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Found 22 papers, 3 papers with code
Model Assessment and Selection under Temporal Distribution Shift
We investigate model assessment and selection in a changing environment, by synthesizing datasets from both the current time period and historical epochs.
CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks
Uncertainty estimation is increasingly attractive for improving the reliability of neural networks.
A Stability Principle for Learning under Non-Stationarity
We develop a versatile framework for statistical learning in non-stationary environments.
Random-Set Convolutional Neural Network (RS-CNN) for Epistemic Deep Learning
Machine learning is increasingly deployed in safety-critical domains where robustness against adversarial attacks is crucial and erroneous predictions could lead to potentially catastrophic consequences.
Pseudo-Labeling for Kernel Ridge Regression under Covariate Shift
We propose to split the labeled data into two subsets and conduct kernel ridge regression on them separately to obtain a collection of candidate models and an imputation model.
Cascaded information enhancement and cross-modal attention feature fusion for multispectral pedestrian detection
On the other hand, the cross-modal attention feature fusion module mines the features of both Color and Thermal modalities to complement each other, then the global features are constructed by adding the cross-modal complemented features element by element, which are attentionally weighted to achieve the effective fusion of the two modal features.
Learning Gaussian Mixtures Using the Wasserstein-Fisher-Rao Gradient Flow
Gaussian mixture models form a flexible and expressive parametric family of distributions that has found applications in a wide variety of applications.
Variable Clustering via Distributionally Robust Nodewise Regression
We study a multi-factor block model for variable clustering and connect it to the regularized subspace clustering by formulating a distributionally robust version of the nodewise regression.
An introduction to optimization under uncertainty -- A short survey
The goal of this survey paper is to briefly touch upon the state of the art in a variety of different methods and refer the reader to other literature for more in-depth treatments of the topics discussed here.
Adaptive Data Fusion for Multi-task Non-smooth Optimization
We study the problem of multi-task non-smooth optimization that arises ubiquitously in statistical learning, decision-making and risk management.
Adaptive and Robust Multi-Task Learning
We study the multi-task learning problem that aims to simultaneously analyze multiple datasets collected from different sources and learn one model for each of them.
Clustering a Mixture of Gaussians with Unknown Covariance
We investigate a clustering problem with data from a mixture of Gaussians that share a common but unknown, and potentially ill-conditioned, covariance matrix.
Robust state and protection-level estimation within tightly coupled GNSS/INS navigation system
In autonomous applications for mobility and transport, a high-rate and highly accurate vehicle-state estimation is achieved by fusing measurements of global navigation satellite systems (GNSS) and inertial sensors.
An $\ell_p$ theory of PCA and spectral clustering
Principal Component Analysis (PCA) is a powerful tool in statistics and machine learning.
Efficient Clustering for Stretched Mixtures: Landscape and Optimality
This paper considers a canonical clustering problem where one receives unlabeled samples drawn from a balanced mixture of two elliptical distributions and aims for a classifier to estimate the labels.
Some compact notations for concentration inequalities and user-friendly results
This paper presents compact notations for concentration inequalities and convenient results to streamline probabilistic analysis.
Communication-Efficient Accurate Statistical Estimation
In addition, we give the conditions under which the one-step CEASE estimator is statistically efficient.
Robust high dimensional factor models with applications to statistical machine learning
Factor models are a class of powerful statistical models that have been widely used to deal with dependent measurements that arise frequently from various applications from genomics and neuroscience to economics and finance.
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion
Focusing on two statistical estimation problems, i. e. solving random quadratic systems of equations and low-rank matrix completion, we establish that gradient descent achieves near-optimal statistical and computational guarantees without explicit regularization.
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution
Recent years have seen a flurry of activities in designing provably efficient nonconvex procedures for solving statistical estimation problems.
Spectral Method and Regularized MLE Are Both Optimal for Top-$K$ Ranking
This paper is concerned with the problem of top-$K$ ranking from pairwise comparisons.
Factor-Adjusted Regularized Model Selection
This paper studies model selection consistency for high dimensional sparse regression when data exhibits both cross-sectional and serial dependency.
Methodology
