Search Results for author:
Found 7 papers, 4 papers with code
Efficient Causal Graph Discovery Using Large Language Models
We propose a novel framework that leverages LLMs for full causal graph discovery.
Network Analysis of the iNaturalist Citizen Science Community
We frame the data from iNaturalist as a bipartite network and use visualizations as well as established network science techniques to gain insights into the structure and interactions between users in citizen science projects.
Forecaster: Towards Temporally Abstract Tree-Search Planning from Pixels
The ability to plan at many different levels of abstraction enables agents to envision the long-term repercussions of their decisions and thus enables sample-efficient learning.
Delta-AI: Local objectives for amortized inference in sparse graphical models
We present a new algorithm for amortized inference in sparse probabilistic graphical models (PGMs), which we call $\Delta$-amortized inference ($\Delta$-AI).
A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control
Reinforcement learning (RL) is a promising approach for optimizing HVAC control.
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RL
Distinct from other contemporary RL approaches to credit assignment, ConSpec takes advantage of the fact that it is easier to retrospectively identify the small set of steps that success is contingent upon (and ignoring other states) than it is to prospectively predict reward at every taken step.
Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learning
We design a clinically relevant intermediate reward that encourages continuous improvement of the patient vitals as well as addresses the challenge of sparse reward in RL.
