4 citations · 7 across the 6 of their papers we have counts for
6 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…
Online Learning with Local Permutations and Delayed Feedback
Ohad Shamir, Liran Szlak
We propose an Online Learning with Local Permutations (OLLP) setting, in which the learner is allowed to slightly permute the \emph{order} of the loss functions generated by an adv…
Multi-Player Bandits -- a Musical Chairs Approach
Jonathan Rosenski, Ohad Shamir, Liran Szlak
We consider a variant of the stochastic multi-armed bandit problem, where multiple players simultaneously choose from the same set of arms and may collide, receiving no reward. Thi…