Yongdai Kim
16 papers · 2012–2026 · 6 venues · across top CS/AI venues
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Achievements
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🌉 Interdisciplinary Bridge · Silver 🗺️ Taxonomy Completionist (33) 🌍 Conference Polyglot · Silver 🐣 Hot Topic Early Bird 🏃 Academic Marathon · Half
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Prolific Year · Productive
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Unstoppable · Bronze
💎
Century Club · Bronze
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Keyword Collector
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Research topics
Machine Learning
(15)
Deep Learning
(8)
Optimization & Theory
(8)
Learning Types · Machine Learning
(7)
Application Areas
(6)
Artificial Intelligence
(4)
Architectures
(4)
Core Methods
(4)
Mathematics & Optimization
(3)
Core AI
(3)
Models
(3)
Learning Types · Deep Learning
(2)
Optimization
(2)
Data Science & Analytics
(1)
Knowledge & Reasoning
(1)
Keywords
adversarial robustness
(4)
adversarial training
(3)
fair clustering
(2)
bayesian neural network
(2)
deep generative model
(2)
integral probability metric
(2)
model selection
(1)
algorithmic fairness
(1)
robust optimization
(1)
domain adaptation
(1)
em algorithm
(1)
uncertainty quantification
(1)
knowledge distillation
(1)
data augmentation
(1)
outlier detection
(1)
variational inference
(1)
markov chain monte carlo
(1)
computational efficiency
(1)
manifold learning
(1)
minimax optimality
(1)
Papers
Fair Model-based Clustering
AAAI 2026
Fair Clustering via Alignment
ICML 2025
Posterior Concentrations of Fully-Connected Bayesian Neural Networks with General Priors on the Weights
JMLR 2025
TAROT: Towards Essentially Domain-Invariant Robustness with Theoretical Justification
CVPR 2025
Tensor Product Neural Networks for Functional ANOVA Model
ICML 2025
IOFM: Using the Interpolation Technique on the Over-Fitted Models to Identify Clean-Annotated Samples
AAAI 2024
ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models
ICML 2024
A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models
JMLR 2023
Covariate balancing using the integral probability metric for causal inference
ICML 2023
Enhancing Adversarial Robustness in Low-Label Regime via Adaptively Weighted Regularization and Knowledge Distillation
ICCV 2023
Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples
ICML 2023
Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference
ICML 2023
Learning fair representation with a parametric integral probability metric
ICML 2022
Kernel-convoluted Deep Neural Networks with Data Augmentation
AAAI 2021
On casting importance weighted autoencoder to an EM algorithm to learn deep generative models
AISTATS 2020
Consistent Model Selection Criteria on High Dimensions
JMLR 2012