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2009.08092
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Distributional Generalization: A New Kind of Generalization
17 September 2020
Preetum Nakkiran
Yamini Bansal
OOD
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Papers citing
"Distributional Generalization: A New Kind of Generalization"
39 / 39 papers shown
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Monitoring Risks in Test-Time Adaptation
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Metod Jazbec
C. A. Naesseth
Eric T. Nalisnick
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592
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Towards Better Generalization via Distributional Input Projection Network
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Yanxin Lu
Xinwei Shen
Tong Zhang
Tong Zhang
292
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0
05 Jun 2025
AdaRank: Disagreement Based Module Rank Prediction for Low-rank Adaptation
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209
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16 Aug 2024
Predicting the Performance of Foundation Models via Agreement-on-the-Line
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Aman Mehra
Rahul Saxena
Taeyoun Kim
Christina Baek
Zico Kolter
Aditi Raghunathan
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251
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0
02 Apr 2024
A Survey on Evaluation of Out-of-Distribution Generalization
Han Yu
Tianyu Wang
Xingxuan Zhang
Jiayun Wu
Peng Cui
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383
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04 Mar 2024
Machine unlearning through fine-grained model parameters perturbation
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2024
Zhiwei Zuo
Zhuo Tang
KenLi Li
Anwitaman Datta
AAML
MU
498
5
0
09 Jan 2024
Beyond Top-Class Agreement: Using Divergences to Forecast Performance under Distribution Shift
Mona Schirmer
Dan Zhang
Eric T. Nalisnick
177
0
0
13 Dec 2023
Resource-constrained knowledge diffusion processes inspired by human peer learning
European Conference on Artificial Intelligence (ECAI), 2023
Ehsan Beikihassan
Amy K. Hoover
Ioannis Koutis
Alipanah Parviz
Niloofar Aghaieabiane
275
0
0
01 Dec 2023
More is Better in Modern Machine Learning: when Infinite Overparameterization is Optimal and Overfitting is Obligatory
James B. Simon
Dhruva Karkada
Nikhil Ghosh
Mikhail Belkin
AI4CE
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507
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0
24 Nov 2023
Unsupervised Accuracy Estimation of Deep Visual Models using Domain-Adaptive Adversarial Perturbation without Source Samples
IEEE International Conference on Computer Vision (ICCV), 2023
JoonHo Lee
J. Woo
H. Moon
Kwonho Lee
237
4
0
19 Jul 2023
On the Joint Interaction of Models, Data, and Features
International Conference on Learning Representations (ICLR), 2023
Yiding Jiang
Christina Baek
J. Zico Kolter
FedML
217
4
0
07 Jun 2023
(Almost) Provable Error Bounds Under Distribution Shift via Disagreement Discrepancy
Neural Information Processing Systems (NeurIPS), 2023
Elan Rosenfeld
Saurabh Garg
UQCV
233
12
0
01 Jun 2023
Inconsistency, Instability, and Generalization Gap of Deep Neural Network Training
Neural Information Processing Systems (NeurIPS), 2023
Rie Johnson
Tong Zhang
210
10
0
31 May 2023
From Tempered to Benign Overfitting in ReLU Neural Networks
Neural Information Processing Systems (NeurIPS), 2023
Guy Kornowski
Gilad Yehudai
Ohad Shamir
300
17
0
24 May 2023
On the Variance of Neural Network Training with respect to Test Sets and Distributions
International Conference on Learning Representations (ICLR), 2023
Keller Jordan
OOD
423
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04 Apr 2023
On the Efficacy of Generalization Error Prediction Scoring Functions
Puja Trivedi
Danai Koutra
Jayaraman J. Thiagarajan
285
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23 Mar 2023
