
Differentially private crosssilo federated learning
Strict privacy is of paramount importance in distributed machine learnin...
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LikelihoodFree Inference with Deep Gaussian Processes
In recent years, surrogate models have been successfully used in likelih...
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Human Strategic Steering Improves Performance of Interactive Optimization
A central concern in an interactive intelligent system is optimization o...
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Variance reduction for distributed stochastic gradient MCMC
Stochastic gradient MCMC methods, such as stochastic gradient Langevin d...
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A HighPerformance Implementation of Bayesian Matrix Factorization with Limited Communication
Matrix factorization is a very common machine learning technique in reco...
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Correlated Feature Selection with Extended Exclusive Group Lasso
In many high dimensional classification or regression problems set in a ...
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Informative Gaussian Scale Mixture Priors for Bayesian Neural Networks
Encoding domain knowledge into the prior over the highdimensional weigh...
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SplitBOLFI for for misspecificationrobust likelihood free inference in high dimensions
Likelihoodfree inference for simulatorbased statistical models has rec...
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Projective Preferential Bayesian Optimization
Bayesian optimization is an effective method for finding extrema of a bl...
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Privacypreserving data sharing via probabilistic modelling
Differential privacy allows quantifying privacy loss from computations o...
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Interactive AI with a Theory of Mind
Understanding each other is the key to success in collaboration. For hum...
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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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Making Bayesian Predictive Models Interpretable: A Decision Theoretic Approach
A salient approach to interpretable machine learning is to restrict mode...
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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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Scalable Bayesian Nonlinear Matrix Completion
Matrix completion aims to predict missing elements in a partially observ...
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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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Active Learning for DecisionMaking from Imbalanced Observational Data
Machine learning can help personalized decision support by learning mode...
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Metaanalysis of Bayesian analyses
Metaanalysis aims to combine results from multiple related statistical ...
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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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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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Representation Transfer for Differentially Private Drug Sensitivity Prediction
Motivation: Human genomic datasets often contain sensitive information t...
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Local dimension reduction of summary statistics for likelihoodfree inference
Approximate Bayesian computation (ABC) and other likelihoodfree inferen...
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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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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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Harmonizable mixture kernels with variational Fourier features
The expressive power of Gaussian processes depends heavily on the choice...
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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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Deep convolutional Gaussian processes
We propose deep convolutional Gaussian processes, a deep Gaussian proces...
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Modelling User's Theory of AI's Mind in Interactive Intelligent Systems
Many interactive intelligent systems, such as recommendation and informa...
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Bayesian Metabolic Flux Analysis reveals intracellular flux couplings
Metabolic flux balance analyses are a standard tool in analysing metabol...
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Variational zeroinflated Gaussian processes with sparse kernels
Zeroinflated datasets, which have an excess of zero outputs, are common...
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User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction
In humanintheloop machine learning, the user provides information bey...
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ELFI: Engine for Likelihood Free Inference
The Engine for LikelihoodFree Inference (ELFI) is a Python software lib...
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NonStationary Spectral Kernels
We propose nonstationary spectral kernels for Gaussian process regressi...
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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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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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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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Distributed Bayesian Matrix Factorization with Minimal Communication
Bayesian matrix factorization (BMF) is a powerful tool for producing low...
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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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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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GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis
The R package GFA provides a full pipeline for factor analysis of multip...
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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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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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