6 citations · 6 across the 2 of their papers we have counts for
15 papers
Hyper Hawkes Processes: Interpretable Models of Marked Temporal Point Processes
Alex Boyd, Andrew Warrington, Taha Kass-Hout +2
Foundational marked temporal point process (MTPP) models, such as the Hawkes process, often use inexpressive model families in order to offer interpretable parameterizations of eve…
Stabilizing Off-Policy Training for Long-Horizon LLM Agent via Turn-Level Importance Sampling and Clipping-Triggered Normalization
Chenliang Li, Adel Elmahdy, Alex Boyd +7
Reinforcement learning (RL) algorithms such as PPO and GRPO are widely used to train large language models (LLMs) for multi-turn agentic tasks. However, in off-policy training pipe…
MammoDINO: Anatomically Aware Self-Supervision for Mammographic Images
Sicheng Zhou, Lei Wu, Cao Xiao +2
Self-supervised learning (SSL) has transformed vision encoder training in general domains but remains underutilized in medical imaging due to limited data and domain specific biase…
Decipher-MR: A Vision-Language Foundation Model for 3D MRI Representations
Zhijian Yang, Noel DSouza, Istvan Megyeri +11
Magnetic Resonance Imaging is a critical imaging modality in clinical diagnosis and research, yet its complexity and heterogeneity hinder scalable, generalizable machine learning.…
Focus on What Matters: Enhancing Medical Vision-Language Models with Automatic Attention Alignment Tuning
Aofei Chang, Le Huang, Alex James Boyd +4
Medical Large Vision-Language Models (Med-LVLMs) often exhibit suboptimal attention distribution on visual inputs, leading to hallucinated or inaccurate outputs. Existing mitigatio…
Any Large Language Model Can Be a Reliable Judge: Debiasing with a Reasoning-based Bias Detector
Haoyan Yang, Runxue Bao, Cao Xiao +4
LLM-as-a-Judge has emerged as a promising tool for automatically evaluating generated outputs, but its reliability is often undermined by potential biases in judgment. Existing eff…