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Found 12 papers, 3 papers with code
Efficient Frameworks for Generalized Low-Rank Matrix Bandit Problems
In the stochastic contextual low-rank matrix bandit problem, the expected reward of an action is given by the inner product between the action's feature matrix and some fixed, but initially unknown $d_1$ by $d_2$ matrix $\Theta^*$ with rank $r \ll \{d_1, d_2\}$, and an agent sequentially takes actions based on past experience to maximize the cumulative reward.
Online Continuous Hyperparameter Optimization for Generalized Linear Contextual Bandits
In stochastic contextual bandits, an agent sequentially makes actions from a time-dependent action set based on past experience to minimize the cumulative regret.
Extending the Use of MDL for High-Dimensional Problems: Variable Selection, Robust Fitting, and Additive Modeling
In the signal processing and statistics literature, the minimum description length (MDL) principle is a popular tool for choosing model complexity.
A Review of Adversarial Attack and Defense for Classification Methods
Despite the efficiency and scalability of machine learning systems, recent studies have demonstrated that many classification methods, especially deep neural networks (DNNs), are vulnerable to adversarial examples; i. e., examples that are carefully crafted to fool a well-trained classification model while being indistinguishable from natural data to human.
Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms
To tackle this problem, we first propose a two-layer bandit structure for auto tuning the exploration parameter and further generalize it to the Syndicated Bandits framework which can learn multiple hyper-parameters dynamically in contextual bandit environment.
Adversarial Examples Detection with Bayesian Neural Network
In this paper, we propose a new framework to detect adversarial examples motivated by the observations that random components can improve the smoothness of predictors and make it easier to simulate the output distribution of a deep neural network.
Uncertainty Quantification in Ensembles of Honest Regression Trees using Generalized Fiducial Inference
The main contribution is a new method that quantifies the uncertainties of the estimates and predictions produced by honest random forests.
Measuring the Algorithmic Convergence of Randomized Ensembles: The Regression Setting
When randomized ensemble methods such as bagging and random forests are implemented, a basic question arises: Is the ensemble large enough?
Optimal Transport Classifier: Defending Against Adversarial Attacks by Regularized Deep Embedding
Recent studies have demonstrated the vulnerability of deep convolutional neural networks against adversarial examples.
Block-wise Partitioning for Extreme Multi-label Classification
Extreme multi-label classification aims to learn a classifier that annotates an instance with a relevant subset of labels from an extremely large label set.
Detecting Abrupt Changes in the Spectra of High-Energy Astrophysical Sources
We embed change points into a marked Poisson process, where photon wavelengths are regarded as marks and both the Poisson intensity parameter and the distribution of the marks are allowed to change.
Applications Instrumentation and Methods for Astrophysics
Matrix Completion with Noisy Entries and Outliers
This paper considers the problem of matrix completion when the observed entries are noisy and contain outliers.
