arXiv:2410.10922v4 Announce Type: replace Abstract: This paper addresses the critical challenge of unlearning in Vertical Federated Learning (VFL), a setting that has received far less attention than its horizontal counterpart. Specifically, we propose the first method tailored to textit{label unlearning}…
arXiv:2602.23630v1 Announce Type: new Abstract: Hyperparameter optimization (HPO) is known to be costly in deep learning, especially when leveraging automated approaches. Most of the existing automated HPO methods are accuracy-based, i.e., accuracy metrics are used to guide the trials of…
arXiv:2504.00510v2 Announce Type: replace Abstract: Neural operators have become increasingly popular in solving textit{partial differential equations} (PDEs) due to their superior capability to capture intricate mappings between function spaces over complex domains. However, the data-hungry nature of operator learning inevitably…
arXiv:2602.23633v1 Announce Type: new Abstract: Stochastic Bilevel Optimization has emerged as a fundamental framework for meta-learning and hyperparameter optimization. Despite the practical prevalence of single-loop algorithms–which update lower and upper variables concurrently–their theoretical understanding, particularly in the stochastic regime, remains…
arXiv:2506.04398v3 Announce Type: replace Abstract: The use of target networks in deep reinforcement learning is a widely popular solution to mitigate the brittleness of semi-gradient approaches and stabilize learning. However, target networks notoriously require additional memory and delay the propagation…
arXiv:2602.23636v1 Announce Type: new Abstract: Ensuring the safety of LLM-generated content is essential for real-world deployment. Most existing guardrail models formulate moderation as a fixed binary classification task, implicitly assuming a fixed definition of harmfulness. In practice, enforcement strictness –…
arXiv:2602.23638v1 Announce Type: new Abstract: Federated LoRA provides a communication-efficient mechanism for fine-tuning large language models on decentralized data. In practice, however, a discrepancy between the factor-wise averaging used to preserve low rank and the mathematically correct aggregation of local…
arXiv:2602.23662v1 Announce Type: new Abstract: Time series anomaly detection (TSAD) has been an important area of research for decades, with reconstruction-based methods, mostly based on generative models, gaining popularity and demonstrating success. Diffusion models have recently attracted attention due to…
arXiv:2510.17480v2 Announce Type: replace Abstract: Decentralized Learning (DL) enables users to collaboratively train models without sharing raw data by iteratively averaging local updates with neighbors in a network graph. This setting is increasingly popular for its scalability and its ability…
arXiv:2602.23663v1 Announce Type: new Abstract: Multi-mode tensor time series (TTS) can be found in many domains, such as search engines and environmental monitoring systems. Learning representations of a TTS benefits various applications, but it is also challenging since the complexities…
