Results 31 to 40 of about 13,855,951 (296)
On the Model-Misspecification in Reinforcement Learning
, 2023 The success of reinforcement learning (RL) crucially depends on effective function approximation when dealing with complex ground-truth models. Existing sample-efficient RL algorithms primarily employ three approaches to function approximation: policy-based, value-based, and model-based methods.
Yunfan Li, Lin Yang
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On model selection and model misspecification in causal inference [PDF]
Statistical Methods in Medical Research, 2010 Standard variable selection procedures, primarily developed for the construction of outcome prediction models, are routinely applied when assessing exposure effects in observational studies. We argue that this tradition is sub-optimal and prone to yield bias in exposure effect estimators as well as their corresponding uncertainty estimators.
Vansteelandt, Stijn +2 more
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A View on Model Misspecification in Uncertainty Quantification
, 2023 An initial version of the current work has been accepted to be presented at BNAIC/BeNeLearn 2022, to which it was submitted on August 27 ...
Yuko Kato, David M. J. Tax, Marco Loog
openaire +2 more sources
Robust Online Control with Model Misspecification
CoRR, 2021 We study online control of an unknown nonlinear dynamical system that is approximated by a time-invariant linear system with model misspecification. Our study focuses on robustness, a measure of how much deviation from the assumed linear approximation can be tolerated by a controller while maintaining finite $\ell_2$-gain.
Xinyi Chen 0001 +3 more
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Optimal Designs’ Robustness to Model Misspecification
, 2021 Simulations considering model misspecification at the design stage (Table 2 in the Manuscript)
Sarah Lotspeich (11426089)
core +1 more source
Model Misspecification in Statistical Analysis [PDF]
, 2017 In my talk, I will discuss two important problems related with model misspecification. How do we provide powerful tests for checking misspecification in hypothesized models?
Zhou, Qian (Michelle)
core +1 more source
The Impact of Model-Misspecification on Model Based Personalised Dosing
, 2016 Model Based Personalised Dosing (MBPD) requires a population pharmacokinetic (PK) or pharmacodynamic model to determine the optimal dose of medication for an individual.
Playford, EG +7 more
core +1 more source
Decision trees compensate for model misspecification
CoRR, 2023 The best-performing models in ML are not interpretable. If we can explain why they outperform, we may be able to replicate these mechanisms and obtain both interpretability and performance. One example are decision trees and their descendent gradient boosting machines (GBMs).
Hugh Panton +2 more
openaire +3 more sources
Applied Economic Perspectives and Policy, EarlyView.ABSTRACT The California dairy industry has experienced considerable consolidation over the past 30 years. During this period, there has also been substantial technological innovation, implementation of the state's Low Carbon Fuel Standard program, and rapid adoption of anaerobic digesters.
Michael McCullough, Jarrett Hart
wiley +1 more source
Generalized Information Matrix Tests for Detecting Model Misspecification
Econometrics, 2016 Generalized Information Matrix Tests (GIMTs) have recently been used for detecting the presence of misspecification in regression models in both randomized controlled trials and observational studies.
Richard M. Golden +3 more
doaj +1 more source