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Found 89 papers, 22 papers with code
Active Learning for Level Set Estimation Using Randomized Straddle Algorithms
The confidence parameter in the proposed method has the advantages of not needing adjustment, not depending on the number of iterations and candidate points, and not being conservative.
Statistical Test for Data Analysis Pipeline by Selective Inference
In this paper, we propose a novel statistical test designed to assess the statistical significance of data analysis pipelines.
Distributionally Robust Safe Sample Screening
DRSSS aims to identify unnecessary training samples, even when the distribution of the training samples changes in the future.
Crystal-LSBO: Automated Design of De Novo Crystals with Latent Space Bayesian Optimization
Generative modeling of crystal structures is significantly challenged by the complexity of input data, which constrains the ability of these models to explore and discover novel crystals.
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.
Statistical Test on Diffusion Model-based Generated Images by Selective Inference
In this study, we propose a method to quantify the reliability of decision-making tasks that rely on images produced by diffusion models within a statistical testing framework.
Statistical Test for Anomaly Detections by Variational Auto-Encoders
In this study, we consider the reliability assessment of anomaly detection (AD) using Variational Autoencoder (VAE).
Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial Libraries
This can be exemplified by the combinatorial libraries that can be explored in multiple locations by multiple tools simultaneously, or downstream characterization in automated synthesis systems.
Statistical Test for Attention Map in Vision Transformer
In this study, we propose a statistical test for ViT's attentions, enabling us to use the attentions as reliable quantitative evidence indicators for ViT's decision-making with a rigorously controlled error rate.
Selective Inference for Changepoint detection by Recurrent Neural Network
In this study, we investigate the quantification of the statistical reliability of detected change points (CPs) in time series using a Recurrent Neural Network (RNN).
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.
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.
CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inference
We propose a novel statistical method for testing the results of anomaly detection (AD) under domain adaptation (DA), which we call CAD-DA -- controllable AD under DA.
Mixing Histopathology Prototypes into Robust Slide-Level Representations for Cancer Subtyping
Whole-slide image analysis via the means of computational pathology often relies on processing tessellated gigapixel images with only slide-level labels available.
Bounded P-values in Parametric Programming-based Selective Inference
This additional conditions often causes the loss of power, and this issue is referred to as over-conditioning in [Fithian et al., 2014].
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.
Human-In-the-Loop for Bayesian Autonomous Materials Phase Mapping
Here, we present a set of methods for integrating human input into an autonomous materials exploration campaign for composition-structure phase mapping.
Adaptive Defective Area Identification in Material Surface Using Active Transfer Learning-based Level Set Estimation
We interpret this problem as an active-learning (AL) of the level set estimation (LSE) problem.
Latent Space Bayesian Optimization with Latent Data Augmentation for Enhanced Exploration
Additionally, we present LCA-VAE, a novel VAE method that creates a latent space with increased consistent points through data augmentation in latent space and penalization of latent inconsistencies.
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).
Valid P-Value for Deep Learning-Driven Salient Region
Various saliency map methods have been proposed to interpret and explain predictions of deep learning models.
Transformer-based Personalized Attention Mechanism for Medical Images with Clinical Records
In medical image diagnosis, identifying the attention region, i. e., the region of interest for which the diagnosis is made, is an important task.
A Confidence Machine for Sparse High-Order Interaction Model
In predictive modeling for high-stake decision-making, predictors must be not only accurate but also reliable.
Benchmarking Active Learning Strategies for Materials Optimization and Discovery
In these systems, machine learning controls experiment design, execution, and analysis in a closed loop.
A Low-Cost Robot Science Kit for Education with Symbolic Regression for Hypothesis Discovery and Validation
We present the next generation in science education, a kit for building a low-cost autonomous scientist.
Statistical Inference for the Dynamic Time Warping Distance, with Application to Abnormal Time-Series Detection
We study statistical inference on the similarity/distance between two time-series under uncertain environment by considering a statistical hypothesis test on the distance obtained from Dynamic Time Warping (DTW) algorithm.
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.
Continuation Path with Linear Convergence Rate
Path-following algorithms are frequently used in composite optimization problems where a series of subproblems, with varying regularization hyperparameters, are solved sequentially.
