pwc-archive/papers-with-abstracts huggingface dataset:
Quasi-parametric rates for Sparse Multivariate Functional Principal Components Analysis
Authors: Ryad Belhakem
Date: 2022-12-19 00:00:00

This work aims to give non-asymptotic results for estimating the first principal component of a multivariate random process. We first define the covariance function and the covariance operator in the multivariate case. We then define a projection operator. This operator can be seen as a reconstruction step from the raw data in the functional data analysis context. Next, we show that the eigenelements can be expressed as the solution to an optimization problem, and we introduce the LASSO variant of this optimization problem and the associated plugin estimator. Finally, we assess the estimator's accuracy. We establish a minimax lower bound on the mean square reconstruction error of the eigenelement, which proves that the procedure has an optimal variance in the minimax sense.
Gradient Descent-Type Methods: Background and Simple Unified Convergence Analysis
Authors: Quoc Tran-Dinh, Marten van Dijk
Date: 2022-12-19 00:00:00
Tasks: Vocal Bursts Type Prediction

In this book chapter, we briefly describe the main components that constitute the gradient descent method and its accelerated and stochastic variants. We aim at explaining these components from a mathematical point of view, including theoretical and practical aspects, but at an elementary level. We will focus on basic variants of the gradient descent method and then extend our view to recent variants, especially variance-reduced stochastic gradient schemes (SGD). Our approach relies on revealing the structures presented inside the problem and the assumptions imposed on the objective function. Our convergence analysis unifies several known results and relies on a general, but elementary recursive expression. We have illustrated this analysis on several common schemes.
Multi-Task Learning for Sparsity Pattern Heterogeneity: Statistical and Computational Perspectives
Authors: Kayhan Behdin, Gabriel Loewinger, Kenneth T. Kishida, Giovanni Parmigiani, Rahul Mazumder
Date: 2022-12-16 00:00:00
Tasks: Multi-Task Learning, Variable Selection

We consider a problem in Multi-Task Learning (MTL) where multiple linear models are jointly trained on a collection of datasets ("tasks"). A key novelty of our framework is that it allows the sparsity pattern of regression coefficients and the values of non-zero coefficients to differ across tasks while still leveraging partially shared structure. Our methods encourage models to share information across tasks through separately encouraging 1) coefficient supports, and/or 2) nonzero coefficient values to be similar. This allows models to borrow strength during variable selection even when non-zero coefficient values differ across tasks. We propose a novel mixed-integer programming formulation for our estimator. We develop custom scalable algorithms based on block coordinate descent and combinatorial local search to obtain high-quality (approximate) solutions for our estimator. Additionally, we propose a novel exact optimization algorithm to obtain globally optimal solutions. We investigate the theoretical properties of our estimators. We formally show how our estimators leverage the shared support information across tasks to achieve better variable selection performance. We evaluate the performance of our methods in simulations and two biomedical applications. Our proposed approaches appear to outperform other sparse MTL methods in variable selection and prediction accuracy. We provide the sMTL package on CRAN.
Marginal-Certainty-aware Fair Ranking Algorithm
Authors: Tao Yang, Zhichao Xu, Zhenduo Wang, Anh Tran, Qingyao Ai
Date: 2022-12-18 00:00:00
Tasks: Fairness

Ranking systems are ubiquitous in modern Internet services, including online marketplaces, social media, and search engines. Traditionally, ranking systems only focus on how to get better relevance estimation. When relevance estimation is available, they usually adopt a user-centric optimization strategy where ranked lists are generated by sorting items according to their estimated relevance. However, such user-centric optimization ignores the fact that item providers also draw utility from ranking systems. It has been shown in existing research that such user-centric optimization will cause much unfairness to item providers, followed by unfair opportunities and unfair economic gains for item providers. To address ranking fairness, many fair ranking methods have been proposed. However, as we show in this paper, these methods could be suboptimal as they directly rely on the relevance estimation without being aware of the uncertainty (i.e., the variance of the estimated relevance). To address this uncertainty, we propose a novel Marginal-Certainty-aware Fair algorithm named MCFair. MCFair jointly optimizes fairness and user utility, while relevance estimation is constantly updated in an online manner. In MCFair, we first develop a ranking objective that includes uncertainty, fairness, and user utility. Then we directly use the gradient of the ranking objective as the ranking score. We theoretically prove that MCFair based on gradients is optimal for the aforementioned ranking objective. Empirically, we find that on semi-synthesized datasets, MCFair is effective and practical and can deliver superior performance compared to state-of-the-art fair ranking methods. To facilitate reproducibility, we release our code https://github.com/Taosheng-ty/WSDM22-MCFair.
Learning with linear mixed model for group recommendation systems
Authors: Baode Gao, Guangpeng Zhan, Hanzhang Wang, Yiming Wang, Shengxin Zhu
Date: 2022-12-17 00:00:00
Tasks: Articles, Recommendation Systems

