Murat Kocaoglu

Murat Kocaoglu

Assistant Professor in School of ECE, Department of Computer Science (by courtesy), Department of Statistics (by courtesy).
Previously Research Staff Member at the MIT-IBM Watson AI Lab.
UT Austin Alumni.

Publications

Machine Learning / AI

[1]
Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand
M. M. Rahman, M. Jordan, M. Kocaoglu
Proc. of NeurIPS’24, 2024
[PDF] [Code]
[2]
Sample Efficient Bayesian Learning of Causal Graphs from Interventions
Z. Zhou, M. Q. Elahi, M. Kocaoglu
Proc. of NeurIPS’24, 2024
[PDF] [Code]
[3]
Partial Structure Discovery is Sufficient for No-regret Learning in Causal Bandits
M. Q. Elahi, M. Ghasemi, M. Kocaoglu
Proc. of NeurIPS’24, 2024
[PDF] [Code]
[4]
Counterfactual Fairness by Combining Factual and Counterfactual Predictions
Z. Zhou, T. Liu, R. Bai, J. Gao, M. Kocaoglu, D. I. Inouye
Proc. of NeurIPS’24, 2024
[PDF] [Code]
[5]
Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference
M. M. Rahman, M. Kocaoglu
Proc. of ICML’24, 2024
[PDF] [Code]
[6]
Conditional Common Entropy for Instrumental Variable Testing and Partial Identification
Z. Jiang, M. Kocaoglu
Proc. of ICML’24, 2024
[PDF] [Code]
[7]
Adaptive Online Experimental Design for Causal Discovery
M. Q. Elahi, L. Wei, M. Kocaoglu, M. Ghasemi
Proc. of ICML’24, 2024
🏆 Spotlight (3.5% Acceptance Rate)
[PDF] [Code]
[8]
Towards Characterizing Domain Counterfactuals for Invertible Latent Causal Models
S. Kulinski, Z. Zhou, R. Bai, M. Kocaoglu, D. I. Inouye
Proc. of ICLR’24, 2024
[PDF] [Code]
[9]
Characterization and Learning of Causal Graphs with Small Conditioning Sets
M. Kocaoglu
Proc. of NeurIPS’23, 2023
[PDF] [Code]
[10]
Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledge
A. Shah, K. Shanmugam, M. Kocaoglu
Proc. of NeurIPS’23, 2023
[PDF] [Code]
[11]
Approximate Allocation Matching for Structural Causal Bandits with Unobserved Confounders
L. Wei, M. Q. Elahi, M. Ghasemi, M. Kocaoglu
Proc. of NeurIPS’23, 2023
[PDF] [Code]
[12]
Causal Discovery in Semi-Stationary Time Series
S. Gao, R. Addanki, T. Yu, R. A. Rossi, M. Kocaoglu
Proc. of NeurIPS’23, 2023
[PDF] [Code]
[13]
Finding Invariant Predictors Efficiently via Causal Structure
K. Lee, M. M. Rahman, M. Kocaoglu
Proc. of UAI’23, 2023
[PDF] [Code]
[14]
Approximate Causal Effect Identification under Weak Confounding
Z. Jiang, L. Wei, M. Kocaoglu
Proc. of ICML'23, 2023
[PDF] [Code]
[15]
Minimum-Entropy Coupling Approximation Guarantees Beyond the Majorization Barrier
S. Compton, D. Katz, B. Qi, K. Greenewald, M. Kocaoglu
Proc. of AISTATS’23, 2023
[PDF]
[16]
Root Cause Analysis of Failures in Microservices through Causal Discovery
