
Making Bayesian Predictive Models Interpretable: A Decision Theoretic Approach
A salient approach to interpretable machine learning is to restrict mode...
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Recovering Pairwise Interactions Using Neural Networks
Recovering pairwise interactions, i.e. pairs of input features whose joi...
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Deep convolutional Gaussian processes
We propose deep convolutional Gaussian processes, a deep Gaussian proces...
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Privacypreserving data sharing via probabilistic modelling
Differential privacy allows quantifying privacy loss from computations o...
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Probabilistic Formulation of the Take The Best Heuristic
The framework of cognitively bounded rationality treats problem solving ...
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Deep learning with differential Gaussian process flows
We propose a novel deep learning paradigm of differential flows that lea...
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Active Learning for DecisionMaking from Imbalanced Observational Data
Machine learning can help personalized decision support by learning mode...
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Embarrassingly parallel MCMC using deep invertible transformations
While MCMC methods have become a main workhorse for Bayesian inference,...
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Representation Transfer for Differentially Private Drug Sensitivity Prediction
Motivation: Human genomic datasets often contain sensitive information t...
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Humanintheloop Active Covariance Learning for Improving Prediction in Small Data Sets
Learning predictive models from small highdimensional data sets is a ke...
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Scalable Probabilistic Matrix Factorization with GraphBased Priors
In matrix factorization, available graph sideinformation may not be wel...
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Harmonizable mixture kernels with variational Fourier features
The expressive power of Gaussian processes depends heavily on the choice...
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Neural NonStationary Spectral Kernel
Standard kernels such as Matérn or RBF kernels only encode simple monoto...
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Approximate Bayesian Computation via Population Monte Carlo and Classification
Approximate Bayesian computation (ABC) methods can be used to sample fro...
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Learning spectrograms with convolutional spectral kernels
We introduce the convolutional spectral kernel (CSK), a novel family of ...
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Scalable Bayesian Nonlinear Matrix Completion
Matrix completion aims to predict missing elements in a partially observ...
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NonStationary Spectral Kernels
We propose nonstationary spectral kernels for Gaussian process regressi...
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Improving drug sensitivity predictions in precision medicine through active expert knowledge elicitation
Predicting the efficacy of a drug for a given individual, using highdim...
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Knowledge Elicitation via Sequential Probabilistic Inference for HighDimensional Prediction
Prediction in a smallsized sample with a large number of covariates, th...
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Interactive Elicitation of Knowledge on Feature Relevance Improves Predictions in Small Data Sets
Providing accurate predictions is challenging for machine learning algor...
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Inferring Cognitive Models from Data using Approximate Bayesian Computation
An important problem for HCI researchers is to estimate the parameter va...
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Inverse Reinforcement Learning from Incomplete Observation Data
Inverse reinforcement learning (IRL) aims to explain observed strategic ...
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Differentially Private Bayesian Learning on Distributed Data
Many applications of machine learning, for example in health care, would...
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A MutuallyDependent Hadamard Kernel for Modelling Latent Variable Couplings
We introduce a novel kernel that models inputdependent couplings across...
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Interpreting Outliers: Localized Logistic Regression for Density Ratio Estimation
We propose an inlierbased outlier detection method capable of both iden...
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Learning Image Relations with Contrast Association Networks
Inferring the relations between two images is an important class of task...
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Natural braininformation interfaces: Recommending information by relevance inferred from human brain signals
Finding relevant information from large document collections such as the...
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Drug response prediction by inferring pathwayresponse associations with Kernelized Bayesian Matrix Factorization
A key goal of computational personalized medicine is to systematically u...
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Efficient differentially private learning improves drug sensitivity prediction
Users of a personalised recommendation system face a dilemma: recommenda...
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PinView: Implicit Feedback in ContentBased Image Retrieval
This paper describes PinView, a contentbased image retrieval system tha...
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Bayesian inference in hierarchical models by combining independent posteriors
Hierarchical models are versatile tools for joint modeling of data sets ...
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Localized Lasso for HighDimensional Regression
We introduce the localized Lasso, which is suited for learning models th...
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Multiview Kernel Completion
In this paper, we introduce the first method that (1) can complete kerne...
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Sparse group factor analysis for biclustering of multiple data sources
Motivation: Modelling methods that find structure in data are necessary ...
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Classification of weak multiview signals by sharing factors in a mixture of Bayesian group factor analyzers
We propose a novel classification model for weak signal data, building u...
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Learning Structures of Bayesian Networks for Variable Groups
Bayesian networks, and especially their structures, are powerful tools f...
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NonStationary Gaussian Process Regression with Hamiltonian Monte Carlo
We present a novel approach for fully nonstationary Gaussian process re...
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Convex Factorization Machine for Regression
We propose the convex factorization machine (CFM), which is a convex var...
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Modellingbased experiment retrieval: A case study with gene expression clustering
Motivation: Public and private repositories of experimental data are gro...
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Classification and Bayesian Optimization for LikelihoodFree Inference
Some statistical models are specified via a data generating process for ...
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Bayesian multitensor factorization
We introduce Bayesian multitensor factorization, a model that is the fi...
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Group Factor Analysis
Factor analysis provides linear factors that describe relationships betw...
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Multiple Output Regression with Latent Noise
In highdimensional data, structured noise caused by observed and unobse...
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Likelihoodfree inference via classification
Increasingly complex generative models are being used across disciplines...
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Toward computational cumulative biology by combining models of biological datasets
A main challenge of datadriven sciences is how to make maximal use of t...
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Retrieval of Experiments by Efficient Estimation of Marginal Likelihood
We study the task of retrieving relevant experiments given a query exper...
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Identification of structural features in chemicals associated with cancer drug response: A systematic datadriven analysis
Motivation: Analysis of relationships of drug structure to biological re...
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Retrieval of Experiments with Sequential Dirichlet Process Mixtures in Model Space
We address the problem of retrieving relevant experiments given a query ...
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Kernelized Bayesian Matrix Factorization
We extend kernelized matrix factorization with a fully Bayesian treatmen...
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TwoWay Latent Grouping Model for User Preference Prediction
We introduce a novel latent grouping model for predicting the relevance ...
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