
The DeCAMFounder: NonLinear Causal Discovery in the Presence of Hidden Variables
Many realworld decisionmaking tasks require learning casual relationsh...
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Efficient Permutation Discovery in Causal DAGs
The problem of learning a directed acyclic graph (DAG) up to Markov equi...
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Causal Imputation via Synthetic Interventions
Consider the problem of determining the effect of a drug on a specific c...
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Do Deeper Convolutional Networks Perform Better?
Overparameterization is a recent topic of much interest in the machine ...
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Joint Inference of Multiple Graphs from Matrix Polynomials
Inferring graph structure from observations on the nodes is an important...
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Linear Convergence and Implicit Regularization of Generalized Mirror Descent with TimeDependent Mirrors
The following questions are fundamental to understanding the properties ...
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Optimal Transport using GANs for Lineage Tracing
In this paper, we present SuperOT, a novel approach to computational li...
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Learning the Effective Dynamics of Complex Multiscale Systems
Simulations of complex multiscale systems are essential for science and ...
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Improved Conditional Flow Models for Molecule to Image Synthesis
In this paper, we aim to synthesize cell microscopy images under differe...
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Balancedness and Alignment are Unlikely in Linear Neural Networks
We study the invariance properties of alignment in linear neural network...
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Causal Structure Discovery from Distributions Arising from Mixtures of DAGs
We consider distributions arising from a mixture of causal models, where...
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OrderingBased Causal Structure Learning in the Presence of Latent Variables
We consider the task of learning a causal graph in the presence of laten...
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PermutationBased Causal Structure Learning with Unknown Intervention Targets
We consider the problem of estimating causal DAG models from a mix of ob...
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Overparameterized Neural Networks Can Implement Associative Memory
Identifying computational mechanisms for memorization and retrieval is a...
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Covariance Matrix Estimation under Total Positivity for Portfolio Selection
Selecting the optimal Markowitz porfolio depends on estimating the covar...
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Algebraic Statistics in Practice: Applications to Networks
Algebraic statistics uses tools from algebra (especially from multilinea...
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Learning HighDimensional Gaussian Graphical Models under Total Positivity without Tuning Parameters
We consider the problem of estimating an undirected Gaussian graphical m...
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Anchored Causal Inference in the Presence of Measurement Error
We consider the problem of learning a causal graph in the presence of me...
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Total positivity in structured binary distributions
We study binary distributions that are multivariate totally positive of ...
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Size of Interventional Markov Equivalence Classes in Random DAG Models
Directed acyclic graph (DAG) models are popular for capturing causal rel...
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ABCDStrategy: Budgeted Experimental Design for Targeted Causal Structure Discovery
Determining the causal structure of a set of variables is critical for b...
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Brownian motion tree models are toric
Felsenstein's classical model for Gaussian distributions on a phylogenet...
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MultiDomain Translation by Learning Uncoupled Autoencoders
Multidomain translation seeks to learn a probabilistic coupling between...
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Scalable Unbalanced Optimal Transport using Generative Adversarial Networks
Generative adversarial networks (GANs) are an expressive class of neural...
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Downsampling leads to Image Memorization in Convolutional Autoencoders
Memorization of data in deep neural networks has become a subject of sig...
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Maximum Likelihood Estimation for Totally Positive LogConcave Densities
We study nonparametric density estimation for two classes of multivariat...
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HighDimensional Joint Estimation of Multiple Directed Gaussian Graphical Models
We consider the problem of jointly estimating multiple related directed ...
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Minimal IMAP MCMC for Scalable Structure Discovery in Causal DAG Models
Learning a Bayesian network (BN) from data can be useful for decisionma...
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Geometry of Discrete Copulas
Multivariate discrete distributions are fundamental to modeling. Discret...
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Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions
We consider the problem of learning causal DAGs in the setting where bot...
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Direct Estimation of Differences in Causal Graphs
We consider the problem of estimating the differences between two causal...
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Patchnet: Interpretable Neural Networks for Image Classification
The ability to visually understand and interpret learned features from c...
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DifferentiallyPrivate Logistic Regression for Detecting MultipleSNP Association in GWAS Databases
Following the publication of an attack on genomewide association studie...
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Caroline Uhler
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