10 accepted papers
Generative models, such as diffusion and autoregressive approaches, have demonstrated impressive capabilities in editing natural images. However, applying these tools to scientific charts rests on a flawed assumption: a chart is not merely an arrangement of pixels but a visual representation of stru…
Forward-mode automatic differentiation (FmAD) and zero-order (ZO) optimization are increasingly proposed as memory-efficient, backpropagation-free alternatives for large language model (LLM) fine-tuning, yet their benefits are typically evaluated only against standard backpropagation (BP), omitting …
LLM-based multi-agent embodied planning remains impractical due to prohibitively high execution latency. We identify failed actions as the dominant bottleneck, stemming from two core challenges: inaccurate state tracking under partial observability and inefficient coordination that produces redundan…
Finetuning large language models (LLMs) in federated learning (FL) settings has become increasingly important as it allows resource-constrained devices to finetune a model using private data. However, finetuning LLMs using backpropagation requires excessive memory (especially from intermediate activ…
In Federated Learning (FL), adaptive optimization is an effective approach to addressing the statistical heterogeneity issue but cannot adapt quickly to concept drifts. In this work, we propose a novel adaptive optimizer called Flash that simultaneously addresses both statistical heterogeneity and t…
Federated learning (FL) suffers from data heterogeneity, where the diverse data distributions across clients make it challenging to train a single global model effectively. Existing personalization approaches aim to address the data heterogeneity issue by creating a personalized model for each clien…
recommendation, and marketing services. Extensive efforts have been made to conquer the cross-modal retrieval problem in the general domain. When it comes to E-commerce, a common practice is to adopt the pretrained model and finetune on E-commerce data. Despite its simplicity, the performance is sub…
The estimation of a narrowband time-varying channel under finite block length and transmission bandwidth is investigated. A novel method is proposed for estimation in the delay-Doppler domain by exploiting structural constraints on low-rank matrix recovery. The proposed algorithm uses Gauss-Seidel i…
The problem of active target detection using low rank methods is explored. In prior work, a strategy was proposed based on matrix completion for randomly sampling a field combined with binary search to localize a target. Herein, two innovations are explored: the consideration of tensor-completion in…
A problem of broad interest is the detection and localization of a target or object from its generated field. In this paper, a detection and localization strategy which exploits the structure of target fields is designed and analyzed. In taking advantage of this structure, one is able to reduce samp…