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James G. Scott
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2020 – today
- 2024
- [i4]Tianyu Chen, Vansh Bansal, James G. Scott:
Conditional diffusions for neural posterior estimation. CoRR abs/2410.19105 (2024) - 2023
- [c5]Mauricio Tec, James G. Scott, Corwin M. Zigler:
Weather2vec: Representation Learning for Causal Inference with Non-local Confounding in Air Pollution and Climate Studies. AAAI 2023: 14504-14513 - 2022
- [i3]Mauricio Tec, James G. Scott, Corwin M. Zigler:
Weather2vec: Representation Learning for Causal Inference with Non-Local Confounding in Air Pollution and Climate Studies. CoRR abs/2209.12316 (2022) - 2021
- [j5]Tyler Buffington, James G. Scott, Ofodike A. Ezekoye:
Combining spatial and sociodemographic regression techniques to predict residential fire counts at the census tract level. Comput. Environ. Urban Syst. 88: 101633 (2021) - 2020
- [j4]Samuel V. Scarpino, James G. Scott, Rosalind M. Eggo, Bruce Clements, Nedialko B. Dimitrov, Lauren Ancel Meyers:
Socioeconomic bias in influenza surveillance. PLoS Comput. Biol. 16(7) (2020)
2010 – 2019
- 2018
- [j3]Raoul R. Wadhwa, Drew F. K. Williamson, Andrew Dhawan, James G. Scott:
TDAstats: R pipeline for computing persistent homology in topological data analysis. J. Open Source Softw. 3(28): 860 (2018) - [c4]Wesley Tansey, Jesse Thomason, James G. Scott:
Maximum-Variance Total Variation Denoising for Interpretable Spatial Smoothing. AAAI 2018: 2460-2467 - [c3]Wesley Tansey, Karl Pichotta, James G. Scott:
Leaf-Smoothed Hierarchical Softmax for Ordinal Prediction. AAAI 2018: 4115-4122 - 2017
- [j2]Oscar Hernan Madrid Padilla, James Sharpnack, James G. Scott, Ryan J. Tibshirani:
The DFS Fused Lasso: Linear-Time Denoising over General Graphs. J. Mach. Learn. Res. 18: 176:1-176:36 (2017) - 2016
- [i2]Wesley Tansey, Edward W. Lowe Jr., James G. Scott:
Diet2Vec: Multi-scale analysis of massive dietary data. CoRR abs/1612.00388 (2016) - 2015
- [i1]Nicholas G. Polson, James G. Scott, Brandon T. Willard:
Proximal Algorithms in Statistics and Machine Learning. CoRR abs/1502.03175 (2015) - 2012
- [j1]James G. Scott:
Benchmarking historical corporate performance. Comput. Stat. Data Anal. 56(6): 1795-1807 (2012) - [c2]Jonathan W. Pillow, James G. Scott:
Fully Bayesian inference for neural models with negative-binomial spiking. NIPS 2012: 1907-1915
2000 – 2009
- 2009
- [c1]Carlos M. Carvalho, Nicholas G. Polson, James G. Scott:
Handling Sparsity via the Horseshoe. AISTATS 2009: 73-80
Coauthor Index
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