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Iljung Kim

dblp:318/0762 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Probabilistic and Bayesian machine learning · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Probabilistic and Bayesian machine learning › statistical inference
density estimation		 0.712023
Variational Weighting for Kernel Density Ratios · NeurIPS 2023
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › density estimation
density ratio estimation		 0.712023
Variational Weighting for Kernel Density Ratios · NeurIPS 2023
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › density estimation
kernel density estimation		 0.712023
Variational Weighting for Kernel Density Ratios · NeurIPS 2023
Machine learning › Probabilistic and Bayesian machine learning › probabilistic inference › approximate inference
variational inference		 0.712023
Variational Weighting for Kernel Density Ratios · NeurIPS 2023

Methods — techniques the papers use, named apart from their topics

kernel density estimation · 0.7calculus of variations · 0.7
YearPublicationVenuePosition
2023 Variational Weighting for Kernel Density Ratios
abstract
Kernel density estimation (KDE) is integral to a range of generative and discriminative tasks in machine learning. Drawing upon tools from the multidimensional calculus of variations, we derive an optimal weight function that reduces bias in standard kernel density estimates for density ratios, leading to improved estimates of prediction posteriors and information-theoretic measures. In the process, we shed light on some fundamental aspects of density estimation, particularly from the perspective of algorithms that employ KDEs as their main building blocks.
Sangwoong Yoon, Frank C. Park 0001, Gunsu S. Yun, Iljung Kim, Yung-Kyun Noh
 NeurIPS4