pwc-archive/papers-with-abstracts huggingface dataset:
Taming Nonconvexity in Kernel Feature Selection -- Favorable Properties of the Laplace Kernel
Authors: Feng Ruan, Keli Liu, Michael I. Jordan
Date: 2021-06-17 00:00:00
Tasks: feature selection, Model Selection

Kernel-based feature selection is an important tool in nonparametric statistics. Despite many practical applications of kernel-based feature selection, there is little statistical theory available to support the method. A core challenge is the objective function of the optimization problems used to define kernel-based feature selection are nonconvex. The literature has only studied the statistical properties of the \emph{global optima}, which is a mismatch, given that the gradient-based algorithms available for nonconvex optimization are only able to guarantee convergence to local minima. Studying the full landscape associated with kernel-based methods, we show that feature selection objectives using the Laplace kernel (and other $\ell_1$ kernels) come with statistical guarantees that other kernels, including the ubiquitous Gaussian kernel (or other $\ell_2$ kernels) do not possess. Based on a sharp characterization of the gradient of the objective function, we show that $\ell_1$ kernels eliminate unfavorable stationary points that appear when using an $\ell_2$ kernel. Armed with this insight, we establish statistical guarantees for $\ell_1$ kernel-based feature selection which do not require reaching the global minima. In particular, we establish model-selection consistency of $\ell_1$-kernel-based feature selection in recovering main effects and hierarchical interactions in the nonparametric setting with $n \sim \log p$ samples.
Identifiability of AMP chain graph models
Authors: Yuhao Wang, Arnab Bhattacharyya
Date: 2021-06-17 00:00:00

We study identifiability of Andersson-Madigan-Perlman (AMP) chain graph models, which are a common generalization of linear structural equation models and Gaussian graphical models. AMP models are described by DAGs on chain components which themselves are undirected graphs. For a known chain component decomposition, we show that the DAG on the chain components is identifiable if the determinants of the residual covariance matrices of the chain components are monotone non-decreasing in topological order. This condition extends the equal variance identifiability criterion for Bayes nets, and it can be generalized from determinants to any super-additive function on positive semidefinite matrices. When the component decomposition is unknown, we describe conditions that allow recovery of the full structure using a polynomial time algorithm based on submodular function minimization. We also conduct experiments comparing our algorithm's performance against existing baselines.
Minimax Estimation of Partially-Observed Vector AutoRegressions
Authors: Guillaume Dalle, Yohann de Castro
Date: 2021-06-17 00:00:00
Tasks: Time Series, Time Series Analysis

High-dimensional time series are a core ingredient of the statistical modeling toolkit, for which numerous estimation methods are known.But when observations are scarce or corrupted, the learning task becomes much harder.The question is: how much harder? In this paper, we study the properties of a partially-observed Vector AutoRegressive process, which is a state-space model endowed with a stochastic observation mechanism.Our goal is to estimate its sparse transition matrix, but we only have access to a small and noisy subsample of the state components.Interestingly, the sampling process itself is random and can exhibit temporal correlations, a feature shared by many realistic data acquisition scenarios.We start by describing an estimator based on the Yule-Walker equation and the Dantzig selector, and we give an upper bound on its non-asymptotic error.Then, we provide a matching minimax lower bound, thus proving near-optimality of our estimator.The convergence rate we obtain sheds light on the role of several key parameters such as the sampling ratio, the amount of noise and the number of non-zero coefficients in the transition matrix.These theoretical findings are commented and illustrated by numerical experiments on simulated data.
Binary classification with corrupted labels
Authors: Yonghoon Lee, Rina Foygel Barber
Date: 2021-06-16 00:00:00
Tasks: Binary Classification, Classification

In a binary classification problem where the goal is to fit an accurate predictor, the presence of corrupted labels in the training data set may create an additional challenge. However, in settings where likelihood maximization is poorly behaved-for example, if positive and negative labels are perfectly separable-then a small fraction of corrupted labels can improve performance by ensuring robustness. In this work, we establish that in such settings, corruption acts as a form of regularization, and we compute precise upper bounds on estimation error in the presence of corruptions. Our results suggest that the presence of corrupted data points is beneficial only up to a small fraction of the total sample, scaling with the square root of the sample size.
Semiparametric count data regression for self-reported mental health
Authors: Daniel R. Kowal, Bohan Wu
Date: 2021-06-16 00:00:00
Tasks: Nutrition, regression, Variable Selection

