4 papers
Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation
Lucile Ter-Minassian, Liran Szlak, Ehud Karavani +2
Interpretability and transparency are essential for incorporating causal effect models from observational data into policy decision-making. They can provide trust for the model in…
Suboptimal and trait-like reinforcement learning strategies correlate with midbrain encoding of prediction errors
Liran Szlak, Kristoffer Aberg, Rony Paz
During probabilistic learning organisms often apply a sub-optimal "probability-matching" strategy, where selection rates match reward probabilities, rather than engaging in the opt…
Replay For Safety
Liran Szlak, Ohad Shamir
Experience replay \citep{lin1993reinforcement, mnih2015human} is a widely used technique to achieve efficient use of data and improved performance in RL algorithms. In experience r…
Convergence Results For Q-Learning With Experience Replay
Liran Szlak, Ohad Shamir
A commonly used heuristic in RL is experience replay (e.g.~\citet{lin1993reinforcement, mnih2015human}), in which a learner stores and re-uses past trajectories as if they were sam…