Yongchan Kwon
16 papers · 2020–2025 · 6 conferences · across top CS/AI conferences
Achievements
Jump to papers ↓+8 more ↓ Show less ↑
π Academic Marathon (5) π Interdisciplinary Bridge π§ Keyword Pioneer π Conference Polyglot (6) π Cross-Pollinator (6)
πΊοΈ
Taxonomy Completionist
(28)
π
Interdisciplinary Bridge
π§
Keyword Pioneer
π
Triple Crown
ποΈ
Keyword Collector
(60)
π₯
Unstoppable
(6)
π
Century Club
(16)
β
The Questioner
Conferences
AISTATS (4)
ICML (4)
NIPS (4)
ICLR (2)
ACL (1)
JMLR (1)
Top co-authors
Research topics
Keywords
data valuation
(5)
shapley value
(4)
mislabeled data detection
(2)
influence function
(2)
binary classification
(1)
anomaly detection
(1)
contrastive learning
(1)
multi-modal learning
(1)
data poisoning
(1)
machine unlearning
(1)
feature attribution
(1)
subpopulation shift
(1)
density estimation
(1)
reinforcement learning from human feedback
(1)
machine learning
(1)
outlier detection
(1)
linear regression
(1)
reconstruction error
(1)
noise reduction
(1)
game theory
(1)
Papers
TimeInf: Time Series Data Contribution via Influence Functions
ICLR 2025
Understanding Impact of Human Feedback via Influence Functions
ACL 2025
Certified Machine Unlearning Under High Dimensional Regime
JMLR 2025
DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
ICLR 2024
2D-OOB: Attributing Data Contribution Through Joint Valuation Framework
NIPS 2024
Rethinking Data Shapley for Data Selection Tasks: Misleads and Merits
ICML 2024
OpenDataVal: a Unified Benchmark for Data Valuation
NIPS 2023
Accuracy on the Curve: On the Nonlinear Correlation of ML Performance Between Data Subpopulations
ICML 2023
Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value
ICML 2023
Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning
AISTATS 2022
WeightedSHAP: analyzing and improving Shapley based feature attributions
NIPS 2022
Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning
NIPS 2022
Competing AI: How does competition feedback affect machine learning?
AISTATS 2021
Efficient Computation and Analysis of Distributional Shapley Values
AISTATS 2021
Principled learning method for Wasserstein distributionally robust optimization with local perturbations
ICML 2020
Lipschitz Continuous Autoencoders in Application to Anomaly Detection
AISTATS 2020