Accurate prediction of users' responses to items is one of the main aims of many computational advising applications. Examples include recommending movies, news articles, songs, jobs, clothes, books and so forth. Accurate prediction of inactive users' responses still remains a challenging problem for many applications. In this paper, we explore the linear mixed model in recommendation system. The recommendation process is naturally modelled as the mixed process between objective effects (fixed effects) and subjective effects (random effects). The latent association between the subjective effects and the users' responses can be mined through the restricted maximum likelihood method. It turns out the linear mixed models can collaborate items' attributes and users' characteristics naturally and effectively. While this model cannot produce the most precisely individual level personalized recommendation, it is relative fast and accurate for group (users)/class (items) recommendation. Numerical examples on GroupLens benchmark problems are presented to show the effectiveness of this method.
Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders
Authors: Qi Le, Enmao Diao, Xinran Wang, Ali Anwar, Vahid Tarokh, Jie Ding
Date: 2022-12-17 00:00:00
Tasks: Decoder, Federated Learning, Marketing, Recommendation Systems

Recommender Systems (RSs) have become increasingly important in many application domains, such as digital marketing. Conventional RSs often need to collect users' data, centralize them on the server-side, and form a global model to generate reliable recommendations. However, they suffer from two critical limitations: the personalization problem that the RSs trained traditionally may not be customized for individual users, and the privacy problem that directly sharing user data is not encouraged. We propose Personalized Federated Recommender Systems (PersonalFR), which introduces a personalized autoencoder-based recommendation model with Federated Learning (FL) to address these challenges. PersonalFR guarantees that each user can learn a personal model from the local dataset and other participating users' data without sharing local data, data embeddings, or models. PersonalFR consists of three main components, including AutoEncoder-based RSs (ARSs) that learn the user-item interactions, Partially Federated Learning (PFL) that updates the encoder locally and aggregates the decoder on the server-side, and Partial Compression (PC) that only computes and transmits active model parameters. Extensive experiments on two real-world datasets demonstrate that PersonalFR can achieve private and personalized performance comparable to that trained by centralizing all users' data. Moreover, PersonalFR requires significantly less computation and communication overhead than standard FL baselines.
Image information and visual quality
Authors: H.R. Sheikh, A.C. Bovik
Date: 2006-02-01 00:00:00
Proceeding: IEEE Transactions on Image Processing 2006 2
Tasks: Full reference image quality assessment, Video Quality Assessment

Measurement of visual quality is of fundamental importance to numerous image and video processing applications. The goal of quality assessment (QA) research is to design algorithms that can automatically assess the quality of images or videos in a perceptually consistent manner. Image QA algorithms generally interpret image quality as fidelity or similarity with a "reference" or "perfect" image in some perceptual space. Such "full-reference" QA methods attempt to achieve consistency in quality prediction by modeling salient physiological and psychovisual features of the human visual system (HVS), or by signal fidelity measures. In this paper, we approach the image QA problem as an information fidelity problem. Specifically, we propose to quantify the loss of image information to the distortion process and explore the relationship between image information and visual quality. QA systems are invariably involved with judging the visual quality of "natural" images and videos that are meant for "human consumption." Researchers have developed sophisticated models to capture the statistics of such natural signals. Using these models, we previously presented an information fidelity criterion for image QA that related image quality with the amount of information shared between a reference and a distorted image. In this paper, we propose an image information measure that quantifies the information that is present in the reference image and how much of this reference information can be extracted from the distorted image. Combining these two quantities, we propose a visual information fidelity measure for image QA. We validate the performance of our algorithm with an extensive subjective study involving 779 images and show that our method outperforms recent state-of-the-art image QA algorithms by a sizeable margin in our simulations. The code and the data from the subjective study are available at the LIVE website.
VSI: A Visual Saliency-Induced Index for Perceptual Image Quality Assessment
Authors: Lin Zhang, Ying Shen, Hongyu Li
Date: 2014-08-07 00:00:00
Proceeding: IEEE Transactions on Image Processing 2014 8
Tasks: Full reference image quality assessment, Image Quality Assessment

