223 citations · 267 across the 16 of their papers we have counts for
18 papers
Future Querying: Can LLMs Serve as Implicit Medical World Models?
Siri Willems, James Butterworth, Lore Goetschalckx +4
Traditional clinical prediction models rely on task-specific pipelines and curated, structured data, which scale poorly and underutilize unstructured text. To address this, we intr…
KernelArc: A Multi-Agent Framework for GPU Kernel Optimization
Joyjit Kundu, Ben Stoffelen, Kaili Wang +2
We present KernelArc, a multi-agent framework for autonomous GPU kernel optimization across heterogeneous workloads. Strategy-specialized agents run in parallel and coordinate thro…
Bayesian Anytime Pareto Set Identification for Multi-Objective Multi-Armed Bandits
Lennert Saerens, Bram Silue, Eleni Litsa +2
Identifying Pareto optimal solutions is critical to support multi-objective decision-making. We introduce the first anytime Multi-Objective Multi-Armed Bandit algorithm for the Par…
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlow
John McLeod, Hrvoje Stojic, Vincent Adam +4
In the past decade, model-free reinforcement learning (RL) has provided solutions to challenging domains such as robotics. Model-based RL shows the prospect of being more sample-ef…
Compatible features for Monotonic Policy Improvement
Marcin B. Tomczak, Sergio Valcarcel Macua, Enrique Munoz de Cote +1
Recent policy optimization approaches have achieved substantial empirical success by constructing surrogate optimization objectives. The Approximate Policy Iteration objective (Sch…
Policy Optimization Through Approximate Importance Sampling
Marcin B. Tomczak, Dongho Kim, Peter Vrancx +1
Recent policy optimization approaches (Schulman et al., 2015a; 2017) have achieved substantial empirical successes by constructing new proxy optimization objectives. These proxy ob…