← Authors

Murat Kocaoglu

35 papers · 2014–2025 · 5 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (5) πŸƒ Academic Marathon (11) 🐝 Cross-Pollinator (6) 🌈 Renaissance Researcher (6) πŸ—ΊοΈ Taxonomy Completionist (35) πŸ† Keyword Champion (5) 🀝 Dynamic Duo (10) πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (20) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (93) πŸ’Ž Century Club (35) πŸ”₯ Unstoppable (9) ⚑ Prolific Year (7) πŸš€ Conference Pioneer

Conferences

NIPS (19) ICML (6) UAI (5) AISTATS (3) ICLR (2)

Top co-authors

Karthikeyan Shanmugam (10) Sriram Vishwanath (5) Kristjan Greenewald (5) Alexandros G Dimakis (4) Dmitriy Katz (4) Md Musfiqur Rahman (4) Muhammad Qasim Elahi (4) Lai Wei (3) Spencer Compton (3) Mahsa Ghasemi (3)

Keywords

causal discovery (14) causal inference (11) causal graph (9) conditional independence (5) structural causal model (5) graphical model (4) latent variable (3) experimental design (3) intervention design (3) latent confounder (2) minimum entropy coupling (2) soft intervention (2) causal bandit (2) information theory (2) regret bound (2) greedy algorithm (2) combinatorial optimization (2) approximation algorithm (2) causal intervention (2) causal dag (2)

Papers

Causal Discovery-Driven Change Point Detection in Time Series AISTATS 2025
FeDCM: Federated Learning of Deep Causal Generative Models UAI 2025
Constraint-based Causal Discovery from a Collection of Conditioning Sets UAI 2025
Root Cause Analysis of Failures from Partial Causal Structures UAI 2025
Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand NIPS 2024
Sample Efficient Bayesian Learning of Causal Graphs from Interventions NIPS 2024
Counterfactual Fairness by Combining Factual and Counterfactual Predictions NIPS 2024
Partial Structure Discovery is Sufficient for No-regret Learning in Causal Bandits NIPS 2024
Towards Characterizing Domain Counterfactuals for Invertible Latent Causal Models ICLR 2024
Adaptive Online Experimental Design for Causal Discovery ICML 2024
Conditional Common Entropy for Instrumental Variable Testing and Partial Identification ICML 2024
Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference ICML 2024
Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledge NIPS 2023
Approximate Allocation Matching for Structural Causal Bandits with Unobserved Confounders NIPS 2023
Characterization and Learning of Causal Graphs with Small Conditioning Sets NIPS 2023
Minimum-Entropy Coupling Approximation Guarantees Beyond the Majorization Barrier AISTATS 2023
Finding Invariant Predictors Efficiently via Causal Structure UAI 2023
Approximate Causal Effect Identification under Weak Confounding ICML 2023
Causal Discovery in Semi-Stationary Time Series NIPS 2023
Root Cause Analysis of Failures in Microservices through Causal Discovery NIPS 2022
Entropic Causal Inference: Graph Identifiability ICML 2022
Conditionally independent data generation UAI 2021
Active Structure Learning of Causal DAGs via Directed Clique Trees NIPS 2020
Entropic Causal Inference: Identifiability and Finite Sample Results NIPS 2020
Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning NIPS 2020
Applications of Common Entropy for Causal Inference NIPS 2020
Sample Efficient Active Learning of Causal Trees NIPS 2019
Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions NIPS 2019
Experimental Design for Cost-Aware Learning of Causal Graphs NIPS 2018
CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training ICLR 2018
Contextual Bandits with Latent Confounders: An NMF Approach AISTATS 2017
Experimental Design for Learning Causal Graphs with Latent Variables NIPS 2017
Cost-Optimal Learning of Causal Graphs ICML 2017
Learning Causal Graphs with Small Interventions NIPS 2015
Sparse Polynomial Learning and Graph Sketching NIPS 2014