116 citations · 266 across the 24 of their papers we have counts for
15 papers · 1 filter
Measuring Effective Data Resolution in Guided Diffusion Posteriors
Ridham Patel, Defu Cao, Jiacheng Pang +1
Guided diffusion samplers are increasingly used to reconstruct physical fields from sparse observations, but standard diagnostics do not say how much of the reconstruction was actu…
Trustworthy Protein-Ligand Binding Affinity Prediction via Reliability-Aware Multi-Engine Fusion
Yongchan Hong, Defu Cao, Wenjin Liu +6
Accurate protein-ligand binding affinity prediction is central to computational drug discovery, yet modern docking engines frequently disagree without indicating which prediction t…
When LLM Meets Time Series: Can LLMs Perform Multi-Step Time Series Reasoning and Inference
Wen Ye, Jinbo Liu, Defu Cao +2
The rapid advancement of Large Language Models (LLMs) has sparked growing interest in their application to time series analysis tasks. However, their ability to perform complex rea…
Foundation Models for Demand Forecasting via Dual-Strategy Ensembling
Wei Yang, Defu Cao, Yan Liu
Accurate demand forecasting is critical for supply chain optimization, yet remains difficult in practice due to hierarchical complexity, domain shifts, and evolving external factor…
Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based Inference
Sam Griesemer, Defu Cao, Zijun Cui +2
Computer simulations have long presented the exciting possibility of scientific insight into complex real-world processes. Despite the power of modern computing, however, it remain…
TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis
Wen Ye, Wei Yang, Defu Cao +4
Time series analysis is crucial in real-world applications, yet traditional methods focus on isolated tasks only, and recent studies on time series reasoning remain limited to eith…
TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model
Defu Cao, Wen Ye, Yizhou Zhang +1
Foundation models, particularly Large Language Models (LLMs), have revolutionized text and video processing, yet time series data presents distinct challenges for such approaches d…
An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series
Qiang Huang, Chuizheng Meng, Defu Cao +3
Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the miti…
TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting
Defu Cao, Furong Jia, Sercan O Arik +4
The past decade has witnessed significant advances in time series modeling with deep learning. While achieving state-of-the-art results, the best-performing architectures vary high…
Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders
Defu Cao, James Enouen, Yujing Wang +4
Causal analysis for time series data, in particular estimating individualized treatment effect (ITE), is a key task in many real-world applications, such as finance, retail, health…
Estimating Treatment Effects in Continuous Time with Hidden Confounders
Defu Cao, James Enouen, Yan Liu
Estimating treatment effects plays a crucial role in causal inference, having many real-world applications like policy analysis and decision making. Nevertheless, estimating treatm…
Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Media
Yizhou Zhang, Defu Cao, Yan Liu
Recent years have witnessed the rise of misinformation campaigns that spread specific narratives on social media to manipulate public opinions on different areas, such as politics…
When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning
Chuizheng Meng, Sungyong Seo, Defu Cao +2
Physics-informed machine learning (PIML), referring to the combination of prior knowledge of physics, which is the high level abstraction of natural phenomenons and human behaviour…
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
Defu Cao, Yujing Wang, Juanyong Duan +8
Multivariate time-series forecasting plays a crucial role in many real-world applications. It is a challenging problem as one needs to consider both intra-series temporal correlati…
Multivariate Time-series Anomaly Detection via Graph Attention Network
Hang Zhao, Yujing Wang, Juanyong Duan +7
Anomaly detection on multivariate time-series is of great importance in both data mining research and industrial applications. Recent approaches have achieved significant progress…