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07/12/2023
Identifiability Guarantees for Causal Disentanglement from Soft Interventions
Causal disentanglement aims to uncover a representation of data using la...
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05/24/2023
Post-processing Private Synthetic Data for Improving Utility on Selected Measures
Existing private synthetic data generation algorithms are agnostic to do...
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05/08/2023
High-Dimensional Smoothed Entropy Estimation via Dimensionality Reduction
We study the problem of overcoming exponential sample complexity in diff...
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04/02/2023
Sharp rates of convergence for the tensor graphical Lasso estimator
Building models and methods for complex data is an important task for ma...
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02/23/2023
Minimum-Entropy Coupling Approximation Guarantees Beyond the Majorization Barrier
Given a set of discrete probability distributions, the minimum entropy c...
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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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06/17/2022
k-Sliced Mutual Information: A Quantitative Study of Scalability with Dimension
Sliced mutual information (SMI) is defined as an average of mutual infor...
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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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01/28/2022
Learning Proximal Operators to Discover Multiple Optima
Finding multiple solutions of non-convex optimization problems is a ubiq...
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10/11/2021
Sliced Mutual Information: A Scalable Measure of Statistical Dependence
Mutual information (MI) is a fundamental measure of statistical dependen...
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06/07/2021
Measuring Generalization with Optimal Transport
Understanding the generalization of deep neural networks is one of the m...
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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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02/25/2021
Improving Approximate Optimal Transport Distances using Quantization
Optimal transport (OT) is a popular tool in machine learning to compare ...
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01/10/2021
Entropic Causal Inference: Identifiability and Finite Sample Results
Entropic causal inference is a framework for inferring the causal direct...
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12/13/2020
k-Variance: A Clustered Notion of Variance
We introduce k-variance, a generalization of variance built on the machi...
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11/03/2020
High-Dimensional Feature Selection for Sample Efficient Treatment Effect Estimation
The estimation of causal treatment effects from observational data is a ...
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11/01/2020
Active Structure Learning of Causal DAGs via Directed Clique Tree
A growing body of work has begun to study intervention design for effici...
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07/10/2020
The Computational Limits of Deep Learning
Deep learning's recent history has been one of achievement: from triumph...
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01/24/2020
Gaussian-Smooth Optimal Transport: Metric Structure and Statistical Efficiency
Optimal transport (OT), and in particular the Wasserstein distance, has ...
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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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09/08/2019
Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity
With the recent evolution of mobile health technologies, health scientis...
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06/01/2019
BreGMN: scaled-Bregman Generative Modeling Networks
The family of f-divergences is ubiquitously applied to generative modeli...
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05/30/2019
Convergence of Smoothed Empirical Measures with Applications to Entropy Estimation
This paper studies convergence of empirical measures smoothed by a Gauss...
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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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10/27/2018
Estimating Differential Entropy under Gaussian Convolutions
This paper studies the problem of estimating the differential entropy h(...
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10/12/2018
Estimating Information Flow in Neural Networks
We study the flow of information and the evolution of internal represent...
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11/10/2017
Time-dependent spatially varying graphical models, with application to brain fMRI data analysis
In this work, we present an additive model for space-time data that spli...
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11/09/2017
Action Centered Contextual Bandits
Contextual bandits have become popular as they offer a middle ground bet...
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01/07/2017
Similarity Function Tracking using Pairwise Comparisons
Recent work in distance metric learning has focused on learning transfor...
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03/11/2016
Nonstationary Distance Metric Learning
Recent work in distance metric learning has focused on learning transfor...
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01/14/2014
Detection of Anomalous Crowd Behavior Using Spatio-Temporal Multiresolution Model and Kronecker Sum Decompositions
In this work we consider the problem of detecting anomalous spatio-tempo...
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07/27/2013