4 citations · 7 across the 9 of their papers we have counts for
9 papers
Variational Quantum Optimization with Continuous Bandits
Marc Wanner, Johan Jonasson, Emil Carlsson +1
We introduce a novel approach to variational Quantum algorithms (VQA) via continuous bandits. VQA are a class of hybrid Quantum-classical algorithms where the parameters of Quantum…
Learning Efficient Recursive Numeral Systems via Reinforcement Learning
Andrea Silvi, Jonathan Thomas, Emil Carlsson +2
It has previously been shown that by using reinforcement learning (RL), agents can derive simple approximate and exact-restricted numeral systems that are similar to human ones (Ca…
Active Preference Learning for Ordering Items In- and Out-of-sample
Herman Bergström, Emil Carlsson, Devdatt Dubhashi +1
Learning an ordering of items based on pairwise comparisons is useful when items are difficult to rate consistently on an absolute scale, for example, when annotators have to make…
Pure Exploration in Bandits with Linear Constraints
Emil Carlsson, Debabrota Basu, Fredrik D. Johansson +1
We address the problem of identifying the optimal policy with a fixed confidence level in a multi-armed bandit setup, when \emph{the arms are subject to linear constraints}. Unlike…
Pragmatic Reasoning in Structured Signaling Games
Emil Carlsson, Devdatt Dubhashi
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 varian…
Cultural evolution via iterated learning and communication explains efficient color naming systems
Emil Carlsson, Devdatt Dubhashi, Terry Regier
It has been argued that semantic systems reflect pressure for efficiency, and a current debate concerns the cultural evolutionary process that produces this pattern. We consider ef…
Towards Learning Abstractions via Reinforcement Learning
Erik Jergéus, Leo Karlsson Oinonen, Emil Carlsson +1
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. We comb…
Thompson Sampling for Bandits with Clustered Arms
Emil Carlsson, Devdatt Dubhashi, Fredrik D. Johansson
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…
Learning Approximate and Exact Numeral Systems via Reinforcement Learning
Emil Carlsson, Devdatt Dubhashi, Fredrik D. Johansson
Recent work (Xu et al., 2020) has suggested that numeral systems in different languages are shaped by a functional need for efficient communication in an information-theoretic sens…