Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Xu, Ruitu"'
Autor:
Fei, Yingjie, Xu, Ruitu
We study risk-sensitive multi-agent reinforcement learning under general-sum Markov games, where agents optimize the entropic risk measure of rewards with possibly diverse risk preferences. We show that using the regret naively adapted from existing
Externí odkaz:
http://arxiv.org/abs/2405.02724
We study a heterogeneous agent macroeconomic model with an infinite number of households and firms competing in a labor market. Each household earns income and engages in consumption at each time step while aiming to maximize a concave utility subjec
Externí odkaz:
http://arxiv.org/abs/2303.04833
We study a Markov matching market involving a planner and a set of strategic agents on the two sides of the market. At each step, the agents are presented with a dynamical context, where the contexts determine the utilities. The planner controls the
Externí odkaz:
http://arxiv.org/abs/2203.03684
Autor:
Fei, Yingjie, Xu, Ruitu
In this paper, we study gap-dependent regret guarantees for risk-sensitive reinforcement learning based on the entropic risk measure. We propose a novel definition of sub-optimality gaps, which we call cascaded gaps, and we discuss their key componen
Externí odkaz:
http://arxiv.org/abs/2203.03110
Stochastic gradient descent with backpropagation is the workhorse of artificial neural networks. It has long been recognized that backpropagation fails to be a biologically plausible algorithm. Fundamentally, it is a non-local procedure -- updating o
Externí odkaz:
http://arxiv.org/abs/2106.06044
In this paper, we establish the ordinary differential equation (ODE) that underlies the training dynamics of Model-Agnostic Meta-Learning (MAML). Our continuous-time limit view of the process eliminates the influence of the manually chosen step size
Externí odkaz:
http://arxiv.org/abs/2006.10921
Studying the landscape of nonconvex cost function is key towards a better understanding of optimization algorithms widely used in signal processing, statistics, and machine learning. Meanwhile, the famous Kuramoto model has been an important mathemat
Externí odkaz:
http://arxiv.org/abs/1809.11083
We investigate a series of learning kernel problems with polynomial combinations of base kernels, which will help us solve regression and classification problems. We also perform some numerical experiments of polynomial kernels with regression and cl
Externí odkaz:
http://arxiv.org/abs/1712.08597
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