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2002.01586
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A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum-
ℓ
1
\ell_1
ℓ
1
-Norm Interpolated Classifiers
Social Science Research Network (SSRN), 2020
5 February 2020
Tengyuan Liang
Pragya Sur
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Papers citing
"A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum-$\ell_1$-Norm Interpolated Classifiers"
50 / 51 papers shown
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Universality of Estimator for High-Dimensional Linear Models with Block Dependency
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Building Conformal Prediction Intervals with Approximate Message Passing
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Asymptotics of Learning with Deep Structured (Random) Features
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Theoretical Analysis of Leave-one-out Cross Validation for Non-differentiable Penalties under High-dimensional Settings
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Arnab Auddy
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Universality in block dependent linear models with applications to nonparametric regression
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Universality of max-margin classifiers
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Unraveling Projection Heads in Contrastive Learning: Insights from Expansion and Shrinkage
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Cong Ma
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Universality laws for Gaussian mixtures in generalized linear models
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Ludovic Stephan
Florent Krzakala
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Lenka Zdeborová
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266
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Deterministic equivalent and error universality of deep random features learning
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Hugo Cui
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311
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Tight bounds for maximum
ℓ
1
\ell_1
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Konstantin Donhauser
Fanny Yang
356
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454
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A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models
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Lijia Zhou
Frederic Koehler
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Danica J. Sutherland
Nathan Srebro
344
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Equivalence of state equations from different methods in High-dimensional Regression
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Songtao Tian
258
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Deep Linear Networks can Benignly Overfit when Shallow Ones Do
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Philip M. Long
256
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On how to avoid exacerbating spurious correlations when models are overparameterized
International Symposium on Information Theory (ISIT), 2022
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260
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High-dimensional Asymptotics of Langevin Dynamics in Spiked Matrix Models
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Tengyuan Liang
Subhabrata Sen
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184
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Random Features Model with General Convex Regularization: A Fine Grained Analysis with Precise Asymptotic Learning Curves
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David Bosch
Ashkan Panahi
Ayça Özçelikkale
Devdatt Dubhash
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328
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A Modern Theory for High-dimensional Cox Regression Models
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Huijuan Zhou
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174
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Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data
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Niladri S. Chatterji
Peter L. Bartlett
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574
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Theoretical characterization of uncertainty in high-dimensional linear classification
Lucas Clarté
Bruno Loureiro
Florent Krzakala
Lenka Zdeborová
313
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Noisy linear inverse problems under convex constraints: Exact risk asymptotics in high dimensions
Annals of Statistics (Ann. Stat.), 2022
Q. Han
249
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Optimistic Rates: A Unifying Theory for Interpolation Learning and Regularization in Linear Regression
Lijia Zhou
Frederic Koehler
Danica J. Sutherland
Nathan Srebro
308
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On Optimal Interpolation In Linear Regression
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Eduard Oravkin
Patrick Rebeschini
153
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21 Oct 2021
A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning
Yehuda Dar
Vidya Muthukumar
Richard G. Baraniuk
296
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Benign Overfitting in Multiclass Classification: All Roads Lead to Interpolation
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Ke Wang
Vidya Muthukumar
Christos Thrampoulidis
321
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21 Jun 2021
Nonasymptotic theory for two-layer neural networks: Beyond the bias-variance trade-off
Huiyuan Wang
Wei Lin
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202
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Learning Gaussian Mixtures with Generalised Linear Models: Precise Asymptotics in High-dimensions
Neural Information Processing Systems (NeurIPS), 2021
Bruno Loureiro
G. Sicuro
Cédric Gerbelot
Alessandro Pacco
Florent Krzakala
Lenka Zdeborová
274
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07 Jun 2021
Support vector machines and linear regression coincide with very high-dimensional features
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Navid Ardeshir
Clayton Sanford
Daniel J. Hsu
210
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AdaBoost and robust one-bit compressed sensing
Mathematical Statistics and Learning (MSL), 2021
Geoffrey Chinot
Felix Kuchelmeister
Matthias Löffler
Sara van de Geer
472
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05 May 2021
Universal Prediction Band via Semi-Definite Programming
Social Science Research Network (SSRN), 2021
Tengyuan Liang
259
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31 Mar 2021
Minimum complexity interpolation in random features models
Michael Celentano
Theodor Misiakiewicz
Andrea Montanari
219
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On the interplay between data structure and loss function in classification problems
Neural Information Processing Systems (NeurIPS), 2021
Stéphane dÁscoli
Marylou Gabrié
Levent Sagun
Giulio Biroli
298
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09 Mar 2021
Label-Imbalanced and Group-Sensitive Classification under Overparameterization
Neural Information Processing Systems (NeurIPS), 2021
Ganesh Ramachandra Kini
Orestis Paraskevas
Samet Oymak
Christos Thrampoulidis
548
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02 Mar 2021
Interpolating Classifiers Make Few Mistakes
Journal of machine learning research (JMLR), 2021
Tengyuan Liang
Benjamin Recht
250
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28 Jan 2021
Provable Benefits of Overparameterization in Model Compression: From Double Descent to Pruning Neural Networks
AAAI Conference on Artificial Intelligence (AAAI), 2020
Xiangyu Chang
Yingcong Li
Samet Oymak
Christos Thrampoulidis
309
58
0
16 Dec 2020
On the robustness of minimum norm interpolators and regularized empirical risk minimizers
Annals of Statistics (Ann. Stat.), 2020
Geoffrey Chinot
Matthias Löffler
Sara van de Geer
399
22
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01 Dec 2020
Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View
Neural Information Processing Systems (NeurIPS), 2020
Christos Thrampoulidis
Samet Oymak
Mahdi Soltanolkotabi
253
45
0
16 Nov 2020
σ
σ
σ
-Ridge: group regularized ridge regression via empirical Bayes noise level cross-validation
Nikolaos Ignatiadis
Panagiotis Lolas
242
5
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29 Oct 2020
Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers
Fan Yang
Hongyang R. Zhang
Sen Wu
Christopher Ré
Weijie J. Su
515
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22 Oct 2020
Precise Statistical Analysis of Classification Accuracies for Adversarial Training
Adel Javanmard
Mahdi Soltanolkotabi
AAML
428
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A Power Analysis of the Conditional Randomization Test and Knockoffs
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Lucas Janson
213
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05 Oct 2020
The Lasso with general Gaussian designs with applications to hypothesis testing
Annals of Statistics (Ann. Stat.), 2020
Michael Celentano
Andrea Montanari
Yuting Wei
563
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Finite-sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime
Journal of machine learning research (JMLR), 2020
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Philip M. Long
271
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Mehler's Formula, Branching Process, and Compositional Kernels of Deep Neural Networks
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Hai Tran-Bach
178
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Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree
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Xiaojun Lin
Jia Liu
297
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