Events

ISTD PhD Oral Defense Seminar by Teo Tzu Hsuan Christopher – Fair Generative Modelling

Baby Shark Fund Open Pitch

Future of Innovation – EPD Distinguished Lecture Series & Innovation Forum in partnership with The James Dyson Foundation

Ling Chun Kai (National University of Singapore) – Learning and Solving Games in the Presence of Teams

Warut Suksompong (National University of Singapore) – Weighted Fair Division: Additive Preferences and Beyond

ISTD PhD Oral Defense Seminar by Ong Kian Eng – Towards Intelligent Analytics for Smarter Animal Behavioral Analysis

Congratulations to Associate Professor Liu Xiaogang’s PhD Student in winning the Royal Society of Chemistry (RSC) Excellent Student Award

DH Asia Webinar Series: “A Sandbox for Humanistic Data Scientists” by Dr. Setsuko Yokoyama

ISTD PhD Oral Defense Seminar by Joel Ong – Modern Portfolio Construction with Advanced Deep Learning Models

A key challenge in data-driven decision-making is the presence of estimation errors in the prediction models, which tend to be amplified by the subsequent optimization model — a phenomenon that is often referred to as the Optimizer’s Curse. A contemporary approach to combat such estimation errors is offered by distributionally robust problem formulations that consider all data-generating distributions close to the empirical distribution derived from historical samples, where ‘closeness’ is determined by the Wasserstein distance. While those techniques show significant promise in problems where all input features are continuous, they scale exponentially when categorical features are present. This work demonstrates that such mixed-feature problems can indeed be solved in polynomial time. We present a practically efficient algorithm to solve mixed-feature problems and compare our method against alternative techniques.