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02/26/2023
Efficient fair PCA for fair representation learning
We revisit the problem of fair principal component analysis (PCA), where...
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02/08/2023
Fortuna: A Library for Uncertainty Quantification in Deep Learning
We present Fortuna, an open-source library for uncertainty quantificatio...
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03/21/2022
Diverse Counterfactual Explanations for Anomaly Detection in Time Series
Data-driven methods that detect anomalies in times series data are ubiqu...
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12/23/2021
More Than Words: Towards Better Quality Interpretations of Text Classifiers
The large size and complex decision mechanisms of state-of-the-art text ...
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11/26/2021
Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models
With the increasing adoption of machine learning (ML) models and systems...
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09/07/2021
Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud
Understanding the predictions made by machine learning (ML) models and t...
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06/23/2021
Multi-objective Asynchronous Successive Halving
Hyperparameter optimization (HPO) is increasingly used to automatically ...
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06/08/2021
On the Lack of Robust Interpretability of Neural Text Classifiers
With the ever-increasing complexity of neural language models, practitio...
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12/15/2020
Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization
Tuning complex machine learning systems is challenging. Machine learning...
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06/09/2020
Fair Bayesian Optimization
Given the increasing importance of machine learning (ML) in our lives, a...
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10/18/2019
Scheduling the Learning Rate via Hypergradients: New Insights and a New Algorithm
We study the problem of fitting task-specific learning rate schedules fr...
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09/18/2019
Voting with Random Classifiers (VORACE)
In many machine learning scenarios, looking for the best classifier that...
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06/25/2019
Learning Fair and Transferable Representations
Developing learning methods which do not discriminate subgroups in the p...
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01/29/2019
General Fair Empirical Risk Minimization
We tackle the problem of algorithmic fairness, where the goal is to avoi...
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10/19/2018
Taking Advantage of Multitask Learning for Fair Classification
A central goal of algorithmic fairness is to reduce bias in automated de...
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02/23/2018
Empirical Risk Minimization under Fairness Constraints
We address the problem of algorithmic fairness: ensuring that sensitive ...
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12/18/2017
A Bridge Between Hyperparameter Optimization and Larning-to-learn
We consider a class of a nested optimization problems involving inner an...
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03/06/2017