Caroline Uhler

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

∙ 05/31/2023

Positivity in Linear Gaussian Structural Equation Models

We study a notion of positivity of Gaussian directed acyclic graphical m...

0 Asad Lodhia, et al. ∙

research

∙ 02/02/2023

Unpaired Multi-Domain Causal Representation Learning

The goal of causal representation learning is to find a representation o...

0 Nils Sturma, et al. ∙

research

∙ 01/25/2023

Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation

Protein-ligand binding prediction is a fundamental problem in AI-driven ...

0 Wengong Jin, et al. ∙

research

∙ 11/29/2022

Linear Causal Disentanglement via Interventions

Causal disentanglement seeks a representation of data involving latent v...

0 Anna Seigal, et al. ∙

research

∙ 11/01/2022

Transfer Learning with Kernel Methods

Transfer learning refers to the process of adapting a model trained on a...

0 Adityanarayanan Radhakrishnan, et al. ∙

research

∙ 09/10/2022

Active Learning for Optimal Intervention Design in Causal Models

An important problem across disciplines is the discovery of intervention...

0 Jiaqi Zhang, et al. ∙

research

∙ 07/04/2022

Causal Structure Discovery between Clusters of Nodes Induced by Latent Factors

We consider the problem of learning the structure of a causal directed a...

0 Chandler Squires, et al. ∙

research

∙ 06/02/2022

Causal Structure Learning: a Combinatorial Perspective

In this review, we discuss approaches for learning causal structure from...

0 Chandler Squires, et al. ∙

research

∙ 04/29/2022

Wide and Deep Neural Networks Achieve Optimality for Classification

While neural networks are used for classification tasks across domains, ...

0 Adityanarayanan Radhakrishnan, et al. ∙

research

∙ 12/30/2021

Local Quadratic Convergence of Stochastic Gradient Descent with Adaptive Step Size

Establishing a fast rate of convergence for optimization methods is cruc...

0 Adityanarayanan Radhakrishnan, et al. ∙

research

∙ 12/01/2021

Maximum Likelihood Estimation for Brownian Motion Tree Models Based on One Sample

We study the problem of maximum likelihood estimation given one data sam...

0 Michael Truell, et al. ∙

research

∙ 07/31/2021

Simple, Fast, and Flexible Framework for Matrix Completion with Infinite Width Neural Networks

Matrix completion problems arise in many applications including recommen...

15 Adityanarayanan Radhakrishnan, et al. ∙

research

∙ 07/05/2021

Matching a Desired Causal State via Shift Interventions

Transforming a causal system from a given initial state to a desired tar...

0 Jiaqi Zhang, et al. ∙

research

∙ 06/29/2021

A Mechanism for Producing Aligned Latent Spaces with Autoencoders

Aligned latent spaces, where meaningful semantic shifts in the input spa...

0 Saachi Jain, et al. ∙

research

∙ 05/28/2021

Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning

Causal structure learning is a key problem in many domains. Causal struc...

0 Scott Sussex, et al. ∙

research

∙ 02/16/2021

The DeCAMFounder: Non-Linear Causal Discovery in the Presence of Hidden Variables

Many real-world decision-making tasks require learning casual relationsh...

0 Raj Agrawal, et al. ∙

research

∙ 11/06/2020

Efficient Permutation Discovery in Causal DAGs

The problem of learning a directed acyclic graph (DAG) up to Markov equi...

0 Chandler Squires, et al. ∙

research

∙ 11/05/2020

Causal Imputation via Synthetic Interventions

Consider the problem of determining the effect of a drug on a specific c...

0 Chandler Squires, et al. ∙

research

∙ 10/19/2020

Do Deeper Convolutional Networks Perform Better?

Over-parameterization is a recent topic of much interest in the machine ...

0 Eshaan Nichani, et al. ∙

research

∙ 10/16/2020

Joint Inference of Multiple Graphs from Matrix Polynomials

Inferring graph structure from observations on the nodes is an important...

0 Madeline Navarro, et al. ∙

research

∙ 09/18/2020

Linear Convergence and Implicit Regularization of Generalized Mirror Descent with Time-Dependent Mirrors

The following questions are fundamental to understanding the properties ...

8 Adityanarayanan Radhakrishnan, et al. ∙

research

∙ 07/23/2020

Optimal Transport using GANs for Lineage Tracing

In this paper, we present Super-OT, a novel approach to computational li...

0 Neha Prasad, et al. ∙

research

∙ 06/24/2020

Learning the Effective Dynamics of Complex Multiscale Systems

Simulations of complex multiscale systems are essential for science and ...

0 Pantelis R. Vlachas, et al. ∙

research

∙ 06/15/2020

Improved Conditional Flow Models for Molecule to Image Synthesis

In this paper, we aim to synthesize cell microscopy images under differe...

