1 citations · 1 across the 8 of their papers we have counts for
10 papers
Asymptotic Max-Min Fair Allocation with Random Utilities
Noam Glazner, Amir Leshem
We investigate the asymptotic behavior of max-min fair allocations for indivisible goods under i.i.d. random utilities. For agents and goods with utilities ${\mU_{i,j}}$ dr…
Sequential-Innovation Reducibility and the Innovation Spectrum
Amir Leshem
Sequential prediction naturally induces an innovation sequence consisting of the prediction errors produced by a causal predictor. We use the collection of all such innovation sequ…
Universal Individual-Sequence Prediction with a Primitive-Recursive Superpredictor
Amir Leshem
We study sequential prediction of individual binary sequences under zero-one loss. No computable master can compete on every sequence with all total computable predictors. We there…
Constrained Multi-Objective Reinforcement Learning with Max-Min Criterion
Giseung Park, Hyunyoung Nam, Woohyeon Byeon +2
Multi-Objective Reinforcement Learning (MORL) extends standard RL by optimizing policies with respect to multiple, often conflicting, objectives. While max-min MORL has emerged as…
Computability Limits of Sequential Hypothesis Testing
Amir Leshem
Sequential hypothesis testing asks for decision rules that update as data arrive. A natural goal is \emph{eventual correctness}: the rule may change its mind early on, but it shoul…
Metastability-Containing Turing Machines
Johannes Bund, Amir Leshem, Moti Medina
Metastability is a spurious mode of operation in digital signals, where an electrical signal fails to settle into a stable state within a specified time, leading to uncertainty and…
Creating a digital poet
Vered Tohar, Tsahi Hayat, Amir Leshem
Can a machine write good poetry? Any positive answer raises fundamental questions about the nature and value of art. We report a seven-month poetry workshop in which a large langua…
Multi-Objective Reinforcement Learning with Max-Min Criterion: A Game-Theoretic Approach
Woohyeon Byeon, Giseung Park, Jongseong Chae +2
In this paper, we propose a provably convergent and practical framework for multi-objective reinforcement learning with max-min criterion. From a game-theoretic perspective, we ref…
The Max-Min Formulation of Multi-Objective Reinforcement Learning: From Theory to a Model-Free Algorithm
Giseung Park, Woohyeon Byeon, Seongmin Kim +3
In this paper, we consider multi-objective reinforcement learning, which arises in many real-world problems with multiple optimization goals. We approach the problem with a max-min…
Two-Stage Resource Allocation in Reconfigurable Intelligent Surface Assisted Hybrid Networks via Multi-Player Bandits
Jingwen Tong, Hongliang Zhang, Liqun Fu +2
This paper considers a resource allocation problem where several Internet-of-Things (IoT) devices send data to a base station (BS) with or without the help of the reconfigurable in…