http://hdl.handle.net/2451/14088 2026-04-19T13:43:00Z 2026-04-19T13:43:00Z Abrahao, Bruno http://hdl.handle.net/2451/75579 2026-02-19T04:46:00Z 2023-01-01T00:00:00Z

Title: Research Statement Authors: Abrahao, Bruno

2023-01-01T00:00:00Z Walter, Ingo Lipshitz, Clive http://hdl.handle.net/2451/61490 2020-09-22T04:39:44Z 2020-01-01T00:00:00Z

Title: US and Canada Public Pension System Data 2008-2018 Authors: Walter, Ingo; Lipshitz, Clive Description: This dataset comprises eleven years of historical data (2008 through 2018) for the 25 largest U.S. public pension plans and the ten largest Canadian public pension plans.

2020-01-01T00:00:00Z Unger, Moshe Tuzhilin, Alexander http://hdl.handle.net/2451/61218 2020-04-10T15:43:15Z 2019-05-01T00:00:00Z

Title: Hierarchical Latent Context Representation for CARS Authors: Unger, Moshe; Tuzhilin, Alexander Abstract: In this paper, we propose a hierarchical representation of latent contextual information that captures contextual situations in which users are recommended particular items. We also introduce an algorithm that converts unstructured latent contextual information into structured hierarchical representations. In addition, we present two general context-aware recommendation algorithms that extend collaborative filtering (CF) approaches and utilize structured and unstructured latent contextual information. In particular, the first algorithm utilizes structured latent contexts and the second one combines the structured and the unstructured latent contextual representations. By using latent contextual information in a recommendation model, we capture and represent both the structure of the latent context in the form of a hierarchy and the values of contextual variables in the form of an unstructured vector. We tested the two proposed methods with two CF-based methods on several context-rich datasets under different experimental settings. We show that using hierarchical latent contextual representations leads to significantly better recommendations than the baselines for the datasets having high- and medium-dimensional contexts. Although this is not the case for the low-dimensional contextual data, the hybrid approach, combining structured and unstructured latent contextual information, significantly outperforms other baselines across all the experimental settings and dimensions of contextual data.

2019-05-01T00:00:00Z Caldentey, Rene Giloni, Avi Hurvich, Clifford http://hdl.handle.net/2451/43902 2019-03-01T16:54:35Z 2019-03-01T00:00:00Z

Title: Inventory Policies and Information Sharing: An Efficient Frontier Approach Authors: Caldentey, Rene; Giloni, Avi; Hurvich, Clifford Abstract: We consider a two-tier inventory management system with one retailer and one supplier. The retailer serves a demand driven by a stationary moving average process (of possibly infinite order) and places periodic inventory replenishment orders to the supplier. In this setting, we study the value of information sharing and its impact on the retailer’s optimal ordering strategy. We argue that information sharing affects performance through two key cost drivers: (i) on-hand inventory variability and (ii) replenishment order variability. We characterize a “Pareto frontier” between these two sources of variability by identifying optimal inventory replenishment strategies that trade-off one type of variability for the other in a cost efficient way. For the case in which the retailer is able to share her complete demand history, we provide a full characterization of the efficient frontier, as well as of an optimal replenishment policy. On the other hand, when the retailer is not able (or willing) to share any demand information we provide a partial characterization of an optimal solution and show that information sharing does not always add value. We also show that the question of identifying conditions under which information sharing does offer value reduces to a delicate analysis of the invertibility (in a time series sense) of a specific stationary process.

2019-03-01T00:00:00Z