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Found 20 papers, 2 papers with code
Mutual Transfer Learning for Massive Data
In this article, we study a new paradigm called mutual transfer learning where among many heterogeneous data domains, every data domain could potentially be the target of interest, and it could also be a useful source to help the learning in other data domains.
Learning Acceptance Regions for Many Classes with Anomaly Detection
Set-valued classification, a new classification paradigm that aims to identify all the plausible classes that an observation belongs to, can be obtained by learning the acceptance regions for all classes.
Enhanced Nearest Neighbor Classification for Crowdsourcing
In machine learning, crowdsourcing is an economical way to label a large amount of data.
Statistical Guarantees of Distributed Nearest Neighbor Classification
It is interesting to note that the weighted voting scheme allows a larger number of subsamples than the majority voting one.
Covariance-engaged Classification of Sets via Linear Programming
The convergence rates of estimation errors and risk of the CLIPS classifier are established to show that having multiple observations in a set leads to faster convergence rates, compared to the standard classification situation in which there is only one observation in the set.
Doubly Robust Direct Learning for Estimating Conditional Average Treatment Effect
The consistency for our CATE estimator is guaranteed if either the main effect model or the propensity score model is correctly specified.
Methodology
Near-optimal Individualized Treatment Recommendations
These numerical studies have shown the usefulness of the proposed A-ITR framework.
Maximum Entropy Diverse Exploration: Disentangling Maximum Entropy Reinforcement Learning
A theoretical investigation shows that the set of policies learned by MEDE capture the same modalities as the optimal maximum entropy policy.
Rates of Convergence for Large-scale Nearest Neighbor Classification
Nearest neighbor is a popular class of classification methods with many desirable properties.
Towards Run Time Estimation of the Gaussian Chemistry Code for SEAGrid Science Gateway
Most codes are not so arbitrary, however, and there has been significant prior research on predicting the run time of applications and workloads.
Diverse Exploration via Conjugate Policies for Policy Gradient Methods
We address the challenge of effective exploration while maintaining good performance in policy gradient methods.
Learning Confidence Sets using Support Vector Machines
The goal of confidence-set learning in the binary classification setting is to construct two sets, each with a specific probability guarantee to cover a class.
On Reject and Refine Options in Multicategory Classification
In many real applications of statistical learning, a decision made from misclassification can be too costly to afford; in this case, a reject option, which defers the decision until further investigation is conducted, is often preferred.
Significance Analysis of High-Dimensional, Low-Sample Size Partially Labeled Data
Although both are challenging questions for the high-dimensional, low-sample size data, there has been some recent development for both.
Sparse Fisher's Linear Discriminant Analysis for Partially Labeled Data
In many real applications, it is costly to manually place labels on observations; hence it is often that only a small portion of labeled data is available while a large number of observations are left without a label.
Noncrossing Ordinal Classification
We show by simulated and data examples that the proposed method can improve the classification performance for ordinal data without the ambiguity caused by boundary crossings.
Stabilized Nearest Neighbor Classifier and Its Statistical Properties
In this paper, we introduce a general measure of classification instability (CIS) to quantify the sampling variability of the prediction made by a classification method.
A Statistical Approach to Set Classification by Feature Selection with Applications to Classification of Histopathology Images
In particular, the method of principal component analysis is used to extract the features of major variation.
Distance-weighted Support Vector Machine
The proposed Distance-weighted Support Vector Machine method can be viewed as a hybrid of SVM and DWD that finds the classification direction by minimizing mainly the DWD loss, and determines the intercept term in the SVM manner.
Flexible High-dimensional Classification Machines and Their Asymptotic Properties
Simulations and real data applications are investigated to illustrate the usefulness of the FLAME classifiers.