"For how many days during the past 30 days was your mental health not good?" The responses to this question measure self-reported mental health and can be linked to important covariates in the National Health and Nutrition Examination Survey (NHANES). However, these count variables present major distributional challenges: the data are overdispersed, zero-inflated, bounded by 30, and heaped in five- and seven-day increments. To meet these challenges, we design a semiparametric estimation and inference framework for count data regression. The data-generating process is defined by simultaneously transforming and rounding (STAR) a latent Gaussian regression model. The transformation is estimated nonparametrically and the rounding operator ensures the correct support for the discrete and bounded data. Maximum likelihood estimators are computed using an EM algorithm that is compatible with any continuous data model estimable by least squares. STAR regression includes asymptotic hypothesis testing and confidence intervals, variable selection via information criteria, and customized diagnostics. Simulation studies validate the utility of this framework. STAR is deployed to study the factors associated with self-reported mental health and demonstrates substantial improvements in goodness-of-fit compared to existing count data regression models.
Pre-processing with Orthogonal Decompositions for High-dimensional Explanatory Variables
Authors: Xu Han, Ethan X Fang, Cheng Yong Tang
Date: 2021-06-16 00:00:00
Tasks: regression, Vocal Bursts Intensity Prediction

Strong correlations between explanatory variables are problematic for high-dimensional regularized regression methods. Due to the violation of the Irrepresentable Condition, the popular LASSO method may suffer from false inclusions of inactive variables. In this paper, we propose pre-processing with orthogonal decompositions (PROD) for the explanatory variables in high-dimensional regressions. The PROD procedure is constructed based upon a generic orthogonal decomposition of the design matrix. We demonstrate by two concrete cases that the PROD approach can be effectively constructed for improving the performance of high-dimensional penalized regression. Our theoretical analysis reveals their properties and benefits for high-dimensional penalized linear regression with LASSO. Extensive numerical studies with simulations and data analysis show the promising performance of the PROD.
Understanding the Effectiveness of Reviews in E-commerce Top-N Recommendation
Authors: Zhichao Xu, Hansi Zeng, Qingyao Ai
Date: 2021-06-17 00:00:00

Modern E-commerce websites contain heterogeneous sources of information, such as numerical ratings, textual reviews and images. These information can be utilized to assist recommendation. Through textual reviews, a user explicitly express her affinity towards the item. Previous researchers found that by using the information extracted from these reviews, we can better profile the users' explicit preferences as well as the item features, leading to the improvement of recommendation performance. However, most of the previous algorithms were only utilizing the review information for explicit-feedback problem i.e. rating prediction, and when it comes to implicit-feedback ranking problem such as top-N recommendation, the usage of review information has not been fully explored. Seeing this gap, in this work, we investigate the effectiveness of textual review information for top-N recommendation under E-commerce settings. We adapt several SOTA review-based rating prediction models for top-N recommendation tasks and compare them to existing top-N recommendation models from both performance and efficiency. We find that models utilizing only review information can not achieve better performances than vanilla implicit-feedback matrix factorization method. When utilizing review information as a regularizer or auxiliary information, the performance of implicit-feedback matrix factorization method can be further improved. However, the optimal model structure to utilize textual reviews for E-commerce top-N recommendation is yet to be determined.
Open Data and the Status Quo -- A Fine-Grained Evaluation Framework for Open Data Quality and an Analysis of Open Data portals in Germany
Authors: Lisa Wenige, Claus Stadler, Michael Martin, Richard Figura, Robert Sauter, Christopher W. Frank
Date: 2021-06-17 00:00:00
Tasks: Metadata quality, valid

This paper presents a framework for assessing data and metadata quality within Open Data portals. Although a few benchmark frameworks already exist for this purpose, they are not yet detailed enough in both breadth and depth to make valid statements about the actual discoverability and accessibility of publicly available data collections. To address this research gap, we have designed a quality framework that is able to evaluate data quality in Open Data portals on dedicated and fine-grained dimensions, such as interoperability, findability, uniqueness or completeness. Additionally, we propose quality measures that allow for valid assessments regarding cross-portal findability and uniqueness of dataset descriptions. We have validated our novel quality framework for the German Open Data landscape and found out that metadata often still lacks meaningful descriptions and is not yet extensively connected to the Semantic Web.
Recovery under Side Constraints
Authors: Khaled Ardah, Martin Haardt, Tianyi Liu, Frederic Matter, Marius Pesavento, Marc E. Pfetsch
Date: 2021-06-17 00:00:00
Tasks: Dictionary Learning, Retrieval

