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Nitesh V. Chawla
University of Notre Dame
299Publications
45H-index
14.8kCitations
Publications 299
Newest
#1Louis Faust (ND: University of Notre Dame)H-Index: 3
#2Keith Feldman (ND: University of Notre Dame)H-Index: 4
Last.Nitesh V. Chawla (ND: University of Notre Dame)H-Index: 45
view all 3 authors...
Background Known colloquially as the “weekend effect,” the association between weekend admissions and increased mortality within hospital settings has become a highly contested topic over the last two decades. Drawing interest from practitioners and researchers alike, a sundry of works have emerged arguing for and against the presence of the effect across various patient cohorts. However, it has become evident that simply studying population characteristics is insufficient for understanding how ...
Aug 4, 2019 in KDD (Knowledge Discovery and Data Mining)
#1Chuxu Zhang (ND: University of Notre Dame)H-Index: 7
#2Dongjin SongH-Index: 11
Last.Nitesh V. Chawla (ND: University of Notre Dame)H-Index: 45
view all 5 authors...
Aug 4, 2019 in KDD (Knowledge Discovery and Data Mining)
#1Daheng Wang (ND: University of Notre Dame)H-Index: 1
#2Tianwen Jiang (ND: University of Notre Dame)H-Index: 1
Last.Meng Jiang (ND: University of Notre Dame)H-Index: 16
view all 4 authors...
Given a project plan and the goal, can we predict the plan's success rate? The key challenge is to learn the feature vectors of billions of the plan's components for effective prediction. However, existing methods did not model the behavior outcomes but component proximities. In this work, we define a measurement of behavior outcomes, which forms a test tube-shaped region to represent "success", in a vector space. We propose a novel representation learning method to learn the embeddings of behav...
Aug 4, 2019 in KDD (Knowledge Discovery and Data Mining)
#1Chao Huang (ND: University of Notre Dame)H-Index: 10
#2Xian Wu (ND: University of Notre Dame)H-Index: 4
Last.Nitesh V. Chawla (ND: University of Notre Dame)H-Index: 45
view all 7 authors...
Online purchase forecasting is of great importance in e-commerce platforms, which is the basis of how to present personalized interesting product lists to individual customers. However, predicting online purchases is not trivial as it is influenced by many factors including: (i) the complex temporal pattern with hierarchical inter-correlations; (ii) arbitrary category dependencies. To address these factors, we develop a Graph Multi-Scale Pyramid Networks (GMP) framework to fully exploit users' l...
Jul 25, 2019 in KDD (Knowledge Discovery and Data Mining)
#1Chuxu Zhang (ND: University of Notre Dame)H-Index: 7
#2Dongjin Song (Princeton University)H-Index: 11
Last.Nitesh V. Chawla (ND: University of Notre Dame)H-Index: 45
view all 5 authors...
Representation learning in heterogeneous graphs aims to pursue a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This task, however, is challenging not only because of the demand to incorporate heterogeneous structural (graph) information consisting of multiple types of nodes and edges, but also due to the need for considering heterogeneous attributes or contents (e.g., text...
#1Mandana Saebi (ND: University of Notre Dame)H-Index: 1
#2Jian Xu (Governors State University)H-Index: 4
Last.Nitesh V. Chawla (ND: University of Notre Dame)H-Index: 45
view all 5 authors...
The introduction and establishment of non-indigenous species (NIS) through global ship movements is a significant threat to marine ecosystems and economies. While ballast-vectored invasions have been partly addressed by some national policies and an international agreement regulating the concentrations of organisms in ballast water, biofouling-vectored invasions remain a large risk. Development of additional realistic and cost-effective ship-borne NIS policies requires an accurate estimation of ...
#1Sanjay ChawlaH-Index: 29
#2Nitesh V. ChawlaH-Index: 45
An increasing number of businesses and organizations are creating new analytics departments to identify, manage, and make best use of patterns within their large data repositories. This is the first book to target data mining practitioners working within these organizations, by introducing the key analytical concepts and emerging techniques in data analytics. Each chapter in the book lists the basic results and key facts relevant to the topic, along with a series of exercises and complete soluti...
#1Frederick Nwanganga (ND: University of Notre Dame)H-Index: 2
#2Nitesh V. Chawla (ND: University of Notre Dame)H-Index: 45
Predicting workload behavior, in response to changes in allocated resources, is a critical part of effective resource management in the cloud. This paper presents a novel approach to predicting the future behavior of a reference workload based on a nearest neighbor similarity search in euclidean space. The proposed approach involves identifying a similar workload candidate, predicting the future behavior of the reference based on the candidate and then validating the prediction using a statistic...
We consider multi-label crowdsourcing learning in two scenarios. In the first scenario, we aim at inferring instances’ groundtruth given the crowds’ annotations. We propose two approaches NAM/RAM (Neighborhood/Relevance Aware Multi-label crowdsourcing) modeling the crowds’ expertise and label correlations from different perspectives. Extended from single-label crowdsourcing methods, NAM models the crowds’ expertise on individual labels, but based on the idea that for rational workers, their anno...
#1Beenish M. Chaudhry (University of Louisiana at Lafayette)H-Index: 3
#2Louis Faust (ND: University of Notre Dame)H-Index: 3
Last.Nitesh V. Chawla (ND: University of Notre Dame)H-Index: 45
view all 3 authors...
Technology-based solutions have been developed to enhance competencies of maternity health workers (MHWs) in the developing countries, but there has been a limited exploration of such tools in the developed world. We developed a web-based application to support care coordination tasks of the prenatal care coordinators (PNCCs) in U.S. using a 3-phase methodology. During the first phase, following key functionalities were identified in collaboration with an expert panel: assessment, information pr...
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