b'Bryon Aragam'

research

∙ 05/31/2023

Neuro-Causal Factor Analysis

Factor analysis (FA) is a statistical tool for studying how observed var...

0 Alex Markham, et al. ∙

research

∙ 05/26/2023

Optimizing NOTEARS Objectives via Topological Swaps

Recently, an intriguing class of non-convex optimization problems has em...

0 Chang Deng, et al. ∙

research

∙ 05/06/2023

Learning Mixtures of Gaussians with Censored Data

We study the problem of learning mixtures of Gaussians with censored dat...

0 Wai Ming Tai, et al. ∙

research

∙ 09/16/2022

DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization

The combinatorial problem of learning directed acyclic graphs (DAGs) fro...

0 Kevin Bello, et al. ∙

research

∙ 06/20/2022

Identifiability of deep generative models under mixture priors without auxiliary information

We prove identifiability of a broad class of deep latent variable models...

0 Bohdan Kivva, et al. ∙

research

∙ 06/12/2022

A non-graphical representation of conditional independence via the neighbourhood lattice

We introduce and study the neighbourhood lattice decomposition of a dist...

0 Arash A. Amini, et al. ∙

research

∙ 03/28/2022

A super-polynomial lower bound for learning nonparametric mixtures

We study the problem of learning nonparametric distributions in a finite...

0 Bryon Aragam, et al. ∙

research

∙ 01/25/2022

Optimal estimation of Gaussian DAG models

We study the optimal sample complexity of learning a Gaussian directed a...

0 Ming Gao, et al. ∙

research

∙ 11/05/2021

Tradeoffs of Linear Mixed Models in Genome-wide Association Studies

Motivated by empirical arguments that are well-known from the genome-wid...

0 Haohan Wang, et al. ∙

research

∙ 11/01/2021

NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters

Context-specific Bayesian networks (i.e. directed acyclic graphs, DAGs) ...

4 Ben Lengerich, et al. ∙

research

∙ 10/12/2021

Efficient Bayesian network structure learning via local Markov boundary search

We analyze the complexity of learning directed acyclic graphical models ...

0 Ming Gao, et al. ∙

research

∙ 10/10/2021

Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families

Greedy algorithms have long been a workhorse for learning graphical mode...

0 Goutham Rajendran, et al. ∙

research

∙ 08/31/2021

Uniform Consistency in Nonparametric Mixture Models

We study uniform consistency in nonparametric mixture models as well as ...

0 Bryon Aragam, et al. ∙

research

∙ 06/29/2021

Learning latent causal graphs via mixture oracles

We study the problem of reconstructing a causal graphical model from dat...

0 Bohdan Kivva, et al. ∙

research

∙ 12/19/2020

Fundamental Limits and Tradeoffs in Invariant Representation Learning

Many machine learning applications involve learning representations that...

0 Han Zhao, et al. ∙

research

∙ 06/22/2020

A polynomial-time algorithm for learning nonparametric causal graphs

We establish finite-sample guarantees for a polynomial-time algorithm fo...

0 Ming Gao, et al. ∙

research

∙ 02/02/2020

DYNOTEARS: Structure Learning from Time-Series Data

In this paper, we revisit the structure learning problem for dynamic Bay...

13 Roxana Pamfil, et al. ∙

research

∙ 12/02/2019

Diagnostic Curves for Black Box Models

In safety-critical applications of machine learning, it is often necessa...

0 David I. Inouye, et al. ∙

research

∙ 10/15/2019

Learning Sample-Specific Models with Low-Rank Personalized Regression

Modern applications of machine learning (ML) deal with increasingly hete...

37 Benjamin Lengerich, et al. ∙

research

∙ 09/29/2019

Learning Sparse Nonparametric DAGs

We develop a framework for learning sparse nonparametric directed acycli...

18 Xun Zheng, et al. ∙

research

∙ 09/03/2019

On perfectness in Gaussian graphical models

Knowing when a graphical model is perfect to a distribution is essential...

0 Arash A. Amini, et al. ∙

research

∙ 10/17/2018

Fault Tolerance in Iterative-Convergent Machine Learning

Machine learning (ML) training algorithms often possess an inherent self...

0 Aurick Qiao, et al. ∙

research

∙ 09/10/2018

Sample Complexity of Nonparametric Semi-Supervised Learning

We study the sample complexity of semi-supervised learning (SSL) and int...

0 Chen Dan, et al. ∙

research

∙ 03/04/2018

DAGs with NO TEARS: Smooth Optimization for Structure Learning

Estimating the structure of directed acyclic graphs (DAGs, also known as...

0 Xun Zheng, et al. ∙

research

∙ 02/12/2018

Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering

Motivated by problems in data clustering, we establish general condition...

0 Bryon Aragam, et al. ∙

research

∙ 11/03/2017

Partial correlation graphs and the neighborhood lattice

We define and study partial correlation graphs (PCGs) with variables in ...

0 Arash A. Amini, et al. ∙

research

∙ 03/11/2017

Learning Large-Scale Bayesian Networks with the sparsebn Package

Learning graphical models from data is an important problem with wide ap...

0 Bryon Aragam, et al. ∙

research

∙ 11/29/2015

Learning Directed Acyclic Graphs with Penalized Neighbourhood Regression

We study a family of regularized score-based estimators for learning the...

0 Bryon Aragam, et al. ∙

research

∙ 01/04/2014

Concave Penalized Estimation of Sparse Gaussian Bayesian Networks

We develop a penalized likelihood estimation framework to estimate the s...

0 Bryon Aragam, et al. ∙

Bryon Aragam

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