Theodor Misiakiewicz

36 papers A* 12Journal 23Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Improved Scaling Laws via Weak-to-Strong Generalization in Random Feature Ridge Regression. CoRR Diyuan Wu, Lehan Chen, Theodor Misiakiewicz, Marco Mondelli
2026	J	`jnl`	Positive Distribution Shift as a Framework for Understanding Tractable Learning. CoRR Marko Medvedev, Idan Attias, Elisabetta Cornacchia, Theodor Misiakiewicz, Gal Vardi, Nathan Srebro
2026	J	`jnl`	Statistical-Computational Trade-offs in Learning Multi-Index Models via Harmonic Analysis. CoRR Hugo Latourelle-Vigeant, Theodor Misiakiewicz
2025	A*	`conf`	A Theory of Learning with Autoregressive Chain of Thought. COLT Nirmit Joshi, Gal Vardi, Adam Block, Surbhi Goel, Zhiyuan Li, Theodor Misiakiewicz, Nathan Srebro
2025	J	`jnl`	A Theory of Learning with Autoregressive Chain of Thought. CoRR Nirmit Joshi, Gal Vardi, Adam Block, Surbhi Goel, Zhiyuan Li, Theodor Misiakiewicz, Nathan Srebro
2025	J	`jnl`	An Optimized Franz-Parisi Criterion and its Equivalence with SQ Lower Bounds. CoRR Siyu Chen, Theodor Misiakiewicz, Ilias Zadik, Peiyuan Zhang
2025	J	`jnl`	Learning single-index models via harmonic decomposition. CoRR Nirmit Joshi, Hugo Koubbi, Theodor Misiakiewicz, Nathan Srebro
2025	J	`jnl`	When does Gaussian equivalence fail and how to fix it: Non-universal behavior of random features with quadratic scaling. CoRR Garrett G. Wen, Hong Hu, Yue M. Lu, Zhou Fan, Theodor Misiakiewicz
2024	J	`jnl`	A non-asymptotic theory of Kernel Ridge Regression: deterministic equivalents, test error, and GCV estimator. CoRR Theodor Misiakiewicz, Basil Saeed
2024	J	`jnl`	Asymptotics of Random Feature Regression Beyond the Linear Scaling Regime. CoRR Hong Hu, Yue M. Lu, Theodor Misiakiewicz
2024	A*	`conf`	Dimension-free deterministic equivalents and scaling laws for random feature regression. NeurIPS Leonardo Defilippis, Bruno Loureiro, Theodor Misiakiewicz
2024	J	`jnl`	Dimension-free deterministic equivalents for random feature regression. CoRR Leonardo Defilippis, Bruno Loureiro, Theodor Misiakiewicz
2024	A*	`conf`	On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries. NeurIPS Nirmit Joshi, Theodor Misiakiewicz, Nati Srebro
2024	J	`jnl`	On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries. CoRR Nirmit Joshi, Theodor Misiakiewicz, Nathan Srebro
2023	A*	`conf`	SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics. COLT Emmanuel Abbe, Enric Boix Adserà, Theodor Misiakiewicz
2023	J	`jnl`	SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics. CoRR Emmanuel Abbe, Enric Boix-Adserà, Theodor Misiakiewicz
2023	J	`jnl`	Six Lectures on Linearized Neural Networks. CoRR Theodor Misiakiewicz, Andrea Montanari
2022	A*	`conf`	Learning with convolution and pooling operations in kernel methods. NeurIPS Theodor Misiakiewicz, Song Mei
2022	A*	`conf`	Precise Learning Curves and Higher-Order Scalings for Dot-product Kernel Regression. NeurIPS Lechao Xiao, Hong Hu, Theodor Misiakiewicz, Yue Lu, Jeffrey Pennington
2022	A*	`conf`	The merged-staircase property: a necessary and nearly sufficient condition for SGD learning of sparse functions on two-layer neural networks. COLT Emmanuel Abbe, Enric Boix Adserà, Theodor Misiakiewicz
2022	J	`jnl`	The merged-staircase property: a necessary and nearly sufficient condition for SGD learning of sparse functions on two-layer neural networks. CoRR Emmanuel Abbe, Enric Boix-Adserà, Theodor Misiakiewicz
2021	J	`jnl`	Learning with convolution and pooling operations in kernel methods. CoRR Theodor Misiakiewicz, Song Mei
2021	A*	`conf`	Learning with invariances in random features and kernel models. COLT Song Mei, Theodor Misiakiewicz, Andrea Montanari
2021	J	`jnl`	Learning with invariances in random features and kernel models. CoRR Song Mei, Theodor Misiakiewicz, Andrea Montanari
2021	J	`jnl`	Minimum complexity interpolation in random features models. CoRR Michael Celentano, Theodor Misiakiewicz, Andrea Montanari
2020	A*	`conf`	When Do Neural Networks Outperform Kernel Methods? NeurIPS Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, Andrea Montanari
2020	J	`jnl`	When Do Neural Networks Outperform Kernel Methods? CoRR Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, Andrea Montanari
2019	A*	`conf`	Limitations of Lazy Training of Two-layers Neural Network. NeurIPS Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, Andrea Montanari
2019	J	`jnl`	Limitations of Lazy Training of Two-layers Neural Networks. CoRR Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, Andrea Montanari
2019	J	`jnl`	Linearized two-layers neural networks in high dimension. CoRR Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, Andrea Montanari
2019	A*	`conf`	Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit. COLT Song Mei, Theodor Misiakiewicz, Andrea Montanari
2019	J	`jnl`	Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit. CoRR Song Mei, Theodor Misiakiewicz, Andrea Montanari
2017	A*	`conf`	Solving SDPs for synchronization and MaxCut problems via the Grothendieck inequality. COLT Song Mei, Theodor Misiakiewicz, Andrea Montanari, Roberto Imbuzeiro Oliveira
2015	J	`jnl`	Concentration to Zero Bit-Error Probability for Regular LDPC Codes on the Binary Symmetric Channel: Proof by Loop Calculus. CoRR Marc Vuffray, Theodor Misiakiewicz
2015	—	`conf`	Concentration to zero bit-error probability for regular LDPC codes on the binary symmetric channel: Proof by loop calculus. Allerton Marc Vuffray, Theodor Misiakiewicz
2015	J	`jnl`	Efficient reconstruction of transmission probabilities in a spreading process from partial observations. CoRR Andrey Y. Lokhov, Theodor Misiakiewicz