Search Results for author:
Found 9 papers, 1 papers with code
Kernel Density Decision Trees
FDTs address the sensitivity and tendency to overfitting of decision trees by representing uncertainty through fuzzy partitions.
Robust Multi-view Representation Learning
Our synthetic and real-world experiments show promising results for the application of these models to robust representation learning.
System-Level Predictive Maintenance: Review of Research Literature and Gap Analysis
This paper reviews current literature in the field of predictive maintenance from the system point of view.
Mutually Regressive Point Processes
Despite many potential applications, existing point process models are limited in their ability to capture complex patterns of interaction.
L-space knots with tunnel number >1 by experiment
In Dunfield's catalog of the hyperbolic manifolds in the SnapPy census which are complements of L-space knots in $S^3$, we determine that $22$ have tunnel number $2$ while the remaining all have tunnel number $1$.
Geometric Topology 57M25
Noise-Tolerant Interactive Learning Using Pairwise Comparisons
We study the problem of interactively learning a binary classifier using noisy labeling and pairwise comparison oracles, where the comparison oracle answers which one in the given two instances is more likely to be positive.
Characterization of Hemodynamic Signal by Learning Multi-View Relationships
We aim to investigate the usefulness of nonlinear multi-view relations to characterize multi-view data in an explainable manner.
Noise-Tolerant Interactive Learning from Pairwise Comparisons
We study the problem of interactively learning a binary classifier using noisy labeling and pairwise comparison oracles, where the comparison oracle answers which one in the given two instances is more likely to be positive.
Performance Bounds for Pairwise Entity Resolution
One significant challenge to scaling entity resolution algorithms to massive datasets is understanding how performance changes after moving beyond the realm of small, manually labeled reference datasets.
