Search Results for author:
Found 15 papers, 5 papers with code
Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions
We consider local kernel metric learning for off-policy evaluation (OPE) of deterministic policies in contextual bandits with continuous action spaces.
Evaluating Out-of-Distribution Detectors Through Adversarial Generation of Outliers
A reliable evaluation method is essential for building a robust out-of-distribution (OOD) detector.
Adversarial Distributions Against Out-of-Distribution Detectors
As an outlier may deviate from the training distribution in unexpected ways, an ideal OOD detector should be able to detect all types of outliers.
Autoencoding Under Normalization Constraints
The specific role of the normalization constraint is to ensure that the out-of-distribution (OOD) regime has a small likelihood when samples are learned using maximum likelihood.
Learning to increase matching efficiency in identifying additional b-jets in the $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ process
The $\text{t}\bar{\text{t}}\text{H}(\text{b}\bar{\text{b}})$ process is an essential channel to reveal the Higgs properties but has an irreducible background from the $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ process, which produces a top quark pair in association with a b quark pair.
Suppressing Outlier Reconstruction in Autoencoders for Out-of-Distribution Detection
This phenomenon, which we refer to as outlier reconstruction, has a detrimental effect on the use of autoencoders for outlier detection, as an autoencoder will misclassify a clear outlier as being in-distribution.
Efficient Neural Network Compression via Transfer Learning for Industrial Optical Inspection
In this paper, we investigate learning the deep neural networks for automated optical inspection in industrial manufacturing.
K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning
Minimax optimization plays a key role in adversarial training of machine learning algorithms, such as learning generative models, domain adaptation, privacy preservation, and robust learning.
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.
Domain Adaptation Using Adversarial Learning for Autonomous Navigation
The objective of this study is to train an autonomous navigation model that uses a simulator (instead of real labeled data) and an inexpensive monocular camera.
Generative Local Metric Learning for Kernel Regression
This paper shows how metric learning can be used with Nadaraya-Watson (NW) kernel regression.
Scalable Iterative Algorithm for Robust Subspace Clustering
Subspace clustering (SC) is a popular method for dimensionality reduction of high-dimensional data, where it generalizes Principal Component Analysis (PCA).
Direct Density-Derivative Estimation and Its Application in KL-Divergence Approximation
Estimation of density derivatives is a versatile tool in statistical data analysis.
Diffusion Decision Making for Adaptive k-Nearest Neighbor Classification
This paper sheds light on some fundamental connections of the diffusion decision making model of neuroscience and cognitive psychology with k-nearest neighbor classification.
Generative Local Metric Learning for Nearest Neighbor Classification
We consider the problem of learning a local metric to enhance the performance of nearest neighbor classification.
