7 papers
TAMA: A Human-AI Collaborative Thematic Analysis Framework Using Multi-Agent LLMs for Clinical Interviews
Huimin Xu, Seungjun Yi, Terence Lim +9
Thematic analysis (TA) is a widely used qualitative approach for uncovering latent meanings in unstructured text data. TA provides valuable insights in healthcare but is resource-i…
Towards Efficient and Expressive Offline RL via Flow-Anchored Noise-conditioned Q-Learning
Sungyoung Lee, Dohyeong Kim, Eshan Balachandar +2
We propose Flow-Anchored Noise-conditioned Q-Learning (FAN), a highly efficient and high-performing offline reinforcement learning (RL) algorithm. Recent work has shown that expres…
VLCs: Managing Parallelism with Virtualized Libraries
Yineng Yan, William Ruys, Hochan Lee +11
As the complexity and scale of modern parallel machines continue to grow, programmers increasingly rely on composition of software libraries to encapsulate and exploit parallelism.…
Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants
Bozhi You, Irene Wang, Zelal Su Mustafaoglu +5
Attention is a fundamental building block of large language models (LLMs), so there have been many efforts to implement it efficiently. For example, FlashAttention leverages tiling…
Optimize Wider, Not Deeper: Consensus Aggregation for Policy Optimization
Zelal Su, Mustafaoglu, Sungyoung Lee +3
Proximal policy optimization (PPO) approximates the trust region update using multiple epochs of clipped SGD. Each epoch may drift further from the natural gradient direction, crea…
Evolutionary Policy Optimization
Zelal Su "Lain" Mustafaoglu, Keshav Pingali, Risto Miikkulainen
A key challenge in reinforcement learning (RL) is managing the exploration-exploitation trade-off without sacrificing sample efficiency. Policy gradient (PG) methods excel in explo…