The trustworthy machine learning (ML) community is increasingly recognizing the crucial
need for models capable of selectively 'unlearning'data points after training. This leads to the …

Abstract Sparsely-gated Mixture of Expert (MoE), an emerging deep model architecture, has
demonstrated a great promise to enable high-accuracy and ultra-efficient model inference …

Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended
scanning times often compromise patient comfort and image quality, especially in …

Massive data is often considered essential for deep learning applications, but it also incurs
significant computational and infrastructural costs. Therefore, dataset pruning (DP) has …

This paper presents a new approach and algorithm for solving a class of constrained Bi-
Level Optimization (BLO) problems in which the lower-level problem involves constraints …

Large Language Models (LLMs) have highlighted the necessity of effective unlearning
mechanisms to comply with data regulations and ethical AI practices. LLM unlearning aims …

Wireless communication systems to date primarily rely on the orthogonality of resources to
facilitate the design and implementation, from user access to data transmission. Emerging …

A novel multiscale consensus-based optimization (CBO) algorithm for solving bi-and tri-level
optimization problems is introduced. Existing CBO techniques are generalized by the …

Bilevel optimization has been recently applied to many machine learning tasks. However,
their applications have been restricted to the supervised learning setting, where static …

This work focuses on addressing two major challenges in the context of large-scale
nonconvex Bi-Level Optimization (BLO) problems, which are increasingly applied in …