Perceptual image quality assessment (IQA) aims to use computational models to measure the image quality in consistent with subjective evaluations. Visual saliency (VS) has been widely studied by psychologists, neurobiologists, and computer scientists during the last decade to investigate, which areas of an image will attract the most attention of the human visual system. Intuitively, VS is closely related to IQA in that suprathreshold distortions can largely affect VS maps of images. With this consideration, we propose a simple but very effective full reference IQA method using VS. In our proposed IQA model, the role of VS is twofold. First, VS is used as a feature when computing the local quality map of the distorted image. Second, when pooling the quality score, VS is employed as a weighting function to reflect the importance of a local region. The proposed IQA index is called visual saliency-based index (VSI). Several prominent computational VS models have been investigated in the context of IQA and the best one is chosen for VSI. Extensive experiments performed on four largescale benchmark databases demonstrate that the proposed IQA index VSI works better in terms of the prediction accuracy than all state-of-the-art IQA indices we can find while maintaining a moderate computational complexity. The MATLAB source code of VSI and the evaluation results are publicly available online at http://sse.tongji.edu.cn/linzhang/IQA/VSI/VSI.htm.
SR-SIM: A fast and high performance IQA index based on spectral residual
Authors: Lin Zhang, Hongyu Li
Date: 2012-09-01 00:00:00
Proceeding: IEEE International Conference on Image Processing 2012 9
Tasks: Full reference image quality assessment, Image Quality Assessment

Automatic image quality assessment (IQA) attempts to use computational models to measure the image quality in consistency with subjective ratings. In the past decades, dozens of IQA models have been proposed. Though some of them can predict subjective image quality accurately, their computational costs are usually very high. To meet real-time requirements, in this paper, we propose a novel fast and effective IQA index, namely spectral residual based similarity (SR-SIM), based on a specific visual saliency model, spectral residual visual saliency. SR-SIM is designed based on the hypothesis that an image's visual saliency map is closely related to its perceived quality. Extensive experiments conducted on three large-scale IQA datasets indicate that SR-SIM could achieve better prediction performance than the other state-of-the-art IQA indices evaluated. Moreover, SR-SIM can have a quite low computational complexity. The Matlab source code of SR-SIM and the evaluation results are available online at http://sse.tongji.edu.cn/linzhang/IQA/SR-SIM/SR-SIM.htm.Collapse
FSIM: A Feature Similarity Index for Image Quality Assessment
Authors: Lin Zhang, Lei Zhang, Xuanqin Mou, David Zhang
Date: 2011-01-31 00:00:00
Proceeding: IEEE Transactions on Image Processing 2011 1
Tasks: Full reference image quality assessment, Image Quality Assessment, Video Quality Assessment

Image quality assessment (IQA) aims to use computational models to measure the image quality consistently with subjective evaluations. The well-known structural similarity index brings IQA from pixel- to structure-based stage. In this paper, a novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features. Specifically, the phase congruency (PC), which is a dimensionless measure of the significance of a local structure, is used as the primary feature in FSIM. Considering that PC is contrast invariant while the contrast information does affect HVS' perception of image quality, the image gradient magnitude (GM) is employed as the secondary feature in FSIM. PC and GM play complementary roles in characterizing the image local quality. After obtaining the local quality map, we use PC again as a weighting function to derive a single quality score. Extensive experiments performed on six benchmark IQA databases demonstrate that FSIM can achieve much higher consistency with the subjective evaluations than state-of-the-art IQA metrics.
Image quality assessment based on DCT subband similarity
Authors: Amnon Balanov, Arik Schwartz, Yair Moshe, Nimrod Peleg
Date: 2015-12-10 00:00:00
Proceeding: IEEE International Conference on Image Processing 2015 12
Tasks: Full reference image quality assessment, Full-Reference Image Quality Assessment, Image Quality Assessment

Measuring image quality becomes increasingly important due to the many applications involving digital imaging and communication. Image quality assessment aims to develop a visual quality metric that correlates well with human visual perception. In this paper, we present a full-reference image quality assessment technique based on DCT Subbands Similarity (DSS). The proposed technique exploits important characteristics of human visual perception by measuring change in structural information in subbands in the discrete cosine transform (DCT) domain and weighting the quality estimates for these subbands. The proposed technique was tested with public image datasets and shows higher correlation with subjective results than state-of-the-art techniques. Another advantage of the proposed technique is its low computational cost.
Gradient magnitude similarity deviation on multiple scales for color image quality assessment
Authors: Bo Zhang, Pedro V. Sander, Amine Bermak
Date: 2017-06-19 00:00:00
Proceeding: IEEE International Conference on Acoustics, Speech and Signal Processing 2017 6
Tasks: Image Quality Assessment

Recently, various image quality assessment (IQA) metrics based on gradient similarity have been developed. In this paper, we extend the work of gradient magnitude similarity deviation (GMSD) and propose a more efficient metric. First, a novel similarity index is proposed, which gives the flexibility to tune the masking parameter to more closely match the human vision system (HVS). Then, we propose a multi-scale GMSD method by incorporating scores of luminance distortion at different scales. Furthermore, a method for measuring chromatic distortions in YIQ color space based on our metric is proposed. The final IQA index, MS-GMSD c , is obtained by combining luminance and chrominance scores. Experimental results on four comprehensive datasets clearly show that, compared with 14 state-of-the-art IQA methods, our method achieves the best performance for both grayscale and chromatic image assessment.
Multiscale structural similarity for image quality assessment
Authors: Z. Wang, E.P. Simoncelli, A.C. Bovik
Date: 2004-05-04 00:00:00
Proceeding: The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers 2004 5
Tasks: Full reference image quality assessment, Image Generation, Image Quality Assessment, Video Quality Assessment

