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Mark Goldstein
Mark Goldstein
Courant Institute, NYU
Подтвержден адрес электронной почты в домене nyu.edu - Главная страница
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Процитировано
Процитировано
Год
Practical whole-system provenance capture
T Pasquier*, X Han, M Goldstein, T Moyer, D Eyers, M Seltzer, J Bacon
Proceedings of the 2017 Symposium on Cloud Computing, 405-418, 2017
2112017
Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers
N Ma*, M Goldstein, MS Albergo, NM Boffi, E Vanden-Eijnden, S Xie
European Conference on Computer Vision (ECCV), 2024
1362024
Understanding failures in out-of-distribution detection with deep generative models
L Zhang*, M Goldstein, R Ranganath
International Conference on Machine Learning (ICML), 12427-12436, 2021
1232021
{FRAPpuccino}: Fault-detection through Runtime Analysis of Provenance
X Han*, T Pasquier, T Ranjan, M Goldstein, M Seltzer
9th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 17), 2017
662017
X-CAL: Explicit Calibration for Survival Analysis
M Goldstein*, X Han*, A Puli*, AJ Perotte, R Ranganath
Advances in Neural Information Processing Systems (NeurIPS) 2020 33, 2020
432020
Where to diffuse, how to diffuse, and how to get back: Automated learning for multivariate diffusions
R Singhal*, M Goldstein*, R Ranganath
International Conference on Learning Representations (ICLR), 2023
212023
Stochastic interpolants with data-dependent couplings
MS Albergo*, M Goldstein*, NM Boffi, R Ranganath, E Vanden-Eijnden
International Conference on Machine Learning (ICML), 2023
192023
Inverse-weighted survival games
X Han*, M Goldstein*, A Puli, T Wies, A Perotte, R Ranganath
Advances in neural information processing systems (NeurIPS) 34, 2160-2172, 2021
14*2021
Probabilistic Forecasting with Stochastic Interpolants and F\" ollmer Processes
Y Chen*, M Goldstein*, M Hua*, MS Albergo, NM Boffi, E Vanden-Eijnden
International Conference on Machine Learning (ICML), 2024
112024
Survival mixture density networks
X Han*, M Goldstein, R Ranganath
Machine Learning for Healthcare Conference (MLHC), 224-248, 2022
112022
Development and external validation of a dynamic risk score for early prediction of cardiogenic shock in cardiac intensive care units using machine learning
Y Hu*, A Lui, M Goldstein, M Sudarshan, A Tinsay, C Tsui, SD Maidman, ...
European Heart Journal: Acute Cardiovascular Care, zuae037, 2024
8*2024
Learning invariant representations with missing data
M Goldstein*, JH Jacobsen, O Chau, A Saporta, AM Puli, R Ranganath, ...
Conference on Causal Learning and Reasoning (CLeaR), 290-301, 2022
82022
What's the score? Automated Denoising Score Matching for Nonlinear Diffusions
R Singhal*, M Goldstein*, R Ranganath
International Conference on Machine Learning (ICML), 2024
42024
Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities
A Saporta*, A Puli, M Goldstein, R Ranganath
Advances in neural information processing systems (NeurIPS), 2024
12024
QTNet: Predicting Drug-Induced QT Prolongation With Artificial Intelligence–Enabled Electrocardiograms
H Zhang*, C Tarabanis, N Jethani, M Goldstein, S Smith, L Chinitz, ...
Clinical Electrophysiology 10 (5), 956-966, 2024
12024
Time After Time: Scalable Effect Estimation for Interventions on When and What to do
Y Wald, Y Efroni, M Goldstein, WAC van Amsterdam, R Ranganath
The Thirteenth International Conference on Learning Representations, 0
Symile-MIMIC: a multimodal clinical dataset of chest X-rays, electrocardiograms, and blood labs from MIMIC-IV
A Saporta, AM Puli, M Goldstein, R Ranganath
GATO: Gates Are Not the Only Option
M Goldstein*, X Han*, R Ranganath
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Статьи 1–18