Search Results for author:
Found 19 papers, 7 papers with code
Meta Adaptive Task Sampling for Few-Domain Generalization
Moreover, we propose a Meta Adaptive Task Sampling (MATS) procedure to differentiate base tasks according to their semantic and domain-shift similarity to the novel task.
Stable Learning via Sparse Variable Independence
The problem of covariate-shift generalization has attracted intensive research attention.
NICO++: Towards Better Benchmarking for Domain Generalization
Most current evaluation methods for domain generalization (DG) adopt the leave-one-out strategy as a compromise on the limited number of domains.
Regulatory Instruments for Fair Personalized Pricing
Personalized pricing is a business strategy to charge different prices to individual consumers based on their characteristics and behaviors.
Integrated Latent Heterogeneity and Invariance Learning in Kernel Space
The ability to generalize under distributional shifts is essential to reliable machine learning, while models optimized with empirical risk minimization usually fail on non-$i. i. d$ testing data.
A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization
Afterward, we prove that under ideal conditions, independence-driven importance weighting algorithms could identify the variables in this set.
Kernelized Heterogeneous Risk Minimization
The ability to generalize under distributional shifts is essential to reliable machine learning, while models optimized with empirical risk minimization usually fail on non-$i. i. d$ testing data.
Towards Out-Of-Distribution Generalization: A Survey
This paper represents the first comprehensive, systematic review of OOD generalization, encompassing a spectrum of aspects from problem definition, methodological development, and evaluation procedures, to the implications and future directions of the field.
Towards Unsupervised Domain Generalization
Domain generalization (DG) aims to help models trained on a set of source domains generalize better on unseen target domains.
Distributionally Robust Learning with Stable Adversarial Training
In this paper, we propose a novel Stable Adversarial Learning (SAL) algorithm that leverages heterogeneous data sources to construct a more practical uncertainty set and conduct differentiated robustness optimization, where covariates are differentiated according to the stability of their correlations with the target.
Heterogeneous Risk Minimization
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.
Deep Stable Learning for Out-Of-Distribution Generalization
Approaches based on deep neural networks have achieved striking performance when testing data and training data share similar distribution, but can significantly fail otherwise.
Ranked #28 on
Domain Generalization
on VLCS
Sample Balancing for Improving Generalization under Distribution Shifts
We propose to address this problem by removing the dependencies between features via reweighting training samples, which results in a more balanced distribution and helps deep models get rid of spurious correlations and, in turn, concentrate more on the true connection between features and labels.
Counterfactual Prediction for Bundle Treatment
Estimating counterfactual outcome of different treatments from observational data is an important problem to assist decision making in a variety of fields.
Algorithmic Decision Making with Conditional Fairness
In practice, there frequently exist a certain set of variables we term as fair variables, which are pre-decision covariates such as users' choices.
Stable Adversarial Learning under Distributional Shifts
Machine learning algorithms with empirical risk minimization are vulnerable under distributional shifts due to the greedy adoption of all the correlations found in training data.
Stable Learning via Sample Reweighting
We consider the problem of learning linear prediction models with model misspecification bias.
Towards Non-I.I.D. Image Classification: A Dataset and Baselines
The experimental results demonstrate that NICO can well support the training of ConvNet model from scratch, and a batch balancing module can help ConvNets to perform better in Non-I. I. D.
Causally Regularized Learning with Agnostic Data Selection Bias
However, this ideal assumption is often violated in real applications, where selection bias may arise between training and testing process.
