Robert E. Tillman

Publication Activity (10 Years)

Years Active: 2008-2024
Publications (10 Years): 9

Top Topics

Heterogeneous Information Networks

Link Prediction

Bayesian Inference

Generative Model

Top Venues

Trans. Mach. Learn. Res.

Publications

Zixiang Chen, Jun Han, Yongqian Li, Yiwen Kou, Eran Halperin, Robert E. Tillman, Quanquan Gu
Guided Discrete Diffusion for Electronic Health Record Generation. CoRR (2024)
Penghang Liu, Rupam Acharyya, Robert E. Tillman, Shunya Kimura, Naoki Masuda, Ahmet Erdem Sariyüce
Temporal Motifs for Financial Networks: A Study on Mercari, JPMC, and Venmo Platforms. CoRR (2023)
Robert E. Tillman, Tucker Balch, Manuela Veloso
Privacy-Preserving Energy-Based Generative Models for Marginal Distribution Protection. Trans. Mach. Learn. Res. 2023 (2023)
Parisa Hassanzadeh, Robert E. Tillman
Generative Models with Information-Theoretic Protection Against Membership Inference Attacks. CoRR (2022)
Robert E. Tillman, Vamsi K. Potluru, Jiahao Chen, Prashant P. Reddy, Manuela Veloso
Heuristics for Link Prediction in Multiplex Networks. ECAI (2020)
Chirag Nagpal, Robert E. Tillman, Prashant P. Reddy, Manuela Veloso
Latent Bayesian Inference for Robust Earnings Estimates. CoRR (2020)
Robert E. Tillman, Vamsi K. Potluru, Jiahao Chen, Prashant P. Reddy, Manuela M. Veloso
Heuristics for Link Prediction in Multiplex Networks. CoRR (2020)
Robert E. Tillman, Prashant P. Reddy, Manuela Veloso
Recommending missing and suspicious links in multiplex financial networks. ICAIF (2020)
Samuel A. Assefa, Danial Dervovic, Mahmoud Mahfouz, Robert E. Tillman, Prashant Reddy, Manuela Veloso
Generating synthetic data in finance: opportunities, challenges and pitfalls. ICAIF (2020)
Robert E. Tillman, Peter Spirtes
Learning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables. AISTATS (2011)
Robert E. Tillman, Peter Spirtes
When causality matters for prediction. NIPS Causality: Objectives and Assessment (2010)
Robert E. Tillman
Structure learning with independent non-identically distributed data. ICML (2009)
Robert E. Tillman, Arthur Gretton, Peter Spirtes
Nonlinear directed acyclic structure learning with weakly additive noise models. NIPS (2009)
Robert E. Tillman, David Danks, Clark Glymour
Integrating Locally Learned Causal Structures with Overlapping Variables. NIPS (2008)