1 citations · 1 across the 5 of their papers we have counts for
5 papers
Generalization Measures under Controlled Covariate Shift: A Regime-Aware Benchmark
Sora Nakai, Youssef Fadhloun, Kacem Mathlouthi +4
Predicting generalization from quantities available before target-test evaluation remains a central challenge in deep learning. The systematic benchmark of Jiang et al. (2020) eval…
DisTaC: Conditioning Task Vectors via Distillation for Robust Model Merging
Kotaro Yoshida, Yuji Naraki, Takafumi Horie +2
Model merging has emerged as an efficient and flexible paradigm for multi-task learning, with numerous methods being proposed in recent years. However, these state-of-the-art techn…
On Fairness of Task Arithmetic: The Role of Task Vectors
Hiroki Naganuma, Kotaro Yoshida, Laura Gomezjurado Gonzalez +3
Model editing techniques, particularly task arithmetic with task vectors, offer an efficient alternative to full fine-tuning by enabling direct parameter updates through simple ari…
Augmenting NER Datasets with LLMs: Towards Automated and Refined Annotation
Yuji Naraki, Ryosuke Yamaki, Yoshikazu Ikeda +4
In the field of Natural Language Processing (NLP), Named Entity Recognition (NER) is recognized as a critical technology, employed across a wide array of applications. Traditional…
Towards Understanding Variants of Invariant Risk Minimization through the Lens of Calibration
Kotaro Yoshida, Hiroki Naganuma
Machine learning models traditionally assume that training and test data are independently and identically distributed. However, in real-world applications, the test distribution o…