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03/01/2023
Fairness Evaluation in Text Classification: Machine Learning Practitioner Perspectives of Individual and Group Fairness
Mitigating algorithmic bias is a critical task in the development and de...
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02/20/2023
Simple Disentanglement of Style and Content in Visual Representations
Learning visual representations with interpretable features, i.e., disen...
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01/15/2023
Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees
We consider the task of training machine learning models with data-depen...
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10/24/2022
Sampling with Mollified Interaction Energy Descent
Sampling from a target measure whose density is only known up to a norma...
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10/13/2022
Outlier-Robust Group Inference via Gradient Space Clustering
Traditional machine learning models focus on achieving good performance ...
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09/14/2022
RMExplorer: A Visual Analytics Approach to Explore the Performance and the Fairness of Disease Risk Models on Population Subgroups
Disease risk models can identify high-risk patients and help clinicians ...
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06/07/2022
How does overparametrization affect performance on minority groups?
The benefits of overparameterization for the overall performance of mode...
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05/26/2022
Understanding new tasks through the lens of training data via exponential tilting
Deploying machine learning models to new tasks is a major challenge desp...
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05/01/2022
Domain Adaptation meets Individual Fairness. And they get along
Many instances of algorithmic bias are caused by distributional shifts. ...
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02/03/2022
Log-Euclidean Signatures for Intrinsic Distances Between Unaligned Datasets
The need for efficiently comparing and representing datasets with unknow...
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01/29/2022
Rewiring with Positional Encodings for Graph Neural Networks
Several recent works use positional encodings to extend the receptive fi...
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01/28/2022
Learning Proximal Operators to Discover Multiple Optima
Finding multiple solutions of non-convex optimization problems is a ubiq...
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10/26/2021
On sensitivity of meta-learning to support data
Meta-learning algorithms are widely used for few-shot learning. For exam...
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10/26/2021
Post-processing for Individual Fairness
Post-processing in algorithmic fairness is a versatile approach for corr...
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08/03/2021
Your fairness may vary: Group fairness of pretrained language models in toxic text classification
We study the performance-fairness trade-off in more than a dozen fine-tu...
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06/11/2021
Measuring the sensitivity of Gaussian processes to kernel choice
Gaussian processes (GPs) are used to make medical and scientific decisio...
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06/05/2021
k-Mixup Regularization for Deep Learning via Optimal Transport
Mixup is a popular regularization technique for training deep neural net...
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03/31/2021
Individually Fair Gradient Boosting
We consider the task of enforcing individual fairness in gradient boosti...
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03/30/2021
Statistical inference for individual fairness
As we rely on machine learning (ML) models to make more consequential de...
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03/19/2021
Individually Fair Ranking
We develop an algorithm to train individually fair learning-to-rank (LTR...
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12/14/2020
Outlier-Robust Optimal Transport
Optimal transport (OT) provides a way of measuring distances between dis...
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11/27/2020
Black Loans Matter: Distributionally Robust Fairness for Fighting Subgroup Discrimination
Algorithmic fairness in lending today relies on group fairness metrics f...
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11/06/2020
There is no trade-off: enforcing fairness can improve accuracy
One of the main barriers to the broader adoption of algorithmic fairness...
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10/23/2020
Online Semi-Supervised Learning with Bandit Feedback
We formulate a new problem at the intersectionof semi-supervised learnin...
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08/28/2020
Continuous Regularized Wasserstein Barycenters
Wasserstein barycenters provide a geometrically meaningful way to aggreg...
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07/22/2020
IBM Federated Learning: an Enterprise Framework White Paper V0.1
Federated Learning (FL) is an approach to conduct machine learning witho...
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07/13/2020
Model Fusion with Kullback–Leibler Divergence
We propose a method to fuse posterior distributions learned from heterog...
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06/25/2020
SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness
In this paper, we cast fair machine learning as invariant machine learni...
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06/19/2020
Two Simple Ways to Learn Individual Fairness Metrics from Data
Individual fairness is an intuitive definition of algorithmic fairness t...
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03/11/2020
Auditing ML Models for Individual Bias and Unfairness
We consider the task of auditing ML models for individual bias/unfairnes...
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02/15/2020
Federated Learning with Matched Averaging
Federated learning allows edge devices to collaboratively learn a shared...
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11/05/2019
Alleviating Label Switching with Optimal Transport
Label switching is a phenomenon arising in mixture model posterior infer...
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11/01/2019
Statistical Model Aggregation via Parameter Matching
We consider the problem of aggregating models learned from sequestered, ...
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09/19/2019
On Efficient Multilevel Clustering via Wasserstein Distances
We propose a novel approach to the problem of multilevel clustering, whi...
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06/28/2019
Learning fair predictors with Sensitive Subspace Robustness
We consider an approach to training machine learning systems that are fa...
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06/26/2019
Hierarchical Optimal Transport for Document Representation
The ability to measure similarity between documents enables intelligent ...
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05/28/2019
Bayesian Nonparametric Federated Learning of Neural Networks
In federated learning problems, data is scattered across different serve...
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05/27/2019
Dirichlet Simplex Nest and Geometric Inference
We propose Dirichlet Simplex Nest, a class of probabilistic models suita...
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09/24/2018
Streaming dynamic and distributed inference of latent geometric structures
We develop new models and algorithms for learning the temporal dynamics ...
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10/09/2017
Conic Scan-and-Cover algorithms for nonparametric topic modeling
We propose new algorithms for topic modeling when the number of topics i...
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09/27/2017
Multi-way Interacting Regression via Factorization Machines
We propose a Bayesian regression method that accounts for multi-way inte...
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06/13/2017
Multilevel Clustering via Wasserstein Means
We propose a novel approach to the problem of multilevel clustering, whi...
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10/27/2016
