Search Results for author:
Found 10 papers, 1 papers with code
Distributionally Robust Safe Screening
In this study, we propose a method Distributionally Robust Safe Screening (DRSS), for identifying unnecessary samples and features within a DR covariate shift setting.
Efficient Model Selection for Predictive Pattern Mining Model by Safe Pattern Pruning
Predictive pattern mining is an approach used to construct prediction models when the input is represented by structured data, such as sets, graphs, and sequences.
Generalized Low-Rank Update: Model Parameter Bounds for Low-Rank Training Data Modifications
Among the class of ML methods known as linear estimators, there exists an efficient model update framework called the low-rank update that can effectively handle changes in a small number of rows and columns within the data matrix.
Bounding Box-based Multi-objective Bayesian Optimization of Risk Measures under Input Uncertainty
In this study, we propose a novel multi-objective Bayesian optimization (MOBO) method to efficiently identify the Pareto front (PF) defined by risk measures for black-box functions under the presence of input uncertainty (IU).
Fast and More Powerful Selective Inference for Sparse High-order Interaction Model
Automated high-stake decision-making such as medical diagnosis requires models with high interpretability and reliability.
Supervised sequential pattern mining of event sequences in sport to identify important patterns of play: an application to rugby union
Given a set of sequences comprised of time-ordered events, sequential pattern mining is useful to identify frequent subsequences from different sequences or within the same sequence.
Learning sparse optimal rule fit by safe screening
In this paper, we propose Safe Optimal Rule Fit (SORF) as an approach to resolve this problem, which is formulated as a convex optimization problem with sparse regularization.
Interval-based Prediction Uncertainty Bound Computation in Learning with Missing Values
A drawback of this naive approach is that the uncertainty in the missing entries is not properly incorporated in the prediction.
Efficiently Bounding Optimal Solutions after Small Data Modification in Large-Scale Empirical Risk Minimization
We study large-scale classification problems in changing environments where a small part of the dataset is modified, and the effect of the data modification must be quickly incorporated into the classifier.
Secure Approximation Guarantee for Cryptographically Private Empirical Risk Minimization
The key property of SAG method is that, given an arbitrary approximate solution, it can provide a non-probabilistic assumption-free bound on the approximation quality under cryptographically secure computation framework.
