pwc-archive/papers-with-abstracts huggingface dataset:
Community detection by spectral methods in multi-layer networks
Authors: Huan Qing
Date: 2024-03-19 00:00:00
Tasks: Clustering, Community Detection, Computational Efficiency, Stochastic Block Model

Community detection in multi-layer networks is a crucial problem in network analysis. In this paper, we analyze the performance of two spectral clustering algorithms for community detection within the framework of the multi-layer degree-corrected stochastic block model (MLDCSBM) framework. One algorithm is based on the sum of adjacency matrices, while the other utilizes the debiased sum of squared adjacency matrices. We also provide their accelerated versions through subsampling to handle large-scale multi-layer networks. We establish consistency results for community detection of the two proposed methods under MLDCSBM as the size of the network and/or the number of layers increases. Our theorems demonstrate the advantages of utilizing multiple layers for community detection. Our analysis also indicates that spectral clustering with the debiased sum of squared adjacency matrices is generally superior to spectral clustering with the sum of adjacency matrices. Furthermore, we provide a strategy to estimate the number of communities in multi-layer networks by maximizing the averaged modularity. Substantial numerical simulations demonstrate the superiority of our algorithm employing the debiased sum of squared adjacency matrices over existing methods for community detection in multi-layer networks, the high computational efficiency of our accelerated algorithms for large-scale multi-layer networks, and the high accuracy of our strategy in estimating the number of communities. Finally, the analysis of several real-world multi-layer networks yields meaningful insights.
An Alternative Graphical Lasso Algorithm for Precision Matrices
Authors: Aramayis Dallakyan, Mohsen Pourahmadi
Date: 2024-03-19 00:00:00
Tasks: regression

The Graphical Lasso (GLasso) algorithm is fast and widely used for estimating sparse precision matrices (Friedman et al., 2008). Its central role in the literature of high-dimensional covariance estimation rivals that of Lasso regression for sparse estimation of the mean vector. Some mysteries regarding its optimization target, convergence, positive-definiteness and performance have been unearthed, resolved and presented in Mazumder and Hastie (2011), leading to a new/improved (dual-primal) DP-GLasso. Using a new and slightly different reparametriztion of the last column of a precision matrix we show that the regularized normal log-likelihood naturally decouples into a sum of two easy to minimize convex functions one of which is a Lasso regression problem. This decomposition is the key in developing a transparent, simple iterative block coordinate descent algorithm for computing the GLasso updates with performance comparable to DP-GLasso. In particular, our algorithm has the precision matrix as its optimization target right at the outset, and retains all the favorable properties of the DP-GLasso algorithm.
Selecting informative conformal prediction sets with false coverage rate control
Authors: Ulysse Gazin, Ruth Heller, Ariane Marandon, Etienne Roquain
Date: 2024-03-18 00:00:00
Tasks: Conformal Prediction, Prediction, Prediction Intervals, regression

In supervised learning, including regression and classification, conformal methods provide prediction sets for the outcome/label with finite sample coverage for any machine learning predictor. We consider here the case where such prediction sets come after a selection process. The selection process requires that the selected prediction sets be `informative' in a well defined sense. We consider both the classification and regression settings where the analyst may consider as informative only the sample with prediction sets small enough, excluding null values, or obeying other appropriate `monotone' constraints. We develop a unified framework for building such informative conformal prediction sets while controlling the false coverage rate (FCR) on the selected sample. While conformal prediction sets after selection have been the focus of much recent literature in the field, the new introduced procedures, called InfoSP and InfoSCOP, are to our knowledge the first ones providing FCR control for informative prediction sets. We show the usefulness of our resulting procedures on real and simulated data.
Context-based Fast Recommendation Strategy for Long User Behavior Sequence in Meituan Waimai
Authors: Zhichao Feng, Junjiie Xie, Kaiyuan Li, Yu Qin, Pengfei Wang, Qianzhong Li, Bin Yin, Xiang Li, Wei Lin, Shangguang Wang
Date: 2024-03-19 00:00:00
Tasks: Recommendation Systems, Sequential Recommendation

