2 citations · 4 across the 8 of their papers we have counts for
8 papers
Beyond Success and Failure: Length-Aware Contrastive Learning for GUI Agents
Chengyang Gu, Le Zhang, Jingbo Zhou +6
Graphical User Interface (GUI) agents powered by Multimodal Large Language Models (MLLMs) have shown strong potential for automating tasks across diverse digital environments, wher…
TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning
Siyang Li, Yize Chen, Zijie Zhu +4
Time Series Foundation Models (TSFMs) have demonstrated strong generalization capability and data efficiency in time series forecasting through large-scale pretraining. However, ad…
STO-RL: Offline RL under Sparse Rewards via LLM-Guided Subgoal Temporal Order
Chengyang Gu, Yuxin Pan, Hui Xiong +1
Offline reinforcement learning (RL) enables policy learning from pre-collected datasets, avoiding costly and risky online interactions, but it often struggles with long-horizon tas…
Towards Generalizable PDE Dynamics Forecasting via Physics-Guided Invariant Learning
Siyang Li, Yize Chen, Yan Guo +2
Advanced deep learning-based approaches have been actively applied to forecast the spatiotemporal physical dynamics governed by partial differential equations (PDEs), which acts as…
SEAL: SEmantic-Augmented Imitation Learning via Language Model
Chengyang Gu, Yuxin Pan, Haotian Bai +2
Hierarchical Imitation Learning (HIL) is a promising approach for tackling long-horizon decision-making tasks. While it is a challenging task due to the lack of detailed supervisor…
Channel-aware Contrastive Conditional Diffusion for Multivariate Probabilistic Time Series Forecasting
Siyang Li, Yize Chen, Hui Xiong
Forecasting faithful trajectories of multivariate time series from practical scopes is essential for reasonable decision-making. Recent methods majorly tailor generative conditiona…
DiffPLF: A Conditional Diffusion Model for Probabilistic Forecasting of EV Charging Load
Siyang Li, Hui Xiong, Yize Chen
Due to the vast electric vehicle (EV) penetration to distribution grid, charging load forecasting is essential to promote charging station operation and demand-side management.Howe…
Large Foundation Models for Power Systems
Chenghao Huang, Siyang Li, Ruohong Liu +2
Foundation models, such as Large Language Models (LLMs), can respond to a wide range of format-free queries without any task-specific data collection or model training, creating va…