stochastic approximation
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Provably Fast Finite Particle Variants of SVGD via Virtual Particle Stochastic Approximation
Although the population (i.e, infinite-particle) limit dynamics of SVGD is well characterized, its behavior in the finite-particle regime is far less understood.
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Provably Fast Finite Particle Variants of SVGD via Virtual Particle Stochastic Approximation
Although the population (i.e, infinite-particle) limit dynamics of SVGD is well characterized, its behavior in the finite-particle regime is far less understood.
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Online Performative Gradient Descent for Learning Nash Equilibria in Decision-Dependent Games Zihan Zhu Duke University Ethan X. Fang Duke University Zhuoran Yang Yale University
We focus on finding the Nash equilibrium of decision-dependent games in the bandit feedback setting.
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Two Time-scale Off-Policy TD Learning: Non-asymptotic Analysis over Markovian Samples
Tengyu Xu, Shaofeng Zou, Yingbin Liang
Neural Information Processing Systems http://nips.cc/
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