Pre-Synaptic Pool Modification (PSPM): A Supervised Learning Procedure for Spiking Neural Networks. - Bryce Bagley; Blake Bordelon; Benjamin Moseley; Ralf Wessel (CoRR, 2018)

Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks. - Blake Bordelon; Abdulkadir Canatar; Cengiz Pehlevan (ICML, 2020)

Statistical Mechanics of Generalization in Kernel Regression. - Abdulkadir Canatar; Blake Bordelon; Cengiz Pehlevan (CoRR, 2020)

Out-of-Distribution Generalization in Kernel Regression. - Abdulkadir Canatar; Blake Bordelon; Cengiz Pehlevan (NeurIPS, 2021)

Efficient online inference for nonparametric mixture models. - Rylan Schaeffer; Blake Bordelon; Mikail Khona; Weiwei Pan; Ila Rani Fiete (UAI, 2021)

Rapid Feature Evolution Accelerates Learning in Neural Networks. - Haozhe Shan; Blake Bordelon (CoRR, 2021)

Neural Networks as Kernel Learners: The Silent Alignment Effect. - Alexander B. Atanasov; Blake Bordelon; Cengiz Pehlevan (ICLR, 2022)

Learning Curves for SGD on Structured Features. - Blake Bordelon; Cengiz Pehlevan (ICLR, 2022)

Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views? - Matthew Farrell; Blake Bordelon; Shubhendu Trivedi; Cengiz Pehlevan (ICLR, 2022)

Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks. - Blake Bordelon; Cengiz Pehlevan (NeurIPS, 2022)

The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich Regimes. - Alexander B. Atanasov; Blake Bordelon; Sabarish Sainathan; Cengiz Pehlevan (ICLR, 2023)

The Influence of Learning Rule on Representation Dynamics in Wide Neural Networks. - Blake Bordelon; Cengiz Pehlevan (ICLR, 2023)

Feature-Learning Networks Are Consistent Across Widths At Realistic Scales. - Nikhil Vyas 001; Alexander B. Atanasov; Blake Bordelon; Depen Morwani; Sabarish Sainathan; Cengiz Pehlevan (NeurIPS, 2023)

Loss Dynamics of Temporal Difference Reinforcement Learning. - Blake Bordelon; Paul Masset; Henry Kuo; Cengiz Pehlevan (NeurIPS, 2023)

Dynamics of Finite Width Kernel and Prediction Fluctuations in Mean Field Neural Networks. - Blake Bordelon; Cengiz Pehlevan (NeurIPS, 2023)

Dynamics of Temporal Difference Reinforcement Learning. - Blake Bordelon; Paul Masset; Henry Kuo; Cengiz Pehlevan (CoRR, 2023)

Depthwise Hyperparameter Transfer in Residual Networks: Dynamics and Scaling Limit. - Blake Bordelon; Lorenzo Noci; Mufan Bill Li; Boris Hanin; Cengiz Pehlevan (ICLR, 2024)

Grokking as the transition from lazy to rich training dynamics. - Tanishq Kumar; Blake Bordelon; Samuel J. Gershman; Cengiz Pehlevan (ICLR, 2024)

A Dynamical Model of Neural Scaling Laws. - Blake Bordelon; Alexander B. Atanasov; Cengiz Pehlevan (ICML, 2024)

Infinite Limits of Multi-head Transformer Dynamics. - Blake Bordelon; Hamza Tahir Chaudhry; Cengiz Pehlevan (NeurIPS, 2024)

Do Mice Grok? Glimpses of Hidden Progress During Overtraining in Sensory Cortex. - Tanishq Kumar; Blake Bordelon; Cengiz Pehlevan; Venkatesh N. Murthy; Samuel J. Gershman (CoRR, 2024)

How Feature Learning Can Improve Neural Scaling Laws. - Blake Bordelon; Alexander B. Atanasov; Cengiz Pehlevan (ICLR, 2025)

Scaling Laws for Precision. - Tanishq Kumar; Zachary Ankner; Benjamin Frederick Spector; Blake Bordelon; Niklas Muennighoff; Mansheej Paul; Cengiz Pehlevan; Christopher Ré; Aditi Raghunathan (ICLR, 2025)

Do Mice Grok? Glimpses of Hidden Progress in Sensory Cortex. - Tanishq Kumar; Blake Bordelon; Cengiz Pehlevan; Venkatesh N. Murthy; Samuel J. Gershman (ICLR, 2025)

Deep Linear Network Training Dynamics from Random Initialization: Data, Width, Depth, and Hyperparameter Transfer. - Blake Bordelon; Cengiz Pehlevan (CoRR, 2025)

Two-Point Deterministic Equivalence for Stochastic Gradient Dynamics in Linear Models. - Alexander B. Atanasov; Blake Bordelon; Jacob A. Zavatone-Veth; Courtney Paquette; Cengiz Pehlevan (CoRR, 2025)

Adaptive kernel predictors from feature-learning infinite limits of neural networks. - Clarissa Lauditi; Blake Bordelon; Cengiz Pehlevan (CoRR, 2025)

Don't be lazy: CompleteP enables compute-efficient deep transformers. - Nolan Dey; Bin Claire Zhang; Lorenzo Noci; Mufan Bill Li; Blake Bordelon; Shane Bergsma; Cengiz Pehlevan; Boris Hanin; Joel Hestness (CoRR, 2025)

Transfer Learning in Infinite Width Feature Learning Networks. - Clarissa Lauditi; Blake Bordelon; Cengiz Pehlevan (CoRR, 2025)