Matthias Seeger | DeepAI

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∙ 05/05/2023

Optimizing Hyperparameters with Conformal Quantile Regression

Many state-of-the-art hyperparameter optimization (HPO) algorithms rely ...

0 David Salinas, et al. ∙

∙ 02/08/2023

Fortuna: A Library for Uncertainty Quantification in Deep Learning

We present Fortuna, an open-source library for uncertainty quantificatio...

0 Gianluca Detommaso, et al. ∙

∙ 11/05/2021

Meta-Forecasting by combining Global Deep Representations with Local Adaptation

While classical time series forecasting considers individual time series...

0 Riccardo Grazzi, et al. ∙

∙ 06/10/2021

A Nonmyopic Approach to Cost-Constrained Bayesian Optimization

Bayesian optimization (BO) is a popular method for optimizing expensive-...

0 Eric Hans Lee, et al. ∙

∙ 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...

0 Anastasia Makarova, et al. ∙

∙ 02/17/2021

BORE: Bayesian Optimization by Density-Ratio Estimation

Bayesian optimization (BO) is among the most effective and widely-used b...

0 Louis C. Tiao, et al. ∙

∙ 12/15/2020

Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization

Tuning complex machine learning systems is challenging. Machine learning...

0 Valerio Perrone, et al. ∙

∙ 12/15/2020

Amazon SageMaker Autopilot: a white box AutoML solution at scale

AutoML systems provide a black-box solution to machine learning problems...

0 Piali Das, et al. ∙

∙ 03/24/2020

Model-based Asynchronous Hyperparameter Optimization

We introduce a model-based asynchronous multi-fidelity hyperparameter op...

0 Louis C. Tiao, et al. ∙

∙ 03/22/2020

Cost-aware Bayesian Optimization

Bayesian optimization (BO) is a class of global optimization algorithms,...

8 Eric Hans Lee, et al. ∙

∙ 02/27/2020

LEEP: A New Measure to Evaluate Transferability of Learned Representations

We introduce a new measure to evaluate the transferability of representa...

0 Cuong V. Nguyen, et al. ∙

∙ 10/15/2019

Constrained Bayesian Optimization with Max-Value Entropy Search

Bayesian optimization (BO) is a model-based approach to sequentially opt...

0 Valerio Perrone, et al. ∙

∙ 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...

0 Valerio Perrone, et al. ∙

∙ 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...

0 Valerio Perrone, et al. ∙

∙ 10/24/2017

Auto-Differentiating Linear Algebra

Development systems for deep learning, such as Theano, Torch, TensorFlow...

0 Matthias Seeger, et al. ∙

∙ 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...

0 Matthias Seeger, et al. ∙

∙ 06/05/2013

Fast Dual Variational Inference for Non-Conjugate LGMs

Latent Gaussian models (LGMs) are widely used in statistics and machine ...

0 Mohammad Emtiyaz Khan, et al. ∙

∙ 06/27/2012

Large Scale Variational Bayesian Inference for Structured Scale Mixture Models

Natural image statistics exhibit hierarchical dependencies across multip...

0 Young Jun Ko, et al. ∙

∙ 03/04/2011

Multiple Kernel Learning: A Unifying Probabilistic Viewpoint

We present a probabilistic viewpoint to multiple kernel learning unifyin...

0 Hannes Nickisch, et al. ∙