Caroline Uhler

Kirankumar Shiragur

CoRR, 2024

Causal Imputation for Counterfactual SCMs: Bridging Graphs and Latent Factor Models.

[BibT_eX]

[DOI]

Alvaro Ribot

CoRR, 2024

Causal Discovery under Off-Target Interventions.

[BibT_eX]

[DOI]

Davin Choo

Kirankumar Shiragur

CoRR, 2024

2023

Active learning for optimal intervention design in causal models.

[BibT_eX]

[DOI]

Louis Cammarata

Themistoklis P. Sapsis

Nat. Mac. Intell., October, 2023

Causal Structure Learning: A Combinatorial Perspective.

[BibT_eX]

[DOI]

Found. Comput. Math., October, 2023

Removing Biases from Molecular Representations via Information Maximization.

[BibT_eX]

[DOI]

CoRR, 2023

Identifiability Guarantees for Causal Disentanglement from Soft Interventions.

[BibT_eX]

[DOI]

Kristjan H. Greenewald

Akash Srivastava

Karthikeyan Shanmugam

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Unpaired Multi-Domain Causal Representation Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Meek Separators and Their Applications in Targeted Causal Discovery.

[BibT_eX]

[DOI]

Kirankumar Shiragur

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Linear Causal Disentanglement via Interventions.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

2022

Identifying 3D Genome Organization in Diploid Organisms via Euclidean Distance Geometry.

[BibT_eX]

[DOI]

SIAM J. Math. Data Sci., 2022

Machine Learning Approaches to Single-Cell Data Integration and Translation.

[BibT_eX]

[DOI]

G. V. Shivashankar

Proc. IEEE, 2022

Multiscale simulations of complex systems by learning their effective dynamics.

[BibT_eX]

[DOI]

Pantelis-Rafail Vlachas

Georgios Arampatzis

Petros Koumoutsakos

Nat. Mach. Intell., 2022

Joint Inference of Multiple Graphs from Matrix Polynomials.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2022

Publisher Correction: Geometry of Log-Concave Density Estimation.

[BibT_eX]

[DOI]

Elina Robeva

Bernd Sturmfels

Discret. Comput. Geom., 2022

Linear Causal Disentanglement via Interventions.

[BibT_eX]

[DOI]

Anna Seigal

CoRR, 2022

Transfer Learning with Kernel Methods.

[BibT_eX]

[DOI]

Max Ruiz Luyten

Neha Prasad

CoRR, 2022

Wide and Deep Neural Networks Achieve Optimality for Classification.

[BibT_eX]

[DOI]

CoRR, 2022

Causal Structure Discovery between Clusters of Nodes Induced by Latent Factors.

[BibT_eX]

[DOI]

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

Causal Imputation via Synthetic Interventions.

[BibT_eX]

[DOI]

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

2021

Local Quadratic Convergence of Stochastic Gradient Descent with Adaptive Step Size.

[BibT_eX]

[DOI]

CoRR, 2021

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

[BibT_eX]

[DOI]

George Stefanakis

CoRR, 2021

A Mechanism for Producing Aligned Latent Spaces with Autoencoders.

[BibT_eX]

[DOI]

Saachi Jain

CoRR, 2021

DCI: learning causal differences between gene regulatory networks.

[BibT_eX]

[DOI]

Anastasiya Belyaeva

Bioinform., 2021

Matching a Desired Causal State via Shift Interventions.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

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

[BibT_eX]

[DOI]

Scott Sussex

Andreas Krause

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis.

[BibT_eX]

[DOI]

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020

Overparameterized neural networks implement associative memory.

[BibT_eX]

[DOI]

Proc. Natl. Acad. Sci. USA, 2020

Predicting cell lineages using autoencoders and optimal transport.

[BibT_eX]

[DOI]

Saradha Venkatachalapathy

Karthik Damodaran

Ali Soylemezoglu

G. V. Shivashankar

PLoS Comput. Biol., 2020

Brownian motion tree models are toric.

[BibT_eX]

[DOI]

Bernd Sturmfels

Piotr Zwiernik

Kybernetika, 2020

Efficient Permutation Discovery in Causal DAGs.

[BibT_eX]

[DOI]

Joshua Amaniampong

CoRR, 2020

Do Deeper Convolutional Networks Perform Better?

[BibT_eX]

[DOI]

Eshaan Nichani

CoRR, 2020

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

[BibT_eX]

[DOI]

CoRR, 2020

Optimal Transport using GANs for Lineage Tracing.

[BibT_eX]

[DOI]

Neha Prasad

CoRR, 2020

Learning the Effective Dynamics of Complex Multiscale Systems.

[BibT_eX]

[DOI]

Pantelis-Rafail Vlachas

Georgios Arampatzis

Petros Koumoutsakos

CoRR, 2020

Improved Conditional Flow Models for Molecule to Image Synthesis.

[BibT_eX]

[DOI]

CoRR, 2020

Balancedness and Alignment are Unlikely in Linear Neural Networks.

