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Found 8 papers, 0 papers with code
Graphical Dirichlet Process
We consider the problem of clustering grouped data with possibly non-exchangeable groups whose dependencies can be characterized by a directed acyclic graph.
Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation
Causal discovery for quantitative data has been extensively studied but less is known for categorical data.
Efficient Personalized Learning for Wearable Health Applications using HyperDimensional Computing
Considering the significant role of wearable devices in monitoring human body parameters, on-device learning can be utilized to build personalized models for behavioral and physiological patterns, and provide data privacy for users at the same time.
QHD: A brain-inspired hyperdimensional reinforcement learning algorithm
QHD provides real-time learning by further decreasing the memory capacity and the batch size.
Ordinal Causal Discovery
Causal discovery for purely observational, categorical data is a long-standing challenging problem.
Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks
To infer causal relationships in zero-inflated count data, we propose a new zero-inflated Poisson Bayesian network (ZIPBN) model.
Consensus Monte Carlo for Random Subsets using Shared Anchors
Motivated by three case studies, we focus on clustering induced by a Dirichlet process mixture sampling model, inference under an Indian buffet process prior with a binomial sampling model, and with a categorical sampling model.
Adversarial Domain Adaptation Being Aware of Class Relationships
In this paper, we propose a novel relationship-aware adversarial domain adaptation (RADA) algorithm, which first utilizes a single multi-class domain discriminator to enforce the learning of inter-class dependency structure during domain-adversarial training and then aligns this structure with the inter-class dependencies that are characterized from training the label predictor on source domain.