14 Karren Yang, et al. ∙

research

∙ 03/13/2020

Balancedness and Alignment are Unlikely in Linear Neural Networks

We study the invariance properties of alignment in linear neural network...

0 Adityanarayanan Radhakrishnan, et al. ∙

research

∙ 01/31/2020

Causal Structure Discovery from Distributions Arising from Mixtures of DAGs

We consider distributions arising from a mixture of causal models, where...

0 Basil Saeed, et al. ∙

research

∙ 10/20/2019

Ordering-Based Causal Structure Learning in the Presence of Latent Variables

We consider the task of learning a causal graph in the presence of laten...

0 Daniel Irving Bernstein, et al. ∙

research

∙ 10/20/2019

Permutation-Based Causal Structure Learning with Unknown Intervention Targets

We consider the problem of estimating causal DAG models from a mix of ob...

0 Chandler Squires, et al. ∙

research

∙ 09/26/2019

Overparameterized Neural Networks Can Implement Associative Memory

Identifying computational mechanisms for memorization and retrieval is a...

8 Adityanarayanan Radhakrishnan, et al. ∙

research

∙ 09/10/2019

Covariance Matrix Estimation under Total Positivity for Portfolio Selection

Selecting the optimal Markowitz porfolio depends on estimating the covar...

0 Raj Agrawal, et al. ∙

research

∙ 06/23/2019

Algebraic Statistics in Practice: Applications to Networks

Algebraic statistics uses tools from algebra (especially from multilinea...

0 Marta Casanellas, et al. ∙

research

∙ 06/12/2019

Learning High-Dimensional Gaussian Graphical Models under Total Positivity without Tuning Parameters

We consider the problem of estimating an undirected Gaussian graphical m...

0 Yuhao Wang, et al. ∙

research

∙ 06/03/2019

Anchored Causal Inference in the Presence of Measurement Error

We consider the problem of learning a causal graph in the presence of me...

0 Basil Saeed, et al. ∙

research

∙ 05/01/2019

Total positivity in structured binary distributions

We study binary distributions that are multivariate totally positive of ...

0 Steffen Lauritzen, et al. ∙

research

∙ 03/05/2019

Size of Interventional Markov Equivalence Classes in Random DAG Models

Directed acyclic graph (DAG) models are popular for capturing causal rel...

0 Dmitriy Katz, et al. ∙

research

∙ 02/27/2019

ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery

Determining the causal structure of a set of variables is critical for b...

0 Raj Agrawal, et al. ∙

research

∙ 02/26/2019

Brownian motion tree models are toric

Felsenstein's classical model for Gaussian distributions on a phylogenet...

0 Bernd Sturmfels, et al. ∙

research

∙ 02/09/2019

Multi-Domain Translation by Learning Uncoupled Autoencoders

Multi-domain translation seeks to learn a probabilistic coupling between...

32 Karren D. Yang, et al. ∙

research

∙ 10/26/2018

Scalable Unbalanced Optimal Transport using Generative Adversarial Networks

Generative adversarial networks (GANs) are an expressive class of neural...

0 Karren D. Yang, et al. ∙

research

∙ 10/16/2018

Downsampling leads to Image Memorization in Convolutional Autoencoders

Memorization of data in deep neural networks has become a subject of sig...

0 Adityanarayanan Radhakrishnan, et al. ∙

research

∙ 06/26/2018

Maximum Likelihood Estimation for Totally Positive Log-Concave Densities

We study nonparametric density estimation for two classes of multivariat...

0 Elina Robeva, et al. ∙

research

∙ 04/03/2018

High-Dimensional Joint Estimation of Multiple Directed Gaussian Graphical Models

We consider the problem of jointly estimating multiple related directed ...

0 Yuhao Wang, et al. ∙

research

∙ 03/15/2018

Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models

Learning a Bayesian network (BN) from data can be useful for decision-ma...

0 Raj Agrawal, et al. ∙

research

∙ 02/20/2018

Geometry of Discrete Copulas

Multivariate discrete distributions are fundamental to modeling. Discret...

0 Elisa Perrone, et al. ∙

research

∙ 02/17/2018

Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions

We consider the problem of learning causal DAGs in the setting where bot...

0 Karren D. Yang, et al. ∙

research

∙ 02/15/2018

Direct Estimation of Differences in Causal Graphs

We consider the problem of estimating the differences between two causal...

0 Yuhao Wang, et al. ∙

research

∙ 05/23/2017

Patchnet: Interpretable Neural Networks for Image Classification

The ability to visually understand and interpret learned features from c...

0 Adityanarayanan Radhakrishnan, et al. ∙

research

∙ 07/30/2014

Differentially-Private Logistic Regression for Detecting Multiple-SNP Association in GWAS Databases

Following the publication of an attack on genome-wide association studie...

0 Fei Yu, et al. ∙

Caroline Uhler

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