1 citations · 1 across the 6 of their papers we have counts for
6 papers
Probabilistic Residual Learning for Online Recommendations
Wenyuan Wang, Yusong Zhao, Zihao Xu +11
Modern recommender systems are typically based on deep learning (DL) models, where a dense encoder learns representations of users and items. As a result, these systems often suffe…
Bayesian Selective Latent Inference for Wastewater-First Influenza Monitoring
Yixuan Zhang, Yang Song, Hao Wang +2
Wastewater influenza surveillance can reveal community circulation before clinical reporting, but wastewater alone is not a fully identifiable proxy for human burden. Existing wast…
iLoRA: Bayesian Low-Rank Adaptation with Latent Interaction Graphs for Microbiome Diagnosis
Yang Song, Yixuan Zhang, Lingfa Meng +5
Parameter-efficient adaptation has made LLMs practical for domain prediction, but standard LoRA still relies on a static low-rank update and does not expose the latent interactions…
Exposing and Mitigating Calibration Biases and Demographic Unfairness in MLLM Few-Shot In-Context Learning for Medical Image Classification
Xing Shen, Justin Szeto, Mingyang Li +2
Multimodal large language models (MLLMs) have enormous potential to perform few-shot in-context learning in the context of medical image analysis. However, safe deployment of these…
On Calibration of LLM-based Guard Models for Reliable Content Moderation
Hongfu Liu, Hengguan Huang, Xiangming Gu +2
Large language models (LLMs) pose significant risks due to the potential for generating harmful content or users attempting to evade guardrails. Existing studies have developed LLM…
STRODE: Stochastic Boundary Ordinary Differential Equation
Hengguan Huang, Hongfu Liu, Hao Wang +2
Perception of time from sequentially acquired sensory inputs is rooted in everyday behaviors of individual organisms. Yet, most algorithms for time-series modeling fail to learn dy…