1 citations · 1 across the 13 of their papers we have counts for
14 papers
When Weak Submodularity Meets Matroids: How Does Standard Greedy Behave?
Qixin Zhang, Shi Fu, Shengminjie Chen +2
Weakly submodular maximization finds numerous applications in machine learning, data mining and statistics. In particular, under a cardinality constraint, the standard greedy algor…
Poisson Exchange Beyond Submodularity: Effective Approximation Algorithms for Offline and Online Subset Selection over Matroids
Shi Fu, Youming Qiao, Dacheng Tao +2
Over the past decade, a growing body of research has shown that -weak submodularity broadly arises in numerous subset selection tasks, including feature selection, neural networ…
A Sharp Barrier for Consistent Submodular Maximization: Any Improvement over Entails Exponential Queries or Linear Recourse
Shi Fu, Qixin Zhang, Dacheng Tao
Consistent submodular maximization studies the tradeoff between solution quality and stability when elements arrive over time. For a monotone submodular objective, which models dim…
Synchronization Strings over the Optimal Alphabet
Huibo Xu, Shi Fu, Youming Qiao +1
Synchronization strings provide deterministic position labels for recovering coordinates after insertions and deletions. Haeupler and Shahrasbi introduced these objects, and subseq…
Feature Priming in Online Linear Regression: Sparse-Regret Lower Bounds and Tight Coordinatewise Rates
Huibo Xu, Shi Fu, Qixin Zhang +1
In high-dimensional online prediction, sparse comparators motivate regret bounds that depend on sparsity rather than ambient dimension. Feature priming seeks such adaptation by rew…
Restricted Eigenvalues Beyond Gaussian Width: Threshold Occupancy under Heavy Tails
Shi Fu, Huibo Xu, Qixin Zhang +1
Restricted eigenvalue (RE) bounds govern stable recovery by norm-regularized estimators. For isotropic sub-Gaussian measurements, the benchmark sample size is , where $w(…
ACE-Brain-0.5: A Unified Embodied Foundational Model for Physical Agentic AI
Brain Team, Ziyang Gong, Haoming Gu +28
Embodied AI is moving from isolated perception or action modules toward physical agents that understand, plan under goals, act through robot bodies, monitor progress, and improve f…
WMAttack: Automated Attack Search for Adversarial Evaluation of World-Model Agents
Zhixiang Guo, Siyuan Liang, Shi Fu +4
Despite the growing use of world models as decision-making agents, their adversarial robustness remains underexplored due to the lack of dedicated automated evaluation methods. A k…
ACE-Brain-0: Spatial Intelligence as a Shared Scaffold for Universal Embodiments
Ziyang Gong, Zehang Luo, Anke Tang +21
Universal embodied intelligence demands robust generalization across heterogeneous embodiments, such as autonomous driving, robotics, and unmanned aerial vehicles (UAVs). However,…
Why Self-Rewarding Works: Theoretical Guarantees for Iterative Alignment of Language Models
Shi Fu, Yingjie Wang, Shengchao Hu +2
Self-Rewarding Language Models (SRLMs) achieve notable success in iteratively improving alignment without external feedback. Yet, despite their striking empirical progress, the cor…
A Theoretical Perspective: How to Prevent Model Collapse in Self-consuming Training Loops
Shi Fu, Yingjie Wang, Yuzhu Chen +2
High-quality data is essential for training large generative models, yet the vast reservoir of real data available online has become nearly depleted. Consequently, models increasin…
HRP: High-Rank Preheating for Superior LoRA Initialization
Yuzhu Chen, Yingjie Wang, Shi Fu +4
This paper studies the crucial impact of initialization in Low-Rank Adaptation (LoRA). Through theoretical analysis, we demonstrate that the fine-tuned result of LoRA is highly sen…
A Theoretical Survey on Foundation Models
Shi Fu, Yuzhu Chen, Yingjie Wang +1
Understanding the inner mechanisms of black-box foundation models (FMs) is essential yet challenging in artificial intelligence and its applications. Over the last decade, the long…
Towards Theoretical Understandings of Self-Consuming Generative Models
Shi Fu, Sen Zhang, Yingjie Wang +2
This paper tackles the emerging challenge of training generative models within a self-consuming loop, wherein successive generations of models are recursively trained on mixtures o…