9 citations · 23 across the 5 of their papers we have counts for
6 papers
Large language model for Bible sentiment analysis: Sermon on the Mount
Mahek Vora, Tom Blau, Vansh Kachhwal +2
The revolution of natural language processing via large language models has motivated its use in multidisciplinary areas that include social sciences and humanities and more specif…
Statistically Efficient Bayesian Sequential Experiment Design via Reinforcement Learning with Cross-Entropy Estimators
Tom Blau, Iadine Chades, Amir Dezfouli +2
Reinforcement learning can learn amortised design policies for designing sequences of experiments. However, current amortised methods rely on estimators of expected information gai…
Optimizing Sequential Experimental Design with Deep Reinforcement Learning
Tom Blau, Edwin V. Bonilla, Iadine Chades +1
Bayesian approaches developed to solve the optimal design of sequential experiments are mathematically elegant but computationally challenging. Recently, techniques using amortizat…
Learning from Demonstration without Demonstrations
Tom Blau, Gilad Francis, Philippe Morere
State-of-the-art reinforcement learning (RL) algorithms suffer from high sample complexity, particularly in the sparse reward case. A popular strategy for mitigating this problem i…
Reinforcement Learning with Probabilistically Complete Exploration
Philippe Morere, Gilad Francis, Tom Blau +1
Balancing exploration and exploitation remains a key challenge in reinforcement learning (RL). State-of-the-art RL algorithms suffer from high sample complexity, particularly in th…
Bayesian Curiosity for Efficient Exploration in Reinforcement Learning
Tom Blau, Lionel Ott, Fabio Ramos
Balancing exploration and exploitation is a fundamental part of reinforcement learning, yet most state-of-the-art algorithms use a naive exploration protocol like -greedy. This…