Data Science
This is the collection for the University of Waterloo's Data Science program.
Recent deposits
-
Optimal Decumulation for Retirees using Tontines: a Dynamic Neural Network Based Approach
(University of Waterloo, 2023-09-19)We introduce a new approach for optimizing neural networks (NN) using data to solve a stochastic control problem with stochastic constraints. We utilize customized activation functions for the output layers of the NN, ... -
A Robust Neural Network Approach to Optimal Decumulation and Factor Investing in Defined Contribution Pension Plans
(University of Waterloo, 2023-09-18)In this thesis, we propose a novel data-driven neural network (NN) optimization framework for solving an optimal stochastic control problem under stochastic constraints. The NN utilizes customized output layer activation ... -
Algorithmic Behaviours of Adagrad in Underdetermined Linear Regression
(University of Waterloo, 2023-08-24)With the high use of over-parameterized data in deep learning, the choice of optimizer in training plays a big role in a model’s ability to generalize well due to the existence of solution selection bias. We consider the ... -
Enhancing Recommender Systems with Causal Inference Methodologies
(University of Waterloo, 2023-08-22)In the current era of data deluge, recommender systems (RSs) are widely recognized as one of the most effective tools for information filtering. However, traditional RSs are founded on associational relationships among ... -
Simple Yet Effective Pseudo Relevance Feedback with Rocchio’s Technique and Text Classification
(University of Waterloo, 2022-08-22)With the continuous growth of the Internet and the availability of large-scale collections, assisting users in locating the information they need becomes a necessity. Generally, an information retrieval system will process ... -
A Particle Filter Method of Inference for Stochastic Differential Equations
(University of Waterloo, 2022-05-31)Stochastic Differential Equations (SDE) serve as an extremely useful modelling tool in areas including ecology, finance, population dynamics, and physics. Yet, parameter inference for SDEs is notoriously difficult due ...