Fetching the paper…
Reading the bibliography…
Transfer learning is a critical part of real-world machine learning deployments and has been extensively studied in experimental works with overparameterized neural networks.
“Towards Robust Waveform-Based Acoustic Models”
Dino Oglic, Zoran Cvetkovic, Peter Sollich, Steve Renals and Bin Yu · 1992
Earlier work this paper cites.
“Understanding deep learning requires rethinking generalization”
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht and Oriol Vinyals · 2017
Earlier work this paper cites.
“Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate”
Mikhail Belkin, Daniel Hsu and Partha Mitra · 2018
Earlier work this paper cites.
“High-dimensional probability”
Roman Vershynin · 2018
Earlier work this paper cites.
“Reconciling modern machine-learning practice and the classical bias–variance trade-off”
Mikhail Belkin, Daniel Hsu, Siyuan Ma and Soumik Mandal · 2019
Earlier work this paper cites.
“Does data interpolation contradict statistical optimality?”
Mikhail Belkin, Alexander Rakhlin and Alexandre. Tsybakov · 2019
Earlier work this paper cites.
“Benchmarking neural network robustness to common corruptions and perturbations”
Dan Hendrycks and Thomas. Dietterich · 2019
Earlier work this paper cites.
“Consistency of Interpolation with Laplace Kernels is a High-Dimensional Phenomenon”
Alexander Rakhlin and Xiyu Zhai · 2019
Earlier work this paper cites.
“Do ImageNet Classifiers Generalize to ImageNet?”
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt and Vaishaal Shankar · 2019
Earlier work this paper cites.
“Benign Overfitting in Linear Regression”
Peter. Bartlett, Philip. Long, Gábor Lugosi and Alexander Tsigler · 2020
Earlier work this paper cites.
“Revisiting minimum description length complexity in overparameterized models”
Raaz Dwivedi, Chandan Singh, Bin Yu and Martin. Wainwright · 2020
Earlier work this paper cites.
“Harmless interpolation of noisy data in regression”
Vidya Muthukumar, Kailas Vodrahalli, Vignesh Subramanian and Anant Sahai · 2020
Earlier work this paper cites.
“The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization”
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt and Justin Gilmer · 2021
Earlier work this paper cites.
“Wilds: A benchmark of in-the-wild distribution shifts”
Pang Koh, Shiori Sagawa, Henrik Marklund, Sang Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard. Phillips and Ian Gao · 2021
Earlier work this paper cites.
“Near-Optimal Linear Regression under Distribution Shift”
Qi Lei, Wei Hu and Jason. Lee · 2021
Earlier work this paper cites.
“Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization”
John Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Koh, Vaishaal Shankar, Percy Liang, Yair Carmon and Ludwig Schmidt · 2021
Cited alongside, same era.
“Overparameterization Improves Robustness to Covariate Shift in High Dimensions”
Nilesh Tripuraneni, Ben Adlam and Jeffrey Pennington · 2021
Cited alongside, same era.
“The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks”
Niladri. Chatterji, Philip. Long and Peter. Bartlett · 2022
Cited alongside, same era.
“Underspecification Presents Challenges for Credibility in Modern Machine Learning”
Alexander D’Amour · 2022
Cited alongside, same era.
“Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data”
Spencer Frei, Niladri Chatterji and Peter Bartlett · 2022
Cited alongside, same era.
“Random Feature Amplification: Feature Learning and Generalization in Neural Networks”
Spencer Frei, Niladri. Chatterji and Peter. Bartlett · 2023
Later among the works it cites.
“Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization”
Spencer Frei, Gal Vardi, Peter. Bartlett and Nathan Srebro · 2023
Later among the works it cites.
“Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension”
Moritz Haas, David Holzmüller, Ulrike von Luxburg and Ingo Steinwart · 2023
Later among the works it cites.
“Generalization Error Without Independence: Denoising, Linear Regression, and Transfer Learning”
Chinmay Kausik, Kshitij Srivastava and Rahul Sonthalia · 2023
Later among the works it cites.
“From Tempered to Benign Overfitting in ReLU Neural Networks”
Guy Kornowski, Gilad Yehudai and Ohad Shamir · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
“Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data”
Spencer Frei, Gal Vardi, Peter. Bartlett, Nathan Srebro and Wei Hu · 2022
Cited alongside, same era.
“Surprises in High-Dimensional Ridgeless Least Squares Interpolation”
Trevor Hastie, Andrea Montanari, Saharon Rosset and Ryan. Tibshirani · 2022
Cited alongside, same era.
“Benign, Tempered, or Catastrophic: Toward a Refined Taxonomy of Overfitting”
Neil Mallinar, James Simon, Amirhesam Abedsoltan, Parthe Pandit, Mikhail Belkin and Preetum Nakkiran · 2022
Cited alongside, same era.
“A new similarity measure for covariate shift with applications to nonparametric regression”
Reese Pathak, Cong Ma and Martin Wainwright · 2022
Cited alongside, same era.
“Is Importance Weighting Incompatible with Interpolating Classifiers?”
Ke Wang, Niladri Chatterji, Saminul Haque and Tatsunori Hashimoto · 2022
Cited alongside, same era.
“Assaying Out-Of-Distribution Generalization in Transfer Learning”
F. Wenzel, Andrea Dittadi, Peter Gehler, Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, Chris Russell, Thomas Brox, Bernt Schiele, Bernhard Scholkopf and Francesco Locatello · 2022
Cited alongside, same era.
“Generalization in Kernel Regression Under Realistic Assumptions”
Daniel Barzilai and Ohad Shamir · 2023
Cited alongside, same era.
“Benign Overfitting in Two-layer ReLU Convolutional Neural Networks”
Yiwen Kou, Zixiang Chen, Yuanzhou Chen and Quanquan Gu · 2023
Later among the works it cites.
“Generalization Ability of Wide Residual Networks”
Jianfa Lai, Zixiong Yu, Songtao Tian and Qian Lin · 2023
Later among the works it cites.
“Accuracy on the Curve: On the Nonlinear Correlation of ML Performance Between Data Subpopulations”
Weixin Liang, Yining Mao, Yongchan Kwon, Xinyu Yang and James. Zou · 2023
Later among the works it cites.
“Optimally tackling covariate shift in RKHS-based nonparametric regression”
Cong Ma, Reese Pathak and Martin. Wainwright · 2023
Later among the works it cites.
“Statistical Learning under Heterogeneous Distribution Shift”
Max Simchowitz, Anurag Ajay, Pulkit Agrawal and Akshay Krishnamurthy · 2023
Later among the works it cites.
“Benign overfitting in ridge regression”
Alexander Tsigler and Peter. Bartlett · 2023
Later among the works it cites.
“Pseudo-Labeling for Kernel Ridge Regression under Covariate Shift”
Kaizheng Wang · 2023
Later among the works it cites.
“Monotonic Risk Relationships under Distribution Shifts for Regularized Risk Minimization”
Daniel LeJeune, Jiayu Liu and Reinhard Heckel · 2024
Closest in time.
“Benign Overfitting and Grokking in ReLU Networks for XOR Cluster Data”
Zhiwei Xu, Yutong Wang, Spencer Frei, Gal Vardi and Wei Hu · 2024
Closest in time.