Murat Kocaoglu

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

Achievements

+13 more ↓

🌍 Conference Polyglot (5) 🏃 Academic Marathon (11) 🧭 Keyword Pioneer 🌉 Interdisciplinary Bridge 🐝 Cross-Pollinator (6)

🐝 Cross-Pollinator (6) 🌈 Renaissance Researcher (6) 🗺️ Taxonomy Completionist (35) 🏆 Keyword Champion (5) 🤝 Dynamic Duo (10) 👑 Triple Crown 🔬 Deep Specialist (20) 📈 Trend Setter 🗃️ Keyword Collector (93) 💎 Century Club (35) 🔥 Unstoppable (9) ⚡ Prolific Year (7) 🚀 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