| P1 |
Belkin et al. |
Overfitting or perfect fitting? risk bounds for classification and regression rules that interpolate. |
NeurIPS |
2018 |
| P2 |
Chatterjee & Mishchenko |
Circuit-based intrinsic methods to detect overfitting. |
ICML |
2020 |
| P3 |
Chatterji & Long |
Foolish crowds support benign overfitting. |
JMLR |
2022 |
| P4 |
Chen et al. |
Robust overfitting may be mitigated by properly learned smoothening. |
ICLR |
2021 |
| P5 |
d'Ascoli et al. |
Triple descent and the two kinds of overfitting: where & why do they appear? |
NeurIPS |
2020 |
| P6 |
Feldman et al. |
The advantages of multiple classes for reducing overfitting from test set reuse. |
ICML |
2019 |
| P7 |
Feldman et al. |
Open problem: how fast can a multiclass test set be overfit? |
COLT |
2019 |
| P8 |
Frei et al. |
Benign overfitting without linearity: neural network classifiers trained by gradient descent for noisy linear data. |
COLT |
2022 |
| P9 |
He et al. |
Sparse double descent: where network pruning aggravates overfitting. |
ICML |
2022 |
| P10 |
Huang et al. |
Sparse progressive distillation: resolving overfitting under pretrain-and-finetune paradigm. |
ACL |
2022 |
| P11 |
Ju et al. |
Overfitting can be harmless for basis pursuit, but only to a degree. |
NeurIPS |
2020 |
| P12 |
Ju et al. |
On the generalization power of overfitted two-layer neural tangent kernel models. |
ICML |
2021 |
| P13 |
Kim et al. |
Understanding catastrophic overfitting in single-step adversarial training. |
AAAI |
2021 |
| P14 |
Koehler et al. |
Uniform convergence of interpolators: Gaussian width, norm bounds and benign overfitting. |
NeurIPS |
2021 |
| P15 |
Liu et al. |
Overfitting the data: compact neural video delivery via content-aware feature modulation. |
ICCV |
2021 |
| P16 |
Mohammed & Cawley |
Over-fitting in model selection with Gaussian process regression. |
ICML |
2017 |
| P17 |
Rice et al. |
Overfitting in adversarially robust deep learning. |
ICML |
2020 |
| P18 |
Roelofs et al. |
A meta-analysis of overfitting in machine learning. |
NeurIPS |
2019 |
| P19 |
Rozendaal et al. |
Overfitting for fun and profit: instance-adaptive data compression. |
ICLR |
2021 |
| P20 |
Russo & Zou |
How much does your data exploration overfit? controlling bias via information usage. |
IEEE Trans. Inf. Theory |
2020 |
| P21 |
Sanyal et al. |
How benign is benign overfitting? |
ICLR |
2021 |
| P22 |
Shamir |
The implicit bias of benign overfitting. |
COLT |
2022 |
| P23 |
Singla et al. |
Low curvature activations reduce overfitting in adversarial training. |
ICCV |
2021 |
| P24 |
Song et al. |
Observational overfitting in reinforcement learning. |
ICLR |
2020 |
| P25 |
Steck |
Autoencoders that don't overfit towards the identity. |
NeurIPS |
2020 |
| P26 |
Sun et al. |
meProp: sparsified back propagation for accelerated deep learning with reduced overfitting. |
ICML |
2017 |
| P27 |
Telgarsky |
Stochastic linear optimization never overfits with quadratically-bounded losses on general data. |
COLT |
2022 |
| P28 |
Wang et al. |
Benign overfitting in multiclass classification: all roads lead to interpolation. |
NeurIPS |
2021 |
| P29 |
Webster et al. |
Detecting overfitting of deep generative networks via latent recovery. |
CVPR |
2019 |
| P30 |
Werpachowski et al. |
Detecting overfitting via adversarial examples. |
NeurIPS |
2019 |
| P31 |
Xu et al. |
Overfitting avoidance in tensor train factorization and completion: prior analysis and inference. |
ICDM |
2021 |
| P32 |
Zhang & Amini |
Label consistency in overfitted generalized k-means. |
NeurIPS |
2021 |
| P33 |
Zhang et al. |
Why overfitting isn't always bad: retrofitting cross-lingual word embeddings to dictionaries. |
ACL |
2020 |