M. A. Ikram, S. Chakraborty, S. Mitra, S. Saini, S. Bagchi, M. Kocaoglu
Proc. of NeurIPS'22, 2022
[PDF]
[17]
Entropic Causal Inference: Graph Identifiability
S. Compton, K. Greenewald, D. Katz, M. Kocaoglu
Proc. of ICML'22, 2022
[PDF]
[18]
Conditionally Independent Data Generation
K. Ahuja, P. Sattigeri, K. Shanmugam, D. Wei, K. N. Ramamurthy, M. Kocaoglu
Proc. of UAI'21, 2021
[PDF]
[19]
Applications of Common Entropy for Causal Inference
M. Kocaoglu, S. Shakkottai, A. G. Dimakis, C. Caramanis, S. Vishwanath
Proc. of NeurIPS'20, 2020
[PDF]
[20]
Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning
A. Jaber, M. Kocaoglu, K. Shanmugam, E. Bareinboim
Proc. of NeurIPS'20, 2020
[PDF]
[21]
Entropic Causal Inference: Identifiability and Finite Sample Results
S. Compton, M. Kocaoglu, Kristjan Greenewald, Dmitriy Katz
Proc. of NeurIPS’20, 2020
[PDF]
[22]
Active Structure Learning of Causal DAGs via Directed Clique Trees
C. Squires, S. Magliacane, K. Greenewald, D. Katz, M. Kocaoglu, K. Shanmugam
Proc. of NeurIPS'20, 2020
[PDF]
[23]
Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions
M. Kocaoglu*, A. Jaber*, K. Shanmugam*, E. Bareinboim
Proc. of NeurIPS'19, 2019
[PDF]
[24]
Sample Efficient Active Learning of Causal Trees
K. Greenewald, D. Katz, K. Shanmugam, S. Magliacane, M. Kocaoglu, E. B. Adsera, G. Bresler
Proc. of NeurIPS'19, 2019
[PDF]
[25]
Experimental Design for Cost-Aware Learning of Causal Graphs
E. Lindgren, M. Kocaoglu, A. G. Dimakis, S. Vishwanath
Proc. of NeurIPS'18, 2018
[PDF]
[26]
CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training
M. Kocaoglu*, C. Snyder*, A. G. Dimakis, S. Vishwanath
Proc. of ICLR'18, 2018
[PDF]
[27]
Experimental Design for Learning Causal Graphs with Latent Variables
M. Kocaoglu, K. Shanmugam, E. Bareinboim
Proc. of NIPS'17, 2017
[PDF]
[28]
Cost-Optimal Learning of Causal Graphs
M. Kocaoglu, A. G. Dimakis, S. Vishwanath
Proc. of ICML'17, 2017
[PDF]
[29]
Entropic Causality and Greedy Minimum Entropy Coupling
M. Kocaoglu, A. G. Dimakis, S. Vishwanath, B. Hassibi
Proc. of ISIT'17, 2017
[PDF]
[30]
Contextual Bandits with Latent Confounders: An NMF Approach
R. Sen, K. Shanmugam, M. Kocaoglu, A. G. Dimakis, S. Shakkottai
Proc. of AISTATS'17, 2017
[PDF]
[31]
Entropic Causal Inference
M. Kocaoglu, A. G. Dimakis, S. Vishwanath, B. Hassibi
Proc. of AAAI'17, 2017
[PDF]
[32]
Learning Causal Graphs with Small Interventions
K. Shanmugam, M. Kocaoglu, A. G. Dimakis, S. Vishwanath
Proc. of NIPS'15, 2015
[PDF]
[33]
Sparse Polynomial Learning and Graph Sketching
M. Kocaoglu*, K. Shanmugam*, A. G. Dimakis, A. Klivans
Proc. of NIPS'14 (Oral), 2014
[PDF]

