Publications

Most of my work are published in open-access machine learning conferences. If you want to access one of my papers behind a paywall, send me an email and I will share it with you.

The links below will take you to the publishers’ pages. For the most recent versions of the papers including corrections, please refer to the arXiv versions here.

Machine Learning

1. M. M. Rahman, M. Jordan, M. Kocaoglu, “Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand,” in Proc. of NeurIPS’24, Vancouver, Canada, Dec. 2024.

2. Z. Zhou, M. Q. Elahi, M. Kocaoglu, “Sample Efficient Bayesian Learning of Causal Graphs from Interventions,” in Proc. of NeurIPS’24, Vancouver, Canada, Dec. 2024.

3. M. Q. Elahi, M. Ghasemi, M. Kocaoglu, “Partial Structure Discovery is Sufficient for No-regret Learning in Causal Bandits,” in Proc. of NeurIPS’24, Vancouver, Canada, Dec. 2024.

4. Z. Zhou, T. Liu, R. Bai, J. Gao, M. Kocaoglu, D. I. Inouye, “Counterfactual Fairness by Combining Factual and Counterfactual Predictions,” in Proc. of NeurIPS’24, Vancouver, Canada, Dec. 2024.

5. M. M. Rahman, M. Kocaoglu, “Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference,” in Proc. of ICML’24, Vienna, Austria, July 2024.

6. Z. Jiang, M. Kocaoglu, “Conditional Common Entropy for Instrumental Variable Testing and Partial Identification,” in Proc. of ICML’24, Vienna, Austria, July 2024.

7. M. Q. Elahi, L. Wei, M. Kocaoglu, M. Ghasemi, “Adaptive Online Experimental Design for Causal Discovery,” in Proc. of ICML’24 as Spotlight (3.5\% acceptance rate), Vienna, Austria, July 2024.

8. S. Kulinski, Z. Zhou, R. Bai, M. Kocaoglu, D. I. Inouye Towards Characterizing Domain Counterfactuals for Invertible Latent Causal Models” in Proc. of ICLR’24, Vienna, Austria, May 2024.

9. M. Kocaoglu, “Characterization and Learning of Causal Graphs with Small Conditioning Sets,” in Proc. of NeurIPS’23, New Orleans, LA, USA, Dec. 2023.

10. A. Shah, K. Shanmugam, M. Kocaoglu, “Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledge,” in Proc. of NeurIPS’23, New Orleans, LA, USA, Dec. 2023.

11. L. Wei, M. Q. Elahi, M. Ghasemi, M. Kocaoglu, “Approximate Allocation Matching for Structural Causal Bandits with Unobserved Confounders,” in Proc. of NeurIPS’23, New Orleans, LA, USA, Dec. 2023.

12. S. Gao, R. Addanki, T. Yu, R. A. Rossi, M. Kocaoglu, “Causal Discovery in Semi-Stationary Time Series,” in Proc. of NeurIPS’23, New Orleans, LA, USA, Dec. 2023.

13. K. Lee, M. M. Rahman, M. Kocaoglu, “Finding Invariant Predictors Efficiently via Causal Structure,” in Proc. of UAI’23, Pittsburgh, PA, USA, Aug. 2023.

14. Z. Jiang, L. Wei, M. Kocaoglu, “Approximate Causal Effect Identification under Weak Confounding,” in Proc. of ICML’23, Honolulu, HI, USA, July 2023.

15. S. Compton, D. Katz, B. Qi, K. Greenewald, M. Kocaoglu, “Minimum-Entropy Coupling Approximation Guarantees Beyond the Majorization Barrier,” in Proc. of AISTATS’23, Valencia, Spain, April 2023.

16. M. A. Ikram, S. Chakraborty, S. Mitra, S. Saini, S. Bagchi, M. Kocaoglu, “Root Cause Analysis of Failures in Microservices through Causal Discovery,” in Proc. of NeurIPS’22, Dec. 2022.

17. S. Compton, K. Greenewald, D. Katz, M. Kocaoglu, “Entropic Causal Inference: Graph Identifiability”, in Proc. of ICML’22, July 2022.

18. K. Ahuja, P. Sattigeri, K. Shanmugam, D. Wei, K. N. Ramamurthy, M. Kocaoglu, “Conditionally Independent Data Generation”, in Proc. of UAI’21, 2021.

19. M. Kocaoglu, S. Shakkottai, A. G. Dimakis, C. Caramanis, S. Vishwanath, “Applications of Common Entropy for Causal Inference,” in Proc. of NeurIPS’20, Online, Dec. 2020.

20. A. Jaber, M. Kocaoglu, K. Shanmugam, E. Bareinboim, “Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning,” in Proc. of NeurIPS’20, Online, Dec. 2020.

21. S. Compton, M. Kocaoglu, Kristjan Greenewald, Dmitriy Katz, “Entropic Causal Inference: Identifiability and Finite Sample Results,” in Proc. of NeurIPS’20, Online, Dec. 2020.

22. C. Squires, S. Magliacane, K. Greenewald, D. Katz, M. Kocaoglu, K. Shanmugam, “Active Structure Learning of Causal DAGs via Directed Clique Trees,” in Proc. of NeurIPS’20, Online, Dec. 2020.

