James Lucas
15 papers · 2018–2025 · 6 conferences · across top CS/AI conferences
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Conferences
ICLR (5)
NIPS (5)
ICML (2)
CVPR (1)
ICCV (1)
JMLR (1)
Top co-authors
Keywords
neural scaling law
(3)
semi-supervised learning
(2)
lipschitz constraint
(2)
adversarial robustness
(2)
data collection
(2)
network architecture
(1)
convergence analysis
(1)
representation learning
(1)
uncertainty quantification
(1)
generative model
(1)
function approximation
(1)
sample complexity
(1)
neural network optimization
(1)
marginal likelihood
(1)
probabilistic pca
(1)
computer vision
(1)
gradient descent
(1)
adaptive learning rate
(1)
posterior distribution
(1)
principal component analysis
(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
Optimizing Data Collection for Machine Learning
NIPS 2022
How Much More Data Do I Need? Estimating Requirements for Downstream Tasks
CVPR 2022
Theoretical bounds on estimation error for meta-learning
ICLR 2021
Regularized linear autoencoders recover the principal components, eventually
NIPS 2020
Aggregated Momentum: Stability Through Passive Damping
ICLR 2019
Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse
NIPS 2019
Lookahead Optimizer: k steps forward, 1 step back
NIPS 2019
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks
NIPS 2019
Sorting Out Lipschitz Function Approximation
ICML 2019
Adversarial Distillation of Bayesian Neural Network Posteriors
ICML 2018