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Maximizing the information learned from finite data selects a simple
  model
v1v2v3 (latest)

Maximizing the information learned from finite data selects a simple model

2 May 2017
Henry H. Mattingly
Mark K. Transtrum
Michael C. Abbott
B. Machta
ArXiv (abs)PDFHTML

Papers citing "Maximizing the information learned from finite data selects a simple model"

10 / 10 papers shown
Universal Batch Learning Under The Misspecification Setting
Universal Batch Learning Under The Misspecification Setting
Shlomi Vituri
Meir Feder
149
2
0
12 May 2024
Playing it safe: information constrains collective betting strategies
Playing it safe: information constrains collective betting strategies
P. Fleig
V. Balasubramanian
210
0
0
18 Apr 2023
Far from Asymptopia
Far from Asymptopia
Michael C. Abbott
B. Machta
150
2
0
06 May 2022
Deep Reference Priors: What is the best way to pretrain a model?
Deep Reference Priors: What is the best way to pretrain a model?International Conference on Machine Learning (ICML), 2022
Yansong Gao
Rahul Ramesh
Pratik Chaudhari
BDL
341
6
0
01 Feb 2022
Statistical aspects of nuclear mass models
Statistical aspects of nuclear mass modelsJournal of Physics G: Nuclear and Particle Physics (J. Phys. G), 2020
Vojtech Kejzlar
L. Neufcourt
W. Nazarewicz
P. Reinhard
202
42
0
11 Feb 2020
Intrinsic regularization effect in Bayesian nonlinear regression scaled
  by observed data
Intrinsic regularization effect in Bayesian nonlinear regression scaled by observed dataPhysical Review Research (PRResearch), 2020
Satoru Tokuda
Kenji Nagata
M. Okada
525
1
0
05 Jan 2020
Variational Predictive Information Bottleneck
Variational Predictive Information BottleneckSymposium on Advances in Approximate Bayesian Inference (AABI), 2019
Alexander A. Alemi
218
21
0
23 Oct 2019
On the complexity of logistic regression models
On the complexity of logistic regression modelsNeural Computation (Neural Comput.), 2019
Nicola Bulso
M. Marsili
Y. Roudi
307
19
0
01 Mar 2019
A high-bias, low-variance introduction to Machine Learning for
  physicists
A high-bias, low-variance introduction to Machine Learning for physicists
Pankaj Mehta
Marin Bukov
Ching-Hao Wang
A. G. Day
C. Richardson
Charles K. Fisher
D. Schwab
AI4CE
453
975
0
23 Mar 2018
A scaling law from discrete to continuous solutions of channel capacity
  problems in the low-noise limit
A scaling law from discrete to continuous solutions of channel capacity problems in the low-noise limitJournal of statistical physics (J. Stat. Phys.), 2017
Michael C. Abbott
B. Machta
154
13
0
25 Oct 2017
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