Institution
HEC Montréal
Education•Montreal, Quebec, Canada•
About: HEC Montréal is a education organization based out in Montreal, Quebec, Canada. It is known for research contribution in the topics: Vehicle routing problem & Corporate governance. The organization has 1221 authors who have published 5708 publications receiving 196862 citations. The organization is also known as: Ecole des Hautes Etudes Commerciales de Montreal & HEC Montreal.
Topics: Vehicle routing problem, Corporate governance, Heuristic (computer science), Context (language use), Monetary policy
Papers published on a yearly basis
Papers
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TL;DR: Tabu search and multistart heuristics for this stochastic districting problem are developed and compared and Computational results show that tabu search is superior overMultistart.
115 citations
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TL;DR: In this article, a multi-head self-attentive neural network with residual connections is proposed to explicitly model the feature interactions in the low-dimensional space, which can be applied to both numerical and categorical input features.
Abstract: Click-through rate (CTR) prediction, which aims to predict the probability of a user clicking on an ad or an item, is critical to many online applications such as online advertising and recommender systems. The problem is very challenging since (1) the input features (e.g., the user id, user age, item id, item category) are usually sparse and high-dimensional, and (2) an effective prediction relies on high-order combinatorial features (\textit{a.k.a.} cross features), which are very time-consuming to hand-craft by domain experts and are impossible to be enumerated. Therefore, there have been efforts in finding low-dimensional representations of the sparse and high-dimensional raw features and their meaningful combinations. In this paper, we propose an effective and efficient method called the \emph{AutoInt} to automatically learn the high-order feature interactions of input features. Our proposed algorithm is very general, which can be applied to both numerical and categorical input features. Specifically, we map both the numerical and categorical features into the same low-dimensional space. Afterwards, a multi-head self-attentive neural network with residual connections is proposed to explicitly model the feature interactions in the low-dimensional space. With different layers of the multi-head self-attentive neural networks, different orders of feature combinations of input features can be modeled. The whole model can be efficiently fit on large-scale raw data in an end-to-end fashion. Experimental results on four real-world datasets show that our proposed approach not only outperforms existing state-of-the-art approaches for prediction but also offers good explainability. Code is available at: \url{this https URL}.
115 citations
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TL;DR: In this paper, the authors extend the typical labor-leisure model used to analyze the decision to skip work to include firm-level policy variables relevant to the absenteeism decision and uncertainty about the cost of absenteeism.
Abstract: This paper provides new evidence on the determinants of absenteeism. The authors extend the typical labor-leisure model used to analyze the decision to skip work to include firm-level policy variables relevant to the absenteeism decision and uncertainty about the cost of absenteeism. Estimates based on data from Statistics Canada's Workplace Employee Survey (1999–2002), with controls for observed and unobserved demographic, job, and firm characteristics (including workplace practices), indicate that work arrangements were important determinants of absence. For example, the authors find strong evidence that standard weekday work hours, work-at-home options, and reduced workweeks were associated with reduced absence, whereas shift work and compressed work weeks were associated with increased absence.
115 citations
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TL;DR: In this paper, the column generation method was used to solve modularity maximization problems. But the authors only considered the problem of finding modules, or clusters, in networks and did not consider how to find the optimal solution.
Abstract: Finding modules, or clusters, in networks currently attracts much attention in several domains. The most studied criterion for doing so, due to Newman and Girvan [Phys. Rev. E 69, 026113 (2004)], is modularity maximization. Many heuristics have been proposed for maximizing modularity and yield rapidly near optimal solution or sometimes optimal ones but without a guarantee of optimality. There are few exact algorithms, prominent among which is a paper by Xu et al. [Eur. Phys. J. B 60, 231 (2007)]. Modularity maximization can also be expressed as a clique partitioning problem and the row generation algorithm of Grotschel and Wakabayashi [Math. Program. 45, 59 (1989)] applied. We propose to extend both of these algorithms using the powerful column generation methods for linear and non linear integer programming. Performance of the four resulting algorithms is compared on problems from the literature. Instances with up to 512 entities are solved exactly. Moreover, the computing time of previously solved problems are reduced substantially.
115 citations
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TL;DR: This paper shows that, contrary to predictions in the literature that the authors can resolve uncertainty very quickly, the time to learn may be on the order of thousands of years when uncertainty surrounds two parameters in the law of motion for temperature.
114 citations
Authors
Showing all 1262 results
Name | H-index | Papers | Citations |
---|---|---|---|
Danny Miller | 133 | 512 | 71238 |
Gilbert Laporte | 128 | 730 | 62608 |
Michael Pollak | 114 | 663 | 57793 |
Yong Yu | 78 | 523 | 26956 |
Pierre Hansen | 78 | 575 | 32505 |
Jean-François Cordeau | 71 | 208 | 19310 |
Robert A. Jarrow | 65 | 356 | 24295 |
Jacques Desrosiers | 63 | 173 | 15926 |
François Soumis | 61 | 290 | 14272 |
Nenad Mladenović | 54 | 320 | 19182 |
Massimo Caccia | 52 | 389 | 16007 |
Guy Desaulniers | 51 | 242 | 8836 |
Ann Langley | 50 | 161 | 15675 |
Jean-Charles Chebat | 48 | 161 | 9062 |
Georges Dionne | 48 | 421 | 7838 |