Item Response Theory (IRT) models aim to assess latent abilities of $n$
examinees along with latent difficulty characteristics of $m$ test items from
categorical data that indicates the quality of their corresponding answers.
Classical psychometric assessments are based on a relatively small number of
examinees and items, say a class of $200$ students solving an exam comprising
$10$ problems. More recent global large scale assessments such as PISA, or
internet studies, may lead to significantly increased numbers of participants.
Additionally, in the context of Machine Learning where algorithms take the role
of examinees and data analysis problems take the role of items, both $n$ and
$m$ may become very large, challenging the efficiency and scalability of
computations. To learn the latent variables in IRT models from large data, we
leverage the similarity of these models to logistic regression, which can be
approximated accurately using small weighted subsets called coresets. We
develop coresets for their use in alternating IRT training algorithms,
facilitating scalable learning from large data.

Item Response Theory (IRT) models leverage coresets for scalable learning of latent abilities and difficulty characteristics in the context of large-scale assessments and data analysis problems.

可扩展的项目反应理论模型学习

Scalable Learning of Item Response Theory Models

Deep dynamic generative models are developed to learn sequential dependencies
in time-series data. The multi-layered model is designed by constructing a
hierarchy of temporal sigmoid belief networks (TSBNs), defined as a sequential
stack of sigmoid belief networks (SBNs). Each SBN has a contextual hidden
state, inherited from the previous SBNs in the sequence, and is used to
regulate its hidden bias. Scalable learning and inference algorithms are
derived by introducing a recognition model that yields fast sampling from the
variational posterior. This recognition model is trained jointly with the
generative model, by maximizing its variational lower bound on the
log-likelihood. Experimental results on bouncing balls, polyphonic music,
motion capture, and text streams show that the proposed approach achieves
state-of-the-art predictive performance, and has the capacity to synthesize
various sequences.

通过构建时序 Sigmoid 置信网络的层次结构，设计深动态生成模型，引入识别模型从变分后验中快速采样，提出的方法在多种序列数据上取得最先进的预测性能，能够合成各种序列。

深度时间 Sigmoid 信念网络用于序列建模

Deep Temporal Sigmoid Belief Networks for Sequence Modeling

Kernel methods are ubiquitous tools in machine learning. However, there is
often little reason for the common practice of selecting a kernel a priori.
Even if a universal approximating kernel is selected, the quality of the finite
sample estimator may be greatly affected by the choice of kernel. Furthermore,
when directly applying kernel methods, one typically needs to compute a $N
\times N$ Gram matrix of pairwise kernel evaluations to work with a dataset of
$N$ instances. The computation of this Gram matrix precludes the direct
application of kernel methods on large datasets, and makes kernel learning
especially difficult. In this paper we introduce Bayesian nonparmetric
kernel-learning (BaNK), a generic, data-driven framework for scalable learning
of kernels. BaNK places a nonparametric prior on the spectral distribution of
random frequencies allowing it to both learn kernels and scale to large
datasets. We show that this framework can be used for large scale regression
and classification tasks. Furthermore, we show that BaNK outperforms several
other scalable approaches for kernel learning on a variety of real world
datasets.

本研究提出 Bayesian 非参数核学习（BaNK），是一种针对扩展核学习，且基于贝叶斯框架和非参数的数据驱动框架，通过对随机频率的谱分布设置非参数先验实现对于大规模数据的分类和回归任务的可扩展高效处理。实验表明，在各种真实世界数据集上，与多种可扩展内核学习方法相比，BaNK 具有更高的性能表现。