989 citations · 1.3k across the 20 of their papers we have counts for
8 papers · 1 filter
Constrained Bayesian Optimization with Max-Value Entropy Search
Valerio Perrone, Iaroslav Shcherbatyi, Rodolphe Jenatton +2
Bayesian optimization (BO) is a model-based approach to sequentially optimize expensive black-box functions, such as the validation error of a deep neural network with respect to i…
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning
Valerio Perrone, Huibin Shen, Matthias Seeger +2
Bayesian optimization (BO) is a successful methodology to optimize black-box functions that are expensive to evaluate. While traditional methods optimize each black-box function in…
Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian Optimization with Warm Start
Valerio Perrone, Rodolphe Jenatton, Matthias Seeger +1
Bayesian optimization (BO) is a model-based approach for gradient-free black-box function optimization. Typically, BO is powered by a Gaussian process (GP), whose algorithmic compl…
Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale
Matthias Seeger, Syama Rangapuram, Yuyang Wang +4
We present a scalable and robust Bayesian inference method for linear state space models. The method is applied to demand forecasting in the context of a large e-commerce platform,…
Fast Dual Variational Inference for Non-Conjugate LGMs
Mohammad Emtiyaz Khan, Aleksandr Y. Aravkin, Michael P. Friedlander +1
Latent Gaussian models (LGMs) are widely used in statistics and machine learning. Bayesian inference in non-conjugate LGMs is difficult due to intractable integrals involving the G…
Multiple Kernel Learning: A Unifying Probabilistic Viewpoint
Hannes Nickisch, Matthias Seeger
We present a probabilistic viewpoint to multiple kernel learning unifying well-known regularised risk approaches and recent advances in approximate Bayesian inference relaxations.…