2 citations · 4 across the 14 of their papers we have counts for
14 papers
Federated Distillation for Whole Slide Image via Gaussian-Mixture Feature Alignment and Curriculum Integration
Luru Jing, Cong Cong, Yanyuan Chen +1
Federated learning (FL) offers a promising framework for collaborative digital pathology by enabling model training across institutions. However, real-world deployments face hetero…
Probabilistic Mechanism Design in Diffusion Auctions
Xinlun Zhang, Zhechen Li, Yongzhi Cao +2
A diffusion auction refers to a selling process conducted over a social network, where each participant submits a bid and may invite other potential buyers to join the auction. Alt…
Revisiting Label Inference Attacks in Vertical Federated Learning: Why They Are Vulnerable and How to Defend
Yige Liu, Dexuan Xu, Zimai Guo +2
Vertical federated learning (VFL) allows an active party with a top model, and multiple passive parties with bottom models to collaborate. In this scenario, passive parties possess…
Is the Trigger Essential? A Feature-Based Triggerless Backdoor Attack in Vertical Federated Learning
Yige Liu, Yiwei Lou, Che Wang +2
As a distributed collaborative machine learning paradigm, vertical federated learning (VFL) allows multiple passive parties with distinct features and one active party with labels…
Differentiable Rule Induction from Raw Sequence Inputs
Kun Gao, Katsumi Inoue, Yongzhi Cao +2
Rule learning-based models are widely used in highly interpretable scenarios due to their transparent structures. Inductive logic programming (ILP), a form of machine learning, ind…
MedMKEB: A Comprehensive Knowledge Editing Benchmark for Medical Multimodal Large Language Models
Dexuan Xu, Jieyi Wang, Zhongyan Chai +4
Recent advances in multimodal large language models (MLLMs) have significantly improved medical AI, enabling it to unify the understanding of visual and textual information. Howeve…
DEFNet: Multitasks-based Deep Evidential Fusion Network for Blind Image Quality Assessment
Yiwei Lou, Yuanpeng He, Rongchao Zhang +3
Blind image quality assessment (BIQA) methods often incorporate auxiliary tasks to improve performance. However, existing approaches face limitations due to insufficient integratio…
Differentially Private Approval-Based Committee Voting
Zhechen Li, Zimai Guo, Lirong Xia +2
In this paper, we investigate tradeoffs among differential privacy (DP) and several representative axioms for approval-based committee voting, including justified representation, p…
Trading Off Voting Axioms for Privacy
Zhechen Li, Ao Liu, Lirong Xia +2
In this paper, we investigate tradeoffs among differential privacy (DP) and several important voting axioms: Pareto efficiency, SD-efficiency, PC-efficiency, Condorcet consistency,…
First-Choice Maximality Meets Ex-ante and Ex-post Fairness
Xiaoxi Guo, Sujoy Sikdar, Lirong Xia +2
For the assignment problem where multiple indivisible items are allocated to a group of agents given their ordinal preferences, we design randomized mechanisms that satisfy first-c…
A separation logic for sequences in pointer programs and its decidability
Tianyue Cao, Bowen Zhang, Zhao Jin +2
Separation logic and its variants can describe various properties on pointer programs. However, when it comes to properties on sequences, one may find it hard to formalize. To deal…
Differentially Private Condorcet Voting
Zhechen Li, Ao Liu, Lirong Xia +2
Designing private voting rules is an important and pressing problem for trustworthy democracy. In this paper, under the framework of differential privacy, we propose a novel famliy…
Learning First-Order Rules with Differentiable Logic Program Semantics
Kun Gao, Katsumi Inoue, Yongzhi Cao +1
Learning first-order logic programs (LPs) from relational facts which yields intuitive insights into the data is a challenging topic in neuro-symbolic research. We introduce a nove…
Favoring Eagerness for Remaining Items: Designing Efficient, Fair, and Strategyproof Mechanisms
Xiaoxi Guo, Sujoy Sikdar, Lirong Xia +2
In the assignment problem, the goal is to assign indivisible items to agents who have ordinal preferences, efficiently and fairly, in a strategyproof manner. In practice, first-cho…