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"Recommendation": models, code, and papers

Bayesian Optimization in a Billion Dimensions via Random Embeddings

Jan 10, 2016
Ziyu Wang, Frank Hutter, Masrour Zoghi, David Matheson, Nando de Freitas

Bayesian optimization techniques have been successfully applied to robotics, planning, sensor placement, recommendation, advertising, intelligent user interfaces and automatic algorithm configuration. Despite these successes, the approach is restricted to problems of moderate dimension, and several workshops on Bayesian optimization have identified its scaling to high-dimensions as one of the holy grails of the field. In this paper, we introduce a novel random embedding idea to attack this problem. The resulting Random EMbedding Bayesian Optimization (REMBO) algorithm is very simple, has important invariance properties, and applies to domains with both categorical and continuous variables. We present a thorough theoretical analysis of REMBO. Empirical results confirm that REMBO can effectively solve problems with billions of dimensions, provided the intrinsic dimensionality is low. They also show that REMBO achieves state-of-the-art performance in optimizing the 47 discrete parameters of a popular mixed integer linear programming solver.

* 33 pages 

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Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents

Mar 06, 2022
Yicheng Zou, Hongwei Liu, Tao Gui, Junzhe Wang, Qi Zhang, Meng Tang, Haixiang Li, Daniel Wang

Text semantic matching is a fundamental task that has been widely used in various scenarios, such as community question answering, information retrieval, and recommendation. Most state-of-the-art matching models, e.g., BERT, directly perform text comparison by processing each word uniformly. However, a query sentence generally comprises content that calls for different levels of matching granularity. Specifically, keywords represent factual information such as action, entity, and event that should be strictly matched, while intents convey abstract concepts and ideas that can be paraphrased into various expressions. In this work, we propose a simple yet effective training strategy for text semantic matching in a divide-and-conquer manner by disentangling keywords from intents. Our approach can be easily combined with pre-trained language models (PLM) without influencing their inference efficiency, achieving stable performance improvements against a wide range of PLMs on three benchmarks.

* Accepted by Findings of ACL 2022, 11 pages 

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Computerization of Clinical Pathways: A Literature Review and Directions for Future Research

Mar 02, 2022
Ayman Alahmar, Ola Alkhatib

Clinical Pathways (CP) are medical management plans developed to standardize patient treatment activities, optimize resource usage, reduce expenses, and improve the quality of healthcare services. Most CPs currently in use are paper-based documents (i.e., not computerized). CP computerization has been an active research topic since the inception of CP use in hospitals. This literature review research aims to examine studies that focused on CP computerization and offers recommendations for future research in this important research area. Some critical research suggestions include centralizing computerized CPs in Healthcare Information Systems (HIS), CP term standardization using international medical terminology systems, developing a global CP-specific digital coding system, creating a unified CP meta-ontology, developing independent Clinical Pathway Management Systems (CPMS), and supporting CPMSs with machine learning sub-systems.

* 2nd. International Symposium of Scientific Research and Innovative Studies (ISSRIS'22), March 2-5, 2022 
* 12 pages, 4 figures, 3 tables 

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A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications

Feb 16, 2022
Jun Xia, Yanqiao Zhu, Yuanqi Du, Stan Z. Li

Pretrained Language Models (PLMs) such as BERT have revolutionized the landscape of Natural Language Processing (NLP). Inspired by their proliferation, tremendous efforts have been devoted to Pretrained Graph Models (PGMs). Owing to the powerful model architectures of PGMs, abundant knowledge from massive labeled and unlabeled graph data can be captured. The knowledge implicitly encoded in model parameters can benefit various downstream tasks and help to alleviate several fundamental issues of learning on graphs. In this paper, we provide the first comprehensive survey for PGMs. We firstly present the limitations of graph representation learning and thus introduce the motivation for graph pre-training. Then, we systematically categorize existing PGMs based on a taxonomy from four different perspectives. Next, we present the applications of PGMs in social recommendation and drug discovery. Finally, we outline several promising research directions that can serve as a guideline for future research.

* 9 pages. Submitted to IJCAI 2022 (Survey Track) 

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An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering

Jan 20, 2022
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Paolo Cremonesi

This work explores the reproducibility of CFGAN. CFGAN and its family of models (TagRec, MTPR, and CRGAN) learn to generate personalized and fake-but-realistic rankings of preferences for top-N recommendations by using previous interactions. This work successfully replicates the results published in the original paper and discusses the impact of certain differences between the CFGAN framework and the model used in the original evaluation. The absence of random noise and the use of real user profiles as condition vectors leaves the generator prone to learn a degenerate solution in which the output vector is identical to the input vector, therefore, behaving essentially as a simple autoencoder. The work further expands the experimental analysis comparing CFGAN against a selection of simple and well-known properly optimized baselines, observing that CFGAN is not consistently competitive against them despite its high computational cost. To ensure the reproducibility of these analyses, this work describes the experimental methodology and publishes all datasets and source code.

