1 citations · 3 across the 9 of their papers we have counts for
9 papers
Deep Self-Evolving Reasoning
Zihan Liu, Shun Zheng, Xumeng Wen +3
Long-form chain-of-thought reasoning has become a cornerstone of advanced reasoning in large language models. While recent verification-refinement frameworks have enabled proprieta…
Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs
Xumeng Wen, Zihan Liu, Shun Zheng +9
Recent advancements in long chain-of-thought (CoT) reasoning, particularly through the Group Relative Policy Optimization algorithm used by DeepSeek-R1, have led to significant int…
Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains
Jiawen Zhang, Zhenwei Zhang, Shun Zheng +3
Time-Series Foundation Models (TSFMs) are rapidly transitioning from research prototypes to core components of critical decision-making systems, driven by their impressive zero-sho…
Scalable In-Context Learning on Tabular Data via Retrieval-Augmented Large Language Models
Xumeng Wen, Shun Zheng, Zhen Xu +2
Recent studies have shown that large language models (LLMs), when customized with post-training on tabular data, can acquire general tabular in-context learning (TabICL) capabiliti…
ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer
Jiawen Zhang, Shun Zheng, Xumeng Wen +3
Numerous industrial sectors necessitate models capable of providing robust forecasts across various horizons. Despite the recent strides in crafting specific architectures for time…
Large Language Model as a Universal Clinical Multi-task Decoder
Yujiang Wu, Hongjian Song, Jiawen Zhang +3
The development of effective machine learning methodologies for enhancing the efficiency and accuracy of clinical systems is crucial. Despite significant research efforts, managing…
IN-Flow: Instance Normalization Flow for Non-stationary Time Series Forecasting
Wei Fan, Shun Zheng, Pengyang Wang +5
Due to the non-stationarity of time series, the distribution shift problem largely hinders the performance of time series forecasting. Existing solutions either rely on using certa…
DEWP: Deep Expansion Learning for Wind Power Forecasting
Wei Fan, Yanjie Fu, Shun Zheng +3
Wind is one kind of high-efficient, environmentally-friendly and cost-effective energy source. Wind power, as one of the largest renewable energy in the world, has been playing a m…
ProbTS: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizons
Jiawen Zhang, Xumeng Wen, Zhenwei Zhang +3
Delivering precise point and distributional forecasts across a spectrum of prediction horizons represents a significant and enduring challenge in the application of time-series for…