8 citations · 8 across the 6 of their papers we have counts for
7 papers
Inherited, Not Invented: Tracing the Provenance of Wait in Reasoning Models
Zhizhuo Kou, Sizai Hou, Long Chen +6
Reinforcement learning (RL) is credited with teaching reasoning models to write {wait} before reconsidering a step, yet large RL accuracy gains do not require the word. This view c…
Target-Aware Calibration Data Selection for Preserving Uncertainty in Quantized Language Models
Zhen Yang, Sizai Hou, Kaiwen Zheng +4
Quantization is widely used to deploy large language models, but its effect on uncertainty behavior, such as confidence, margins, and abstention, is rarely treated as a primary obj…
Privacy-preserving Prompt Personalization in Federated Learning for Multimodal Large Language Models
Sizai Hou, Songze Li, Baturalp Buyukates
Prompt learning is a crucial technique for adapting pre-trained multimodal language models (MLLMs) to user tasks. Federated prompt personalization (FPP) is further developed to add…
DeDe: Detecting Backdoor Samples for SSL Encoders via Decoders
Sizai Hou, Songze Li, Duanyi Yao
Self-supervised learning (SSL) is pervasively exploited in training high-quality upstream encoders with a large amount of unlabeled data. However, it is found to be susceptible to…
PriRoAgg: Achieving Robust Model Aggregation with Minimum Privacy Leakage for Federated Learning
Sizai Hou, Songze Li, Tayyebeh Jahani-Nezhad +1
Federated learning (FL) has recently gained significant momentum due to its potential to leverage large-scale distributed user data while preserving user privacy. However, the typi…
URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learning
Duanyi Yao, Songze Li, Xueluan Gong +2
Launching effective malicious attacks in VFL presents unique challenges: 1) Firstly, given the distributed nature of clients' data features and models, each client rigorously guard…
Secure Federated Clustering
Songze Li, Sizai Hou, Baturalp Buyukates +1
We consider a foundational unsupervised learning task of -means data clustering, in a federated learning (FL) setting consisting of a central server and many distributed clients…