
kMixup Regularization for Deep Learning via Optimal Transport
Mixup is a popular regularization technique for training deep neural net...
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Individually Fair Gradient Boosting
We consider the task of enforcing individual fairness in gradient boosti...
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Statistical inference for individual fairness
As we rely on machine learning (ML) models to make more consequential de...
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Individually Fair Ranking
We develop an algorithm to train individually fair learningtorank (LTR...
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OutlierRobust Optimal Transport
Optimal transport (OT) provides a way of measuring distances between dis...
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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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There is no tradeoff: enforcing fairness can improve accuracy
One of the main barriers to the broader adoption of algorithmic fairness...
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Online SemiSupervised Learning with Bandit Feedback
We formulate a new problem at the intersectionof semisupervised learnin...
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Continuous Regularized Wasserstein Barycenters
Wasserstein barycenters provide a geometrically meaningful way to aggreg...
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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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Model Fusion with Kullback–Leibler Divergence
We propose a method to fuse posterior distributions learned from heterog...
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SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness
In this paper, we cast fair machine learning as invariant machine learni...
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Two Simple Ways to Learn Individual Fairness Metrics from Data
Individual fairness is an intuitive definition of algorithmic fairness t...
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Auditing ML Models for Individual Bias and Unfairness
We consider the task of auditing ML models for individual bias/unfairnes...
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Federated Learning with Matched Averaging
Federated learning allows edge devices to collaboratively learn a shared...
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Alleviating Label Switching with Optimal Transport
Label switching is a phenomenon arising in mixture model posterior infer...
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Statistical Model Aggregation via Parameter Matching
We consider the problem of aggregating models learned from sequestered, ...
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On Efficient Multilevel Clustering via Wasserstein Distances
We propose a novel approach to the problem of multilevel clustering, whi...
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Learning fair predictors with Sensitive Subspace Robustness
We consider an approach to training machine learning systems that are fa...
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Hierarchical Optimal Transport for Document Representation
The ability to measure similarity between documents enables intelligent ...
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Bayesian Nonparametric Federated Learning of Neural Networks
In federated learning problems, data is scattered across different serve...
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Dirichlet Simplex Nest and Geometric Inference
We propose Dirichlet Simplex Nest, a class of probabilistic models suita...
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Streaming dynamic and distributed inference of latent geometric structures
We develop new models and algorithms for learning the temporal dynamics ...
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Conic ScanandCover algorithms for nonparametric topic modeling
We propose new algorithms for topic modeling when the number of topics i...
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Multiway Interacting Regression via Factorization Machines
We propose a Bayesian regression method that accounts for multiway inte...
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Multilevel Clustering via Wasserstein Means
We propose a novel approach to the problem of multilevel clustering, whi...
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Geometric Dirichlet Means algorithm for topic inference
We propose a geometric algorithm for topic learning and inference that i...
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Mikhail Yurochkin
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