Search Results for author:
Found 21 papers, 4 papers with code
A Simple and Fast Baseline for Tuning Large XGBoost Models
XGBoost, a scalable tree boosting algorithm, has proven effective for many prediction tasks of practical interest, especially using tabular datasets.
A Nonmyopic Approach to Cost-Constrained Bayesian Optimization
Bayesian optimization (BO) is a popular method for optimizing expensive-to-evaluate black-box functions.
A multi-objective perspective on jointly tuning hardware and hyperparameters
In three benchmarks where hardware is selected in addition to hyperparameters, we obtain runtime and cost reductions of at least 5. 8x and 8. 8x, respectively.
Automatic Termination for Hyperparameter Optimization
Across an extensive range of real-world HPO problems and baselines, we show that our termination criterion achieves a better trade-off between the test performance and optimization time.
Lexical semantic change for Ancient Greek and Latin
In this chapter we build on GASC, a recent computational approach to semantic change based on a dynamic Bayesian mixture model.
Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization
To democratize access to machine learning systems, it is essential to automate the tuning.
Amazon SageMaker Autopilot: a white box AutoML solution at scale
AutoML systems provide a black-box solution to machine learning problems by selecting the right way of processing features, choosing an algorithm and tuning the hyperparameters of the entire pipeline.
Pareto-efficient Acquisition Functions for Cost-Aware Bayesian Optimization
Bayesian optimization (BO) is a popular method to optimize expensive black-box functions.
Gaussian Density Parametrization Flow: Particle and Stochastic Approaches
Bayesian inference is intractable for most practical problems and requires approximation schemes with several trade-offs.
Fair Bayesian Optimization
Moreover, our method can be used in synergy with such specialized fairness techniques to tune their hyperparameters.
Cost-aware Bayesian Optimization
Bayesian optimization (BO) is a class of global optimization algorithms, suitable for minimizing an expensive objective function in as few function evaluations as possible.
Constrained Bayesian Optimization with Max-Value Entropy Search
We propose constrained Max-value Entropy Search (cMES), a novel information theoretic-based acquisition function implementing this formulation.
A Quantile-based Approach for Hyperparameter Transfer Learning
In this work, we introduce a novel approach to achieve transfer learning across different \emph{datasets} as well as different \emph{objectives}.
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning
Despite its simplicity, we show that our approach considerably boosts BO by reducing the size of the search space, thus accelerating the optimization of a variety of black-box optimization problems.
A Copula approach for hyperparameter transfer learning
In this work, we introduce a novel approach to achieve transfer learning across different datasets as well as different metrics.
GASC: Genre-Aware Semantic Change for Ancient Greek
Word meaning changes over time, depending on linguistic and extra-linguistic factors.
Scalable Hyperparameter Transfer Learning
Bayesian optimization (BO) is a model-based approach for gradient-free black-box function optimization, such as hyperparameter optimization.
A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks
To achieve this, two inferential challenges need to be addressed: (1) population data are exchangeable, calling for methods that efficiently exploit the symmetries of the data, and (2) computing likelihoods is intractable as it requires integrating over a set of correlated, extremely high-dimensional latent variables.
Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian Optimization with Warm Start
Bayesian optimization (BO) is a model-based approach for gradient-free black-box function optimization.
Poisson Random Fields for Dynamic Feature Models
We present the Wright-Fisher Indian buffet process (WF-IBP), a probabilistic model for time-dependent data assumed to have been generated by an unknown number of latent features.
Relativistic Monte Carlo
Based on this, we develop relativistic stochastic gradient descent by taking the zero-temperature limit of relativistic stochastic gradient Hamiltonian Monte Carlo.
