7 papers · 1 filter
Personalized Federated Direct Preference Optimization via LoRA Adapters
Zhanhong Jiang, Soumik Sarkar
Federated Direct Preference Optimization (FedDPO) enables privacy-preserving alignment of language models across distributed agents without centralizing sensitive preference data.…
Continuous-Time Probabilistic Correctors for Uncertainty-Aware Physics-Based Spacecraft Trajectory Forecasting
Muhammad Bilal Shahid, Zhanhong Jiang, Soumik Sarkar +1
Long-horizon spacecraft trajectory forecasting suffers from error accumulation due to the absence of corrective observations in the forecast regime, making reliable uncertainty est…
Lighting-aware Unified Model for Instance Segmentation
Qisai Liu, Alloy Das, Zhanhong Jiang +4
Foundation models like the Segment Anything Model (SAM) demonstrate impressive zero-shot generalization but frequently degrade under diverse real-world illumination, particularly f…
TabQL: In-Context Q-Learning with Tabular Foundation Models
Qisai Liu, Zhanhong Jiang, Timilehin Ayanlade +4
We propose Tabular Q-Learning (TabQL), a reinforcement learning framework that replaces the conventional parametric Q-network in Deep Q-Learning (DQN) with a tabular foundation mod…
COOPO: Cyclic Offline-Online Policy Optimization Algorithm
Qisai Liu, Zhanhong Jiang, Joshua Russell Waite +3
Offline reinforcement learning struggles with distributional shift and constrained performance due to static dataset limitations, while online RL demands prohibitive environment in…
Uncertainty-aware neural emulation reveals climate-resilient maize traits at scale
Mojdeh Saadati, Juan Panelo, Gustavo Visentini +3
Global food security depends on predicting crop responses to climate variability, yet process based crop models remain too computationally expensive for large scale exploration of…
ADKO: Agentic Decentralized Knowledge Optimization
Lucas Nerone Rillo, Zhanhong Jiang, Nastaran Saadati +4
We present Agentic Decentralized Knowledge Optimization (ADKO), a framework for collaborative black-box optimization across autonomous agents that achieves sample efficiency, priva…