← Back to all news
arXiv Machine Learning By Girish Keshav Palshikar

Alliance Beats Isolation: Unifying Heterogeneous Allied Datasets Improves Classifier Performance

Read the original on arXiv Machine Learning →

The paper introduces a method for combining heterogeneous, allied datasets—datasets that share the same class labels but have disjoint objects and largely distinct feature spaces—into a single unified feature space. By applying matrix completion to this merged space, the authors create a unified dataset that enables knowledge transfer between the original datasets. Experiments across multiple dataset pairs and classifiers show that models trained on the unified representation consistently outperform those trained separately on each dataset.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Sep 22

Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation

The paper introduces FedPFT, a federated learning framework that tackles the feature‑classifier mismatch problem by using personalized prompts processed through a shared self‑attention transformation module. Unlike prior methods that either degrade the feature extractor or address the mismatch only after training, FedPFT aligns local features with the global classifier during training, improving aggregation and model performance. Experiments demonstrate that FedPFT surpasses state‑of‑the‑art methods by up to 5.07%, and gains up to 7.08% when combined with collaborative contrastive learning.

By Xinghao Wu, Xuefeng Liu, Jianwei Niu, Guogang Zhu, Mingjia Shi, Shaojie Tang, Jing Yuan
fine-tuningbenchmarkssafety
More like this →
arXiv Machine Learning
Aug 11

Coupled Training with Privileged Information and Unlabeled Data

arXiv:2605. 23268v2 Announce Type: replace-cross Abstract: In many prediction problems, we have extra information during training (for example, measurements that are expensive or slow to collect) that will not be available when the model is deployed.

By Jiahao Shi, Omar Hagrass, Jason M. Klusowski
More like this →
arXiv Machine Learning
Aug 31

Beyond Non-IID: Learner--Client Distribution Mismatch in Federated Learning

The paper addresses the mismatch between learner and client data distributions in federated learning, noting that traditional client selection methods often ignore this misalignment. It introduces a dynamic, influence-aware client selection framework that uses a small proxy dataset to estimate each client's utility for the learner’s objective, prioritizing informative sources while mitigating noise and heterogeneity. Experiments on CIFAR-10 with heterogeneous partitions show the proposed method outperforms static and dynamic baselines, achieving faster convergence and higher accuracy.

By Yiming Xie, Lili Su, Ningfang Mi
fine-tuning
More like this →