Robert E. Tillman

According to our database, Robert E. Tillman authored at least 13 papers between 2008 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Guided Discrete Diffusion for Electronic Health Record Generation.

Zixiang Chen, Jun Han, Yongqian Li, Yiwen Kou, Eran Halperin, Robert E. Tillman, Quanquan Gu

CoRR, 2024

2023

Privacy-Preserving Energy-Based Generative Models for Marginal Distribution Protection.

Robert E. Tillman, Tucker Balch, Manuela Veloso

Trans. Mach. Learn. Res., 2023

Temporal Motifs for Financial Networks: A Study on Mercari, JPMC, and Venmo Platforms.

Penghang Liu, Rupam Acharyya, Robert E. Tillman, Shunya Kimura, Naoki Masuda, Ahmet Erdem Sariyüce

CoRR, 2023

2022

Generative Models with Information-Theoretic Protection Against Membership Inference Attacks.

Parisa Hassanzadeh, Robert E. Tillman

CoRR, 2022

2020

Latent Bayesian Inference for Robust Earnings Estimates.

Chirag Nagpal, Robert E. Tillman, Prashant P. Reddy, Manuela Veloso

CoRR, 2020

Recommending missing and suspicious links in multiplex financial networks.

Robert E. Tillman, Prashant P. Reddy, Manuela Veloso

Proceedings of the ICAIF '20: The First ACM International Conference on AI in Finance, 2020

Generating synthetic data in finance: opportunities, challenges and pitfalls.

Samuel A. Assefa, Danial Dervovic, Mahmoud Mahfouz, Robert E. Tillman, Prashant Reddy, Manuela Veloso

Proceedings of the ICAIF '20: The First ACM International Conference on AI in Finance, 2020

Heuristics for Link Prediction in Multiplex Networks.

Robert E. Tillman, Vamsi K. Potluru, Jiahao Chen, Prashant P. Reddy, Manuela Veloso

Proceedings of the ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020, 2020

2011

Learning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables.

Robert E. Tillman, Peter Spirtes

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

2010

When causality matters for prediction.

Robert E. Tillman, Peter Spirtes

Proceedings of the Causality: Objectives and Assessment (NIPS 2008 Workshop), 2010

2009

Nonlinear directed acyclic structure learning with weakly additive noise models.

Robert E. Tillman, Arthur Gretton, Peter Spirtes

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Structure learning with independent non-identically distributed data.

Robert E. Tillman

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008

Integrating Locally Learned Causal Structures with Overlapping Variables.

Robert E. Tillman, David Danks, Clark Glymour

Proceedings of the Advances in Neural Information Processing Systems 21, 2008