23. M. Kocaoglu*, A. Jaber*, K. Shanmugam*, E. Bareinboim, “Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions,” in Proc. of NeurIPS’19, Vancouver, Canada, Dec. 2019.

24. K. Greenewald, D. Katz, K. Shanmugam, S. Magliacane, M. Kocaoglu, E. B. Adsera, G. Bresler, “Sample Efficient Active Learning of Causal Trees,” in Proc. of NeurIPS’19, Vancouver, Canada, Dec. 2019.

25. E. Lindgren, M. Kocaoglu, A. G. Dimakis, S. Vishwanath, “Experimental Design for Cost-Aware Learning of Causal Graphs” in Proc. of NeurIPS’18, Montreal, Canada, Dec. 2018.

26. M. Kocaoglu*, C. Snyder*, A. G. Dimakis, S. Vishwanath, “CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training,” in Proc. of ICLR’18, Vancouver, Canada, May 2018.

27. E. Lindgren, M. Kocaoglu, A. G. Dimakis, S. Vishwanath, “Submodularity and Minimum Cost Intervention Design for Learning Causal Graphs,” in DISCML’17 Workshop in NIPS’17, Dec. 2017.

28. M. Kocaoglu, K. Shanmugam, E. Bareinboim, “Experimental Design for Learning Causal Graphs with Latent Variables,” in Proc. of NeurIPS’17, 2017.

29. M. Kocaoglu, A. G. Dimakis, S. Vishwanath, “Cost-Optimal Learning of Causal Graphs,” in Proc. of ICML’17, 2017.

30. M. Kocaoglu, A. G. Dimakis, S. Vishwanath, B. Hassibi, “Entropic Causality and Greedy Minimum Entropy Coupling,” in Proc. of ISIT’17, 2017.

31. K. Shanmugam, M. Kocaoglu, A. G. Dimakis, S. Sanghavi, “Sparse Quadratic Logistic Regression in Sub-quadratic Time,” arXiv preprint, 2017.

32. R. Sen, K. Shanmugam, M. Kocaoglu, A. G. Dimakis, S. Shakkottai, “Contextual Bandits with Latent Confounders: An NMF Approach,” in Proc. of AISTATS’17, 2017.

33. M. Kocaoglu, A. G. Dimakis, S. Vishwanath, B. Hassibi, “Entropic Causal Inference,” in Proc. of AAAI’17, San Francisco, USA, Feb. 2017.

34. M. Kocaoglu, A. G. Dimakis, S. Vishwanath, “Learning Causal Graphs with Constraints,” in NeurIPS’16 Workshop: What If? Inference and Learning of Hypothetical and Counterfactual Interventions in Complex Systems, Barcelona, Spain, Dec. 2016.

35. K. Shanmugam, M. Kocaoglu, A. G. Dimakis, S. Vishwanath, “Learning Causal Graphs with Small Interventions,” in Proc. of NeurIPS’15, Montreal, Canada, Dec. 2015.

36. M. Kocaoglu*, K. Shanmugam*, A. G. Dimakis, A. Klivans, “Sparse Polynomial Learning and Graph Sketching,” in Proc. of NeurIPS’14 (Oral), Montreal, Canada, Dec. 2014.

Communications

Journals

1. M. Kocaoglu, O. B. Akan, “Energy Minimization with Network Coding,” in IEEE Systems Journal, Special Issue on Green Communications, Computing and Systems, 2015.

2. M. Kocaoglu, B. Gulbahar, O. B. Akan, “Stochastic Resonance in Graphene Bi-layer Optical Nanoreceivers,” in IEEE Transactions on Nanotechnology, vol. 13, no. 6, pp. 1107-1117, November 2014.

3. D. Malak, M. Kocaoglu, O. B. Akan, “Communication Theoretic Analysis of Synaptic Channel for Cortical Neurons,” in Nano Communication Networks Journal (Elsevier), vol. 4, no. 3, pp. 131-141, September 2013.

4. M. Kocaoglu, O. B. Akan, “Minimum Energy Channel Codes for Nanoscale Wireless Communications,” IEEE Transactions on Wireless Communications, vol. 12, no. 4, pp. 1492-1500, April 2013.

5. M. Kocaoglu, D. Malak, O. B. Akan, “Fundamentals of Green Communications and Computing: Modeling and Simulation,” Computer, vol. 45, no. 9, pp. 40-46, September 2012.

Conferences

1. M. Kocaoglu, C. Oksuz, O. B. Akan, “Effect of Channel Conditions on Inventory Database Update in Supply Chains.” IEEE BlackSeaCom’13, 2013.

2. M. Kocaoglu, D. Malak, “On the Node Density Limits and Rate-Delay-Energy Tradeoffs in Ad Hoc Nanonetworks with Minimum Energy Coding,” in Proc. IEEE MoNaCom 2012 (in conjunction with IEEE ICC 2012), Ottawa, Canada, June 2012.

3. M. Kocaoglu, O. B. Akan, “Minimum Energy Coding for Wireless NanoSensor Networks,” in Proc. IEEE INFOCOM 2012 (Mini Conference), Orlando, FL, USA, Mar. 2012.

PhD Thesis

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

M. Sc. Thesis

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