13 accepted papers
Temporal embeddings play a crucial role in dynamic graph neural networks (DGNNs) by capturing the temporal dynamics of interactions. However, existing Random Fourier Feature (RFF)-based methods in DGNNs directly sample Fourier frequencies from a fixed, data-independent distribution $p(\omega)$, negl
To schedule LLM inference, the \textit{shortest job first} (SJF) principle is favorable by prioritizing requests with short output lengths to avoid head-of-line (HOL) blocking. Existing methods usually predict a single output length for each request to facilitate scheduling. We argue that such a \te…
Text-attributed graph (TAG) provides a text description for each graph node, and few- and zero-shot node classification on TAGs have many applications in fields such as academia and social networks. Existing work utilizes various graph-based augmentation techniques to train the node and text embeddi
Smart contract is a kind of self-executing code based on blockchain technology with a wide range of application scenarios, but the traditional generation method relies on manual coding and expert auditing, which has a high threshold and low efficiency. Although Large Language Models (LLMs) show grea
Vertical federated learning (VFL) trains model when the features of data samples are scattered over multiple clients. To improve efficiency, a promising approach is to find a coreset of the data samples and use it as a smaller training set. However, existing methods produce a large coreset when ther…
Split learning is a prominent framework for vertical federated learning, where multiple clients collaborate with a central server for model training by exchanging intermediate embeddings. Recently, it is shown that an adversarial server can exploit the intermediate embeddings to train surrogate mode
Among the many variants of graph neural network (GNN) architectures capable of modeling data with cross-instance relations, an important subclass involves layers designed such that the forward pass iteratively reduces a graph-regularized energy function of interest. In this way, node embeddings prod…
Text-to-SQL transforms the user queries from natural language to executable SQL programs, enabling non-experts to interact with complex databases. Existing prompt-based methods craft meticulous text guidelines and examples to facilitate SQL generation, but their accuracy is hindered by the large sem
Face restoration (FR) recovers high resolution (HR) faces from low resolution (LR) faces and is challenging due to its ill-posed nature. With years of development, existing methods can produce quality HR faces with realistic details. However, we observe that key facial attributes (e.g., age and gend…
Facial expression recognition (FER) is challenging due to the class imbalance caused by data collection. Existing studies tackle the data bias problem using only labeled facial expression dataset. Orthogonal to existing FER methods, we propose to utilize large unlabeled face recognition (FR) dataset…
Neyshabur and Srebro proposed SIMPLE-LSH, which is the state-of-the-art hashing based algorithm for maximum inner product search (MIPS). We found that the performance of SIMPLE-LSH, in both theory and practice, suffers from long tails in the 2-norm distribution of real datasets. We propose NORM-RANG…
Several control schemes have been proposed for optical tweezers, but most existing methods assume that the optical trapping is maintained throughout the manipulation, and Brownian motion is ignored for the simplification of stability analysis. However, the optical trapping is not effective when a bi
Various control approaches have been developed for micro/nanomanipulations using optical tweezers. Most existing methods assume that the micro/nanoparticles stay trapped during manipulations, and stochastic perturbations (Brownian motion) are usually ignored for the simplification of model dynamics.…