This paper addresses sparse signal reconstruction under various types of structural side constraints with applications in multi-antenna systems. Side constraints may result from prior information on the measurement system and the sparse signal structure. They may involve the structure of the sensing matrix, the structure of the non-zero support values, the temporal structure of the sparse representationvector, and the nonlinear measurement structure. First, we demonstrate how a priori information in form of structural side constraints influence recovery guarantees (null space properties) using L1-minimization. Furthermore, for constant modulus signals, signals with row-, block- and rank-sparsity, as well as non-circular signals, we illustrate how structural prior information can be used to devise efficient algorithms with improved recovery performance and reduced computational complexity. Finally, we address the measurement system design for linear and nonlinear measurements of sparse signals. Moreover, we discuss the linear mixing matrix design based on coherence minimization. Then we extend our focus to nonlinear measurement systems where we design parallel optimization algorithms to efficiently compute stationary points in the sparse phase retrieval problem with and without dictionary learning.
Embedding-based Product Retrieval in Taobao Search
Authors: Sen Li, Fuyu Lv, Taiwei Jin, Guli Lin, Keping Yang, Xiaoyi Zeng, Xiao-Ming Wu, Qianli Ma
Date: 2021-06-17 00:00:00
Tasks: Open-Ended Question Answering, Retrieval

Nowadays, the product search service of e-commerce platforms has become a vital shopping channel in people's life. The retrieval phase of products determines the search system's quality and gradually attracts researchers' attention. Retrieving the most relevant products from a large-scale corpus while preserving personalized user characteristics remains an open question. Recent approaches in this domain have mainly focused on embedding-based retrieval (EBR) systems. However, after a long period of practice on Taobao, we find that the performance of the EBR system is dramatically degraded due to its: (1) low relevance with a given query and (2) discrepancy between the training and inference phases. Therefore, we propose a novel and practical embedding-based product retrieval model, named Multi-Grained Deep Semantic Product Retrieval (MGDSPR). Specifically, we first identify the inconsistency between the training and inference stages, and then use the softmax cross-entropy loss as the training objective, which achieves better performance and faster convergence. Two efficient methods are further proposed to improve retrieval relevance, including smoothing noisy training data and generating relevance-improving hard negative samples without requiring extra knowledge and training procedures. We evaluate MGDSPR on Taobao Product Search with significant metrics gains observed in offline experiments and online A/B tests. MGDSPR has been successfully deployed to the existing multi-channel retrieval system in Taobao Search. We also introduce the online deployment scheme and share practical lessons of our retrieval system to contribute to the community.
Current Challenges and Future Directions in Podcast Information Access
Authors: Rosie Jones, Hamed Zamani, Markus Schedl, Ching-Wei Chen, Sravana Reddy, Ann Clifton, Jussi Karlgren, Helia Hashemi, Aasish Pappu, Zahra Nazari, Longqi Yang, Oguz Semerci, Hugues Bouchard, Ben Carterette
Date: 2021-06-17 00:00:00

Podcasts are spoken documents across a wide-range of genres and styles, with growing listenership across the world, and a rapidly lowering barrier to entry for both listeners and creators. The great strides in search and recommendation in research and industry have yet to see impact in the podcast space, where recommendations are still largely driven by word of mouth. In this perspective paper, we highlight the many differences between podcasts and other media, and discuss our perspective on challenges and future research directions in the domain of podcast information access.
The NNI Query-by-Example System for MediaEval 2015
Authors: Jingyong Hou, Van Tung Pham, Cheung-Chi Leung, Lei Wang, HaiHua Xu, Hang Lv, Lei Xie, Zhonghua Fu, Chongjia Ni, Xiong Xiao, Hongjie Chen, Shaofei Zhang, Sining Sun, Yougen Yuan, Pengcheng Li, Tin Lay Nwe, Sunil Sivadas, Bin Ma, Eng Siong Chng, Haizhou Li
Date: 2015-09-14 00:00:00
Proceeding: MediaEval 2015 Workshop 2015 9
Tasks: Keyword Spotting