In the recommender system of Meituan Waimai, we are dealing with ever-lengthening user behavior sequences, which pose an increasing challenge to modeling user preference effectively. Existing sequential recommendation models often fail to capture long-term dependencies or are too complex, complicating the fulfillment of Meituan Waimai's unique business needs. To better model user interests, we consider selecting relevant sub-sequences from users' extensive historical behaviors based on their preferences. In this specific scenario, we've noticed that the contexts in which users interact have a significant impact on their preferences. For this purpose, we introduce a novel method called Context-based Fast Recommendation Strategy to tackle the issue of long sequences. We first identify contexts that share similar user preferences with the target context and then locate the corresponding PoIs based on these identified contexts. This approach eliminates the necessity to select a sub-sequence for every candidate PoI, thereby avoiding high time complexity. Specifically, we implement a prototype-based approach to pinpoint contexts that mirror similar user preferences. To amplify accuracy and interpretability, we employ JS divergence of PoI attributes such as categories and prices as a measure of similarity between contexts. A temporal graph integrating both prototype and context nodes helps incorporate temporal information. We then identify appropriate prototypes considering both target contexts and short-term user preferences. Following this, we utilize contexts aligned with these prototypes to generate a sub-sequence, aimed at predicting CTR and CTCVR scores with target attention. Since its inception in 2023, this strategy has been adopted in Meituan Waimai's display recommender system, leading to a 4.6% surge in CTR and a 4.2% boost in GMV.
Listwise Generative Retrieval Models via a Sequential Learning Process
Authors: Yubao Tang, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Wei Chen, Xueqi Cheng
Date: 2024-03-19 00:00:00
Tasks: Retrieval

Recently, a novel generative retrieval (GR) paradigm has been proposed, where a single sequence-to-sequence model is learned to directly generate a list of relevant document identifiers (docids) given a query. Existing GR models commonly employ maximum likelihood estimation (MLE) for optimization: this involves maximizing the likelihood of a single relevant docid given an input query, with the assumption that the likelihood for each docid is independent of the other docids in the list. We refer to these models as the pointwise approach in this paper. While the pointwise approach has been shown to be effective in the context of GR, it is considered sub-optimal due to its disregard for the fundamental principle that ranking involves making predictions about lists. In this paper, we address this limitation by introducing an alternative listwise approach, which empowers the GR model to optimize the relevance at the docid list level. Specifically, we view the generation of a ranked docid list as a sequence learning process: at each step we learn a subset of parameters that maximizes the corresponding generation likelihood of the $i$-th docid given the (preceding) top $i-1$ docids. To formalize the sequence learning process, we design a positional conditional probability for GR. To alleviate the potential impact of beam search on the generation quality during inference, we perform relevance calibration on the generation likelihood of model-generated docids according to relevance grades. We conduct extensive experiments on representative binary and multi-graded relevance datasets. Our empirical results demonstrate that our method outperforms state-of-the-art GR baselines in terms of retrieval performance.
Enriching User Shopping History: Empowering E-commerce with a Hierarchical Recommendation System
Authors: Irem Islek, sule gunduz oguducu
Date: 2024-03-15 00:00:00
Tasks: Recommendation Systems

Recommendation systems can provide accurate recommendations by analyzing user shopping history. A richer user history results in more accurate recommendations. However, in real applications, users prefer e-commerce platforms where the item they seek is at the lowest price. In other words, most users shop from multiple e-commerce platforms simultaneously; different parts of the user's shopping history are shared between different e-commerce platforms. Consequently, we assume in this study that any e-commerce platform has a complete record of the user's history but can only access some parts of it. If a recommendation system is able to predict the missing parts first and enrich the user's shopping history properly, it will be possible to recommend the next item more accurately. Our recommendation system leverages user shopping history to improve prediction accuracy. The proposed approach shows significant improvements in both NDCG@10 and HR@10.
Foundation Models and Information Retrieval in Digital Pathology
Authors: H. R. Tizhoosh
Date: 2024-03-13 00:00:00
Tasks: Information Retrieval, Retrieval

The paper reviews the state-of-the-art of foundation models, LLMs, generative AI, information retrieval and CBIR in digital pathology
TMU at TREC Clinical Trials Track 2023
Authors: Aritra Kumar Lahiri, Emrul Hasan, Qinmin Vivian Hu, Cherie Ding
Date: 2024-03-12 00:00:00

This paper describes Toronto Metropolitan University's participation in the TREC Clinical Trials Track for 2023. As part of the tasks, we utilize advanced natural language processing techniques and neural language models in our experiments to retrieve the most relevant clinical trials. We illustrate the overall methodology, experimental settings, and results of our implementation for the run submission as part of Team - V-TorontoMU.
Group Movie Selection using Multi-channel Emotion Recognition
Authors: Elnara Kadyrgali, Adilet Yerkin, Yerdauit Torekhan, Pakizar Shamoi
Date: 2024-03-11 00:00:00
Tasks: Decision Making, Emotion Recognition, Movie Recommendation, Recommendation Systems

Social activities often done in groups include watching television or movies. Choosing a film that appeals to the emotional inclinations of a varied group can be tricky. One of the most difficult aspects of making group movie suggestions is achieving agreement among members. At the same time, emotion is the most important component that connects the film and the viewer. Current research proposes a methodology for group movie selection that employs emotional analysis from numerous sources, such as film posters, soundtracks, and text. Our research stands at the intersection of emotion recognition technology in music, text, color images, and group decision-making, providing a practical tool for navigating the complex dynamics of film selection in a group setting. The survey participants were given emotion categories and asked to select the emotions that best suited a particular movie. Preliminary comparison results between real and predicted scores show the effectiveness of using emotion detection for group movie recommendation. Such systems have the potential to enhance movie recommendation systems.
Presenting Terrorizer: an algorithm for consolidating company names in patent assignees
Authors: Grazia Sveva Ascione, Valerio Sterzi
Date: 2024-03-07 00:00:00

