Heterogeneous Risk Minimization

• URL: http://arxiv.org/abs/2105.03818v1
• Date: Sun, 9 May 2021 02:51:36 GMT
• Title: Heterogeneous Risk Minimization
• Authors: Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, Zheyan Shen
• Abstract summary: Invariant learning methods for out-of-distribution generalization have been proposed by leveraging multiple training environments to find invariant relationships. Modern datasets are assembled by merging data from multiple sources without explicit source labels. We propose Heterogeneous Risk Minimization (HRM) framework to achieve joint learning of latent heterogeneity among the data and invariant relationship.
• Score: 25.5458915855661
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Machine learning algorithms with empirical risk minimization usually suffer from poor generalization performance due to the greedy exploitation of correlations among the training data, which are not stable under distributional shifts. Recently, some invariant learning methods for out-of-distribution (OOD) generalization have been proposed by leveraging multiple training environments to find invariant relationships. However, modern datasets are frequently assembled by merging data from multiple sources without explicit source labels. The resultant unobserved heterogeneity renders many invariant learning methods inapplicable. In this paper, we propose Heterogeneous Risk Minimization (HRM) framework to achieve joint learning of latent heterogeneity among the data and invariant relationship, which leads to stable prediction despite distributional shifts. We theoretically characterize the roles of the environment labels in invariant learning and justify our newly proposed HRM framework. Extensive experimental results validate the effectiveness of our HRM framework.

Related papers

• Collaborative Heterogeneous Causal Inference Beyond Meta-analysis [68.4474531911361]
  We propose a collaborative inverse propensity score estimator for causal inference with heterogeneous data. Our method shows significant improvements over the methods based on meta-analysis when heterogeneity increases.
  arXiv  Detail & Related papers  (2024-04-24T09:04:36Z)
• Tackling Diverse Minorities in Imbalanced Classification [80.78227787608714]
  Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers. We propose generating synthetic samples iteratively by mixing data samples from both minority and majority classes. We demonstrate the effectiveness of our proposed framework through extensive experiments conducted on seven publicly available benchmark datasets.
  arXiv  Detail & Related papers  (2023-08-28T18:48:34Z)
• Kernelized Heterogeneous Risk Minimization [25.5458915855661]
  We propose a Kernelized Heterogeneous Risk Minimization (KerHRM) algorithm, which achieves both the latent exploration and invariant learning in kernel space. We theoretically justify our algorithm and empirically validate the effectiveness of our algorithm with extensive experiments.
  arXiv  Detail & Related papers  (2021-10-24T12:26:50Z)
• Distributionally Robust Learning with Stable Adversarial Training [34.74504615726101]
  Machine learning algorithms with empirical risk minimization are vulnerable under distributional shifts. We propose a novel Stable Adversarial Learning (SAL) algorithm that leverages heterogeneous data sources to construct a more practical uncertainty set.
  arXiv  Detail & Related papers  (2021-06-30T03:05:45Z)
• Meta-Learned Invariant Risk Minimization [12.6484257912092]
  Empirical Risk Minimization (ERM) based machine learning algorithms have suffered from weak generalization performance on data obtained from out-of-distribution (OOD) In this paper, we propose a novel meta-learning based approach for IRM. We show that our algorithm not only has better OOD generalization performance than IRMv1 and all IRM variants, but also addresses the weakness of IRMv1 with improved stability.
  arXiv  Detail & Related papers  (2021-03-24T02:52:48Z)
• Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification [13.456851070400024]
  Invariant Risk Minimization (IRM) is a domain generalization algorithm that employs a causal discovery inspired method to find robust predictors. We show that IRM achieves better out-of-distribution accuracy and fairness than Empirical Risk Minimization (ERM) methods.
  arXiv  Detail & Related papers  (2020-11-12T16:42:14Z)
• The Risks of Invariant Risk Minimization [52.7137956951533]
  Invariant Risk Minimization is an objective based on the idea for learning deep, invariant features of data. We present the first analysis of classification under the IRM objective--as well as these recently proposed alternatives--under a fairly natural and general model. We show that IRM can fail catastrophically unless the test data are sufficiently similar to the training distribution--this is precisely the issue that it was intended to solve.
  arXiv  Detail & Related papers  (2020-10-12T14:54:32Z)
• Accounting for Unobserved Confounding in Domain Generalization [107.0464488046289]
  This paper investigates the problem of learning robust, generalizable prediction models from a combination of datasets. Part of the challenge of learning robust models lies in the influence of unobserved confounders. We demonstrate the empirical performance of our approach on healthcare data from different modalities.
  arXiv  Detail & Related papers  (2020-07-21T08:18:06Z)
• Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization [55.278153228758434]
  Real-world datasets are heteroskedastic and imbalanced. Addressing heteroskedasticity and imbalance simultaneously is under-explored. We propose a data-dependent regularization technique for heteroskedastic datasets.
  arXiv  Detail & Related papers  (2020-06-29T01:09:50Z)
• When Relation Networks meet GANs: Relation GANs with Triplet Loss [110.7572918636599]
  Training stability is still a lingering concern of generative adversarial networks (GANs) In this paper, we explore a relation network architecture for the discriminator and design a triplet loss which performs better generalization and stability. Experiments on benchmark datasets show that the proposed relation discriminator and new loss can provide significant improvement on variable vision tasks.
  arXiv  Detail & Related papers  (2020-02-24T11:35:28Z)

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.