← Authors

Caroline Uhler

33 papers · 2017–2025 · 8 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (8) πŸƒ Academic Marathon (8) 🐝 Cross-Pollinator (12) 🌈 Renaissance Researcher (8) πŸ—ΊοΈ Taxonomy Completionist (40) πŸ”¬ Deep Specialist (18) 🀝 Dynamic Duo (13) πŸ‘‘ Triple Crown πŸ† Keyword Champion (2) 🧬 Topic Evolution πŸ’Ž Century Club (33) πŸ—ƒοΈ Keyword Collector (103) πŸ”₯ Unstoppable (9) ⚑ Prolific Year (5) πŸš€ Conference Pioneer πŸ“ˆ Trend Setter

Conferences

NIPS (10) AISTATS (7) ICML (6) CLEAR (3) ICLR (3) UAI (2) CVPR (1) JMLR (1)

Top co-authors

Chandler Squires (13) Jiaqi Zhang (7) Yuhao Wang (6) Kirankumar Shiragur (5) Karren Yang (4) Karthikeyan Shanmugam (3) Raj Agrawal (3) Basil Saeed (3) Tommi Jaakkola (2) Sharut Gupta (2)

Keywords

causal discovery (11) causal inference (8) causal graph (6) markov equivalence class (6) interventional datum (6) directed acyclic graph (6) latent variable (5) gene regulatory network (4) causal structure discovery (3) structure learning (3) causal structure learning (3) graphical model (3) causal disentanglement (3) causal dag (2) experimental design (2) approximation algorithm (2) causal model (2) causal structure (2) latent factor model (2) targeted causal discovery (2)

Papers

Probabilistic Factorial Experimental Design for Combinatorial Interventions ICML 2025
Synthetic Potential Outcomes and Causal Mixture Identifiability AISTATS 2025
An Information Criterion for Controlled Disentanglement of Multimodal Data ICLR 2025
Membership Testing in Markov Equivalence Classes via Independence Queries AISTATS 2024
Causal Discovery under Off-Target Interventions AISTATS 2024
Identifiability Guarantees for Causal Disentanglement from Purely Observational Data NIPS 2024
Causal Imputation for Counterfactual SCMs: Bridging Graphs and Latent Factor Models CLEAR 2024
Removing Biases from Molecular Representations via Information Maximization ICLR 2024
Causal Discovery with Fewer Conditional Independence Tests ICML 2024
Learning Mixtures of Unknown Causal Interventions NIPS 2024
Linear Causal Disentanglement via Interventions ICML 2023
Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation NIPS 2023
Unpaired Multi-Domain Causal Representation Learning NIPS 2023
Meek Separators and Their Applications in Targeted Causal Discovery NIPS 2023
Identifiability Guarantees for Causal Disentanglement from Soft Interventions NIPS 2023
Joint Inference of Multiple Graphs from Matrix Polynomials JMLR 2022
Causal Structure Discovery between Clusters of Nodes Induced by Latent Factors CLEAR 2022
Causal Imputation via Synthetic Interventions CLEAR 2022
Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning NIPS 2021
Matching a Desired Causal State via Shift Interventions NIPS 2021
Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis CVPR 2021
Permutation-Based Causal Structure Learning with Unknown Intervention Targets UAI 2020
Learning High-dimensional Gaussian Graphical Models under Total Positivity without Adjustment of Tuning Parameters AISTATS 2020
Ordering-Based Causal Structure Learning in the Presence of Latent Variables AISTATS 2020
Causal Structure Discovery from Distributions Arising from Mixtures of DAGs ICML 2020
Anchored Causal Inference in the Presence of Measurement Error UAI 2020
Size of Interventional Markov Equivalence Classes in random DAG models AISTATS 2019
Scalable Unbalanced Optimal Transport using Generative Adversarial Networks ICLR 2019
ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery AISTATS 2019
Direct Estimation of Differences in Causal Graphs NIPS 2018
Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models ICML 2018
Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions ICML 2018
Permutation-based Causal Inference Algorithms with Interventions NIPS 2017