James Lucas
15 papers · 2018–2025 · 6 venues · across top CS/AI venues
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Keyword Collector
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Research topics
Machine Learning
(12)
Optimization & Theory Β· Machine Learning
(9)
Deep Learning
(8)
Artificial Intelligence
(6)
Architectures
(3)
Learning Paradigms
(3)
Optimization & Theory Β· Deep Learning
(3)
Computer Vision
(2)
Analysis
(2)
Application Areas
(2)
Core AI
(2)
Core Methods
(2)
Learning Types
(2)
Models
(2)
Mathematics & Optimization
(1)
Keywords
neural scaling law
(3)
data collection
(2)
lipschitz constraint
(2)
semi-supervised learning
(2)
gradient descent
(2)
adversarial robustness
(2)
function approximation
(1)
computer vision
(1)
representation learning
(1)
principal component analysis
(1)
domain adaptation
(1)
bayesian inference
(1)
link prediction
(1)
uncertainty quantification
(1)
convergence analysis
(1)
network architecture
(1)
hierarchy extraction
(1)
sample complexity
(1)
graph representation
(1)
object detection
(1)
Papers
Optimizing Data Collection for Machine Learning
JMLR 2025
Graph Metanetworks for Processing Diverse Neural Architectures
ICLR 2024
Transferring Labels to Solve Annotation Mismatches Across Object Detection Datasets
ICLR 2024
ATT3D: Amortized Text-to-3D Object Synthesis
ICCV 2023
Spacetime Representation Learning
ICLR 2023
How Much More Data Do I Need? Estimating Requirements for Downstream Tasks
CVPR 2022
Optimizing Data Collection for Machine Learning
NeurIPS 2022
Theoretical bounds on estimation error for meta-learning
ICLR 2021
Regularized linear autoencoders recover the principal components, eventually
NeurIPS 2020
Aggregated Momentum: Stability Through Passive Damping
ICLR 2019
Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse
NeurIPS 2019
Lookahead Optimizer: k steps forward, 1 step back
NeurIPS 2019
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks
NeurIPS 2019
Sorting Out Lipschitz Function Approximation
ICML 2019
Adversarial Distillation of Bayesian Neural Network Posteriors
ICML 2018