3 citations · 4 across the 12 of their papers we have counts for
12 papers
LatentGym: A Testbed For Cross-Task Experiential Learning With Controllable Latent Structure
Daksh Mittal, Tommaso Castellani, Thomson Yen +7
We envision continually learning agentic systems that become more useful over time: as they encounter sequences of related tasks, they should infer the hidden structure shared acro…
Cross-Domain Policy Optimization via Bellman Consistency and Hybrid Critics
Ming-Hong Chen, Kuan-Chen Pan, You-De Huang +2
Cross-domain reinforcement learning (CDRL) is meant to improve the data efficiency of RL by leveraging the data samples collected from a source domain to facilitate the learning in…
Semi-Supervised Cross-Domain Imitation Learning
Li-Min Chu, Kai-Siang Ma, Ming-Hong Chen +1
Cross-domain imitation learning (CDIL) accelerates policy learning by transferring expert knowledge across domains, which is valuable in applications where the collection of expert…
Textual Equilibrium Propagation for Deep Compound AI Systems
Minghui Chen, Wenlong Deng, James Zou +2
Large language models (LLMs) are increasingly deployed as part of compound AI systems that coordinate multiple modules (e.g., retrievers, tools, verifiers) over long-horizon workfl…
A Closer Look at Personalized Fine-Tuning in Heterogeneous Federated Learning
Minghui Chen, Hrad Ghoukasian, Ruinan Jin +3
Federated Learning (FL) enables decentralized, privacy-preserving model training but struggles to balance global generalization and local personalization due to non-identical data…
For-Value: Efficient Forward-Only Data Valuation for finetuning LLMs and VLMs
Wenlong Deng, Qi Zeng, Jiaming Zhang +5
Data valuation is essential for enhancing the transparency and accountability of large language models (LLMs) and vision-language models (VLMs). However, existing methods typically…
Enhancing Clinical Multiple-Choice Questions Benchmarks with Knowledge Graph Guided Distractor Generation
Running Yang, Wenlong Deng, Minghui Chen +2
Clinical tasks such as diagnosis and treatment require strong decision-making abilities, highlighting the importance of rigorous evaluation benchmarks to assess the reliability of…
Can Textual Gradient Work in Federated Learning?
Minghui Chen, Ruinan Jin, Wenlong Deng +4
Recent studies highlight the promise of LLM-based prompt optimization, especially with TextGrad, which automates differentiation'' via texts and backpropagates textual feedback. Th…
Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning
Minghui Chen, Meirui Jiang, Xin Zhang +3
Federated learning (FL) is a learning paradigm that enables collaborative training of models using decentralized data. Recently, the utilization of pre-trained weight initializatio…
DARE the Extreme: Revisiting Delta-Parameter Pruning For Fine-Tuned Models
Wenlong Deng, Yize Zhao, Vala Vakilian +3
Storing open-source fine-tuned models separately introduces redundancy and increases response times in applications utilizing multiple models. Delta-parameter pruning (DPP), partic…
Debiased Noise Editing on Foundation Models for Fair Medical Image Classification
Ruinan Jin, Wenlong Deng, Minghui Chen +1
In the era of Foundation Models' (FMs) rising prominence in AI, our study addresses the challenge of biases in medical images while the model operates in black-box (e.g., using FM…
Forgettable Federated Linear Learning with Certified Data Unlearning
Ruinan Jin, Minghui Chen, Qiong Zhang +1
Federated Learning (FL) enables collaborative model training across distributed clients while preserving user privacy. Recently, Federated Unlearning (FU) has emerged to address th…