Greg Yang
18 papers · 2019–2025 · 3 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
Jump to papers ↓+6 more ↓ Show less ↑
🗺️ Taxonomy Completionist (24) 🏃 Academic Marathon · Sprint 🌍 Conference Polyglot · Bronze 🌉 Interdisciplinary Bridge · Bronze 🐣 Hot Topic Early Bird
🏆
Keyword Champion
(2)
🗃️
Keyword Collector
(79)
📈
Trend Setter
💎
Century Club · Bronze
🔥
Unstoppable · Bronze
⚡
Prolific Year · Productive
Venues
Top co-authors
Research topics
Machine Learning
(18)
Optimization & Theory · Machine Learning
(16)
Deep Learning
(14)
Architectures
(7)
Optimization & Theory · Deep Learning
(6)
Artificial Intelligence
(3)
Application Areas
(3)
Core Methods
(3)
Learning Types · Deep Learning
(3)
Techniques
(3)
Core AI
(2)
Learning Types · Machine Learning
(2)
Mathematics & Optimization
(1)
Bayesian & Probabilistic · Machine Learning
(1)
Bayesian & Probabilistic · Artificial Intelligence
(1)
Keywords
neural tangent kernel
(6)
feature learning
(5)
neural network
(4)
neural network optimization
(3)
infinite-width limit
(3)
gradient descent
(3)
randomized smoothing
(3)
gaussian process
(3)
adversarial robustness
(3)
infinite-width neural network
(3)
certified robustness
(2)
representation learning
(2)
infinite width
(2)
stochastic gradient descent
(2)
tensor program
(2)
hyperparameter transfer
(1)
adversarial training
(1)
ridge regression
(1)
batch normalization
(1)
bayesian inference
(1)
Papers
Global Convergence and Rich Feature Learning in L-Layer Infinite-Width Neural Networks under μ Parametrization
ICML 2025
Tensor Programs VI: Feature Learning in Infinite Depth Neural Networks
ICLR 2024
Adaptive Optimization in the ∞-Width Limit
ICLR 2023
Width and Depth Limits Commute in Residual Networks
ICML 2023
3DB: A Framework for Debugging Computer Vision Models
NeurIPS 2022
Efficient Computation of Deep Nonlinear Infinite-Width Neural Networks that Learn Features
ICLR 2022
High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation
NeurIPS 2022
Non-Gaussian Tensor Programs
NeurIPS 2022
Tensor Programs IIb: Architectural Universality Of Neural Tangent Kernel Training Dynamics
ICML 2021
Tensor Programs IV: Feature Learning in Infinite-Width Neural Networks
ICML 2021
Denoised Smoothing: A Provable Defense for Pretrained Classifiers
NeurIPS 2020
On Infinite-Width Hypernetworks
NeurIPS 2020
Randomized Smoothing of All Shapes and Sizes
ICML 2020
A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks
NeurIPS 2019
A Mean Field Theory of Batch Normalization
ICLR 2019
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes
ICLR 2019
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers
NeurIPS 2019
Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes
NeurIPS 2019