Kristjan Greenewald

research

∙ 07/12/2023

Identifiability Guarantees for Causal Disentanglement from Soft Interventions

Causal disentanglement aims to uncover a representation of data using la...

0 Jiaqi Zhang, et al. ∙

research

∙ 05/24/2023

Post-processing Private Synthetic Data for Improving Utility on Selected Measures

Existing private synthetic data generation algorithms are agnostic to do...

0 Hao Wang, et al. ∙

research

∙ 05/08/2023

High-Dimensional Smoothed Entropy Estimation via Dimensionality Reduction

We study the problem of overcoming exponential sample complexity in diff...

0 Kristjan Greenewald, et al. ∙

research

∙ 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...

0 Kristjan Greenewald, et al. ∙

research

∙ 02/23/2023

Minimum-Entropy Coupling Approximation Guarantees Beyond the Majorization Barrier

Given a set of discrete probability distributions, the minimum entropy c...

0 Spencer Compton, et al. ∙

research

∙ 10/13/2022

Outlier-Robust Group Inference via Gradient Space Clustering

Traditional machine learning models focus on achieving good performance ...

0 Yuchen Zeng, et al. ∙

research

∙ 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...

0 Ziv Goldfeld, et al. ∙

research

∙ 02/03/2022

Log-Euclidean Signatures for Intrinsic Distances Between Unaligned Datasets

The need for efficiently comparing and representing datasets with unknow...

0 Tal Shnitzer, et al. ∙

research

∙ 01/28/2022

Learning Proximal Operators to Discover Multiple Optima

Finding multiple solutions of non-convex optimization problems is a ubiq...

0 Lingxiao Li, et al. ∙

research

∙ 10/11/2021

Sliced Mutual Information: A Scalable Measure of Statistical Dependence

Mutual information (MI) is a fundamental measure of statistical dependen...

0 Ziv Goldfeld, et al. ∙

research

∙ 06/07/2021

Measuring Generalization with Optimal Transport

Understanding the generalization of deep neural networks is one of the m...

0 Ching-Yao Chuang, et al. ∙

research

∙ 06/05/2021

k-Mixup Regularization for Deep Learning via Optimal Transport

Mixup is a popular regularization technique for training deep neural net...

0 Kristjan Greenewald, et al. ∙

research

∙ 02/25/2021

Improving Approximate Optimal Transport Distances using Quantization

Optimal transport (OT) is a popular tool in machine learning to compare ...

0 Gaspard Beugnot, et al. ∙

research

∙ 01/10/2021

Entropic Causal Inference: Identifiability and Finite Sample Results

Entropic causal inference is a framework for inferring the causal direct...

0 Spencer Compton, et al. ∙

research

∙ 12/13/2020

k-Variance: A Clustered Notion of Variance

We introduce k-variance, a generalization of variance built on the machi...

0 Justin Solomon, et al. ∙

research

∙ 11/03/2020

High-Dimensional Feature Selection for Sample Efficient Treatment Effect Estimation

The estimation of causal treatment effects from observational data is a ...

9 Kristjan Greenewald, et al. ∙

research

∙ 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...

0 Chandler Squires, et al. ∙

research

∙ 07/10/2020

The Computational Limits of Deep Learning

Deep learning's recent history has been one of achievement: from triumph...

378 Neil C. Thompson, et al. ∙

research

∙ 01/24/2020

Gaussian-Smooth Optimal Transport: Metric Structure and Statistical Efficiency

Optimal transport (OT), and in particular the Wasserstein distance, has ...

0 Ziv Goldfeld, et al. ∙

research

∙ 11/01/2019

Statistical Model Aggregation via Parameter Matching

We consider the problem of aggregating models learned from sequestered, ...

0 Mikhail Yurochkin, et al. ∙

research

∙ 09/08/2019

Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity

With the recent evolution of mobile health technologies, health scientis...

7 Peng Liao, et al. ∙

research

∙ 06/01/2019

BreGMN: scaled-Bregman Generative Modeling Networks

The family of f-divergences is ubiquitously applied to generative modeli...

0 Akash Srivastava, et al. ∙

research

∙ 05/30/2019

Convergence of Smoothed Empirical Measures with Applications to Entropy Estimation

This paper studies convergence of empirical measures smoothed by a Gauss...

0 Ziv Goldfeld, et al. ∙

research

∙ 05/28/2019

Bayesian Nonparametric Federated Learning of Neural Networks

In federated learning problems, data is scattered across different serve...

0 Mikhail Yurochkin, et al. ∙

research

∙ 10/27/2018

Estimating Differential Entropy under Gaussian Convolutions

This paper studies the problem of estimating the differential entropy h(...

0 Ziv Goldfeld, et al. ∙

research

∙ 10/12/2018

Estimating Information Flow in Neural Networks

We study the flow of information and the evolution of internal represent...

0 Ziv Goldfeld, et al. ∙

research

∙ 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...

0 Kristjan Greenewald, et al. ∙

research

∙ 11/09/2017

Action Centered Contextual Bandits

Contextual bandits have become popular as they offer a middle ground bet...

0 Kristjan Greenewald, et al. ∙

research

∙ 01/07/2017

Similarity Function Tracking using Pairwise Comparisons

Recent work in distance metric learning has focused on learning transfor...

0 Kristjan Greenewald, et al. ∙

research

∙ 03/11/2016

Nonstationary Distance Metric Learning

Recent work in distance metric learning has focused on learning transfor...

0 Kristjan Greenewald, et al. ∙

research

∙ 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...

0 Kristjan Greenewald, et al. ∙

research

∙ 07/27/2013

Kronecker Sum Decompositions of Space-Time Data

In this paper we consider the use of the space vs. time Kronecker produc...

0 Kristjan Greenewald, et al. ∙

Kristjan Greenewald

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