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Found 7 papers, 1 papers with code
Active Preference Learning for Ordering Items In- and Out-of-sample
Learning an ordering of items based on noisy pairwise comparisons is useful when item-specific labels are difficult to assign, for example, when annotators have to make subjective assessments.
Pure Exploration in Bandits with Linear Constraints
Both these algorithms try to track an optimal allocation based on the lower bound and computed by a weighted projection onto the boundary of a normal cone.
Cultural evolution via iterated learning and communication explains efficient color naming systems
It has been argued that semantic systems reflect pressure for efficiency, and a current debate concerns the cultural evolutionary process that produces this pattern.
Pragmatic Reasoning in Structured Signaling Games
In this work we introduce a structured signaling game, an extension of the classical signaling game with a similarity structure between meanings in the context, along with a variant of the Rational Speech Act (RSA) framework which we call structured-RSA (sRSA) for pragmatic reasoning in structured domains.
Towards Learning Abstractions via Reinforcement Learning
In this paper we take the first steps in studying a new approach to synthesis of efficient communication schemes in multi-agent systems, trained via reinforcement learning.
Thompson Sampling for Bandits with Clustered Arms
We propose algorithms based on a multi-level Thompson sampling scheme, for the stochastic multi-armed bandit and its contextual variant with linear expected rewards, in the setting where arms are clustered.
Learning Approximate and Exact Numeral Systems via Reinforcement Learning
The agents gradually learn to communicate using reinforcement learning and the resulting numeral systems are shown to be efficient in the information-theoretic framework of Regier et al. (2015); Gibson et al. (2017).
