| 2025 | ICLR | How Feature Learning Can Improve Neural Scaling Laws. | Blake Bordelon, Alexander B. Atanasov, Cengiz Pehlevan |
| 2025 | ICLR | Scaling Laws for Precision. | Tanishq Kumar, Zachary Ankner, Benjamin Frederick Spector, Blake Bordelon, Niklas Muennighoff, Mansheej Paul, Cengiz Pehlevan, Christopher R, Aditi Raghunathan |
| 2025 | ICLR | Do Mice Grok? Glimpses of Hidden Progress in Sensory Cortex. | Tanishq Kumar, Blake Bordelon, Cengiz Pehlevan, Venkatesh N. Murthy, Samuel J. Gershman |
| 2025 | ICML | Deep Linear Network Training Dynamics from Random Initialization: Data, Width, Depth, and Hyperparameter Transfer. | Blake Bordelon, Cengiz Pehlevan |
| 2025 | ICML | A Model of Place Field Reorganization During Reward Maximization. | M. Ganesh Kumar, Blake Bordelon, Jacob A. Zavatone-Veth, Cengiz Pehlevan |
| 2025 | ICML | Adaptive kernel predictors from feature-learning infinite limits of neural networks. | Clarissa Lauditi, Blake Bordelon, Cengiz Pehlevan |
| 2024 | ICLR | Depthwise Hyperparameter Transfer in Residual Networks: Dynamics and Scaling Limit. | Blake Bordelon, Lorenzo Noci, Mufan Bill Li, Boris Hanin, Cengiz Pehlevan |
| 2024 | ICLR | Grokking as the transition from lazy to rich training dynamics. | Tanishq Kumar, Blake Bordelon, Samuel J. Gershman, Cengiz Pehlevan |
| 2024 | ICML | A Dynamical Model of Neural Scaling Laws. | Blake Bordelon, Alexander B. Atanasov, Cengiz Pehlevan |
| 2023 | ICLR | The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich Regimes. | Alexander B. Atanasov, Blake Bordelon, Sabarish Sainathan, Cengiz Pehlevan |
| 2023 | ICLR | The Influence of Learning Rule on Representation Dynamics in Wide Neural Networks. | Blake Bordelon, Cengiz Pehlevan |
| 2022 | ICLR | Neural Networks as Kernel Learners: The Silent Alignment Effect. | Alexander B. Atanasov, Blake Bordelon, Cengiz Pehlevan |
| 2022 | ICLR | Learning Curves for SGD on Structured Features. | Blake Bordelon, Cengiz Pehlevan |
| 2022 | ICLR | 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 |
| 2021 | UAI | Efficient online inference for nonparametric mixture models. | Rylan Schaeffer, Blake Bordelon, Mikail Khona, Weiwei Pan, Ila Rani Fiete |
| 2020 | ICML | Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks. | Blake Bordelon, Abdulkadir Canatar, Cengiz Pehlevan |