32 citations · 73 across the 12 of their papers we have counts for
13 papers
Arbitrariness Lies Beyond the Fairness-Accuracy Frontier
Carol Xuan Long, Hsiang Hsu, Wael Alghamdi +1
Machine learning tasks may admit multiple competing models that achieve similar performance yet produce conflicting outputs for individual samples -- a phenomenon known as predicti…
Arbitrary Decisions are a Hidden Cost of Differentially Private Training
Bogdan Kulynych, Hsiang Hsu, Carmela Troncoso +1
Mechanisms used in privacy-preserving machine learning often aim to guarantee differential privacy (DP) during model training. Practical DP-ensuring training methods use randomizat…
Automated Segmentation and Recurrence Risk Prediction of Surgically Resected Lung Tumors with Adaptive Convolutional Neural Networks
Marguerite B. Basta, Sarfaraz Hussein, Hsiang Hsu +1
Lung cancer is the leading cause of cancer related mortality by a significant margin. While new technologies, such as image segmentation, have been paramount to improved detection…
Beyond Adult and COMPAS: Fairness in Multi-Class Prediction
Wael Alghamdi, Hsiang Hsu, Haewon Jeong +4
We consider the problem of producing fair probabilistic classifiers for multi-class classification tasks. We formulate this problem in terms of "projecting" a pre-trained (and pote…
Rashomon Capacity: A Metric for Predictive Multiplicity in Classification
Hsiang Hsu, Flavio du Pin Calmon
Predictive multiplicity occurs when classification models with statistically indistinguishable performances assign conflicting predictions to individual samples. When used for deci…
Robust Hybrid Learning With Expert Augmentation
Antoine Wehenkel, Jens Behrmann, Hsiang Hsu +3
Hybrid modelling reduces the misspecification of expert models by combining them with machine learning (ML) components learned from data. Similarly to many ML algorithms, hybrid mo…
CPR: Classifier-Projection Regularization for Continual Learning
Sungmin Cha, Hsiang Hsu, Taebaek Hwang +2
We propose a general, yet simple patch that can be applied to existing regularization-based continual learning methods called classifier-projection regularization (CPR). Inspired b…
To Split or Not to Split: The Impact of Disparate Treatment in Classification
Hao Wang, Hsiang Hsu, Mario Diaz +1
Disparate treatment occurs when a machine learning model yields different decisions for individuals based on a sensitive attribute (e.g., age, sex). In domains where prediction acc…
Obfuscation via Information Density Estimation
Hsiang Hsu, Shahab Asoodeh, Flavio du Pin Calmon
Identifying features that leak information about sensitive attributes is a key challenge in the design of information obfuscation mechanisms. In this paper, we propose a framework…
Correspondence Analysis Using Neural Networks
Hsiang Hsu, Salman Salamatian, Flavio P. Calmon
Correspondence analysis (CA) is a multivariate statistical tool used to visualize and interpret data dependencies. CA has found applications in fields ranging from epidemiology to…
Correspondence Analysis of Government Expenditure Patterns
Hsiang Hsu, Flavio P. Calmon, José Cândido Silveira Santos Filho +2
We analyze expenditure patterns of discretionary funds by Brazilian congress members. This analysis is based on a large dataset containing over million expenses made publicly a…
Generalizing Correspondence Analysis for Applications in Machine Learning
Hsiang Hsu, Salman Salamatian, Flavio P. Calmon
Correspondence analysis (CA) is a multivariate statistical tool used to visualize and interpret data dependencies by finding maximally correlated embeddings of pairs of random vari…
Generalizing Bottleneck Problems
Hsiang Hsu, Shahab Asoodeh, Salman Salamatian +1
Given a pair of random variables and two convex functions and , we introduce two bottleneck functionals as the lower and upper boundaries of the two-d…