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Robustness Evaluation of Transformer-based Form Field Extractors via Form Attacks

Oct 08, 2021
Le Xue, Mingfei Gao, Zeyuan Chen, Caiming Xiong, Ran Xu

We propose a novel framework to evaluate the robustness of transformer-based form field extraction methods via form attacks. We introduce 14 novel form transformations to evaluate the vulnerability of the state-of-the-art field extractors against form attacks from both OCR level and form level, including OCR location/order rearrangement, form background manipulation and form field-value augmentation. We conduct robustness evaluation using real invoices and receipts, and perform comprehensive research analysis. Experimental results suggest that the evaluated models are very susceptible to form perturbations such as the variation of field-values (~15% drop in F1 score), the disarrangement of input text order(~15% drop in F1 score) and the disruption of the neighboring words of field-values(~10% drop in F1 score). Guided by the analysis, we make recommendations to improve the design of field extractors and the process of data collection.

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TOKCS: Tool for Organizing Key Characteristics of VAM-HRI Systems

Aug 07, 2021
Thomas R. Groechel, Michael E. Walker, Christine T. Chang, Eric Rosen, Jessica Zosa Forde

Frameworks have begun to emerge to categorize Virtual, Augmented, and Mixed Reality (VAM) technologies that provide immersive, intuitive interfaces to facilitate Human-Robot Interaction. These frameworks, however, fail to capture key characteristics of the growing subfield of VAM-HRI and can be difficult to consistently apply. This work builds upon these prior frameworks through the creation of a Tool for Organizing Key Characteristics of VAM-HRI Systems (TOKCS). TOKCS discretizes the continuous scales used within prior works for more consistent classification and adds additional characteristics related to a robot's internal model, anchor locations, manipulability, and the system's software and hardware. To showcase the tool's capability, TOKCS is applied to find trends and takeaways from the fourth VAM-HRI workshop. These trends highlight the expressive capability of TOKCS while also helping frame newer trends and future work recommendations for VAM-HRI research.

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Strategyproof Learning: Building Trustworthy User-Generated Datasets

Jun 04, 2021
Sadegh Farhadkhani, Rachid Guerraoui, Lê-Nguyên Hoang

Today's large-scale machine learning algorithms harness massive amounts of user-generated data to train large models. However, especially in the context of content recommendation with enormous social, economical and political incentives to promote specific views, products or ideologies, strategic users might be tempted to fabricate or mislabel data in order to bias algorithms in their favor. Unfortunately, today's learning schemes strongly incentivize such strategic data misreporting. This is a major concern, as it endangers the trustworthiness of the entire training datasets, and questions the safety of any algorithm trained on such datasets. In this paper, we show that, perhaps surprisingly, incentivizing data misreporting is not a fatality. We propose the first personalized collaborative learning framework, Licchavi, with provable strategyproofness guarantees through a careful design of the underlying loss function. Interestingly, we also prove that Licchavi is Byzantine resilient: it tolerates a minority of users that provide arbitrary data.

* 31 pages 

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A Clustering Framework for Residential Electric Demand Profiles

May 17, 2021
Mayank Jain, Tarek AlSkaif, Soumyabrata Dev

The availability of residential electric demand profiles data, enabled by the large-scale deployment of smart metering infrastructure, has made it possible to perform more accurate analysis of electricity consumption patterns. This paper analyses the electric demand profiles of individual households located in the city Amsterdam, the Netherlands. A comprehensive clustering framework is defined to classify households based on their electricity consumption pattern. This framework consists of two main steps, namely a dimensionality reduction step of input electricity consumption data, followed by an unsupervised clustering algorithm of the reduced subspace. While any algorithm, which has been used in the literature for the aforementioned clustering task, can be used for the corresponding step, the more important question is to deduce which particular combination of algorithms is the best for a given dataset and a clustering task. This question is addressed in this paper by proposing a novel objective validation strategy, whose recommendations are then cross-verified by performing subjective validation.

* Published in Proc. International Conference on Smart Energy Systems and Technologies (SEST), 2020 

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