Jiachen Li
6 papers in the PaperMetrix corpus
Papers by this author
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Multi-task Batch Reinforcement Learning with Metric Learning
2019 · arXiv (Cornell University)
We tackle the Multi-task Batch Reinforcement Learning problem. Given multiple datasets collected from different tasks, we train a multi-task policy to perform well in unseen tasks sampled from the same distribution. The task identities of …
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HybridCom: Improve Federated Learning Efficiency on Unstable Data
2024
Federated learning (FL) has made significant advancements in recent years. However, its efficiency on unstable distributed data remains a critical challenge. This stems from oversights in existing FL frameworks regarding the instability of global and …
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Adversarial Attacks on Parts of Speech: An Empirical Study in Text-to-Image Generation
2024 · arXiv (Cornell University)
Recent studies show that text-to-image (T2I) models are vulnerable to adversarial attacks, especially with noun perturbations in text prompts. In this study, we investigate the impact of adversarial attacks on different POS tags within text …
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Importance Sampling-Guided Meta-Training for Intelligent Agents in Highly Interactive Environments
2024 · IEEE Robotics and Automation Letters
Training intelligent agents to navigate highly interactive environments presents significant challenges. While guided meta reinforcement learning (RL) approach that first trains a guiding policy to train the ego agent has proven effective in improving generalizability …
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Smart Predict-Then-Control: Control-Aware Surrogate Refinement for System Identification
2025 · arXiv (Cornell University)
This paper introduces Smart Predict Then Control (SPC), a control aware refinement procedure for model based control. SPC refines a prediction oriented model by optimizing a surrogate objective that evaluates candidate models through the control …
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CancerGUIDE: Cancer Guideline Understanding via Internal Disagreement Estimation
2025 · arXiv (Cornell University)
The National Comprehensive Cancer Network (NCCN) provides evidence-based guidelines for cancer treatment. Translating complex patient presentations into guideline-compliant treatment recommendations is time-intensive, requires specialized expertise, and is prone to error. Advances in large language model …