← Authors

AmirEmad Ghassami

15 papers · 2017–2024 · 7 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (7) 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer πŸ—ΊοΈ Taxonomy Completionist (14) 🌍 Conference Polyglot (7) πŸƒ Academic Marathon (7) 🀝 Dynamic Duo (11) πŸ”¬ Deep Specialist (12) 🧬 Topic Evolution πŸš€ Conference Pioneer πŸ’Ž Century Club (15) πŸ”₯ Unstoppable (8) πŸ—ƒοΈ Keyword Collector (55)

Conferences

NIPS (5) ICML (3) JMLR (2) UAI (2) AAAI (1) AISTATS (1) CLEAR (1)

Top co-authors

Negar Kiyavash (11) Kun Zhang (7) Saber Salehkaleybar (5) Ilya Shpitser (4) Yuqin Yang (2) Alan Yang (2) Ehsan Mokhtarian (2) Elyse Rosenbaum (1) Elias Bareinboim (1) Jaron Jia Rong Lee (1)

Keywords

causal discovery (7) causal structure learning (4) causal inference (4) structure learning (3) experiment design (2) latent variable (2) markov equivalence (2) graphical model (2) structural causal model (2) directed acyclic graph (2) causal effect estimation (1) cycle detection (1) conditional mutual information (1) bayesian network (1) constraint optimization (1) causal structure (1) continuous optimization (1) structural equation model (1) gaussian graphical model (1) greedy algorithm (1)

Papers

Identification and Estimation for Nonignorable Missing Data: A Data Fusion Approach ICML 2024
A General Identification Algorithm For Data Fusion Problems Under Systematic Selection UAI 2024
A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models JMLR 2023
Causal Discovery in Linear Structural Causal Models with Deterministic Relations CLEAR 2022
Causal Discovery in Linear Latent Variable Models Subject to Measurement Error NIPS 2022
Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference AISTATS 2022
Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias NIPS 2021
Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables JMLR 2020
Model-Augmented Conditional Mutual Information Estimation for Feature Selection UAI 2020
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs NIPS 2020
Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs ICML 2020
Counting and Sampling from Markov Equivalent DAGs Using Clique Trees AAAI 2019
Budgeted Experiment Design for Causal Structure Learning ICML 2018
Multi-domain Causal Structure Learning in Linear Systems NIPS 2018
Learning Causal Structures Using Regression Invariance NIPS 2017