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Double Randomized Underdamped Langevin with Dimension-Independent Convergence Guarantee Y uanshi Liu, Cong Fang, Tong Zhang School of Intelligence Science and Technology, Peking University

Neural Information Processing Systems

Sampling from a high-dimensional distribution serves as one of the key components in statistics, machine learning, and scientific computing, and constitutes the foundation of the fields including Bayesian statistics and generative models [Liu and Liu, 2001, Brooks et al., 2011, Song et al.,


Double Randomized Underdamped Langevin with Dimension-Independent Convergence Guarantee Y uanshi Liu, Cong Fang, Tong Zhang School of Intelligence Science and Technology, Peking University

Neural Information Processing Systems

Sampling from a high-dimensional distribution serves as one of the key components in statistics, machine learning, and scientific computing, and constitutes the foundation of the fields including Bayesian statistics and generative models [Liu and Liu, 2001, Brooks et al., 2011, Song et al.,



Regret Matching +: (In)Stability and Fast Convergence in Games

Neural Information Processing Systems

However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no-regret algorithms such as online mirror descent, which satisfy stability.