9 accepted papers
Rehearsal-based methods are the cornerstone of modern online class-incremental learning (OCIL), yet they face a fundamental challenge: the gradient of the current task often conflicts with that of the rehearsal data from the memory buffer, leading to catastrophic forgetting. Recent works have implic
Transferable jailbreaking attacks enable red-teaming of black-box large language models by optimizing adversarial prompts on open-source surrogates. A natural approach to improve transferability is multi-model training---optimizing against multiple source models simultaneously. Yet this approach has…
Offline policy learning from logged data is a critical paradigm for enabling effective decision-making without costly online exploration. However, its application has been largely confined to single-objective problems, a stark contrast to real-world scenarios where decision-making inherently involve
Continual post-training (CPT) is a popular and effective technique for adapting foundation models like multimodal large language models to ever-evolving downstream tasks. While existing research primarily focus on methods like data replay, model expansion, or parameter regularization, the fundamenta…
Despite being a cost-effective modality for volumetric imaging, freehand three-dimensional (3D) ultrasound produces inherently sparse data due to the significant elevational gaps left by tracked 2D sweeps. This sparsity poses a unique challenge for Implicit Neural Representations (INRs). While succe
Online Continual Learning (OCL) requires models to learn sequentially from data streams with limited memory. Rehearsal-based methods, particularly Experience Replay (ER), are commonly used in OCL scenarios. This paper revisits ER through the lens of $\epsilon$-constraint optimization, revealing that…
Continual learning aims to learn multiple tasks sequentially. A key challenge in continual learning is balancing between two objectives: retaining knowledge from old tasks (stability) and adapting to new tasks (plasticity). Experience replay methods, which store and replay past data alongside new da…
Traditional long-tailed learning methods often perform poorly when dealing with inconsistencies between training and test data distributions, and they cannot flexibly adapt to different user preferences for trade-offs between head and tail classes. To address this issue, we propose a novel long-tail…
Real-world data generally follows a long-tailed distribution, which makes traditional high-performance training strategies unable to show their usual effects. Various insights have been proposed to alleviate this challenging distribution. However, some observations indicate that models trained on lo…