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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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05/31/2023
Positivity in Linear Gaussian Structural Equation Models
We study a notion of positivity of Gaussian directed acyclic graphical m...
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02/02/2023
Unpaired Multi-Domain Causal Representation Learning
The goal of causal representation learning is to find a representation o...
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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 ...
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11/29/2022
Linear Causal Disentanglement via Interventions
Causal disentanglement seeks a representation of data involving latent v...
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11/01/2022
Transfer Learning with Kernel Methods
Transfer learning refers to the process of adapting a model trained on a...
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09/10/2022
Active Learning for Optimal Intervention Design in Causal Models
An important problem across disciplines is the discovery of intervention...
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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...
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06/02/2022
Causal Structure Learning: a Combinatorial Perspective
In this review, we discuss approaches for learning causal structure from...
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04/29/2022
Wide and Deep Neural Networks Achieve Optimality for Classification
While neural networks are used for classification tasks across domains, ...
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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...
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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...
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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...
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07/05/2021
Matching a Desired Causal State via Shift Interventions
Transforming a causal system from a given initial state to a desired tar...
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06/29/2021
A Mechanism for Producing Aligned Latent Spaces with Autoencoders
Aligned latent spaces, where meaningful semantic shifts in the input spa...
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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...
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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...
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11/06/2020
Efficient Permutation Discovery in Causal DAGs
The problem of learning a directed acyclic graph (DAG) up to Markov equi...
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11/05/2020
Causal Imputation via Synthetic Interventions
Consider the problem of determining the effect of a drug on a specific c...
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10/19/2020
Do Deeper Convolutional Networks Perform Better?
Over-parameterization is a recent topic of much interest in the machine ...
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10/16/2020
Joint Inference of Multiple Graphs from Matrix Polynomials
Inferring graph structure from observations on the nodes is an important...
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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 ...
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07/23/2020
Optimal Transport using GANs for Lineage Tracing
In this paper, we present Super-OT, a novel approach to computational li...
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06/24/2020
Learning the Effective Dynamics of Complex Multiscale Systems
Simulations of complex multiscale systems are essential for science and ...
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06/15/2020
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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03/13/2020
Balancedness and Alignment are Unlikely in Linear Neural Networks
We study the invariance properties of alignment in linear neural network...
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01/31/2020
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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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...
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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...
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09/26/2019
Overparameterized Neural Networks Can Implement Associative Memory
Identifying computational mechanisms for memorization and retrieval is a...
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09/10/2019
Covariance Matrix Estimation under Total Positivity for Portfolio Selection
Selecting the optimal Markowitz porfolio depends on estimating the covar...
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06/23/2019
Algebraic Statistics in Practice: Applications to Networks
Algebraic statistics uses tools from algebra (especially from multilinea...
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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...
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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...
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05/01/2019
Total positivity in structured binary distributions
We study binary distributions that are multivariate totally positive of ...
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03/05/2019
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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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...
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02/26/2019
Brownian motion tree models are toric
Felsenstein's classical model for Gaussian distributions on a phylogenet...
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02/09/2019
Multi-Domain Translation by Learning Uncoupled Autoencoders
Multi-domain translation seeks to learn a probabilistic coupling between...
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10/26/2018
Scalable Unbalanced Optimal Transport using Generative Adversarial Networks
Generative adversarial networks (GANs) are an expressive class of neural...
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10/16/2018
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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06/26/2018
Maximum Likelihood Estimation for Totally Positive Log-Concave Densities
We study nonparametric density estimation for two classes of multivariat...
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04/03/2018
High-Dimensional Joint Estimation of Multiple Directed Gaussian Graphical Models
We consider the problem of jointly estimating multiple related directed ...
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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...
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02/20/2018
Geometry of Discrete Copulas
Multivariate discrete distributions are fundamental to modeling. Discret...
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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...
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02/15/2018
Direct Estimation of Differences in Causal Graphs
We consider the problem of estimating the differences between two causal...
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05/23/2017
Patchnet: Interpretable Neural Networks for Image Classification
The ability to visually understand and interpret learned features from c...
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07/30/2014