The structural similarity image quality paradigm is based on the assumption that the human visual system is highly adapted for extracting structural information from the scene, and therefore a measure of structural similarity can provide a good approximation to perceived image quality. This paper proposes a multiscale structural similarity method, which supplies more flexibility than previous single-scale methods in incorporating the variations of viewing conditions. We develop an image synthesis method to calibrate the parameters that define the relative importance of different scales. Experimental comparisons demonstrate the effectiveness of the proposed method.
Image quality assessment: from error visibility to structural similarity
Authors: Zhou Wang, A.C. Bovik, H.R. Sheikh; E.P. Simoncelli
Date: 2004-04-13 00:00:00
Proceeding: IEEE Transactions on Image Processing 2004 4
Tasks: Full reference image quality assessment, Image Quality Assessment, SSIM, Video Quality Assessment

Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/.
FovVideoVDP: A visible difference predictor for wide field-of-view video
Authors: Rafał K. Mantiuk, Gyorgy Denes, ALEXANDRE CHAPIRO, Anton Kaplanyan, GIZEM RUFO, ROMAIN BACHY, Trisha Lian, Anjul Patney
Date: 2021-04-01 00:00:00
Proceeding: ACM Transactions on Graphics 2021 4
Tasks: Sensitivity

FovVideoVDP is a video difference metric that models the spatial, temporal, and peripheral aspects of perception. While many other metrics are available, our work provides the first practical treatment of these three central aspects of vision simultaneously. The complex interplay between spatial and temporal sensitivity across retinal locations is especially important for displays that cover a large field-of-view, such as Virtual and Augmented Reality displays, and associated methods, such as foveated rendering. Our metric is derived from psychophysical studies of the early visual system, which model spatio-temporal contrast sensitivity, cortical magnification and contrast masking. It accounts for physical specification of the display (luminance, size, resolution) and viewing distance. To validate the metric, we collected a novel foveated rendering dataset which captures quality degradation due to sampling and reconstruction. To demonstrate our algorithm’s generality, we test it on 3 independent foveated video datasets, and on a large image quality dataset, achieving the best performance across all datasets when compared to the state-of-the-art.
Toward A Practical Perceptual Video Quality Metric
Authors: Netflix
Date: 2016-06-06 00:00:00
Proceeding: Netflix Technology Blog 2016 6

At Netflix we care about video quality, and we care about measuring video quality accurately at scale. Our method, Video Multimethod Assessment Fusion (VMAF), seeks to reflect the viewer’s perception of our streaming quality. We are open-sourcing this tool and invite the research community to collaborate with us on this important project.
QMR:Q-learning based Multi-objective optimization Routing protocol for Flying Ad Hoc Networks
Authors: Jianmin Liu, Qi Wang, ChenTao He, Katia Jaffrès-Runser, Yida Xu, Zhenyu Li, Yongjun Xu
Date: 2019-11-27 00:00:00
Proceeding: Computer Communications 2019 11
Tasks: Q-Learning

A network with reliable and rapid communication is critical for Unmanned Aerial Vehicles (UAVs). Flying Ad Hoc Networks (FANETs) consisting of UAVs is a new paradigm of wireless communication. However, the highly dynamic topology of FANETs and limited energy of UAVs have brought great challenges to the routing design of FANETs. It is difficult for existing routing protocols for Mobile Ad Hoc Networks (MANETs) and Vehicular Ad Hoc Networks (VANETs) to adapt the high dynamics of FANETs. Moreover, few of existing routing protocols simultaneously meet the requirement of low delay and low energy consumption of FANETs. This paper proposes a novel Q-learning based Multi-objective optimization Routing protocol for FANETs to provide low-delay and low-energy service guarantees. Most of existing Q-learning based protocols use a fixed value for the Q-learning parameters. In contrast, Q-learning parameters can be adaptively adjusted in the proposed protocol to adapt to the high dynamics of FANETs. In addition, a new exploration and exploitation mechanism is also proposed to explore some undiscovered potential optimal routing path while exploiting the acquired knowledge. Instead of using past neighbor relationships, the proposed method re-estimates neighbor relationships in the routing decision process to select the more reliable next hop. Simulation results show that the proposed method can provide higher packet arrival ratio, lower delay and energy consumption than existing good performing Q-learning based routing method.