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We study a localized notion of uniform convergence known as an "optimistic rate" (Panchenko 2002; Srebro et al.
“Uniform convergence may be unable to explain generalization in deep learning”
Vaishnavh Nagarajan and J. Kolter · 1902
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“Uniform convergence may be unable to explain generalization in deep learning”
Vaishnavh Nagarajan and J. Kolter · 1902
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“Two models of double descent for weak features”
Mikhail Belkin, Daniel Hsu and Ji Xu · 1903
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“Surprises in high-dimensional ridgeless least squares interpolation”
Trevor Hastie, Andrea Montanari, Saharon Rosset and Ryan Tibshirani · 1903
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“Harmless interpolation of noisy data in regression”
Vidya Muthukumar, Kailas Vodrahalli, Vignesh Subramanian and Anant Sahai · 1903
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“Surprises in high-dimensional ridgeless least squares interpolation”
Trevor Hastie, Andrea Montanari, Saharon Rosset and Ryan Tibshirani · 1903
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“Two models of double descent for weak features”
Mikhail Belkin, Daniel Hsu and Ji Xu · 1903
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“Harmless interpolation of noisy data in regression”
Vidya Muthukumar, Kailas Vodrahalli, Vignesh Subramanian and Anant Sahai · 1903
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“Benign overfitting in linear regression”
Peter. Bartlett, Philip. Long, Gábor Lugosi and Alexander Tsigler · 1906
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“Benign overfitting in linear regression”
Peter. Bartlett, Philip. Long, Gábor Lugosi and Alexander Tsigler · 1906
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“A model of double descent for high-dimensional binary linear classification”
Zeyu Deng, Abla Kammoun and Christos Thrampoulidis · 1911
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Andrea Montanari, Feng Ruan, Youngtak Sohn and Jun Yan · 1911
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Andrea Montanari, Feng Ruan, Youngtak Sohn and Jun Yan · 1911
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“A model of double descent for high-dimensional binary linear classification”
Zeyu Deng, Abla Kammoun and Christos Thrampoulidis · 1911
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Jeffrey Negrea, Gintare Dziugaite and Daniel. Roy · 1912
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Jeffrey Negrea, Gintare Dziugaite and Daniel. Roy · 1912
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“Estimation of dependences based on empirical data”
Vladimir Vapnik · 1982
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“Estimation of dependences based on empirical data”
Vladimir Vapnik · 1982
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“Some inequalities for Gaussian processes and applications”
Yehoram Gordon · 1985
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“Some inequalities for Gaussian processes and applications”
Yehoram Gordon · 1985
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“Moments for the Inverted Wishart Distribution”
Dietrich von Rosen · 1988
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“Moments for the Inverted Wishart Distribution”
Dietrich von Rosen · 1988
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“Rademacher and Gaussian complexities: Risk bounds and structural results”
Peter. Bartlett and Shahar Mendelson · 2002
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“Overfitting Can Be Harmless for Basis Pursuit: Only to a Degree”
Peizhong Ju, Xiaojun Lin and Jia Liu · 2002
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Tengyuan Liang and Pragya Sur · 2002
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“Some Extensions of an Inequality of Vapnik and Chervonenkis”
Dmitriy Panchenko · 2002
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“Effective dimension and generalization of kernel learning”
Tong Zhang · 2002
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“Rademacher and Gaussian complexities: Risk bounds and structural results”
Peter. Bartlett and Shahar Mendelson · 2002
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“Some Extensions of an Inequality of Vapnik and Chervonenkis”
Dmitriy Panchenko · 2002
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“Effective dimension and generalization of kernel learning”
Tong Zhang · 2002
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“Overfitting Can Be Harmless for Basis Pursuit: Only to a Degree”
Peizhong Ju, Xiaojun Lin and Jia Liu · 2002
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Tengyuan Liang and Pragya Sur · 2002
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“On the performance of kernel classes”
Shahar Mendelson · 2003
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“On the performance of kernel classes”
Shahar Mendelson · 2003
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“Local rademacher complexities”
Peter. Bartlett, Olivier Bousquet and Shahar Mendelson · 2005
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“Local rademacher complexities”
Peter. Bartlett, Olivier Bousquet and Shahar Mendelson · 2005
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“Empirical minimization”
Peter. Bartlett and Shahar Mendelson · 2006
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“On Uniform Convergence and Low-Norm Interpolation Learning”
Lijia Zhou, Danica. Sutherland and Nathan Srebro · 2006
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“Empirical minimization”
Peter. Bartlett and Shahar Mendelson · 2006
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“The Gaussian min-max theorem in the presence of convexity”, 2014
Christos Thrampoulidis, Samet Oymak and Babak Hassibi · 2014
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“Probability in High Dimension”, Lecture notes, Princeton University, 2014
Ramon van Handel · 2014
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“Living on the edge: Phase transitions in convex programs with random data”
Dennis Amelunxen, Martin Lotz, Michael McCoy and Joel Tropp · 2014
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“Learning without concentration”
Shahar Mendelson · 2014
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“The Gaussian min-max theorem in the presence of convexity”, 2014
Christos Thrampoulidis, Samet Oymak and Babak Hassibi · 2014
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Lijia Zhou, Danica. Sutherland and Nathan Srebro · 2006
Cited alongside, same era.
