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Found 22 papers, 4 papers with code
Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes
This observation motivates us to improve the MCMC-based estimation for skew GP, for which we show the practical efficiency of Gibbs sampling and derive the low variance MC estimator.
Distance Metric Learning for Graph Structured Data
Hence, we propose a supervised distance metric learning method for the graph classification problem.
Safe Triplet Screening for Distance Metric Learning
Distance metric learning can optimize a metric over a set of triplets, each one of which is defined by a pair of same class instances and an instance in a different class.
Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining
The SPP method allows us to efficiently find a superset of all the predictive patterns in the database that are needed for the optimal predictive model.
Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling
A significant advantage of considering them simultaneously rather than individually is that they have a synergy effect in the sense that the results of the previous safe feature screening can be exploited for improving the next safe sample screening performances, and vice-versa.
Homotopy Continuation Approaches for Robust SV Classification and Regression
An advantage of our homotopy approach is that it can be interpreted as simulated annealing, a common approach for finding a good local optimal solution in non-convex optimization problems.
Safe Feature Pruning for Sparse High-Order Interaction Models
An SFS rule has a property that, if a feature satisfies the rule, then the feature is guaranteed to be non-active in the LASSO solution, meaning that it can be safely screened-out prior to the LASSO training process.
Regularization Path of Cross-Validation Error Lower Bounds
Careful tuning of a regularization parameter is indispensable in many machine learning tasks because it has a significant impact on generalization performances.
Manifold-based Similarity Adaptation for Label Propagation
In this approach, edge weights represent both similarity and local reconstruction weight simultaneously, both being reasonable for label propagation.
Multiple Incremental Decremental Learning of Support Vector Machines
Conventional single cremental decremental SVM can update the trained model efficiently when single data point is added to or removed from the training set.
Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its parallelization
In this paper, we focus on the information-based approach, which is a popular and empirically successful approach in BO.
Statistically Discriminative Sub-trajectory Mining
Given two groups of trajectories, the goal of this problem is to extract moving patterns in the form of sub-trajectories which are more similar to sub-trajectories of one group and less similar to those of the other.
Multi-objective Bayesian Optimization using Pareto-frontier Entropy
The entropy search is successful approach to Bayesian optimization.
Active learning for level set estimation under cost-dependent input uncertainty
As part of a quality control process in manufacturing it is often necessary to test whether all parts of a product satisfy a required property, with as few inspections as possible.
Cost-effective search for lower-error region in material parameter space using multifidelity Gaussian process modeling
Hence, we considered estimating a region of material parameter space in which a computational model produces precipitates having shapes similar to those observed in the experimental images.
Sequential- and Parallel- Constrained Max-value Entropy Search via Information Lower Bound
Max-value entropy search (MES) is one of the state-of-the-art approaches in Bayesian optimization (BO).
Bayesian Optimization for Cascade-type Multi-stage Processes
A cascade process is a multistage process in which the output of one stage is used as an input for the subsequent stage.
Bayesian Optimization for Distributionally Robust Chance-constrained Problem
In black-box function optimization, we need to consider not only controllable design variables but also uncontrollable stochastic environment variables.
Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Bounds
Gaussian process upper confidence bound (GP-UCB) is a theoretically promising approach for black-box optimization; however, the confidence parameter $\beta$ is considerably large in the theorem and chosen heuristically in practice.
Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds
We show that PIMS achieves the tighter BCR bound and avoids the hyperparameter tuning, unlike GP-UCB.
Multi-Objective Bayesian Optimization with Active Preference Learning
There are a lot of real-world black-box optimization problems that need to optimize multiple criteria simultaneously.
Learning Attributed Graphlets: Predictive Graph Mining by Graphlets with Trainable Attribute
LAGRA learns importance weights for small attributed subgraphs, called attributed graphlets (AGs), while simultaneously optimizing their attribute vectors.
