
Attentive Clustering Processes
Amortized approaches to clustering have recently received renewed attent...
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Bootstrapping Neural Processes
Unlike in the traditional statistical modeling for which a user typicall...
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Neural Complexity Measures
While various complexity measures for diverse model classes have been pr...
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Stochastic Subset Selection
Current machine learning algorithms are designed to work with huge volum...
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The NormalGeneralised GammaPareto process: A novel purejump Lévy process with flexible tail and jumpactivity properties
Purejump Lévy processes are popular classes of stochastic processes whi...
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Costeffective Interactive Attention Learning with Neural Attention Processes
We propose a novel interactive learning framework which we refer to as I...
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Graph Embedding VAE: A Permutation Invariant Model of Graph Structure
Generative models of graph structure have applications in biology and so...
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Deep Amortized Clustering
We propose a deep amortized clustering (DAC), a neural architecture whic...
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A unified construction for series representations and finite approximations of completely random measures
Infiniteactivity completely random measures (CRMs) have become importan...
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Beyond the Chinese Restaurant and PitmanYor processes: Statistical Models with Double Powerlaw Behavior
Bayesian nonparametric approaches, in particular the PitmanYor process ...
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A Bayesian model for sparse graphs with flexible degree distribution and overlapping community structure
We consider a nonprojective class of inhomogeneous random graph models ...
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Set Transformer
Many machine learning tasks such as multiple instance learning, 3D shape...
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Mixed Effect Composite RNNGP: A Personalized and Reliable Prediction Model for Healthcare
We present a personalized and reliable prediction model for healthcare, ...
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Adaptive Network Sparsification via Dependent Variational BetaBernoulli Dropout
While variational dropout approaches have been shown to be effective for...
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Transductive Propagation Network for Fewshot Learning
Fewshot learning aims to build a learner that quickly generalizes to no...
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UncertaintyAware Attention for Reliable Interpretation and Prediction
Attention mechanism is effective in both focusing the deep learning mode...
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DropMax: Adaptive Stochastic Softmax
We propose DropMax, a stochastic version of softmax classifier which at ...
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Bayesian inference on random simple graphs with power law degree distributions
We present a model for random simple graphs with a degree distribution t...
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TreeGuided MCMC Inference for Normalized Random Measure Mixture Models
Normalized random measures (NRMs) provide a broad class of discrete rand...
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Bayesian Hierarchical Clustering with Exponential Family: SmallVariance Asymptotics and Reducibility
Bayesian hierarchical clustering (BHC) is an agglomerative clustering me...
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Juho Lee
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