AmirEmad Ghassami
15 papers · 2017–2024 · 7 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (15) π Academic Marathon Β· Sprint π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
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Deep Specialist
(13)
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Dynamic Duo
(11)
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Conference Pioneer
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Unstoppable Β· Bronze
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Century Club Β· Bronze
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Keyword Collector
(63)
Top co-authors
Research topics
Artificial Intelligence
(13)
Core AI
(13)
Machine Learning
(11)
Knowledge & Reasoning
(9)
Reasoning
(9)
Optimization & Theory
(6)
Learning Types
(5)
Mathematics & Optimization
(4)
Mathematics
(4)
Core Methods
(3)
Bayesian & Probabilistic Β· Artificial Intelligence
(1)
Bayesian & Probabilistic Β· Machine Learning
(1)
Optimization
(1)
Keywords
causal discovery
(7)
causal inference
(5)
causal structure learning
(4)
structure learning
(3)
experiment design
(2)
structural causal model
(2)
missing data
(2)
selection bia
(2)
latent variable
(2)
markov equivalence
(2)
graphical model
(2)
data fusion
(2)
directed acyclic graph
(2)
bayesian network
(1)
conditional mutual information
(1)
causal effect estimation
(1)
cycle detection
(1)
constraint optimization
(1)
causal structure
(1)
continuous optimization
(1)
Papers
A General Identification Algorithm For Data Fusion Problems Under Systematic Selection
UAI 2024
Identification and Estimation for Nonignorable Missing Data: A Data Fusion Approach
ICML 2024
A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models
JMLR 2023
Causal Discovery in Linear Latent Variable Models Subject to Measurement Error
NeurIPS 2022
Causal Discovery in Linear Structural Causal Models with Deterministic Relations
CLeaR 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
NeurIPS 2021
Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs
ICML 2020
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
NeurIPS 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
NeurIPS 2018
Learning Causal Structures Using Regression Invariance
NeurIPS 2017