The problem of disambiguation of company names poses a significant challenge in extracting useful information from patents. This issue biases research outcomes as it mostly underestimates the number of patents attributed to companies, particularly multinational corporations which file patents under a plethora of names, including alternate spellings of the same entity and, eventually, companies' subsidiaries. To date, addressing these challenges has relied on labor-intensive dictionary based or string matching approaches, leaving the problem of patents' assignee harmonization on large datasets mostly unresolved. To bridge this gap, this paper describes the Terrorizer algorithm, a text-based algorithm that leverages natural language processing (NLP), network theory, and rule-based techniques to harmonize the variants of company names recorded as patent assignees. In particular, the algorithm follows the tripartite structure of its antecedents, namely parsing, matching and filtering stage, adding an original "knowledge augmentation" phase which is used to enrich the information available on each assignee name. We use Terrorizer on a set of 325'917 companies' names who are assignees of patents granted by the USPTO from 2005 to 2022. The performance of Terrorizer is evaluated on four gold standard datasets. This validation step shows us two main things: the first is that the performance of Terrorizer is similar over different kind of datasets, proving that our algorithm generalizes well. Second, when comparing its performance with the one of the algorithm currently used in PatentsView for the same task (Monath et al., 2021), it achieves a higher F1 score. Finally, we use the Tree-structured Parzen Estimator (TPE) optimization algorithm for the hyperparameters' tuning. Our final result is a reduction in the initial set of names of over 42%.
Beyond Beats: A Recipe to Song Popularity? A machine learning approach
Authors: Niklas Sebastian, Jung, Florian Mayer
Date: 2024-03-01 00:00:00
Tasks: regression

Music popularity prediction has garnered significant attention in both industry and academia, fuelled by the rise of data-driven algorithms and streaming platforms like Spotify. This study aims to explore the predictive power of various machine learning models in forecasting song popularity using a dataset comprising 30,000 songs spanning different genres from 1957 to 2020. Methods: We employ Ordinary Least Squares (OLS), Multivariate Adaptive Regression Splines (MARS), Random Forest, and XGBoost algorithms to analyse song characteristics and their impact on popularity. Results: Ordinary Least Squares (OLS) regression analysis reveals genre as the primary influencer of popularity, with notable trends over time. MARS modelling highlights the complex relationship between variables, particularly with features like instrumentalness and duration. Random Forest and XGBoost models underscore the importance of genre, especially EDM, in predicting popularity. Despite variations in performance, Random Forest emerges as the most effective model, improving prediction accuracy by 7.1% compared to average scores. Despite the importance of genre, predicting song popularity remains challenging, as observed variations in music-related features suggest complex interactions between genre and other factors. Consequently, while certain characteristics like loudness and song duration may impact popularity scores, accurately predicting song success remains elusive.
Evaluating Robustness of Generative Search Engine on Adversarial Factual Questions
Authors: Xuming Hu, Xiaochuan Li, Junzhe Chen, Yinghui Li, Yangning Li, Xiaoguang Li, Yasheng Wang, Qun Liu, Lijie Wen, Philip S. Yu, Zhijiang Guo
Date: 2024-02-25 00:00:00
Tasks: Retrieval, Retrieval-augmented Generation

Generative search engines have the potential to transform how people seek information online, but generated responses from existing large language models (LLMs)-backed generative search engines may not always be accurate. Nonetheless, retrieval-augmented generation exacerbates safety concerns, since adversaries may successfully evade the entire system by subtly manipulating the most vulnerable part of a claim. To this end, we propose evaluating the robustness of generative search engines in the realistic and high-risk setting, where adversaries have only black-box system access and seek to deceive the model into returning incorrect responses. Through a comprehensive human evaluation of various generative search engines, such as Bing Chat, PerplexityAI, and YouChat across diverse queries, we demonstrate the effectiveness of adversarial factual questions in inducing incorrect responses. Moreover, retrieval-augmented generation exhibits a higher susceptibility to factual errors compared to LLMs without retrieval. These findings highlight the potential security risks of these systems and emphasize the need for rigorous evaluation before deployment.
On the Quality of Deep Representations for Kepler Light Curves Using Variational Auto-Encoders
Authors: Francisco Mena, Patricio Olivares, Margarita Bugueño, Gabriel Molina, Mauricio Aray
Date: 2021-10-14 00:00:00
Proceeding: Signal 2021 10
Tasks: Astronomy, Disentanglement, Time Series