Topic Analysis of Superconductivity Literature by Semantic Non-negative Matrix Factorization
We utilize a recently developed topic modeling method called SeNMFk, extending the standard Non-negative Matrix Factorization (NMF) methods by incorporating the semantic structure of the text, and adding a robust system for determining the number of topics.
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.
Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping
Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades.
Valid and Exact Statistical Inference for Multi-dimensional Multiple Change-Points by Selective Inference
In this paper, we study statistical inference of change-points (CPs) in multi-dimensional sequence.
Exact Statistical Inference for the Wasserstein Distance by Selective Inference
In this paper, we study statistical inference for the Wasserstein distance, which has attracted much attention and has been applied to various machine learning tasks.
Case-based Similar Image Retrieval for Weakly Annotated Large Histopathological Images of Malignant Lymphoma Using Deep Metric Learning
To address this problem, we employ attention-based multiple instance learning, which enables us to focus on tumor-specific regions when the similarity between cases is computed.
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.
More Powerful Conditional Selective Inference for Generalized Lasso by Parametric Programming
The basic concept of conditional SI is to make the inference conditional on the selection event, which enables an exact and valid statistical inference to be conducted even when the hypothesis is selected based on the data.
Conditional Selective Inference for Robust Regression and Outlier Detection using Piecewise-Linear Homotopy Continuation
In practical data analysis under noisy environment, it is common to first use robust methods to identify outliers, and then to conduct further analysis after removing the outliers.
Root-finding Approaches for Computing Conformal Prediction Set
Conformal prediction constructs a confidence set for an unobserved response of a feature vector based on previous identically distributed and exchangeable observations of responses and features.
Mapping causal patterns in crystalline solids
The evolution of the atomic structures of the combinatorial library of Sm-substituted thin film BiFeO3 along the phase transition boundary from the ferroelectric rhombohedral phase to the non-ferroelectric orthorhombic phase is explored using scanning transmission electron microscopy (STEM).
Data Analysis, Statistics and Probability Materials Science
Active learning for distributionally robust level-set estimation
A natural measure of robustness is the probability that $f(\bm x, \bm w)$ exceeds a given threshold $h$, which is known as the \emph{probability threshold robustness} (PTR) measure in the literature on robust optimization.
More Powerful and General Selective Inference for Stepwise Feature Selection using the Homotopy Continuation Approach
Conditional SI has been mainly studied in the context of feature selection such as stepwise feature selection (SFS).
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.
Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inference
To overcome this difficulty, we introduce a conditional selective inference (SI) framework -- a new statistical inference framework for data-driven hypotheses that has recently received considerable attention -- to compute exact (non-asymptotic) valid p-values for the segmentation results.
Mean-Variance Analysis in Bayesian Optimization under Uncertainty
As an AL problem in such an uncertain environment, we study Mean-Variance Analysis in Bayesian Optimization (MVA-BO) setting.
Bayesian Quadrature Optimization for Probability Threshold Robustness Measure
In many product development problems, the performance of the product is governed by two types of parameters called design parameter and environmental parameter.
On-the-fly Closed-loop Autonomous Materials Discovery via Bayesian Active Learning
Active learning - the field of machine learning (ML) dedicated to optimal experiment design, has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics [1].
Parametric Programming Approach for More Powerful and General Lasso Selective Inference
Unfortunately, the main limitation of the original SI approach for Lasso is that the inference is conducted not only conditional on the selected features but also on their signs -- this leads to loss of power because of over-conditioning.
CRYSPNet: Crystal Structure Predictions via Neural Network
Structure is the most basic and important property of crystalline solids; it determines directly or indirectly most materials characteristics.
Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programming
In this paper, we introduce a novel method to perform statistical inference on the significance of the CPs, estimated by a Dynamic Programming (DP)-based optimal CP detection algorithm.
Causal analysis of competing atomistic mechanisms in ferroelectric materials from high-resolution Scanning Transmission Electron Microscopy data
Machine learning has emerged as a powerful tool for the analysis of mesoscopic and atomically resolved images and spectroscopy in electron and scanning probe microscopy, with the applications ranging from feature extraction to information compression and elucidation of relevant order parameters to inversion of imaging data to reconstruct structural models.
Materials Science
Distance Metric Learning for Graph Structured Data
Hence, we propose a supervised distance metric learning method for the graph classification problem.
Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Images
We propose a new method for cancer subtype classification from histopathological images, which can automatically detect tumor-specific features in a given whole slide image (WSI).
Bayesian Active Learning for Structured Output Design
In this paper, we propose an active learning method for an inverse problem that aims to find an input that achieves a desired structured-output.
Bayesian Experimental Design for Finding Reliable Level Set under Input Uncertainty
In the manufacturing industry, it is often necessary to repeat expensive operational testing of machine in order to identify the range of input conditions under which the machine operates properly.
Computing Full Conformal Prediction Set with Approximate Homotopy
If you are predicting the label $y$ of a new object with $\hat y$, how confident are you that $y = \hat y$?
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.
Computing Valid p-values for Image Segmentation by Selective Inference
To overcome this difficulty, we introduce a statistical approach called selective inference, and develop a framework to compute valid p-values in which the segmentation bias is properly accounted for.
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.
Active learning for enumerating local minima based on Gaussian process derivatives
We study active learning (AL) based on Gaussian Processes (GPs) for efficiently enumerating all of the local minimum solutions of a black-box function.
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.
Safe Grid Search with Optimal Complexity
Popular machine learning estimators involve regularization parameters that can be challenging to tune, and standard strategies rely on grid search for this task.
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.
Unsupervised Phase Mapping of X-ray Diffraction Data by Nonnegative Matrix Factorization Integrated with Custom Clustering
Analyzing large X-ray diffraction (XRD) datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries.
Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator
In the paper, we propose a post selection inference (PSI) framework for divergence measure, which can select a set of statistically significant features that discriminate two distributions.
Selecting the Best in GANs Family: a Post Selection Inference Framework
"Which Generative Adversarial Networks (GANs) generates the most plausible images?"
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.
Machine learning modeling of superconducting critical temperature
Separate regression models are developed to predict the values of $T_{\mathrm{c}}$ for cuprate, iron-based, and "low-$T_{\mathrm{c}}$" compounds.
Selective Inference for Sparse High-Order Interaction Models
Finding statistically significant high-order interactions in predictive modeling is important but challenging task because the possible number of high-order interactions is extremely large (e. g., $> 10^{17}$).
Selective Inference for Change Point Detection in Multi-dimensional Sequences
We study the problem of detecting change points (CPs) that are characterized by a subset of dimensions in a multi-dimensional sequence.
Stochastic Primal Dual Coordinate Method with Non-Uniform Sampling Based on Optimality Violations
We study primal-dual type stochastic optimization algorithms with non-uniform sampling.
Post Selection Inference with Kernels
We propose a novel kernel based post selection inference (PSI) algorithm, which can not only handle non-linearity in data but also structured output such as multi-dimensional and multi-label outputs.
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.
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.
Selective Inference Approach for Statistically Sound Predictive Pattern Mining
The main obstacle of this problem is in the difficulty of taking into account the selection bias, i. e., the bias arising from the fact that patterns are selected from extremely large number of candidates in databases.
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.
Quick sensitivity analysis for incremental data modification and its application to leave-one-out CV in linear classification problems
This property is quite advantageous in a typical sensitivity analysis task where only a small number of instances are updated.
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.
An Algorithmic Framework for Computing Validation Performance Bounds by Using Suboptimal Models
If a reasonably good suboptimal model is available, our algorithm can compute lower and upper bounds of many useful quantities for making inferences on the unknown target model.
Safe Sample Screening for Support Vector Machines
Sparse classifiers such as the support vector machines (SVM) are efficient in test-phases because the classifier is characterized only by a subset of the samples called support vectors (SVs), and the rest of the samples (non SVs) have no influence on the classification result.
Parametric Task Learning
We introduce a novel formulation of multi-task learning (MTL) called parametric task learning (PTL) that can systematically handle infinitely many tasks parameterized by a continuous parameter.
Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering
However, Bayesian learning is often obstructed by computational difficulty: the rigorous Bayesian learning is intractable in many models, and its variational Bayesian (VB) approximation is prone to suffer from local minima.
Density-Difference Estimation
A naive approach is a two-step procedure of first estimating two densities separately and then computing their difference.
Target Neighbor Consistent Feature Weighting for Nearest Neighbor Classification
We consider feature selection and weighting for nearest neighbor classifiers.
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.
