2024 Robust generalization

Robust generalization

Author: mlnz

August undefined, 2024

WebStandard vs. robust generalization. Recent work [11, 16, 5, 7] has focused on the sample complexity of learning a predictor that has high robust accuracy (robust generaliza-tion),adifferentobjective. Incontrast,westudytheﬁnitesam-ple behavior of adversarially trained predictors on the stan-dard learning objective (standard generalization ... WebApr 30, 2024 · To better understand this phenomenon, we study adversarially robust learning from the viewpoint of generalization. We show that already in a simple natural data model, the sample complexity of robust learning can be …

In Search of Robust Measures of Generalization - NIPS

WebWe then experiment with dropout, a standard generalization technique, on top of robust training methods on two image datasets where there is a signiﬁcant generalization gap. We see that dropout in particular narrows the generalization gaps of TRADES and RST, and improves test accuracy, test adversarial accuracy as well as test Lipschitzness. WebApr 30, 2024 · To better understand this phenomenon, we study adversarially robust learning from the viewpoint of generalization. We show that already in a simple natural data model, the sample complexity of robust learning can be … ot and sleep

Robustness and Generalization Request PDF - ResearchGate

WebFeb 11, 2024 · We introduce the Generalization and Specialization (GENSPEC) algorithm, a robust feature-based counterfactual LTR method that pursues per-query memorization when it is safe to do so. GENSPEC optimizes a single feature-based model for generalization: robust performance across all queries, and many tabular models for specialization: each ... WebSep 2, 2024 · The standard generalization gap is defined as the difference of the standard accuracy between training data and test data, and similarly, the adversarial robust generalization gap is defined as the difference of the robust accuracy on the PGD attack between training data and test data. The results on CIFAR-10 dataset are reported in … WebOct 8, 2024 · All in all, I think that Generalization is a broader term for Robustness, Resilience, and Domain Adaptation. From a Machine-Learning/AI engineering perspective … ot and co

RobustBench: a standardized adversarial robustness benchmark

[1005.2243] Robustness and Generalization - arXiv.org

WebMar 23, 2024 · Robust generalization aims to tackle the most challenging data distributions which are rare in the training set and contain severe noises, i.e., photon-limited corruptions. Common solutions such as distributionally robust optimization (DRO) focus on the worst-case empirical risk to ensure low training error on the uncommon noisy distributions. WebSep 28, 2024 · Despite the preliminary understandings devoted on adversarially robust generalization, little is known from the architectural perspective. Thus, this paper tries to bridge the gap by ... ot and dbt ot and hippotherapy

"WebBut big-generalization theorists should also take ... In Chapter 2, Goldberg advances the claim that broader and more robust generalizations hold for surface forms than for any form from which the surface might be claimed to be syntactically or semantically derived. She argues this convincing in the domain argument structure, " - Robust generalization

Robust generalization

Adversarial Weight Perturbation Helps Robust …

WebMar 1, 2024 · [Submitted on 1 Mar 2024] Transformed Low-Rank Parameterization Can Help Robust Generalization for Tensor Neural Networks Andong Wang, Chao Li, Mingyuan Bai, Zhong Jin, Guoxu Zhou, Qibin Zhao Achieving efficient and robust multi-channel data learning is a challenging task in data science. WebDec 6, 2024 · In this paper, we investigate the weight loss landscape from a new perspective, and identify a clear correlation between the flatness of weight loss landscape and robust generalization gap. Several well-recognized adversarial training improvements, such as early stopping, designing new objective functions, or leveraging unlabeled data, all ...

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WebMay 27, 2024 · By demonstrating an exponential separation between the network size for achieving low robust training and generalization error, our results reveal that the hardness of robust generalization... Webrobust optimization (DRO) [36]. Contributions. We highlight the following main contributions of this paper: We introduce a new modelling framework for distribution shifts via the …

WebApr 12, 2024 · Building models that solve a diverse set of tasks has become a dominant paradigm in the domains of vision and language. In natural language processing, large pre-trained models, such as PaLM, GPT-3 and Gopher, have demonstrated remarkable zero-shot learning of new language tasks.Similarly, in computer vision, models like CLIP and … WebMar 24, 2024 · To address the issue, we decouple the natural generalization and the robust generalization from joint training and formulate different training strategies for each one. Specifically, instead of minimizing a global loss on the expectation over these two generalization errors, we propose a bi-expert framework called \emph {Generalist} where …

Webrobust generalization. We complement our theoretical anal-ysis with experiments on CIFAR10, CIFAR100, SVHN, and Tiny ImageNet, and show that AVmixup signiﬁcantly im … WebMost existing online knowledge distillation(OKD) techniques typically requiresophisticated modules to produce diverse knowledge for improving students'generalization ability. In this paper, we strive to fully utilize multi-modelsettings instead of well-designed modules to achieve a distillation effect withexcellent generalization performance. Generally, model …

WebAbstract We derive generalization bounds for learning algorithms based on their robust-ness: the property that if a testing sample is “similar” to a training sample, then the …

WebDomain Generalization for Robust Model-Based Offline RL ... Our results show that models trained with REx exhibit improved domain generalization performance when compared … rock dog around the park hdtodayWebrobust one. To study this question, we analyze robust generalization in two distributional models. By focusing on speciﬁc distributions, we can establish information-theoretic … rock dog assistirWebSep 2, 2024 · To realize the framework, we propose a Regional Adversarial Training (RAT) defense method that first utilizes the attack path generated by the typical iterative attack method of projected gradient descent (PGD), and constructs an adversarial region based on the attack path. ot and carpal tunnelWebNov 15, 2011 · This provides a novel approach, different from complexity or stability arguments, to study generalization of learning algorithms. One advantage of the robustness approach, compared to previous methods, is the geometric intuition it conveys. ot and playWebIn Search of Robust Measures of Generalization Gintare Karolina Dziugaite 1⇤, Alexandre Drouin , Brady Neal,2 3, Nitarshan Rajkumar2,3, Ethan Caballero 2,3, Linbo Wang4, Ioannis … rock dog and cat eagle rockWebthe exact sample complexity requirements for generalization. We ﬁnd that even for a simple data distribution such as a mixture of two class-conditional Gaussians, the sample complexity of robust generalization is signiﬁcantly larger than that of standard generalization. Our lower bound holds for any model and learning algorithm. Hence no ... ot and it protocolsWebDomain generalization (DG) aims to learn transferable knowledge from multiple source domains and generalize it to the unseen target domain. To achieve such expectation, the intuitive solution is to seek domain-invariant representations via generative adversarial mechanism or minimization of crossdomain discrepancy. However, the widespread … rock dog and cat eagle rock ca phone