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Variational Item Response Theory: Fast, Accurate, and Expressive

Variational Item Response Theory: Fast, Accurate, and Expressive

1 February 2020
Mike Wu
R. Davis
B. Domingue
Chris Piech
Noah D. Goodman
    OffRL
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Papers citing "Variational Item Response Theory: Fast, Accurate, and Expressive"

7 / 7 papers shown
Title
Generative Adversarial Networks for High-Dimensional Item Factor Analysis: A Deep Adversarial Learning Algorithm
Nanyu Luo
Feng Ji
DRL
41
0
0
15 Feb 2025
Survey of Computerized Adaptive Testing: A Machine Learning Perspective
Survey of Computerized Adaptive Testing: A Machine Learning Perspective
Qi Liu
Zhuang Yan
Haoyang Bi
Zhenya Huang
Weizhe Huang
...
Z. Pardos
Haiping Ma
Mengxiao Zhu
Shijin Wang
Enhong Chen
AI4Ed
49
9
0
31 Mar 2024
Provably Scalable Black-Box Variational Inference with Structured
  Variational Families
Provably Scalable Black-Box Variational Inference with Structured Variational Families
Joohwan Ko
Kyurae Kim
W. Kim
Jacob R. Gardner
BDL
33
2
0
19 Jan 2024
Transferable Curricula through Difficulty Conditioned Generators
Transferable Curricula through Difficulty Conditioned Generators
Sidney Tio
Pradeep Varakantham
17
4
0
22 Jun 2023
Computational modeling of semantic change
Computational modeling of semantic change
Nina Tahmasebi
Haim Dubossarsky
34
6
0
13 Apr 2023
py-irt: A Scalable Item Response Theory Library for Python
py-irt: A Scalable Item Response Theory Library for Python
John P. Lalor
Pedro Rodriguez
30
10
0
02 Mar 2022
MCMC using Hamiltonian dynamics
MCMC using Hamiltonian dynamics
Radford M. Neal
185
3,266
0
09 Jun 2012
1