Workshops

[1]
Partial Structure Discovery is Sufficient for No-regret Learning in Causal Bandits
M. Q. Elahi, M. Ghasemi, M. Kocaoglu
ICML 2024 Workshop: Foundations of Reinforcement Learning and Control– Connections and Perspective, 2024
[2]
Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand
M. M. Rahman, M. Jordan, M. Kocaoglu
ICML 2024 Workshop on Structured Probabilistic Inference and Generative Modeling, 2024
[3]
Conditional Common Entropy for Instrumental Variable Testing and Partial Identification
Z. Jiang, M. Kocaoglu
ICML 2024 Workshop on Structured Probabilistic Inference and Generative Modeling, 2024
[4]
Constraint-based Causal Discovery from a Collection of Conditioning Sets
K. Lee, B. Ribeiro, M. Kocaoglu
9th Causal Inference Workshop at UAI 2024, 2024
[5]
RCPC: A Sound Causal Discovery Algorithm under Orientation Unfaithfulness
K. Lee, M. Kocaoglu
9th Causal Inference Workshop at UAI 2024, 2024
[6]
Causal Discovery in Semi-Stationary Time Series
S. Gao, R. Addanki, T. Yu, R. A. Rossi, M. Kocaoglu
UAI 2023 Workshop on Causal inference for Time-series Data, 2023
[7]
Approximate Causal Effect Identification under Weak Confounding
Z. Jiang, L. Wei, M. Kocaoglu
ICML 2023 Workshop on Spurious Correlations, Invariance, and Stability, 2023
[8]
Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledge
A. Shah, K. Shanmugam, M. Kocaoglu
ICML 2023 Workshop on Spurious Correlations, Invariance, and Stability, 2023
[9]
Towards Modular Learning of Deep Causal Generative Models
M. M. Rahman, M. Kocaoglu
ICML 2023 Workshop on Spurious Correlations, Invariance, and Stability, 2023
[10]
Towards Modular Learning of Deep Causal Generative Models
M. M. Rahman, M. Kocaoglu
ICML 2023 Workshop on Structured Probabilistic Inference & Generative Modeling, 2023
[11]
Entropic Causal Inference: Identifiability for Trees and Complete Graphs
S. Compton, M. Kocaoglu, K. Greenewald, D. Katz
ITR3 Workshop at ICML-21, 2021
[12]
Submodularity and Minimum Cost Intervention Design for Learning Causal Graphs
E. Lindgren, M. Kocaoglu, A. G. Dimakis, S. Vishwanath
DISCML'17 Workshop in NIPS'17, 2017
[PDF]
[13]
Learning Causal Graphs with Constraints
M. Kocaoglu, A. G. Dimakis, S. Vishwanath, M. Kocaoglu
NIPS'16 Workshop: What If? Inference and Learning of Hypothetical and Counterfactual Interventions in Complex Systems, 2016
[PDF]

Communications

Journals

[1]
Energy Minimization with Network Coding
M. Kocaoglu, O. B. Akan, ,
IEEE Systems Journal, Special Issue on Green Communications, Computing and Systems, 2015
[2]
Stochastic Resonance in Graphene Bi-layer Optical Nanoreceivers
M. Kocaoglu, B. Gulbahar, O. B. Akan
IEEE Transactions on Nanotechnology, 2014
[3]
Communication Theoretic Analysis of Synaptic Channel for Cortical Neurons
D. Malak, M. Kocaoglu, O. B. Akan
Nano Communication Networks Journal (Elsevier), 2013
[4]
Minimum Energy Channel Codes for Nanoscale Wireless Communications
M. Kocaoglu, O. B. Akan
IEEE Transactions on Wireless Communications, 2013
[5]
Fundamentals of Green Communications and Computing: Modeling and Simulation
M. Kocaoglu, D. Malak, O. B. Akan
IEEE Computer, 2012

Conferences

[1]
Effect of Channel Conditions on Inventory Database Update in Supply Chains
M. Kocaoglu, C. Oksuz, O. B. Akan
IEEE BlackSeaCom'13, 2013
[2]
On the Node Density Limits and Rate-Delay-Energy Tradeoffs in Ad Hoc Nanonetworks with Minimum Energy Coding
M. Kocaoglu, D. Malak
Proc. IEEE MoNaCom 2012 (in conjunction with IEEE ICC 2012), 2012
[3]
Minimum Energy Coding for Wireless NanoSensor Networks
M. Kocaoglu, O. B. Akan
Proc. IEEE INFOCOM 2012 (Mini Conference), 2012

PhD Thesis

Causality: From Learning to Generative Models
M. Kocaoglu
PhD Thesis, The University of Texas at Austin
Austin, TX, USA, 2018

M. Sc. Thesis

Minimum Energy Channel and Network Coding with Applications in Nanoscale Communications
M. Kocaoglu
M. Sc. Thesis, Koc University
Istanbul, Turkey, 2012