Concentration to zero bit-error probability for regular LDPC codes on the binary symmetric channel: Proof by loop calculus. - Marc Vuffray; Theodor Misiakiewicz (Allerton, 2015)

Efficient reconstruction of transmission probabilities in a spreading process from partial observations. - Andrey Y. Lokhov; Theodor Misiakiewicz (CoRR, 2015)

Solving SDPs for synchronization and MaxCut problems via the Grothendieck inequality. - Song Mei; Theodor Misiakiewicz; Andrea Montanari; Roberto Imbuzeiro Oliveira (COLT, 2017)

Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit. - Song Mei; Theodor Misiakiewicz; Andrea Montanari (COLT, 2019)

Limitations of Lazy Training of Two-layers Neural Network. - Behrooz Ghorbani; Song Mei; Theodor Misiakiewicz; Andrea Montanari (NeurIPS, 2019)

Linearized two-layers neural networks in high dimension. - Behrooz Ghorbani; Song Mei; Theodor Misiakiewicz; Andrea Montanari (CoRR, 2019)

Limitations of Lazy Training of Two-layers Neural Networks. - Behrooz Ghorbani; Song Mei; Theodor Misiakiewicz; Andrea Montanari (CoRR, 2019)

When Do Neural Networks Outperform Kernel Methods? - Behrooz Ghorbani; Song Mei; Theodor Misiakiewicz; Andrea Montanari (NeurIPS, 2020)

Learning with invariances in random features and kernel models. - Song Mei; Theodor Misiakiewicz; Andrea Montanari (COLT, 2021)

Minimum complexity interpolation in random features models. - Michael Celentano; Theodor Misiakiewicz; Andrea Montanari (CoRR, 2021)

The merged-staircase property: a necessary and nearly sufficient condition for SGD learning of sparse functions on two-layer neural networks. - Emmanuel Abbe; Enric Boix Adserà; Theodor Misiakiewicz (COLT, 2022)

Learning with convolution and pooling operations in kernel methods. - Theodor Misiakiewicz; Song Mei (NeurIPS, 2022)

Precise Learning Curves and Higher-Order Scalings for Dot-product Kernel Regression. - Lechao Xiao; Hong Hu; Theodor Misiakiewicz; Yue Lu; Jeffrey Pennington (NeurIPS, 2022)

SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics. - Emmanuel Abbe; Enric Boix Adserà; Theodor Misiakiewicz (COLT, 2023)

Six Lectures on Linearized Neural Networks. - Theodor Misiakiewicz; Andrea Montanari (CoRR, 2023)

Dimension-free deterministic equivalents and scaling laws for random feature regression. - Leonardo Defilippis; Bruno Loureiro; Theodor Misiakiewicz (NeurIPS, 2024)

On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries. - Nirmit Joshi; Theodor Misiakiewicz; Nati Srebro (NeurIPS, 2024)

Asymptotics of Random Feature Regression Beyond the Linear Scaling Regime. - Hong Hu; Yue M. Lu; Theodor Misiakiewicz (CoRR, 2024)

A non-asymptotic theory of Kernel Ridge Regression: deterministic equivalents, test error, and GCV estimator. - Theodor Misiakiewicz; Basil Saeed (CoRR, 2024)

Dimension-free deterministic equivalents for random feature regression. - Leonardo Defilippis; Bruno Loureiro; Theodor Misiakiewicz (CoRR, 2024)

A Theory of Learning with Autoregressive Chain of Thought. - Nirmit Joshi; Gal Vardi; Adam Block; Surbhi Goel; Zhiyuan Li 005; Theodor Misiakiewicz; Nathan Srebro (COLT, 2025)

An Optimized Franz-Parisi Criterion and its Equivalence with SQ Lower Bounds. - Siyu Chen; Theodor Misiakiewicz; Ilias Zadik; Peiyuan Zhang (CoRR, 2025)

Learning single-index models via harmonic decomposition. - Nirmit Joshi; Hugo Koubbi; Theodor Misiakiewicz; Nathan Srebro (CoRR, 2025)