Search Results for author:
Found 8 papers, 4 papers with code
Improved Evidential Deep Learning via a Mixture of Dirichlet Distributions
This paper explores a modern predictive uncertainty estimation approach, called evidential deep learning (EDL), in which a single neural network model is trained to learn a meta distribution over the predictive distribution by minimizing a specific objective function.
Operator SVD with Neural Networks via Nested Low-Rank Approximation
Computing eigenvalue decomposition (EVD) of a given linear operator, or finding its leading eigenvalues and eigenfunctions, is a fundamental task in many machine learning and scientific computing problems.
One-Nearest-Neighbor Search is All You Need for Minimax Optimal Regression and Classification
Recently, Qiao, Duan, and Cheng~(2019) proposed a distributed nearest-neighbor classification method, in which a massive dataset is split into smaller groups, each processed with a $k$-nearest-neighbor classifier, and the final class label is predicted by a majority vote among these groupwise class labels.
Parameter-free Online Linear Optimization with Side Information via Universal Coin Betting
A class of parameter-free online linear optimization algorithms is proposed that harnesses the structure of an adversarial sequence by adapting to some side information.
Feedback Recurrent AutoEncoder
In this work, we propose a new recurrent autoencoder architecture, termed Feedback Recurrent AutoEncoder (FRAE), for online compression of sequential data with temporal dependency.
Wyner VAE: A Variational Autoencoder with Succinct Common Representation Learning
A new variational autoencoder (VAE) model is proposed that learns a succinct common representation of two correlated data variables for conditional and joint generation tasks.
Learning with Succinct Common Representation Based on Wyner's Common Information
A new bimodal generative model is proposed for generating conditional and joint samples, accompanied with a training method with learning a succinct bottleneck representation.
Nearest neighbor density functional estimation from inverse Laplace transform
A new approach to $L_2$-consistent estimation of a general density functional using $k$-nearest neighbor distances is proposed, where the functional under consideration is in the form of the expectation of some function $f$ of the densities at each point.
