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05/05/2023
Optimizing Hyperparameters with Conformal Quantile Regression
Many state-of-the-art hyperparameter optimization (HPO) algorithms rely ...
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02/08/2023
Fortuna: A Library for Uncertainty Quantification in Deep Learning
We present Fortuna, an open-source library for uncertainty quantificatio...
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11/05/2021
Meta-Forecasting by combining Global Deep Representations with Local Adaptation
While classical time series forecasting considers individual time series...
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06/10/2021
A Nonmyopic Approach to Cost-Constrained Bayesian Optimization
Bayesian optimization (BO) is a popular method for optimizing expensive-...
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04/16/2021
Overfitting in Bayesian Optimization: an empirical study and early-stopping solution
Bayesian Optimization (BO) is a successful methodology to tune the hyper...
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02/17/2021
BORE: Bayesian Optimization by Density-Ratio Estimation
Bayesian optimization (BO) is among the most effective and widely-used b...
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12/15/2020
Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization
Tuning complex machine learning systems is challenging. Machine learning...
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12/15/2020
Amazon SageMaker Autopilot: a white box AutoML solution at scale
AutoML systems provide a black-box solution to machine learning problems...
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03/24/2020
Model-based Asynchronous Hyperparameter Optimization
We introduce a model-based asynchronous multi-fidelity hyperparameter op...
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03/22/2020
Cost-aware Bayesian Optimization
Bayesian optimization (BO) is a class of global optimization algorithms,...
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02/27/2020
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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10/15/2019
Constrained Bayesian Optimization with Max-Value Entropy Search
Bayesian optimization (BO) is a model-based approach to sequentially opt...
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09/27/2019
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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12/08/2017
Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian Optimization with Warm Start
Bayesian optimization (BO) is a model-based approach for gradient-free b...
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10/24/2017
Auto-Differentiating Linear Algebra
Development systems for deep learning, such as Theano, Torch, TensorFlow...
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09/22/2017
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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06/05/2013
Fast Dual Variational Inference for Non-Conjugate LGMs
Latent Gaussian models (LGMs) are widely used in statistics and machine ...
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06/27/2012
Large Scale Variational Bayesian Inference for Structured Scale Mixture Models
Natural image statistics exhibit hierarchical dependencies across multip...
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03/04/2011
