22 papers
Probabilistic Multi-Source Data Selection via Learnable Categorical Priors: Variance Reduction and Guaranteed Derandomization
Shizhan Cai, Qixin Zhang, Siyuan Ma +3
Bilevel data selection is a fundamental task with numerous applications in machine learning and statistics. A common strategy for tackling this problem is to relax the original bil…
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…
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(…
OmniQEC: discovering practical quantum error-correcting codes by an AI scientist
Ge Yan, Shanchuan Li, Pengyue Ma +5
Quantum error correction (QEC) is indispensable for scalable fault-tolerant quantum computing. However, discovering QEC codes that remain effective is challenging, as logical perfo…
Stochastic Pauli-path simulator for large-scale quantum optimization
Kaining Zhang, Xinbiao Wang, Kunsheng Li +4
Pauli-based simulators offer a promising route to large-scale classical simulation of quantum circuits in the low-magic regime. Yet their applicability remains largely limited to f…
Seeing Time: Benchmarking Chronological Reasoning and Shortcut Biases in Vision-Language Models
Haoyu Zhou, Qing Qing, Caichong Li +6
Recent advancements in Vision-Language Models (VLMs) have significantly enhanced their ability to interpret complex visual semantics, yet their capacity for chronological reasoning…
TERGAD: Structure-Aware Text-Enhanced Representations for Graph Anomaly Detection
Wen Shi, Zhe Wang, Huafei Huang +6
Graph Anomaly Detection (GAD) aims to identify atypical graph entities, such as nodes, edges, or substructures, that deviate significantly from the majority. While existing text-ri…
Provably Learning Diffusion Models under the Manifold Hypothesis: Collapse and Refine
Wei Huang, Andi Han, Mingyuan Bai +4
Diffusion models generate high-dimensional data with remarkable quality, yet how their training efficiently learns the score function, bypassing the curse of dimensionality when da…
Discovering Physical Directions in Weight Space: Composing Neural PDE Experts
Pengkai Wang, Pengwei Liu, Yuanyi Wang +7
Recent advances in neural operators have made partial differential equation (PDE) surrogate modeling increasingly scalable and transferable through large-scale pretraining and in-c…
CMKL: Modality-Aware Continual Learning for Evolving Biomedical Knowledge Graphs
Yousef A. Radwan, Yao Li, Qing Qing +5
Biomedical knowledge graphs are increasingly large, dynamic, and multimodal, driven by rapid advances in biotechnology such as high-throughput sequencing. Machine learning models c…
Learning Multi-Relational Graph Representations for DNA Methylation-Based Biological Age Estimation
Qing Qing, Xikun Zhang, Zhongyuan Zhang +7
Aging clocks aim to estimate biological age, a measure of physiological state distinct from chronological age, from observable biomarkers, and are widely used for health assessment…
FORGE: Fine-grained Multimodal Evaluation for Manufacturing Scenarios
Xiangru Jian, Hao Xu, Wei Pang +13
The manufacturing sector is increasingly adopting Multimodal Large Language Models (MLLMs) to transition from simple perception to autonomous execution, yet current evaluations fai…
Multinoulli Extension: A Lossless Continuous Relaxation for Partition-Constrained Subset Selection
Qixin Zhang, Wei Huang, Yan Sun +3
Identifying the most representative subset for a close-to-submodular objective while satisfying the predefined partition constraint is a fundamental task with numerous applications…
Near-Oracle KV Selection via Pre-hoc Sparsity for Long-Context Inference
Yifei Gao, Lei Wang, Rong-Cheng Tu +3
A core bottleneck in large language model (LLM) inference is the cost of attending over the ever-growing key-value (KV) cache. Although near-oracle top-k KV selection can preserve…
Efficient Differentiable Causal Discovery via Reliable Super-Structure Learning
Pingchuan Ma, Qixin Zhang, Shuai Wang +1
Recently, differentiable causal discovery has emerged as a promising approach to improve the accuracy and efficiency of existing methods. However, when applied to high-dimensional…
Effective Policy Learning for Multi-Agent Online Coordination Beyond Submodular Objectives
Qixin Zhang, Yan Sun, Can Jin +5
In this paper, we present two effective policy learning algorithms for multi-agent online coordination(MA-OC) problem. The first one, \texttt{MA-SPL}, not only can achieve the opti…
MaskPro: Linear-Space Probabilistic Learning for Strict (N:M)-Sparsity on LLMs
Yan Sun, Qixin Zhang, Zhiyuan Yu +3
The rapid scaling of large language models~(LLMs) has made inference efficiency a primary bottleneck in the practical deployment. To address this, semi-structured sparsity offers a…
Near-Optimal Online Learning for Multi-Agent Submodular Coordination: Tight Approximation and Communication Efficiency
Qixin Zhang, Zongqi Wan, Yu Yang +2
Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning, robot…
Convergent Differential Privacy Analysis for General Federated Learning
Yan Sun, Qixin Zhang, Li Shen +1
The powerful cooperation of federated learning (FL) and differential privacy~(DP) provides a promising paradigm for the large-scale private clients. However, existing analyses in F…
Contextual Bandits for Unbounded Context Distributions
Puning Zhao, Rongfei Fan, Shaowei Wang +4
Nonparametric contextual bandit is an important model of sequential decision making problems. Under -Tsybakov margin condition, existing research has established a regret bound…