Jaehoon Lee
18 papers · 2018–2026 · 5 conferences · across top CS/AI conferences
Achievements
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π Academic Marathon (7) π Interdisciplinary Bridge π§ Keyword Pioneer π Conference Polyglot (5) π Cross-Pollinator (12)
πΊοΈ
Taxonomy Completionist
(32)
π
Interdisciplinary Bridge
π§
Keyword Pioneer
π
Keyword Champion
(2)
π
Conference Pioneer
π₯
Unstoppable
(5)
π
Century Club
(17)
β
The Questioner
Conferences
ICLR (8)
NIPS (5)
ACL (3)
ICML (1)
JMLR (1)
Top co-authors
Keywords
large language model
(2)
kernel methods
(2)
infinite width limit
(2)
neural tangent kernel
(2)
in-context learning
(1)
dense retrieval
(1)
double descent
(1)
knowledge graph
(1)
embedding learning
(1)
convolutional neural network
(1)
gradient norm
(1)
weight decay
(1)
out-of-distribution detection
(1)
generative adversarial network
(1)
demonstration retrieval
(1)
gradient descent
(1)
tabular data synthesis
(1)
data parallelism
(1)
reference-free evaluation
(1)
dataset distillation
(1)
Papers
ReFEree: Reference-Free and Fine-Grained Method for Evaluating Factual Consistency in Real-World Code Summarization
ACL 2026
When Should Dense Retrievers Be Updated in Evolving Corpora? Detecting Out-of-Distribution Corpora Using GradNormIR
ACL 2025
Scaling LLM Test-Time Compute Optimally Can be More Effective than Scaling Parameters for Reasoning
ICLR 2025
DCG-SQL: Enhancing In-Context Learning for Text-to-SQL with Deep Contextual Schema Link Graph
ACL 2025
Scaling Exponents Across Parameterizations and Optimizers
ICML 2024
Small-scale proxies for large-scale Transformer training instabilities
ICLR 2024
LORD: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential Equations
ICLR 2022
Fast Neural Kernel Embeddings for General Activations
NIPS 2022
Dataset Distillation with Infinitely Wide Convolutional Networks
NIPS 2021
Invertible Tabular GANs: Killing Two Birds with One Stone for Tabular Data Synthesis
NIPS 2021
Dataset Meta-Learning from Kernel Ridge-Regression
ICLR 2021
Exploring the Uncertainty Properties of Neural Networksβ Implicit Priors in the Infinite-Width Limit
ICLR 2021
Neural Tangents: Fast and Easy Infinite Neural Networks in Python
ICLR 2020
Finite Versus Infinite Neural Networks: an Empirical Study
NIPS 2020
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent
NIPS 2019
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes
ICLR 2019
Measuring the Effects of Data Parallelism on Neural Network Training
JMLR 2019
Deep Neural Networks as Gaussian Processes
ICLR 2018