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Calibrating Bayesian Learning via Regularization, Confidence Minimization, and Selective Inference

Calibrating Bayesian Learning via Regularization, Confidence Minimization, and Selective Inference

3 January 2025
Jiayi Huang
Sangwoo Park
Osvaldo Simeone
ArXivPDFHTML

Papers citing "Calibrating Bayesian Learning via Regularization, Confidence Minimization, and Selective Inference"

6 / 6 papers shown
Title
Neuromorphic Split Computing with Wake-Up Radios: Architecture and
  Design via Digital Twinning
Neuromorphic Split Computing with Wake-Up Radios: Architecture and Design via Digital Twinning
Member Ieee Jiechen Chen
Member Ieee Sangwoo Park
Fellow Ieee Petar Popovski
Ieee H. Vincent Poor Life Fellow
Fellow Ieee Osvaldo Simeone
21
4
0
02 Apr 2024
Conformal Language Modeling
Conformal Language Modeling
Victor Quach
Adam Fisch
Tal Schuster
Adam Yala
J. Sohn
Tommi Jaakkola
Regina Barzilay
74
55
0
16 Jun 2023
Improving model calibration with accuracy versus uncertainty
  optimization
Improving model calibration with accuracy versus uncertainty optimization
R. Krishnan
Omesh Tickoo
UQCV
172
140
0
14 Dec 2020
PAC$^m$-Bayes: Narrowing the Empirical Risk Gap in the Misspecified
  Bayesian Regime
PACm^mm-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime
Warren Morningstar
Alexander A. Alemi
Joshua V. Dillon
65
16
0
19 Oct 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
268
4,940
0
05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
243
9,042
0
06 Jun 2015
1