On Finding Rhythmic Patterns in Musical Lines. - Bernard Mont-Reynaud; Mark Goldstein (ICMC, 1985)

Real-Time Additive Synthesis Controlled by a Mixture of Neural-Networks and Direct Manipulation of Physical and Perceptual Attributes. - Adrian Freed; Mark Goldstein; Michael Goodwin; Michael Lee; Keith A. McMillen; Xavier Rodet; David Wessel; Matthew Wright 002 (ICMC, 1994)

Gestural coherence and musical interaction design. - Mark Goldstein (SMC, 1998)

Practical whole-system provenance capture. - Thomas F. J.-M. Pasquier; Xueyuan Han; Mark Goldstein; Thomas Moyer; David M. Eyers; Margo I. Seltzer; Jean Bacon (SoCC, 2017)

FRAPpuccino: Fault-detection through Runtime Analysis of Provenance. - Xueyuan Han; Thomas F. J.-M. Pasquier; Tanvi Ranjan; Mark Goldstein; Margo I. Seltzer (HotCloud, 2017)

X-CAL: Explicit Calibration for Survival Analysis. - Mark Goldstein; Xintian Han; Aahlad Manas Puli; Adler J. Perotte; Rajesh Ranganath (NeurIPS, 2020)

Understanding Failures in Out-of-Distribution Detection with Deep Generative Models. - Lily H. Zhang; Mark Goldstein; Rajesh Ranganath (ICML, 2021)

Inverse-Weighted Survival Games. - Xintian Han; Mark Goldstein; Aahlad Manas Puli; Thomas Wies; Adler J. Perotte; Rajesh Ranganath (NeurIPS, 2021)

Learning Invariant Representations with Missing Data. - Mark Goldstein; Jörn-Henrik Jacobsen; Olina Chau; Adriel Saporta; Aahlad Manas Puli; Rajesh Ranganath; Andrew C. Miller (CLeaR, 2022)

Survival Mixture Density Networks. - Xintian Han; Mark Goldstein; Rajesh Ranganath (MLHC, 2022)

Where to Diffuse, How to Diffuse, and How to Get Back: Automated Learning for Multivariate Diffusions. - Raghav Singhal; Mark Goldstein; Rajesh Ranganath (ICLR, 2023)

A dynamic risk score for early prediction of cardiogenic shock using machine learning. - Yuxuan Hu; Albert Lui; Mark Goldstein; Mukund Sudarshan; Andrea Tinsay; Cindy Tsui; Samuel Maidman; John Medamana; Neil Jethani; Aahlad Manas Puli; Vuthy Nguy; Yindalon Aphinyanaphongs; et al. (CoRR, 2023)

SiT: Exploring Flow and Diffusion-Based Generative Models with Scalable Interpolant Transformers. - Nanye Ma; Mark Goldstein; Michael S. Albergo; Nicholas M. Boffi; Eric Vanden-Eijnden; Saining Xie (ECCV (77), 2024)

Stochastic Interpolants with Data-Dependent Couplings. - Michael S. Albergo; Mark Goldstein; Nicholas Matthew Boffi; Rajesh Ranganath; Eric Vanden-Eijnden (ICML, 2024)

Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes. - Yifan Chen; Mark Goldstein; Mengjian Hua; Michael S. Albergo; Nicholas Matthew Boffi; Eric Vanden-Eijnden (ICML, 2024)

What's the score? Automated Denoising Score Matching for Nonlinear Diffusions. - Raghav Singhal; Mark Goldstein; Rajesh Ranganath (ICML, 2024)

Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities. - Adriel Saporta; Aahlad Manas Puli; Mark Goldstein; Rajesh Ranganath (NeurIPS, 2024)

Time After Time: Deep-Q Effect Estimation for Interventions on When and What to do. - Yoav Wald; Mark Goldstein; Yonathan Efroni; Wouter A. C. van Amsterdam; Rajesh Ranganath (ICLR, 2025)