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A New Look at an Old Problem: A Universal Learning Approach to Linear
  Regression

A New Look at an Old Problem: A Universal Learning Approach to Linear Regression

International Symposium on Information Theory (ISIT), 2019
12 May 2019
Koby Bibas
Yaniv Fogel
M. Feder
ArXiv (abs)PDFHTML

Papers citing "A New Look at an Old Problem: A Universal Learning Approach to Linear Regression"

23 / 23 papers shown
High Dimensional Binary Classification under Label Shift: Phase
  Transition and Regularization
High Dimensional Binary Classification under Label Shift: Phase Transition and RegularizationSampling Theory, Signal Processing, and Data Analysis (SampTA), 2022
Jiahui Cheng
Minshuo Chen
Hao Liu
Tuo Zhao
Wenjing Liao
406
1
0
01 Dec 2022
Beyond Ridge Regression for Distribution-Free Data
Beyond Ridge Regression for Distribution-Free Data
Koby Bibas
M. Feder
237
0
0
17 Jun 2022
Generalization for multiclass classification with overparameterized
  linear models
Generalization for multiclass classification with overparameterized linear modelsNeural Information Processing Systems (NeurIPS), 2022
Vignesh Subramanian
Rahul Arya
A. Sahai
AI4CE
259
12
0
03 Jun 2022
Bias-variance decomposition of overparameterized regression with random
  linear features
Bias-variance decomposition of overparameterized regression with random linear featuresPhysical Review E (Phys. Rev. E), 2022
J. Rocks
Pankaj Mehta
197
12
0
10 Mar 2022
Fine-Tuning can Distort Pretrained Features and Underperform
  Out-of-Distribution
Fine-Tuning can Distort Pretrained Features and Underperform Out-of-DistributionInternational Conference on Learning Representations (ICLR), 2022
Ananya Kumar
Aditi Raghunathan
Robbie Jones
Tengyu Ma
Abigail Z. Jacobs
OODD
502
895
0
21 Feb 2022
Single Layer Predictive Normalized Maximum Likelihood for
  Out-of-Distribution Detection
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection
Koby Bibas
M. Feder
Tal Hassner
OODD
208
28
0
18 Oct 2021
Utilizing Adversarial Targeted Attacks to Boost Adversarial Robustness
Utilizing Adversarial Targeted Attacks to Boost Adversarial Robustness
Uriya Pesso
Koby Bibas
M. Feder
AAML
186
2
0
04 Sep 2021
Mitigating deep double descent by concatenating inputs
Mitigating deep double descent by concatenating inputs
John Chen
Qihan Wang
Anastasios Kyrillidis
BDL
234
3
0
02 Jul 2021
Double Descent Optimization Pattern and Aliasing: Caveats of Noisy
  Labels
Double Descent Optimization Pattern and Aliasing: Caveats of Noisy Labels
Florian Dubost
Erin Hong
Max Pike
Siddharth Sharma
Siyi Tang
Nandita Bhaskhar
Christopher Lee-Messer
D. Rubin
NoLa
317
2
0
03 Jun 2021
The Geometry of Over-parameterized Regression and Adversarial
  Perturbations
The Geometry of Over-parameterized Regression and Adversarial Perturbations
J. Rocks
Pankaj Mehta
AAML
220
10
0
25 Mar 2021
Distribution Free Uncertainty for the Minimum Norm Solution of
  Over-parameterized Linear Regression
Distribution Free Uncertainty for the Minimum Norm Solution of Over-parameterized Linear Regression
Koby Bibas
M. Feder
229
5
0
14 Feb 2021
Efficient Data-Dependent Learnability
Efficient Data-Dependent Learnability
Yaniv Fogel
T. Shapira
M. Feder
105
0
0
20 Nov 2020
Amortized Conditional Normalized Maximum Likelihood: Reliable Out of
  Distribution Uncertainty Estimation
Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation
Aurick Zhou
Sergey Levine
BDLOODUQCV
169
12
0
05 Nov 2020
Memorizing without overfitting: Bias, variance, and interpolation in
  over-parameterized models
Memorizing without overfitting: Bias, variance, and interpolation in over-parameterized modelsPhysical Review Research (PRResearch), 2020
J. Rocks
Pankaj Mehta
556
59
0
26 Oct 2020
Increasing Depth Leads to U-Shaped Test Risk in Over-parameterized
  Convolutional Networks
Increasing Depth Leads to U-Shaped Test Risk in Over-parameterized Convolutional Networks
Eshaan Nichani
Adityanarayanan Radhakrishnan
Caroline Uhler
349
9
0
19 Oct 2020
Learning, compression, and leakage: Minimising classification error via
  meta-universal compression principles
Learning, compression, and leakage: Minimising classification error via meta-universal compression principles
F. Rosas
P. Mediano
Michael C. Gastpar
261
12
0
14 Oct 2020
Benign overfitting in ridge regression
Benign overfitting in ridge regression
Alexander Tsigler
Peter L. Bartlett
454
205
0
29 Sep 2020
Optimal Regularization Can Mitigate Double Descent
Optimal Regularization Can Mitigate Double DescentInternational Conference on Learning Representations (ICLR), 2020
Preetum Nakkiran
Prayaag Venkat
Sham Kakade
Tengyu Ma
445
148
0
04 Mar 2020
More Data Can Hurt for Linear Regression: Sample-wise Double Descent
More Data Can Hurt for Linear Regression: Sample-wise Double Descent
Preetum Nakkiran
251
72
0
16 Dec 2019
Deep Double Descent: Where Bigger Models and More Data Hurt
Deep Double Descent: Where Bigger Models and More Data HurtInternational Conference on Learning Representations (ICLR), 2019
Preetum Nakkiran
Gal Kaplun
Yamini Bansal
Tristan Yang
Boaz Barak
Ilya Sutskever
583
1,096
0
04 Dec 2019
Deep pNML: Predictive Normalized Maximum Likelihood for Deep Neural
  Networks
Deep pNML: Predictive Normalized Maximum Likelihood for Deep Neural Networks
Koby Bibas
Yaniv Fogel
M. Feder
BDL
190
21
0
28 Apr 2019
Universal Supervised Learning for Individual Data
Universal Supervised Learning for Individual Data
Yaniv Fogel
M. Feder
FedMLSSL
123
11
0
22 Dec 2018
Optimal ridge penalty for real-world high-dimensional data can be zero
  or negative due to the implicit ridge regularization
Optimal ridge penalty for real-world high-dimensional data can be zero or negative due to the implicit ridge regularization
D. Kobak
Jonathan Lomond
Benoit Sanchez
418
97
0
28 May 2018
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