research
∙
06/15/2023
Arbitrariness Lies Beyond the Fairness-Accuracy Frontier
Machine learning tasks may admit multiple competing models that achieve ...
share
research
∙
02/28/2023
Arbitrary Decisions are a Hidden Cost of Differentially-Private Training
Mechanisms used in privacy-preserving machine learning often aim to guar...
research
∙
09/17/2022
Automated Segmentation and Recurrence Risk Prediction of Surgically Resected Lung Tumors with Adaptive Convolutional Neural Networks
Lung cancer is the leading cause of cancer related mortality by a signif...
research
∙
06/15/2022
Beyond Adult and COMPAS: Fairness in Multi-Class Prediction
We consider the problem of producing fair probabilistic classifiers for ...
research
∙
06/02/2022
Rashomon Capacity: A Metric for Predictive Multiplicity in Probabilistic Classification
Predictive multiplicity occurs when classification models with nearly in...
research
∙
02/08/2022
Robust Hybrid Learning With Expert Augmentation
Hybrid modelling reduces the misspecification of expert models by combin...
research
∙
06/12/2020
CPR: Classifier-Projection Regularization for Continual Learning
We propose a general, yet simple patch that can be applied to existing r...
research
∙
02/12/2020
To Split or Not to Split: The Impact of Disparate Treatment in Classification
Disparate treatment occurs when a machine learning model produces differ...
research
∙
10/17/2019
Obfuscation via Information Density Estimation
Identifying features that leak information about sensitive attributes is...
research
∙
02/21/2019
Correspondence Analysis Using Neural Networks
Correspondence analysis (CA) is a multivariate statistical tool used to ...
research
∙
11/29/2018
Correspondence Analysis of Government Expenditure Patterns
We analyze expenditure patterns of discretionary funds by Brazilian cong...
research
∙
06/21/2018
Deep Orthogonal Representations: Fundamental Properties and Applications
Several representation learning and, more broadly, dimensionality reduct...
research
∙
02/16/2018