The Price of Implicit Bias in Adversarially Robust Generalization

• URL: http://arxiv.org/abs/2406.04981v1
• Date: Fri, 7 Jun 2024 14:44:37 GMT
• Title: The Price of Implicit Bias in Adversarially Robust Generalization
• Authors: Nikolaos Tsilivis, Natalie Frank, Nathan Srebro, Julia Kempe,
• Abstract summary: We study the implicit bias of optimization in robust empirical risk minimization (robust ERM) We show that the implicit bias of optimization in robust ERM can significantly affect the robustness of the model.
• Score: 25.944485657150146
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the implicit bias of optimization in robust empirical risk minimization (robust ERM) and its connection with robust generalization. In classification settings under adversarial perturbations with linear models, we study what type of regularization should ideally be applied for a given perturbation set to improve (robust) generalization. We then show that the implicit bias of optimization in robust ERM can significantly affect the robustness of the model and identify two ways this can happen; either through the optimization algorithm or the architecture. We verify our predictions in simulations with synthetic data and experimentally study the importance of implicit bias in robust ERM with deep neural networks.

Related papers

• An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models [32.04194224236952]
  We propose an information-theoretic objective function called Sparse Rate Reduction (SRR) We show that SRR has a positive correlation coefficient and outperforms other baseline measures, such as path-norm and sharpness-based ones. We show that generalization can be improved using SRR as regularization on benchmark image classification datasets.
  arXiv  Detail & Related papers  (2024-11-26T07:44:57Z)
• Revisiting Spurious Correlation in Domain Generalization [12.745076668687748]
  We build a structural causal model (SCM) to describe the causality within data generation process. We further conduct a thorough analysis of the mechanisms underlying spurious correlation. In this regard, we propose to control confounding bias in OOD generalization by introducing a propensity score weighted estimator.
  arXiv  Detail & Related papers  (2024-06-17T13:22:00Z)
• Bayesian Nonparametrics Meets Data-Driven Distributionally Robust Optimization [29.24821214671497]
  Training machine learning and statistical models often involve optimizing a data-driven risk criterion. We propose a novel robust criterion by combining insights from Bayesian nonparametric (i.e., Dirichlet process) theory and a recent decision-theoretic model of smooth ambiguity-averse preferences. For practical implementation, we propose and study tractable approximations of the criterion based on well-known Dirichlet process representations.
  arXiv  Detail & Related papers  (2024-01-28T21:19:15Z)
• A PAC-Bayesian Perspective on the Interpolating Information Criterion [54.548058449535155]
  We show how a PAC-Bayes bound is obtained for a general class of models, characterizing factors which influence performance in the interpolating regime. We quantify how the test error for overparameterized models achieving effectively zero training error depends on the quality of the implicit regularization imposed by e.g. the combination of model, parameter-initialization scheme.
  arXiv  Detail & Related papers  (2023-11-13T01:48:08Z)
• Causality-oriented robustness: exploiting general additive interventions [3.871660145364189]
  In this paper, we focus on causality-oriented robustness and propose Distributional Robustness via Invariant Gradients (DRIG) In a linear setting, we prove that DRIG yields predictions that are robust among a data-dependent class of distribution shifts. We extend our approach to the semi-supervised domain adaptation setting to further improve prediction performance.
  arXiv  Detail & Related papers  (2023-07-18T16:22:50Z)
• Optimizing Information-theoretical Generalization Bounds via Anisotropic Noise in SGLD [73.55632827932101]
  We optimize the information-theoretical generalization bound by manipulating the noise structure in SGLD. We prove that with constraint to guarantee low empirical risk, the optimal noise covariance is the square root of the expected gradient covariance.
  arXiv  Detail & Related papers  (2021-10-26T15:02:27Z)
• Autoregressive Score Matching [113.4502004812927]
  We propose autoregressive conditional score models (AR-CSM) where we parameterize the joint distribution in terms of the derivatives of univariable log-conditionals (scores) For AR-CSM models, this divergence between data and model distributions can be computed and optimized efficiently, requiring no expensive sampling or adversarial training. We show with extensive experimental results that it can be applied to density estimation on synthetic data, image generation, image denoising, and training latent variable models with implicit encoders.
  arXiv  Detail & Related papers  (2020-10-24T07:01:24Z)
• CASTLE: Regularization via Auxiliary Causal Graph Discovery [89.74800176981842]
  We introduce Causal Structure Learning (CASTLE) regularization and propose to regularize a neural network by jointly learning the causal relationships between variables. CASTLE efficiently reconstructs only the features in the causal DAG that have a causal neighbor, whereas reconstruction-based regularizers suboptimally reconstruct all input features.
  arXiv  Detail & Related papers  (2020-09-28T09:49:38Z)
• When Does Preconditioning Help or Hurt Generalization? [74.25170084614098]
  We show how the textitimplicit bias of first and second order methods affects the comparison of generalization properties. We discuss several approaches to manage the bias-variance tradeoff, and the potential benefit of interpolating between GD and NGD.
  arXiv  Detail & Related papers  (2020-06-18T17:57:26Z)
• Multiplicative noise and heavy tails in stochastic optimization [62.993432503309485]
  empirical optimization is central to modern machine learning, but its role in its success is still unclear. We show that it commonly arises in parameters of discrete multiplicative noise due to variance. A detailed analysis is conducted in which we describe on key factors, including recent step size, and data, all exhibit similar results on state-of-the-art neural network models.
  arXiv  Detail & Related papers  (2020-06-11T09:58:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.