Computabilities of Validity and Satisfiability in Probability Logics over Finite and Countable Models. - Greg Yang (CoRR, 2014)

Computability of validity and satisfiability in probability logics over finite and countable models. - Greg Yang (J. Appl. Non Class. Logics, 2015)

Lie Access Neural Turing Machine. - Greg Yang (CoRR, 2016)

Lie-Access Neural Turing Machines. - Greg Yang; Alexander M. Rush (ICLR (Poster), 2017)

Mean Field Residual Networks: On the Edge of Chaos. - Greg Yang; Samuel S. Schoenholz (NIPS, 2017)

A Homological Theory of Functions. - Greg Yang (CoRR, 2017)

A Homological Theory of Functions: Nonuniform Boolean Complexity Separation and VC Dimension Bound Via Algebraic Topology, and a Homological Farkas Lemma. - Greg Yang (ITCS, 2018)

Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes. - Roman Novak; Lechao Xiao; Yasaman Bahri; Jaehoon Lee 001; Greg Yang; Jiri Hron; Daniel A. Abolafia; Jeffrey Pennington; Jascha Sohl-Dickstein (ICLR (Poster), 2019)

A Mean Field Theory of Batch Normalization. - Greg Yang; Jeffrey Pennington; Vinay Rao; Jascha Sohl-Dickstein; Samuel S. Schoenholz (ICLR (Poster), 2019)

Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers. - Hadi Salman; Jerry Li 001; Ilya P. Razenshteyn; Pengchuan Zhang; Huan Zhang; Sébastien Bubeck; Greg Yang (NeurIPS, 2019)

A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks. - Hadi Salman; Greg Yang; Huan Zhang 001; Cho-Jui Hsieh; Pengchuan Zhang (NeurIPS, 2019)

Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes. - Greg Yang (NeurIPS, 2019)

Dynamical Isometry and a Mean Field Theory of LSTMs and GRUs. - Dar Gilboa; Bo Chang 002; Minmin Chen; Greg Yang; Samuel S. Schoenholz; Ed H. Chi; Jeffrey Pennington (CoRR, 2019)

NAIL: A General Interactive Fiction Agent. - Matthew J. Hausknecht; Ricky Loynd; Greg Yang; Adith Swaminathan; Jason D. Williams (CoRR, 2019)

Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation. - Greg Yang (CoRR, 2019)

A Fine-Grained Spectral Perspective on Neural Networks. - Greg Yang; Hadi Salman (CoRR, 2019)

Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes. - Greg Yang (CoRR, 2019)

Randomized Smoothing of All Shapes and Sizes. - Greg Yang; Tony Duan; J. Edward Hu; Hadi Salman; Ilya P. Razenshteyn; Jerry Li 001 (ICML, 2020)

On Infinite-Width Hypernetworks. - Etai Littwin; Tomer Galanti; Lior Wolf; Greg Yang (NeurIPS, 2020)

Denoised Smoothing: A Provable Defense for Pretrained Classifiers. - Hadi Salman; Mingjie Sun; Greg Yang; Ashish Kapoor; J. Zico Kolter (NeurIPS, 2020)

Free resolutions of function classes via order complexes. - Justin Chen; Christopher Eur; Greg Yang; Mengyuan Zhang 004 (Adv. Appl. Math., 2020)

Black-box Smoothing: A Provable Defense for Pretrained Classifiers. - Hadi Salman; Mingjie Sun; Greg Yang; Ashish Kapoor; J. Zico Kolter (CoRR, 2020)

Improved Image Wasserstein Attacks and Defenses. - J. Edward Hu; Adith Swaminathan; Hadi Salman; Greg Yang (CoRR, 2020)

Tensor Programs II: Neural Tangent Kernel for Any Architecture. - Greg Yang (CoRR, 2020)

Tensor Programs III: Neural Matrix Laws. - Greg Yang (CoRR, 2020)

Feature Learning in Infinite-Width Neural Networks. - Greg Yang; J. Edward Hu (CoRR, 2020)

Tensor Programs IV: Feature Learning in Infinite-Width Neural Networks. - Greg Yang; Edward J. Hu (ICML, 2021)

Tensor Programs IIb: Architectural Universality Of Neural Tangent Kernel Training Dynamics. - Greg Yang; Etai Littwin (ICML, 2021)

Implicit Acceleration and Feature Learning in Infinitely Wide Neural Networks with Bottlenecks. - Etai Littwin; Omid Saremi; Shuangfei Zhai; Vimal Thilak; Hanlin Goh; Joshua M. Susskind; Greg Yang (CoRR, 2021)

CLUES: Few-Shot Learning Evaluation in Natural Language Understanding. - Subhabrata Mukherjee; Xiaodong Liu 003; Guoqing Zheng; Saghar Hosseini; Hao Cheng 002; Greg Yang; Christopher Meek; Ahmed Hassan Awadallah; Jianfeng Gao 001 (CoRR, 2021)

Efficient Computation of Deep Nonlinear Infinite-Width Neural Networks that Learn Features. - Greg Yang; Michael Santacroce; Edward J. Hu (ICLR, 2022)

High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation. - Jimmy Ba; Murat A. Erdogdu; Taiji Suzuki; Zhichao Wang; Denny Wu; Greg Yang (NeurIPS, 2022)

Non-Gaussian Tensor Programs. - Eugene A. Golikov; Greg Yang (NeurIPS, 2022)

3DB: A Framework for Debugging Computer Vision Models. - Guillaume Leclerc; Hadi Salman; Andrew Ilyas; Sai Vemprala; Logan Engstrom; Vibhav Vineet; Kai Yuanqing Xiao; Pengchuan Zhang; Shibani Santurkar; Greg Yang; Ashish Kapoor; Aleksander Madry (NeurIPS, 2022)

Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer. - Greg Yang; Edward J. Hu; Igor Babuschkin; Szymon Sidor; Xiaodong Liu 003; David Farhi; Nick Ryder; Jakub Pachocki; Weizhu Chen; Jianfeng Gao 001 (CoRR, 2022)

Adaptive Optimization in the ∞-Width Limit. - Etai Littwin; Greg Yang (ICLR, 2023)

Width and Depth Limits Commute in Residual Networks. - Soufiane Hayou; Greg Yang (ICML, 2023)

Tensor Programs IVb: Adaptive Optimization in the Infinite-Width Limit. - Greg Yang; Etai Littwin (CoRR, 2023)

A Spectral Condition for Feature Learning. - Greg Yang; James B. Simon; Jeremy Bernstein (CoRR, 2023)

Tensor Programs VI: Feature Learning in Infinite Depth Neural Networks. - Greg Yang; Dingli Yu; Chen Zhu; Soufiane Hayou (ICLR, 2024)

Global Convergence and Rich Feature Learning in L-Layer Infinite-Width Neural Networks under μP Parametrization. - Zixiang Chen; Greg Yang; Qingyue Zhao; Quanquan Gu (CoRR, 2025)

Cost-Effective, Low Latency Vector Search with Azure Cosmos DB. - Nitish Upreti; Krishnan Sundaram; Hari Sudan Sundar; Samer Boshra; Balachandar Perumalswamy; Shivam Atri; Martin Chisholm; Revti Raman Singh; Greg Yang; Subramanyam Pattipaka; Tamara Hass; Nitesh Dudhey; et al. (CoRR, 2025)