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Found 32 papers, 5 papers with code
Universal Feature Selection for Simultaneous Interpretability of Multitask Datasets
Extracting meaningful features from complex, high-dimensional datasets across scientific domains remains challenging.
Unified Binary and Multiclass Margin-Based Classification
The notion of margin loss has been central to the development and analysis of algorithms for binary classification.
Fusing Sparsity with Deep Learning for Rotating Scatter Mask Gamma Imaging
Many nuclear safety applications need fast, portable, and accurate imagers to better locate radiation sources.
Mixture Proportion Estimation Beyond Irreducibility
The task of mixture proportion estimation (MPE) is to estimate the weight of a component distribution in a mixture, given observations from both the component and mixture.
Label Embedding via Low-Coherence Matrices
Label embedding is a framework for multiclass classification problems where each label is represented by a distinct vector of some fixed dimension, and training involves matching model output to the vector representing the correct label.
Learning from Label Proportions by Learning with Label Noise
Learning from label proportions (LLP) is a weakly supervised classification problem where data points are grouped into bags, and the label proportions within each bag are observed instead of the instance-level labels.
Supervised PCA: A Multiobjective Approach
Methods for supervised principal component analysis (SPCA) aim to incorporate label information into principal component analysis (PCA), so that the extracted features are more useful for a prediction task of interest.
Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations
Recent research has established sufficient conditions for finite mixture models to be identifiable from grouped observations.
Learning from Label Proportions: A Mutual Contamination Framework
Learning from label proportions (LLP) is a weakly supervised setting for classification in which unlabeled training instances are grouped into bags, and each bag is annotated with the proportion of each class occurring in that bag.
Calibrated Surrogate Losses for Adversarially Robust Classification
Adversarially robust classification seeks a classifier that is insensitive to adversarial perturbations of test patterns.
Learning from Multiple Corrupted Sources, with Application to Learning from Label Proportions
We study binary classification in the setting where the learner is presented with multiple corrupted training samples, with possibly different sample sizes and degrees of corruption, and introduce an approach based on minimizing a weighted combination of corruption-corrected empirical risks.
PAC Reinforcement Learning without Real-World Feedback
This work studies reinforcement learning in the Sim-to-Real setting, in which an agent is first trained on a number of simulators before being deployed in the real world, with the aim of decreasing the real-world sample complexity requirement.
A Generalization Error Bound for Multi-class Domain Generalization
Domain generalization is the problem of assigning labels to an unlabeled data set, given several similar data sets for which labels have been provided.
Simple Regret Minimization for Contextual Bandits
Contextual bandits are a sub-class of MABs where, at every time step, the learner has access to side information that is predictive of the best arm.
A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptation
In the problem of domain adaptation for binary classification, the learner is presented with labeled examples from a source domain, and must correctly classify unlabeled examples from a target domain, which may differ from the source.
Domain Generalization by Marginal Transfer Learning
In the problem of domain generalization (DG), there are labeled training data sets from several related prediction problems, and the goal is to make accurate predictions on future unlabeled data sets that are not known to the learner.
Dictionary-Free MRI PERK: Parameter Estimation via Regression with Kernels
This paper introduces a fast, general method for dictionary-free parameter estimation in quantitative magnetic resonance imaging (QMRI) via regression with kernels (PERK).
Decontamination of Mutual Contamination Models
Many machine learning problems can be characterized by mutual contamination models.
Nonparametric Preference Completion
We consider the task of collaborative preference completion: given a pool of items, a pool of users and a partially observed item-user rating matrix, the goal is to recover the \emph{personalized ranking} of each user over all of the items.
Multi-Task Learning for Contextual Bandits
Contextual bandits are a form of multi-armed bandit in which the agent has access to predictive side information (known as the context) for each arm at each time step, and have been used to model personalized news recommendation, ad placement, and other applications.
Consistent Kernel Density Estimation with Non-Vanishing Bandwidth
Consistency of the kernel density estimator requires that the kernel bandwidth tends to zero as the sample size grows.
Adaptive Questionnaires for Direct Identification of Optimal Product Design
In this work, we (1) demonstrate that accurate preference estimation is neither necessary nor sufficient for identifying the optimal design, (2) introduce a novel adaptive questionnaire that leverages knowledge about engineering feasibility and manufacturing costs to directly determine the optimal design, and (3) interpret product design in terms of a nonlinear segmentation of part-worth space, and use this interpretation to illuminate the intrinsic difficulty of optimal design in the presence of noisy questionnaire responses.
Mixture Proportion Estimation via Kernel Embedding of Distributions
Mixture proportion estimation (MPE) is the problem of estimating the weight of a component distribution in a mixture, given samples from the mixture and component.
A Mutual Contamination Analysis of Mixed Membership and Partial Label Models
We examine the decontamination problem in two mutual contamination models that describe popular machine learning tasks: recovering the base distributions up to a permutation in a mixed membership model, and recovering the base distributions exactly in a partial label model for classification.
On the consistency of inversion-free parameter estimation for Gaussian random fields
Gaussian random fields are a powerful tool for modeling environmental processes.
Optimal change point detection in Gaussian processes
By contrast, the standard CUSUM method, which does not account for the covariance structure, is shown to be asymptotically optimal only in the increasing domain.
Disease Prediction based on Functional Connectomes using a Scalable and Spatially-Informed Support Vector Machine
Using the fused Lasso or GraphNet regularizer with the hinge-loss leads to a structured sparse support vector machine (SVM) with embedded feature selection.
Class Proportion Estimation with Application to Multiclass Anomaly Rejection
The first problem studied is that of class proportion estimation, which is the problem of estimating the class proportions in an unlabeled testing data set given labeled examples of each class.
Classification with Asymmetric Label Noise: Consistency and Maximal Denoising
For any label noise problem, there is a unique pair of true class-conditional distributions satisfying the proposed conditions, and we argue that this pair corresponds in a certain sense to maximal denoising of the observed distributions.
Generalizing from Several Related Classification Tasks to a New Unlabeled Sample
We develop a distribution-free, kernel-based approach to the problem.
Extensions of Generalized Binary Search to Group Identification and Exponential Costs
Generalized Binary Search (GBS) is a well known greedy algorithm for identifying an unknown object while minimizing the number of yes" or "no" questions posed about that object, and arises in problems such as active learning and active diagnosis.
Performance analysis for L\_2 kernel classification
We provide statistical performance guarantees for a recently introduced kernel classifier that optimizes the $L_2$ or integrated squared error (ISE) of a difference of densities.