“On sparse reconstruction from Fourier and Gaussian measurements”
Mark Rudelson and Roman Vershynin · 2008
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“On sparse reconstruction from Fourier and Gaussian measurements”
Mark Rudelson and Roman Vershynin · 2008
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“Simultaneous analysis of Lasso and Dantzig selector”
Peter Bickel, Ya’acov Ritov and Alexandre Tsybakov · 2009
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“Benign overfitting in ridge regression”, 2020
Alexander Tsigler and Peter. Bartlett · 2009
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“On the conditions used to prove oracle results for the Lasso”
Sara Van and Peter Bühlmann · 2009
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“Probability in High Dimension”, Lecture notes, Princeton University, 2014
Ramon van Handel · 2014
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“In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning”
Behnam Neyshabur, Ryota Tomioka and Nathan Srebro · 2015
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“High dimensional statistics”
Phillippe Rigollet and Jan-Christian Hütter · 2015
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“Regularized linear regression: A precise analysis of the estimation error”
Christos Thrampoulidis, Samet Oymak and Babak Hassibi · 2015
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“In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning”
Behnam Neyshabur, Ryota Tomioka and Nathan Srebro · 2015
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“High dimensional statistics”
Phillippe Rigollet and Jan-Christian Hütter · 2015
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“Regularized linear regression: A precise analysis of the estimation error”
Christos Thrampoulidis, Samet Oymak and Babak Hassibi · 2015
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“Understanding deep learning requires rethinking generalization”
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht and Oriol Vinyals · 2017
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“Understanding deep learning requires rethinking generalization”
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht and Oriol Vinyals · 2017
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Mikhail Belkin, Daniel. Hsu and Partha Mitra · 2018
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“Universality laws for randomized dimension reduction, with applications”
Samet Oymak and Joel Tropp · 2018
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“Precise error analysis of regularized M M -estimators in high dimensions”
Christos Thrampoulidis, Ehsan Abbasi and Babak Hassibi · 2018
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“High-dimensional probability: An introduction with applications in data science”
Roman Vershynin · 2018
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Mikhail Belkin, Daniel. Hsu and Partha Mitra · 2018
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“Universality laws for randomized dimension reduction, with applications”
Samet Oymak and Joel Tropp · 2018
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“Precise error analysis of regularized M M -estimators in high dimensions”
Christos Thrampoulidis, Ehsan Abbasi and Babak Hassibi · 2018
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“High-dimensional probability: An introduction with applications in data science”
Roman Vershynin · 2018
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“Reconciling modern machine learning practice and the bias-variance trade-off”
Mikhail Belkin, Daniel Hsu, Siyuan Ma and Soumik Mandal · 2019
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“High-dimensional statistics: A non-asymptotic viewpoint”
Martin Wainwright · 2019
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“Reconciling modern machine learning practice and the bias-variance trade-off”
Mikhail Belkin, Daniel Hsu, Siyuan Ma and Soumik Mandal · 2019
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“High-dimensional statistics: A non-asymptotic viewpoint”
Martin Wainwright · 2019
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“Foolish Crowds Support Benign Overfitting”, 2021
Niladri. Chatterji and Philip. Long · 2021
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“On the Power of Preconditioning in Sparse Linear Regression”, 2021
Jonathan Kelner, Frederic Koehler, Raghu Meka and Dhruv Rohatgi · 2021
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“Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting”
Frederic Koehler, Lijia Zhou, Danica. Sutherland and Nathan Srebro · 2021
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“Foolish Crowds Support Benign Overfitting”, 2021
Niladri. Chatterji and Philip. Long · 2021
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“On the Power of Preconditioning in Sparse Linear Regression”, 2021
Jonathan Kelner, Frederic Koehler, Raghu Meka and Dhruv Rohatgi · 2021
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“Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting”
Frederic Koehler, Lijia Zhou, Danica. Sutherland and Nathan Srebro · 2021
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