Neural Optimism for Genetic Perturbation Experiments

This work provides a theoretically sound framework for iteratively exploring the space of perturbations in pooled batches in order to maximize a target phenotype under an experimental budget.

Model Selection for Contextual Bandits and Reinforcement Learning

In the problem of model selection the objective is to design ways to select in online fashion the best suitable algorithm to solve a specific problem instance.

Beyond the Standard Assumptions in Reinforcement Learning

In Reinforcement Learning it is standard to assume the reward to be an additive function of per state feedback. In this work we challenge this standard assumption.