Theodor Misiakiewicz

According to our database, Theodor Misiakiewicz authored at least 27 papers between 2015 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

Improved Scaling Laws via Weak-to-Strong Generalization in Random Feature Ridge Regression.

Diyuan Wu, Lehan Chen, Theodor Misiakiewicz, Marco Mondelli

CoRR, March, 2026

Statistical-Computational Trade-offs in Learning Multi-Index Models via Harmonic Analysis.

Hugo Latourelle-Vigeant, Theodor Misiakiewicz

CoRR, February, 2026

Positive Distribution Shift as a Framework for Understanding Tractable Learning.

Marko Medvedev, Idan Attias, Elisabetta Cornacchia, Theodor Misiakiewicz, Gal Vardi, Nathan Srebro

CoRR, February, 2026

2025

When does Gaussian equivalence fail and how to fix it: Non-universal behavior of random features with quadratic scaling.

Garrett G. Wen, Hong Hu, Yue M. Lu, Zhou Fan, Theodor Misiakiewicz

CoRR, December, 2025

Learning single-index models via harmonic decomposition.

Nirmit Joshi, Hugo Koubbi, Theodor Misiakiewicz, Nathan Srebro

CoRR, June, 2025

An Optimized Franz-Parisi Criterion and its Equivalence with SQ Lower Bounds.

Siyu Chen, Theodor Misiakiewicz, Ilias Zadik, Peiyuan Zhang

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

A Theory of Learning with Autoregressive Chain of Thought.

Nirmit Joshi, Gal Vardi, Adam Block, Surbhi Goel, Zhiyuan Li, Theodor Misiakiewicz, Nathan Srebro

Proceedings of the Thirty Eighth Annual Conference on Learning Theory, 2025

2024

Dimension-free deterministic equivalents for random feature regression.

Leonardo Defilippis, Bruno Loureiro, 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

Asymptotics of Random Feature Regression Beyond the Linear Scaling Regime.

Hong Hu, Yue M. Lu, Theodor Misiakiewicz

CoRR, 2024

On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries.

Nirmit Joshi, Theodor Misiakiewicz, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Dimension-free deterministic equivalents and scaling laws for random feature regression.

Leonardo Defilippis, Bruno Loureiro, Theodor Misiakiewicz

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

Six Lectures on Linearized Neural Networks.

Theodor Misiakiewicz, Andrea Montanari

CoRR, 2023

SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics.

Emmanuel Abbe, Enric Boix Adserà, Theodor Misiakiewicz

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

Precise Learning Curves and Higher-Order Scalings for Dot-product Kernel Regression.

Lechao Xiao, Hong Hu, Theodor Misiakiewicz, Yue Lu, Jeffrey Pennington

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Learning with convolution and pooling operations in kernel methods.

Theodor Misiakiewicz, Song Mei

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021

Minimum complexity interpolation in random features models.

Michael Celentano, Theodor Misiakiewicz, Andrea Montanari

CoRR, 2021

Learning with invariances in random features and kernel models.

Song Mei, Theodor Misiakiewicz, Andrea Montanari

Proceedings of the Conference on Learning Theory, 2021

2020

When Do Neural Networks Outperform Kernel Methods?

Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, Andrea Montanari

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Limitations of Lazy Training of Two-layers Neural Networks.

Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, Andrea Montanari

CoRR, 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 Network.

Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, Andrea Montanari

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit.

Song Mei, Theodor Misiakiewicz, Andrea Montanari

Proceedings of the Conference on Learning Theory, 2019

2017

Solving SDPs for synchronization and MaxCut problems via the Grothendieck inequality.

Song Mei, Theodor Misiakiewicz, Andrea Montanari, Roberto Imbuzeiro Oliveira

Proceedings of the 30th Conference on Learning Theory, 2017

2015

Efficient reconstruction of transmission probabilities in a spreading process from partial observations.

Andrey Y. Lokhov, Theodor Misiakiewicz

CoRR, 2015

Concentration to zero bit-error probability for regular LDPC codes on the binary symmetric channel: Proof by loop calculus.

Marc Vuffray, Theodor Misiakiewicz

Proceedings of the 53rd Annual Allerton Conference on Communication, 2015