Search Results for author:
Found 7 papers, 5 papers with code
Large language model for Bible sentiment analysis: Sermon on the Mount
Sentiment analysis provides a mechanism to study the emotions expressed in text.
Statistically Efficient Bayesian Sequential Experiment Design via Reinforcement Learning with Cross-Entropy Estimators
We propose the use of an alternative estimator based on the cross-entropy of the joint model distribution and a flexible proposal distribution.
Unsupervised machine learning framework for discriminating major variants of concern during COVID-19
This paper presents a framework that utilizes unsupervised machine learning methods to discriminate and visualize the associations between major COVID-19 variants based on their genome sequences.
Optimizing Sequential Experimental Design with Deep Reinforcement Learning
Bayesian approaches developed to solve the optimal design of sequential experiments are mathematically elegant but computationally challenging.
Learning from Demonstration without Demonstrations
To address this shortcoming, we propose Probabilistic Planning for Demonstration Discovery (P2D2), a technique for automatically discovering demonstrations without access to an expert.
Reinforcement Learning with Probabilistically Complete Exploration
Balancing exploration and exploitation remains a key challenge in reinforcement learning (RL).
Bayesian Curiosity for Efficient Exploration in Reinforcement Learning
Balancing exploration and exploitation is a fundamental part of reinforcement learning, yet most state-of-the-art algorithms use a naive exploration protocol like $\epsilon$-greedy.