Light curve analysis usually involves extracting manually designed features associated with physical parameters and visual inspection. The large amount of data collected nowadays in astronomy by different surveys represents a major challenge of characterizing these signals. Therefore, finding good informative representation for them is a key non-trivial task. Some studies have tried unsupervised machine learning approaches to generate this representation without much effectiveness. In this article, we show that variational auto-encoders can learn these representations by taking the difference between successive timestamps as an additional input. We present two versions of such auto-encoders: Variational Recurrent Auto-Encoder plus time (VRAE𝑡) and re-Scaling Variational Recurrent Auto Encoder plus time (S-VRAE𝑡). The objective is to achieve the most likely low-dimensional representation of the time series that matched latent variables and, in order to reconstruct it, should compactly contain the pattern information. In addition, the S-VRAE𝑡 embeds the re-scaling preprocessing of the time series into the model in order to use the Flux standard deviation in the learning of the light curves structure. To assess our approach, we used the largest transit light curve dataset obtained during the 4 years of the Kepler mission and compared to similar techniques in signal processing and light curves. The results show that the proposed methods obtain improvements in terms of the quality of the deep representation of phase-folded transit light curves with respect to their deterministic counterparts. Specifically, they present a good balance between the reconstruction task and the smoothness of the curve, validated with the root mean squared error, mean absolute error, and auto-correlation metrics. Furthermore, there was a good disentanglement in the representation, as validated by the Pearson correlation and mutual information metrics. Finally, a useful representation to distinguish categories was validated with the 𝐹1 score in the task of classifying exoplanets. Moreover, the S-VRAE𝑡 model increases all the advantages of VRAE𝑡, achieving a classification performance quite close to its maximum model capacity and generating light curves that are visually comparable to a Mandel–Agol fit. Thus, the proposed methods present a new way of analyzing and characterizing light curves.
Classical Machine Learning Techniques in the Search of Extrasolar Planets
Authors: Francisco Mena, Margarita Bugueño, Mauricio Aray
Date: 2019-12-01 00:00:00
Proceeding: CLEI Electronic Journal 2019 12
Tasks: Feature Engineering

The field of astronomical data analysis has experienced an important paradigm shift in the recent years. The automation of certain analysis procedures is no longer a desirable feature for reducing the human effort, but a must have asset for coping with the extremely large datasets that new instrumentation technologies are producing. In particular, the detection of transit planets --- bodies that move across the face of another body --- is an ideal setup for intelligent automation. Knowing if the variation within a light curve is evidence of a planet, requires applying advanced pattern recognition methods to a very large number of candidate stars. Here we present a supervised learning approach to refine the results produced by a case-by-case analysis of light-curves, harnessing the generalization power of machine learning techniques to predict the currently unclassified light-curves. The method uses feature engineering to find a suitable representation for classification, and different performance criteria to evaluate them and decide. Our results show that this automatic technique can help to speed up the very time-consuming manual process that is currently done by expert scientists.
Revisiting Machine Learning from Crowds a Mixture Model for Grouping Annotations
Authors: Francisco Mena, Ricardo Ñanculef
Date: 2019-10-22 00:00:00
Proceeding: Lecture Notes in Computer Science 2019 10

Today, supervised learning is widely used for pattern recognition, computer vision and other tasks. In this setting, data need to be explicitly annotated. Unfortunately, obtaining accurate labels can be difficult, expensive and time-consuming. As a result, many machine learning projects rely on labelling processes that involve crowds, i.e. multiple subjective and inexpert annotators. Handling this noise in a principled way is an important challenge for machine learning, called learning from crowds. In this paper, we present a model that learns patterns of label noise by grouping annotations. In contrast to previous art, we do not model specific labeling patterns for each annotator but explain the data using a fixed-size mixture model. This approach allows to handle a sparse distribution of labels among annotators and obtain a model with less parameters that can scale better to large-scale scenarios. Experiments on real and simulated data illustrate the advantages of our approach.
A Binary Variational Autoencoder for Hashing
Authors: Francisco Mena, Ricardo Ñanculef
Date: 2019-10-22 00:00:00
Proceeding: Lecture Notes in Computer Science 2019 10
Tasks: Quantization, Retrieval, Text Retrieval

Searching a large dataset to find elements that are similar to a sample object is a fundamental problem in computer science. Hashing algorithms deal with this problem by representing data with similarity-preserving binary codes that can be used as indices into a hash table. Recently, it has been shown that variational autoencoders (VAEs) can be successfully trained to learn such codes in unsupervised and semi-supervised scenarios. In this paper, we show that a variational autoencoder with binary latent variables leads to a more natural and effective hashing algorithm that its continuous counterpart. The model reduces the quantization error introduced by continuous formulations but is still trainable with standard back-propagation. Experiments on text retrieval tasks illustrate the advantages of our model with respect to previous art.