Universal Function Approximation by Deep Neural Nets with Bounded Width and ReLU Activations. - Boris Hanin (CoRR, 2017)

Approximating Continuous Functions by ReLU Nets of Minimal Width. - Boris Hanin; Mark Sellke (CoRR, 2017)

Which Neural Net Architectures Give Rise to Exploding and Vanishing Gradients? - Boris Hanin (NeurIPS, 2018)

How to Start Training: The Effect of Initialization and Architecture. - Boris Hanin; David Rolnick (NeurIPS, 2018)

Complexity of Linear Regions in Deep Networks. - Boris Hanin; David Rolnick (ICML, 2019)

Deep ReLU Networks Have Surprisingly Few Activation Patterns. - Boris Hanin; David Rolnick (NeurIPS, 2019)

Nonlinear Approximation and (Deep) ReLU Networks. - Ingrid Daubechies; Ronald A. DeVore; Simon Foucart; Boris Hanin; Guergana Petrova (CoRR, 2019)

Finite Depth and Width Corrections to the Neural Tangent Kernel. - Boris Hanin; Mihai Nica (ICLR, 2020)

Data augmentation as stochastic optimization. - Boris Hanin; Yi Sun 010 (CoRR, 2020)

How Data Augmentation affects Optimization for Linear Regression. - Boris Hanin; Yi Sun 010 (NeurIPS, 2021)

The Principles of Deep Learning Theory. - Daniel A. Roberts; Sho Yaida; Boris Hanin (CoRR, 2021)

Random Neural Networks in the Infinite Width Limit as Gaussian Processes. - Boris Hanin (CoRR, 2021)

Ridgeless Interpolation with Shallow ReLU Networks in $1D$ is Nearest Neighbor Curvature Extrapolation and Provably Generalizes on Lipschitz Functions. - Boris Hanin (CoRR, 2021)

Neural network approximation. - Ronald A. DeVore; Boris Hanin; Guergana Petrova (Acta Numer., 2021)

Deep ReLU Networks Preserve Expected Length. - Boris Hanin; Ryan S. Jeong; David Rolnick (ICLR, 2022)

Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis. - Wuyang Chen 001; Wei Huang 034; Xinyu Gong; Boris Hanin; Zhangyang Wang (NeurIPS, 2022)

Correlation Functions in Random Fully Connected Neural Networks at Finite Width. - Boris Hanin (CoRR, 2022)

Bayesian Interpolation with Deep Linear Networks. - Boris Hanin; Alexander Zlokapa (CoRR, 2022)

Maximal Initial Learning Rates in Deep ReLU Networks. - Gaurav Iyer; Boris Hanin; David Rolnick (ICML, 2023)

Depth Dependence of μP Learning Rates in ReLU MLPs. - Samy Jelassi; Boris Hanin; Ziwei Ji; Sashank J. Reddi; Srinadh Bhojanapalli; Sanjiv Kumar (CoRR, 2023)

Quantitative CLTs in Deep Neural Networks. - Stefano Favaro; Boris Hanin; Domenico Marinucci; Ivan Nourdin; Giovanni Peccati (CoRR, 2023)

Les Houches Lectures on Deep Learning at Large & Infinite Width. - Yasaman Bahri; Boris Hanin; Antonin Brossollet; Vittorio Erba; Christian Keup; Rosalba Pacelli; James B. Simon (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)

Principled Architecture-aware Scaling of Hyperparameters. - Wuyang Chen 001; Junru Wu; Zhangyang Wang; Boris Hanin (ICLR, 2024)

Are More LLM Calls All You Need? Towards the Scaling Properties of Compound AI Systems. - Lingjiao Chen; Jared Quincy Davis; Boris Hanin; Peter Bailis; Ion Stoica; Matei A. Zaharia; James Y. Zou (NeurIPS, 2024)

Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systems. - Lingjiao Chen; Jared Quincy Davis; Boris Hanin; Peter Bailis; Ion Stoica; Matei Zaharia; James Zou 001 (CoRR, 2024)

Bayesian Inference with Deep Weakly Nonlinear Networks. - Boris Hanin; Alexander Zlokapa (CoRR, 2024)

Networks of Networks: Complexity Class Principles Applied to Compound AI Systems Design. - Jared Quincy Davis; Boris Hanin; Lingjiao Chen; Peter Bailis; Ion Stoica; Matei Zaharia (CoRR, 2024)

Unintentional Unalignment: Likelihood Displacement in Direct Preference Optimization. - Noam Razin; Sadhika Malladi; Adithya Bhaskar; Danqi Chen 001; Sanjeev Arora; Boris Hanin (ICLR, 2025)

Principles for Initialization and Architecture Selection in Graph Neural Networks with ReLU Activations. - Gage DeZoort; Boris Hanin (SIAM J. Math. Data Sci., 2025)

BARE: Combining Base and Instruction-Tuned Language Models for Better Synthetic Data Generation. - Alan Zhu 001; Parth Asawa; Jared Quincy Davis; Lingjiao Chen; Boris Hanin; Ion Stoica; Joseph E. Gonzalez; Matei Zaharia (CoRR, 2025)

Optimizing Model Selection for Compound AI Systems. - Lingjiao Chen; Jared Quincy Davis; Boris Hanin; Peter Bailis; Matei Zaharia; James Zou 001; Ion Stoica (CoRR, 2025)

Deep Nets as Hamiltonians. - Mike Winer; Boris Hanin (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)