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Found 11 papers, 2 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.
A Framework for Multi-stage Bonus Allocation in meal delivery Platform
The semi-black-box acceptance probability model is employed to forecast the relationship between the bonus allocated to order and its acceptance probability, the Lagrangian dual-based dynamic programming algorithm aims to calculate the empirical Lagrangian multiplier for each allocation stage offline based on the historical data set, and the online allocation algorithm uses the results attained in the offline part to calculate a proper delivery bonus for each order.
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.
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.
Adversarial Eigen Attack on Black-Box Models
In this paper, we propose a novel setting of transferable black-box attack: attackers may use external information from a pre-trained model with available network parameters, however, different from previous studies, no additional training data is permitted to further change or tune the pre-trained model.
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.
Learning to Select Base Classes for Few-shot Classification
Few-shot learning has attracted intensive research attention in recent years.
Learning to Learn Image Classifiers with Visual Analogy
We then propose an out-of-sample embedding method to learn the embedding of a new class represented by a few samples through its visual analogy with base classes and derive the classification parameters for the new class.
