consistency assessment criteria of MHLW (2007) in the context of two MRCTs and provide their evaluation and regional sample size calculation. Numerical studies demonstrate the proposed regional sample size attains the desired probability of showing regional consistency. A hypothetical example is provided for illustration of application. We provide an R package for implementation. Copyright © 2024, The Authors. All rights reserved.

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Confidence intervals and hypothesis testing for high-dimensional quantile regression: convolution smoothing and debiasing

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The Journal of Machine Learning Research

The Journal of Machine Learning Research 2023年第1期24卷 11626-11674页

作者： Yibo Yan Xiaozhou Wang Riquan Zhang School of Statistics East China Normal University Shanghai China School of Statistics Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE East China Normal University Shanghai China School of Statistics and Information Shanghai University of International Business and Economics Shanghai China

ℓ1-penalized quantile regression (ℓ1-QR) is a useful tool for modeling the relationship between input and output variables when detecting heterogeneous effects in the high-dimensional setting. Hypothesis tests can then be formulated based on the debiased ℓ1-QR estimator that reduces the bias induced by Lasso penalty. However, the nonsmoothness of the quantile loss brings great challenges to the computation, especially when the data dimension is high. Recently, the convolution-type smoothed quantile regression (SQR) model has been proposed to overcome such shortcoming, and people developed theory of estimation and variable selection therein. In this work, we combine the debiased method with SQR model and come up with the debiased ℓ1-SQR estimator, based on which we then establish confidence intervals and hypothesis testing in the high-dimensional setup. Theoretically, we provide the non-asymptotic Bahadur representation for our proposed estimator and also the Berry-Esseen bound, which implies the empirical coverage rates for the studentized confidence intervals. Furthermore, we build up the theory of hypothesis testing on both a single variable and a group of variables. Finally, we exhibit extensive numerical experiments on both simulated and real data to demonstrate the good performance of our method.

关键词： high-dimensional quantile regression convolution-based smoothing debiased method hypothesis testing non-asymptotic statistics

SPD-DDPM: Denoising Diffusion Probabilistic Models in the Symmetric Positive Definite Space

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arXiv 2023年

作者： Li, Yunchen Yu, Zhou He, Gaoqi Shen, Yunhang Li, Ke Sun, Xing Lin, Shaohui East China Normal University China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education China Youtu Lab Tencent Shanghai China

Symmetric positive definite (SPD) matrices have shown important value and applications in statistics and machine learning, such as FMRI analysis and traffic prediction. Previous works on SPD matrices mostly focus on discriminative models, where predictions are made directly on E(X|y), where y is a vector and X is an SPD matrix. However, these methods are challenging to handle for large-scale data, as they need to access and process the whole data. In this paper, inspired by denoising diffusion probabilistic model (DDPM), we propose a novel generative model, termed SPD-DDPM, by introducing Gaussian distribution in the SPD space to estimate E(X|y). Moreover, our model is able to estimate p(X) unconditionally and flexibly without giving y. On the one hand, the model conditionally learns p(X|y) and utilizes the mean of samples to obtain E(X|y) as a prediction. On the other hand, the model unconditionally learns the probability distribution of the data p(X) and generates samples that conform to this distribution. Furthermore, we propose a new SPD net which is much deeper than the previous networks and allows for the inclusion of conditional factors. Experiment results on toy data and real taxi data demonstrate that our models effectively fit the data distribution both unconditionally and unconditionally and provide accurate predictions. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

Large and moderate deviations for record numbers in some non–nearest neighbor random walks

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arXiv 2019年

作者： Li, Yuqiang Yao, Qiang Key Laboratory of Advanced Theory and Application in Statistics and Data Science–MOE School of Statistics East China Normal University China NYU–ECNU Institute of Mathematical Sciences NYU Shanghai China

The deviation principles of record numbers in random walk models have not been completely investigated, especially for the non–nearest neighbor cases. In this paper, we derive the asymptotic probabilities of large and moderate deviations for the number of "weak records" (or "ladder points") in two kinds of one–dimensional non–nearest neighbor random walks. The proofs depend only on the direct analysis of random walks. We illustrate that the traditional method of analyzing the local time of Brownian motions, which is often adopted for the simple random walks, would lead to wrong conjectures for our *** Codes 60F10, 60G50 Copyright © 2019, The Authors. All rights reserved.

关键词： Brownian movement