Title: Using Optimal Transport To Mitigate Unfair Predictions and Quantify Counterfactual Fairness

Time: 11:00am 

Venue: Room 301, Run Run Shaw Building

Speaker(s): Prof. Arthur Charpentier

Remark(s): 

Abstract

Many industries are heavily reliant on predictions of risks based on characteristics of potential customers. Although the use of said models is common, researchers have long pointed out that such practices perpetuate discrimination based on sensitive features such as gender or race. Given that such discrimination can often be attributed to historical data biases, an elimination or at least mitigation, is desirable. With the shift from more traditional models to machine-learning based predictions, calls for greater mitigation have grown anew, as simply excluding sensitive variables in the pricing process can be shown to be ineffective.

In the first part of this seminar, we propose to mitigate possible discrimination (related to so call group fairness, related to discrepancies in score distributions) through the use of Wasserstein barycenters instead of simple scaling. To demonstrate the effects and effectiveness of the approach we employ it on real data and discuss its implications.

In the second part, we will focus on another aspect of discrimination usually called counterfactual fairness, where the goal is to quantify a potential discrimination if that person had not been Black or if that person had not been a woman. The standard approach, called ceteris paribus (everything remains unchanged) is not sufficient to take into account indirect discrimination, and therefore, we consider a mutates mutants approach based on optimal transport. With multiple features, optimal transport becomes more challenging and we suggest a sequential approach based on probabilistic graphical models

About the speaker

Professor Arthur Charpentier
Department of Mathematics
University of Quebec at Montreal

Title: Advancing Exploration in Reinforcement Learning

Time: 02:00pm 

Venue: CB 328

Speaker(s): Prof. Leong Hou U

Remark(s): 

Abstract

Exploration remains a key barrier to deploying reinforcement learning in realistic embodied settings, where agents must act under high-dimensional visual observations, sparse and delayed rewards, and often overactuated control interfaces. This talk presents a line of research that makes exploration more practical and scalable by progressively introducing structure into both representation and intrinsic motivation. We first revisit metric-based intrinsic bonuses and propose an effective discrepancy metric with adaptive scaling to improve robustness on hard exploration benchmarks. We then move beyond raw novelty by learning compact representations in a behavioral metric space and rewarding value-diverse, behaviorally distinct trajectories for scalable exploration in high-dimensional environments. To address long-horizon embodied tasks, we introduce latent “foresight” via diffusion-based self-prediction and a latent-space exploration reward, demonstrating gains in navigation/manipulation and real-world indoor deployment. Finally, for overactuated musculoskeletal control, we discover disentangled synergy patterns and learn policies entirely in a synergy-aware latent action space, improving efficiency and generalization.

About the speaker

Leong Hou U is currently an Associate Professor in the Department of Computer and Information Science at the University of Macau, Director of the Data Science Center. His research focuses on interdisciplinary areas at the intersection of artificial intelligence and data engineering, including traffic data optimization, spatiotemporal databases, large-scale data visualization, graph neural networks, and reinforcement learning. His team has published over 80 papers in leading journals and conferences such as SIGMOD, VLDB, ICDE, NeurIPS, AAAI, ICLR, IJCAI, and KDD. In recent years, the team has led and participated in multiple national and regional key R&D projects, including the National Key R&D Program on efficient integration and dynamic cognition technologies for urban public services, the Macau Science and Technology Development Fund key project on collaborative intelligence–driven autonomous driving, and a 2024 project on urban traffic perception fusion and intelligent reasoning that received the Second Prize of the Science and Technology Invention Award. He is also actively engaged in the international research community, serving in program and organizing committees for major conferences such as BigData, IJCAI, ICDE, DASFAA, and PAKDD, and has been a committee member of the China Association of Young Scientists (Information and Electronic Science) and the Urban Planning Committee of the Macao SAR Government since 2020, promoting the integration of scientific research with urban development policy.