This paper describes the system developed by the NNI team for the Query-by-Example Search on Speech Task (QUESST) in the MediaEval 2015 evaluation. Our submitted system mainly used bottleneck features/stacked bottleneck features (BNF/SBNF) trained from various resources. We investigated noise robustness techniques to deal with the noisy data of this year. The submitted system obtained the actual normalized cross entropy (actCnxe) of 0.761 and the actual Term Weighted Value (actTWV) of 0.270 on all types of queries of the evaluation data.
The IIT-B Query-by-Example System for MediaEval 2015
Authors: Hitesh Tulsiani, Preeti Rao
Date: 2015-09-14 00:00:00
Proceeding: MediaEval 2015 Workshop 2015 9
Tasks: Keyword Spotting

This paper describes the system developed at I.I.T. Bombay for Query-by-Example Search on Speech Task (QUESST) within the MediaEval 2015 evaluation framework. Our system preprocesses the data to remove noise and performs subsequence DTW on posterior/bottleneck features obtained using four phone recognition systems to detect the queries. Scores from each of these subsystems are fused to get the single score per query-utterance pair which is then calibrated with respect to the cross entropy evaluation metric.
TUKE at MediaEval 2015 QUESST
Authors: Jozef Vavrek, Peter Viszlay, Martin Lojka, Matúš Pleva, Jozef Juhár, Milan Rusko
Date: 2015-09-14 00:00:00
Proceeding: MediaEval 2015 Workshop 2015 9
Tasks: Dynamic Time Warping, Keyword Spotting

In this paper, we present our retrieving system for QUery by Example Search on Speech Task (QUESST), comprising the posteriorgram-based modeling approach along with the weighted fast sequential dynamic time warping algorithm (WFS-DTW). For this year, our main effort was directed toward developing language-dependent keyword matching system, utilizing all available information about spoken languages, considering all queries and utterance files. Despite the fact that the retrieving algorithm is the same as we used in previous year, a big novelty resides in the way of utilizing the information about all languages spoken in the retrieving database. Two low-resource systems using language dependent acoustic unit modeling (AUM) approaches have been submitted. The first one, called supervised, employs four well-trained phonetic decoders using acoustic models trained on time-aligned and annotated speech. The second one, defined as unsupervised, uses blind phonetic segmentation for the specific language where the information about spoken language is extracted from Mediaeval 2013 and Mediaeval 2014 databases. Considering the influence on the overall retrieving performance, the acoustic model adaptation to the specific language through retraining procedure was investigated for both approaches as well.
The SPL-IT-UC Query by Example Search on Speech system for MediaEval 2015
Authors: Jorge Proença, Luis Castela, Fernando Perdigão
Date: 2015-09-14 00:00:00
Proceeding: MediaEval 2015 Workshop 2015 9
Tasks: Dynamic Time Warping, Keyword Spotting

This document describes the system built by the SPL-IT-UC team from the Signal Processing Lab of Instituto de Telecomunicações (pole of Coimbra) and University of Coimbra for the Query by Example Search on Speech Task (QUESST) of MediaEval 2015. The submitted system filters considerable background noise by applying spectral subtraction, uses five phonetic recognizers from which posterior probabilities are extracted as features, implements novel modifications of Dynamic Time Warping (DTW) that focus on complex queries, and uses linear calibration and fusion to optimize results. This year’s task proved extra challenging in terms of acoustic conditions and match cases, though we observe the best results when merging all complex approaches.
ELiRF at MediaEval 2015: Query by Example Search on Speech Task (QUESST)
Authors: Sergio Laguna, Marcos Calvo, Lluís-F. Hurtado, Emilio Sanchis
Date: 2015-09-14 00:00:00
Proceeding: MediaEval 2015 Workshop 2015 9
Tasks: Dynamic Time Warping, Keyword Spotting

n this paper, we present the systems that the Natural Language Engineering and Pattern Recognition group (ELiRF) has submitted to the MediaEval 2015 Query by Example Search on Speech Task. All of them are based on a Subsequence Dynamic Time Warping algorithm. The systems use information from outside the task (low-resources systems).
GTM-UVigo Systems for the Query-by-Example Search on Speech Task at MediaEval 2015
Authors: Paula Lopez-Otero, Laura Docio-Fernandez, Carmen Garcia-Mateo
Date: 2015-09-14 00:00:00
Proceeding: MediaEval 2015 Workshop 2015 9
Tasks: Decoder, Dynamic Time Warping, Keyword Spotting