[BibT_eX]

[DOI]

Eshaan Nichani

Daniel Irving Bernstein

CoRR, 2020

Permutation-Based Causal Structure Learning with Unknown Intervention Targets.

[BibT_eX]

[DOI]

Yuhao Wang

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Anchored Causal Inference in the Presence of Measurement Error.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Causal Structure Discovery from Distributions Arising from Mixtures of DAGs.

[BibT_eX]

[DOI]

Basil Saeed

Snigdha Panigrahi

Proceedings of the 37th International Conference on Machine Learning, 2020

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

[BibT_eX]

[DOI]

Daniel Irving Bernstein

Basil Saeed

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Learning High-dimensional Gaussian Graphical Models under Total Positivity without Adjustment of Tuning Parameters.

[BibT_eX]

[DOI]

Yuhao Wang

Uma Roy

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Geometry of discrete copulas.

[BibT_eX]

[DOI]

Elisa Perrone

Liam Solus

J. Multivar. Anal., 2019

Geometry of Log-Concave Density Estimation.

[BibT_eX]

[DOI]

Elina Robeva

Bernd Sturmfels

Discret. Comput. Geom., 2019

Overparameterized Neural Networks Can Implement Associative Memory.

[BibT_eX]

[DOI]

CoRR, 2019

Multi-Domain Translation by Learning Uncoupled Autoencoders.

[BibT_eX]

[DOI]

CoRR, 2019

Scalable Unbalanced Optimal Transport using Generative Adversarial Networks.

[BibT_eX]

[DOI]

Proceedings of the 7th International Conference on Learning Representations, 2019

Size of Interventional Markov Equivalence Classes in random DAG models.

[BibT_eX]

[DOI]

Dmitriy Katz

Karthikeyan Shanmugam

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

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

[BibT_eX]

[DOI]

Raj Agrawal

Karthikeyan Shanmugam

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Generalized Permutohedra from Probabilistic Graphical Models.

[BibT_eX]

[DOI]

SIAM J. Discret. Math., 2018

Counting Markov equivalence classes for DAG models on trees.

[BibT_eX]

[DOI]

Liam Solus

Discret. Appl. Math., 2018

Downsampling leads to Image Memorization in Convolutional Autoencoders.

[BibT_eX]

[DOI]

CoRR, 2018

Direct Estimation of Differences in Causal Graphs.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions.

[BibT_eX]

[DOI]

Abigail Katoff

Proceedings of the 35th International Conference on Machine Learning, 2018

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

[BibT_eX]

[DOI]

Raj Agrawal

Tamara Broderick

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Patchnet: Interpretable Neural Networks for Image Classification.

[BibT_eX]

[DOI]

Charles Durham

Ali Soylemezoglu

CoRR, 2017

Counting Markov Equivalence Classes by Number of Immoralities.

[BibT_eX]

[DOI]

Liam Solus

Saradha Venkatachalapathy

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Permutation-based Causal Inference Algorithms with Interventions.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Network Analysis Identifies Regulatory Hotspots in Regions of Chromosome Interactions.

[BibT_eX]

[DOI]

Anastasiya Belyaeva

Mallika Nagarajan

G. V. Shivashankar

Proceedings of the 8th ACM International Conference on Bioinformatics, 2017

Joint inference of networks from stationary graph signals.

[BibT_eX]

[DOI]

Proceedings of the 51st Asilomar Conference on Signals, Systems, and Computers, 2017

2015

Faithfulness and learning hypergraphs from discrete distributions.

[BibT_eX]

[DOI]

Anna Klimova

Tamás Rudas

Comput. Stat. Data Anal., 2015

2014

Scalable privacy-preserving data sharing methodology for genome-wide association studies.

[BibT_eX]

[DOI]

Fei Yu

Stephen E. Fienberg

Aleksandra B. Slavkovic

J. Biomed. Informatics, 2014

Hypersurfaces and Their Singularities in Partial Correlation Testing.

[BibT_eX]

[DOI]

Found. Comput. Math., 2014

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

[BibT_eX]

[DOI]

Proceedings of the Privacy in Statistical Databases, 2014

Sphere Packing with Limited Overlap.

[BibT_eX]

[DOI]

Mabel Iglesias Ham

Michael Kerber

Proceedings of the 26th Canadian Conference on Computational Geometry, 2014

2013

Packing Ellipsoids with Overlap.

[BibT_eX]

[DOI]

Stephen J. Wright

SIAM Rev., 2013

Privacy-Preserving Data Sharing for Genome-Wide Association Studies.

[BibT_eX]

[DOI]

Aleksandra B. Slavkovic

Stephen E. Fienberg

J. Priv. Confidentiality, 2013

Learning directed acyclic graphs based on sparsest permutations.

[BibT_eX]

[DOI]

Garvesh Raskutti

CoRR, 2013

2011

Privacy Preserving GWAS Data Sharing.

[BibT_eX]

[DOI]

Stephen E. Fienberg

Aleksandra B. Slavkovic