Interpolation Learning With Minimum Description Length
N. Manoj
Nathan Srebro
136
4
0
14 Feb 2023
Conditioning Predictive Models: Risks and Strategies
Evan Hubinger
Adam Jermyn
Johannes Treutlein
Rubi Hudson
Kate Woolverton
325
8
0
02 Feb 2023
Demystifying Disagreement-on-the-Line in High Dimensions
International Conference on Machine Learning (ICML), 2023
Dong-Hwan Lee
Behrad Moniri
Xinmeng Huang
Guang Cheng
Hamed Hassani
376
12
0
31 Jan 2023
Variation-based Cause Effect Identification
Mohamed Amine ben Salem
Karim Barsim
Bin Yang
CML
165
0
0
22 Nov 2022
Neural networks trained with SGD learn distributions of increasing complexity
International Conference on Machine Learning (ICML), 2022
Maria Refinetti
Alessandro Ingrosso
Sebastian Goldt
UQCV
372
57
0
21 Nov 2022
Data Feedback Loops: Model-driven Amplification of Dataset Biases
International Conference on Machine Learning (ICML), 2022
Rohan Taori
Tatsunori B. Hashimoto
415
65
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08 Sep 2022
Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting
Neil Rohit Mallinar
James B. Simon
Amirhesam Abedsoltan
Parthe Pandit
M. Belkin
Preetum Nakkiran
389
40
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14 Jul 2022
Agreement-on-the-Line: Predicting the Performance of Neural Networks under Distribution Shift
Neural Information Processing Systems (NeurIPS), 2022
Christina Baek
Yiding Jiang
Aditi Raghunathan
Zico Kolter
481
111
0
27 Jun 2022
What You See is What You Get: Principled Deep Learning via Distributional Generalization
Neural Information Processing Systems (NeurIPS), 2022
B. Kulynych
Yao-Yuan Yang
Yaodong Yu
Jarosław Błasiok
Preetum Nakkiran
OOD
338
11
0
07 Apr 2022
Knowledge Distillation: Bad Models Can Be Good Role Models
Neural Information Processing Systems (NeurIPS), 2022
Gal Kaplun
Eran Malach
Preetum Nakkiran
Shai Shalev-Shwartz
FedML
210
16
0
28 Mar 2022
Deconstructing Distributions: A Pointwise Framework of Learning
International Conference on Learning Representations (ICLR), 2022
Gal Kaplun
Nikhil Ghosh
Saurabh Garg
Boaz Barak
Preetum Nakkiran
OOD
243
24
0
20 Feb 2022
Predicting Out-of-Distribution Error with the Projection Norm
International Conference on Machine Learning (ICML), 2022
Yaodong Yu
Zitong Yang
Alexander Wei
Yi-An Ma
Jacob Steinhardt
OODD
296
51
0
11 Feb 2022
A Note on "Assessing Generalization of SGD via Disagreement"
Andreas Kirsch
Y. Gal
FedML
UQCV
216
19
0
03 Feb 2022
Datamodels: Predicting Predictions from Training Data
Andrew Ilyas
Sung Min Park
Logan Engstrom
Guillaume Leclerc
Aleksander Madry
TDI
457
188
0
01 Feb 2022
Towards Adversarial Evaluations for Inexact Machine Unlearning
Shashwat Goel
Christian Schroeder de Witt
Amartya Sanyal
Ser-Nam Lim
Juil Sock
Ponnurangam Kumaraguru
AAML
ELM
MU
419
81
0
17 Jan 2022
Assessing Generalization of SGD via Disagreement
International Conference on Learning Representations (ICLR), 2021
Yiding Jiang
Vaishnavh Nagarajan
Christina Baek
J. Zico Kolter
456
132
0
25 Jun 2021
Revisiting Model Stitching to Compare Neural Representations
Neural Information Processing Systems (NeurIPS), 2021
Yamini Bansal
Preetum Nakkiran
Boaz Barak
FedML
442
170
0
14 Jun 2021
Early-stopped neural networks are consistent
Neural Information Processing Systems (NeurIPS), 2021
Ziwei Ji
Justin D. Li
Matus Telgarsky
252
50
0
10 Jun 2021
Explaining Neural Scaling Laws
Proceedings of the National Academy of Sciences of the United States of America (PNAS), 2021
Yasaman Bahri
Ethan Dyer
Jared Kaplan
Jaehoon Lee
Utkarsh Sharma
413
412
0
12 Feb 2021
The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers
Preetum Nakkiran
Behnam Neyshabur
Hanie Sedghi
OffRL
282
11
0
16 Oct 2020
Disparate Vulnerability to Membership Inference Attacks
Proceedings on Privacy Enhancing Technologies (PoPETs), 2019
B. Kulynych
Mohammad Yaghini
Giovanni Cherubin
Michael Veale
Carmela Troncoso
646
53
0
02 Jun 2019
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