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Nicholas G. Polson
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2020 – today
- 2024
- [i18]Jyotishka Datta, Nicholas G. Polson:
Bayesian Deep ICE. CoRR abs/2406.17058 (2024) - 2023
- [j7]Aditya Gupta
, Shiva Maharaj, Nicholas Polson, Vadim Sokolov
On the Value of Chess Squares. Entropy 25(10): 1374 (2023) - [i17]Aditya Gupta, Shiva Maharaj, Nicholas Polson, Vadim Sokolov:
The Value of Chess Squares. CoRR abs/2307.05330 (2023) - [i16]Nick Polson, Vadim Sokolov:
Deep Learning: A Tutorial. CoRR abs/2310.06251 (2023) - 2022
- [j6]Shiva Maharaj, Nick Polson, Alex Turk:
Chess AI: Competing Paradigms for Machine Intelligence. Entropy 24(4): 550 (2022) - [i15]Matthew F. Dixon, Nicholas G. Polson, Kemen Goicoechea:
Deep Partial Least Squares for Empirical Asset Pricing. CoRR abs/2206.10014 (2022) - [i14]Nicholas G. Polson, Vadim Sokolov, Jianeng Xu:
Quantum Bayes AI. CoRR abs/2208.08068 (2022) - 2021
- [i13]Shiva Maharaj, Nick Polson:
Karpov's Queen Sacrifices and AI. CoRR abs/2109.08149 (2021) - [i12]Shiva Maharaj, Nick Polson, Alex Turk:
Chess AI: Competing Paradigms for Machine Intelligence. CoRR abs/2109.11602 (2021) - [i11]Anindya Bhadra, Jyotishka Datta, Nicholas G. Polson, Vadim Sokolov, Jianeng Xu:
Merging Two Cultures: Deep and Statistical Learning. CoRR abs/2110.11561 (2021) - 2020
- [j5]Matthew F. Dixon
Short Communication: Deep Fundamental Factor Models. SIAM J. Financial Math. 11(3) (2020)
2010 – 2019
- 2019
- [j4]Anindya Bhadra, Jyotishka Datta, Yunfan Li, Nicholas G. Polson, Brandon T. Willard:
Prediction Risk for the Horseshoe Regression. J. Mach. Learn. Res. 20: 78:1-78:39 (2019) - [i10]Matthew F. Dixon, Nicholas G. Polson:
Deep Fundamental Factor Models. CoRR abs/1903.07677 (2019) - [i9]Yuexi Wang, Nicholas G. Polson, Vadim O. Sokolov:
Scalable Data Augmentation for Deep Learning. CoRR abs/1903.09668 (2019) - 2018
- [j3]Nozer D. Singpurwalla, Nicholas G. Polson, Refik Soyer:
From Least Squares to Signal Processing and Particle Filtering. Technometrics 60(2): 146-160 (2018) - [j2]Nicholas Polson, Vadim Sokolov
Bayesian Particle Tracking of Traffic Flows. IEEE Trans. Intell. Transp. Syst. 19(2): 345-356 (2018) - [c2]Veronika Rocková, Nicholas Polson:
Posterior Concentration for Sparse Deep Learning. NeurIPS 2018: 938-949 - [i8]Nicholas Polson, Veronika Rocková:
Posterior Concentration for Sparse Deep Learning. CoRR abs/1803.09138 (2018) - [i7]Guanhao Feng, Jingyu He, Nicholas G. Polson:
Deep Learning for Predicting Asset Returns. CoRR abs/1804.09314 (2018) - [i6]Nicholas G. Polson, Vadim O. Sokolov:
Deep Learning. CoRR abs/1807.07987 (2018) - [i5]Nicholas Polson, Vadim Sokolov:
Deep Learning: Computational Aspects. CoRR abs/1808.08618 (2018) - 2017
- [i4]Nicholas Polson, Vadim Sokolov:
Deep Learning: A Bayesian Perspective. CoRR abs/1706.00473 (2017) - 2016
- [i3]J. B. Heaton, Nicholas G. Polson, J. H. Witte:
Deep Learning in Finance. CoRR abs/1602.06561 (2016) - [i2]J. B. Heaton, Nicholas G. Polson, J. H. Witte:
Deep Portfolio Theory. CoRR abs/1605.07230 (2016) - 2015
- [i1]Nicholas G. Polson, James G. Scott, Brandon T. Willard:
Proximal Algorithms in Statistics and Machine Learning. CoRR abs/1502.03175 (2015) - 2014
- [j1]Tahir Ekin
Augmented Markov Chain Monte Carlo Simulation for Two-Stage Stochastic Programs with Recourse. Decis. Anal. 11(4): 250-264 (2014)
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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