In this paper, we present the systems developed by GTMUVigo team for the query by example search on speech task (QUESST) at MediaEval 2015. The systems consist in a fusion of 11 dynamic time warping based systems that use phoneme posteriorgrams for speech representation; the primary system introduces a technique to select the most relevant phonetic units on each phoneme decoder, leading to an improvement of the search results.
BUT QUESST 2015 System Description
Authors: Miroslav Skácel, Igor Szöke
Date: 2015-09-14 00:00:00
Proceeding: MediaEval 2015 Workshop 2015 9
Tasks: Dynamic Time Warping, Keyword Spotting, regression

All our systems are based on Dynamic Time Warping (DTW). These systems use bottle-neck features (BN) as input. The bottle-neck feature extractors were trained on GlobalPhone Czech, Portuguese, Russian and Spanish languages, so our approach is in low-resource category. We also aimed on T1/T2/T3 types of query search for late submission systems. System calibration and fusion were based on binary logistic regression.
NTU System at MediaEval 2015: Zero Resource Query by Example Spoken Term Detection Using Deep and Recurrent Neural Networks
Authors: Cheng-Tao Chung, Yang-De Chen
Date: 2015-09-14 00:00:00
Proceeding: MediaEval 2015 Workshop 2015 9
Tasks: Keyword Spotting

This note serves as a documentation describing the methods the authors of this paper implemented for the Query by Example Search on Speech Task (QUESST) as a part of MediaEval 2015. In this work, we combined DTW, DNN and RNN in one framework to perform query by example spoken term detection in a zero resource setting.
CUNY Systems for the Query-by-Example Search on Speech Task at MediaEval 2015
Authors: Min Ma, Andrew Rosenberg
Date: 2015-09-14 00:00:00
Proceeding: MediaEval 2015 Workshop 2015 9
Tasks: Keyword Spotting

This paper describes two query-by-example systems developed by Speech Lab, Queens College (CUNY). Our systems aimed to respond with quick search results from the selected reference files. Three phonetic recognizers (Czech, Hungarian and Russian) were utilized to get phoneme sequences of both query and reference speech files. Each query sequence were compared with all the reference sequences using both global and local aligners. In the first system, we predicted the most probable reference files based on the sequence alignment results; In the second system, we pruned out the subsequences from the reference sequences that yielded best local symbolic alignments, then 39-dimension MFCC features were extracted for both query and the subsequences. Both the two systems employed an optimized DTW, and obtained Cnxe of 0.9989 and 1.0674 on the test data respectively.
Time series modelling to forecast the confirmed and recovered cases of COVID-19
Authors: Mohsen Malekia, Mohammad Reza, Mahmoudibc Darren, Wraithd Kim-HungPhoe
Date: 2020-03-09 00:00:00
Proceeding: Travel Medicine and Infectious Disease Volume 37, September–October 2020, 101742 2020 3
Tasks: Time Series, Time Series Analysis

Abstract
Coronaviruses are enveloped RNA viruses from the Coronaviridae family affecting neurological, gastrointestinal, hepatic and respiratory systems. In late 2019 a new member of this family belonging to the Betacoronavirus genera (referred to as COVID-19) originated and spread quickly across the world calling for strict containment plans and policies. In most countries in the world, the outbreak of the disease has been serious and the number of confirmed COVID-19 cases has increased daily, while, fortunately the recovered COVID-19 cases have also increased. Clearly, forecasting the “confirmed” and “recovered” COVID-19 cases helps planning to control the disease and plan for utilization of health care resources. Time series models based on statistical methodology are useful to model time-indexed data and for forecasting. Autoregressive time series models based on two-piece scale mixture normal distributions, called TP–SMN–AR models, is a flexible family of models involving many classical symmetric/asymmetric and light/heavy tailed autoregressive models. In this paper, we use this family of models to analyze the real world time series data of confirmed and recovered COVID-19 cases.