19 citations · 23 across the 14 of their papers we have counts for
14 papers
GeoCo-SAVi: Geometry-Consistent Slot Attention for Explicitly Editable Object Representations
Haoxiang Huang, Zhekai Wang, Xiang Liu +6
Object-centric video models represent scenes with slots, yet exposed geometry can vary in meaning with appearance. In Invariant Slot Attention (ISA), explicit position and scale ca…
ConfAL-WM: Confidence-Guided Active Learning for Action-Conditioned World Models
Xiang Liu, Sen Cui, Kunwei Wu +2
Action-conditioned world models have become an important foundation for embodied prediction, planning, and synthetic data generation, but their errors under new task and scene dist…
Gold Points Sniper: Self-guided Visual Reasoning in VLM for Fine-grained Action Understanding
Haodi Liu, Xinhang Yang, Kunda Yan +3
Robots operating in everyday environments must understand fine-grained human actions, intentions, and contextual cues from broad views where people occupy only small regions, a cap…
Deliberate Evolution: Agentic Reasoning for Sample-Efficient Symbolic Regression with LLMs
Xinyu Pang, Zhanke Zhou, Xuan Li +5
Symbolic regression (SR) discovers compact mathematical expressions from data, yet recent LLM-based evolutionary methods remain sample-inefficient because they rely mainly on scala…
ECG-WM: A Physiology-Informed ECG World Model for Clinical Intervention Simulation
Zhikang Chen, Yue Wang, Sen Cui +4
Electrocardiogram (ECG)-based models have achieved strong performance in diagnostic tasks, yet they remain limited in modeling how cardiac dynamics evolve under external interventi…
Scene2Demo: Self-Evolving Embodied Data Generation via Object-Action Graph
Xiang Liu, Sen Cui, Guocai Yao +4
We present Scene2Demo, an automated framework for offline embodied data generation. Given a single RGB scene image and a user query, Scene2Demo constructs an interactive simulated…
CALM: Consensus-Aware Localized Merging for Multi-Task Learning
Kunda Yan, Min Zhang, Sen Cui +4
Model merging aims to integrate the strengths of multiple fine-tuned models into a unified model while preserving task-specific capabilities. Existing methods, represented by task…
Balancing Similarity and Complementarity for Federated Learning
Kunda Yan, Sen Cui, Abudukelimu Wuerkaixi +5
In mobile and IoT systems, Federated Learning (FL) is increasingly important for effectively using data while maintaining user privacy. One key challenge in FL is managing statisti…
Bipartite Ranking Fairness through a Model Agnostic Ordering Adjustment
Sen Cui, Weishen Pan, Changshui Zhang +1
Algorithmic fairness has been a serious concern and received lots of interest in machine learning community. In this paper, we focus on the bipartite ranking scenario, where the in…
Correcting the User Feedback-Loop Bias for Recommendation Systems
Weishen Pan, Sen Cui, Hongyi Wen +3
Selection bias is prevalent in the data for training and evaluating recommendation systems with explicit feedback. For example, users tend to rate items they like. However, when ra…
Explaining Algorithmic Fairness Through Fairness-Aware Causal Path Decomposition
Weishen Pan, Sen Cui, Jiang Bian +2
Algorithmic fairness has aroused considerable interests in data mining and machine learning communities recently. So far the existing research has been mostly focusing on the devel…
Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning
Sen Cui, Weishen Pan, Jian Liang +2
Federated learning (FL) has gain growing interests for its capability of learning from distributed data sources collectively without the need of accessing the raw data samples acro…
Collaboration Equilibrium in Federated Learning
Sen Cui, Jian Liang, Weishen Pan +3
Federated learning (FL) refers to the paradigm of learning models over a collaborative research network involving multiple clients without sacrificing privacy. Recently, there have…
Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility
Sen Cui, Weishen Pan, Changshui Zhang +1
Bipartite ranking, which aims to learn a scoring function that ranks positive individuals higher than negative ones from labeled data, is widely adopted in various applications whe…