2 citations · 3 across the 15 of their papers we have counts for
15 papers
EvoTS-Agent: A Self-Evolving LLM Agent for Financial Time Series Change Point Detection
Lei Jiang, Ye Wei, Xinyu Xi +7
Financial time series exhibit non-stationary and heterogeneous statistical properties, making change-point detection challenging because no single unsupervised algorithm performs c…
MOSAIC: Modular Orchestration for Structured Agentic Intelligence and Composition
Yifan Bao, Xinyu Xi, Xinyu Liu +8
Automated data science is a structured model-selection problem. A solution must choose data transformations, feature representations, architecture, training procedure, evaluation p…
De-attribute to Forget for LLM Unlearning
Xinyang Lu, Jiabao Pan, Rachael Hwee Ling Sim +3
The rapid development of large language models (LLMs) has raised concerns on the use of inappropriate data for training, which has led to a growing interest in LLM unlearning. Many…
Weight-Informed Self-Explaining Clustering for Mixed-Type Tabular Data
Lehao Li, Qiang Huang, Yihao Ang +3
Clustering mixed-type tabular data is fundamental for exploratory analysis, yet remains challenging due to misaligned numerical-categorical representations, uneven and context-depe…
IDRBench: Benchmarking the Interactive Capabilities of Deep Research Agents
Yingchaojie Feng, Qiang Huang, Xiaoya Xie +4
Large Language Model (LLM)-based deep research agents perform multi-step reasoning, web exploration, and long-form report generation. In these long-horizon workflows, early deviati…
RFOD: Random Forest-based Outlier Detection for Tabular Data
Yihao Ang, Peicheng Yao, Yifan Bao +4
Outlier detection in tabular data is crucial for safeguarding data integrity in high-stakes domains such as cybersecurity, financial fraud detection, and healthcare, where anomalie…
Structured Agentic Workflows for Financial Time-Series Modeling with LLMs and Reflective Feedback
Yihao Ang, Yifan Bao, Lei Jiang +4
Time-series data is central to decision-making in financial markets, yet building high-performing, interpretable, and auditable models remains a major challenge. While Automated Ma…
CTBench: Cryptocurrency Time Series Generation Benchmark
Yihao Ang, Qiang Wang, Qiang Huang +5
Synthetic time series are essential tools for data augmentation, stress testing, and algorithmic prototyping in quantitative finance. However, in cryptocurrency markets, characteri…
Position: Text Embeddings Should Capture Implicit Semantics, Not Just Surface Meaning
Yiqun Sun, Qiang Huang, Anthony K. H. Tung +1
This position paper argues that text embedding research should move beyond surface meaning and embrace implicit semantics as a central modeling objective. Text embeddings are a fou…
Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding based on Guided Space Transformation
Yingchaojie Feng, Yiqun Sun, Yandong Sun +4
In this work, we investigate an important task named instruction-following text embedding, which generates dynamic text embeddings that adapt to user instructions, highlighting spe…
PRISM: A Framework for Producing Interpretable Political Bias Embeddings with Political-Aware Cross-Encoder
Yiqun Sun, Qiang Huang, Anthony K. H. Tung +1
Semantic Text Embedding is a fundamental NLP task that encodes textual content into vector representations, where proximity in the embedding space reflects semantic similarity. Whi…
Towards Controllable Time Series Generation
Yifan Bao, Yihao Ang, Qiang Huang +2
Time Series Generation (TSG) has emerged as a pivotal technique in synthesizing data that accurately mirrors real-world time series, becoming indispensable in numerous applications…
Diversity-Aware -Maximum Inner Product Search Revisited
Qiang Huang, Yanhao Wang, Yiqun Sun +1
The -Maximum Inner Product Search (MIPS) serves as a foundational component in recommender systems and various data mining tasks. However, while most existing MIPS approac…
From Zero to Hero: Detecting Leaked Data through Synthetic Data Injection and Model Querying
Biao Wu, Qiang Huang, Anthony K. H. Tung
Safeguarding the Intellectual Property (IP) of data has become critically important as machine learning applications continue to proliferate, and their success heavily relies on th…
TSGBench: Time Series Generation Benchmark
Yihao Ang, Qiang Huang, Yifan Bao +2
Synthetic Time Series Generation (TSG) is crucial in a range of applications, including data augmentation, anomaly detection, and privacy preservation. Although significant strides…