306 citations · 2.8k across the 181 of their papers we have counts for
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Multi Instance Learning For Unbalanced Data
Mark Kozdoba, Edward Moroshko, Lior Shani +4
In the context of Multi Instance Learning, we analyze the Single Instance (SI) learning objective. We show that when the data is unbalanced and the family of classifiers is suffici…
Exploration Conscious Reinforcement Learning Revisited
Lior Shani, Yonathan Efroni, Shie Mannor
The Exploration-Exploitation tradeoff arises in Reinforcement Learning when one cannot tell if a policy is optimal. Then, there is a constant need to explore new actions instead of…
Inspiration Learning through Preferences
Nir Baram, Shie Mannor
Current imitation learning techniques are too restrictive because they require the agent and expert to share the same action space. However, oftentimes agents that act differently…
On-Line Learning of Linear Dynamical Systems: Exponential Forgetting in Kalman Filters
Mark Kozdoba, Jakub Marecek, Tigran Tchrakian +1
Kalman filter is a key tool for time-series forecasting and analysis. We show that the dependence of a prediction of Kalman filter on the past is decaying exponentially, whenever t…
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning
Tom Zahavy, Matan Haroush, Nadav Merlis +2
Learning how to act when there are many available actions in each state is a challenging task for Reinforcement Learning (RL) agents, especially when many of the actions are redund…
How to Combine Tree-Search Methods in Reinforcement Learning
Yonathan Efroni, Gal Dalal, Bruno Scherrer +1
Finite-horizon lookahead policies are abundantly used in Reinforcement Learning and demonstrate impressive empirical success. Usually, the lookahead policies are implemented with s…