
BORE: Bayesian Optimization by DensityRatio Estimation
Bayesian optimization (BO) is among the most effective and widelyused b...
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Amazon SageMaker Automatic Model Tuning: Scalable Blackbox Optimization
Tuning complex machine learning systems is challenging. Machine learning...
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Amazon SageMaker Autopilot: a white box AutoML solution at scale
AutoML systems provide a blackbox solution to machine learning problems...
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Modelbased Asynchronous Hyperparameter Optimization
We introduce a modelbased asynchronous multifidelity hyperparameter op...
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Costaware Bayesian Optimization
Bayesian optimization (BO) is a class of global optimization algorithms,...
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LEEP: A New Measure to Evaluate Transferability of Learned Representations
We introduce a new measure to evaluate the transferability of representa...
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Constrained Bayesian Optimization with MaxValue Entropy Search
Bayesian optimization (BO) is a modelbased approach to sequentially opt...
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Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning
Bayesian optimization (BO) is a successful methodology to optimize black...
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Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian Optimization with Warm Start
Bayesian optimization (BO) is a modelbased approach for gradientfree b...
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AutoDifferentiating Linear Algebra
Development systems for deep learning, such as Theano, Torch, TensorFlow...
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Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale
We present a scalable and robust Bayesian inference method for linear st...
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Fast Dual Variational Inference for NonConjugate LGMs
Latent Gaussian models (LGMs) are widely used in statistics and machine ...
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Large Scale Variational Bayesian Inference for Structured Scale Mixture Models
Natural image statistics exhibit hierarchical dependencies across multip...
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Multiple Kernel Learning: A Unifying Probabilistic Viewpoint
We present a probabilistic viewpoint to multiple kernel learning unifyin...
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Matthias Seeger
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