← Authors

Greg Yang

18 papers · 2019–2025 · 3 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (14) πŸ—ΊοΈ Taxonomy Completionist (24) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (3) 🐣 Hot Topic Early Bird πŸƒ Academic Marathon (6) 🧭 Keyword Pioneer πŸ† Keyword Champion (2) πŸ”₯ Unstoppable (7) πŸ’Ž Century Club (18) ⚑ Prolific Year (5) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (59)

Conferences

NIPS (8) ICLR (5) ICML (5)

Top co-authors

Hadi Salman (5) Etai Littwin (3) Pengchuan Zhang (3) Soufiane Hayou (2) Ashish Kapoor (2) Ilya Razenshteyn (2) Jerry Li (2) Huan Zhang (2) Jascha Sohl-dickstein (2) Jeffrey Pennington (2)

Keywords

neural tangent kernel (4) randomized smoothing (3) neural network (3) adversarial robustness (3) neural network optimization (2) feature learning (2) representation learning (2) infinite width (2) certified robustness (2) gaussian process (2) infinite-width limit (2) gradient descent (2) model debugging (1) function space (1) neural network theory (1) transfer learning (1) deep learning (1) covariance structure (1) adversarial training (1) linear programming (1)

Papers

Global Convergence and Rich Feature Learning in $L$-Layer Infinite-Width Neural Networks under $ΞΌ$ Parametrization ICML 2025
Tensor Programs VI: Feature Learning in Infinite Depth Neural Networks ICLR 2024
Adaptive Optimization in the $\infty$-Width Limit ICLR 2023
Width and Depth Limits Commute in Residual Networks ICML 2023
Non-Gaussian Tensor Programs NIPS 2022
3DB: A Framework for Debugging Computer Vision Models NIPS 2022
High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation NIPS 2022
Efficient Computation of Deep Nonlinear Infinite-Width Neural Networks that Learn Features ICLR 2022
Tensor Programs IV: Feature Learning in Infinite-Width Neural Networks ICML 2021
Tensor Programs IIb: Architectural Universality Of Neural Tangent Kernel Training Dynamics ICML 2021
On Infinite-Width Hypernetworks NIPS 2020
Denoised Smoothing: A Provable Defense for Pretrained Classifiers NIPS 2020
Randomized Smoothing of All Shapes and Sizes ICML 2020
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes ICLR 2019
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers NIPS 2019
Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes NIPS 2019
A Mean Field Theory of Batch Normalization ICLR 2019
A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks NIPS 2019