27 citations · 49 across the 19 of their papers we have counts for
6 papers · 1 filter
CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Data
Shifeng Xie, Vasilii Feofanov, Ambroise Odonnat +7
Time series foundation models (TSFMs) have recently gained significant attention due to their strong zero-shot capabilities and widespread real-world applications. Such models typi…
CAMA: Enhancing Mathematical Reasoning in Large Language Models with Causal Knowledge
Lei Zan, Keli Zhang, Ruichu Cai +1
Large Language Models (LLMs) have demonstrated strong performance across a wide range of tasks, yet they still struggle with complex mathematical reasoning, a challenge fundamental…
Causal-aware Large Language Models: Enhancing Decision-Making Through Learning, Adapting and Acting
Wei Chen, Jiahao Zhang, Haipeng Zhu +5
Large language models (LLMs) have shown great potential in decision-making due to the vast amount of knowledge stored within the models. However, these pre-trained models are prone…
An Identifiable Cost-Aware Causal Decision-Making Framework Using Counterfactual Reasoning
Ruichu Cai, Xi Chen, Jie Qiao +5
Decision making under abnormal conditions is a critical process that involves evaluating the current state and determining the optimal action to restore the system to a normal stat…
Post-Training Corrections for Improved Time-Series Forecasting
Hamza Cherkaoui, Malik Tiomoko, Giuseppe Paolo +4
Time-series forecasting is a critical task in various business domains, but it remains inherently challenging. Typically, large forecasting models are trained in a single, resource…
CMCTS: A Constrained Monte Carlo Tree Search Framework for Mathematical Reasoning in Large Language Model
Qingwen Lin, Boyan Xu, Guimin Hu +4
This paper introduces the Constrained Monte Carlo Tree Search (CMCTS) framework to enhance the mathematical reasoning capabilities of Large Language Models (LLM). By incorporating…