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Found 18 papers, 0 papers with code
Convergence Analysis for General Probability Flow ODEs of Diffusion Models in Wasserstein Distances
Score-based generative modeling with probability flow ordinary differential equations (ODEs) has achieved remarkable success in a variety of applications.
Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models
We find that the experimental results are in good agreement with our theoretical predictions on the iteration complexity, and the models with our newly proposed forward processes can outperform existing models.
Regret Bounds for Markov Decision Processes with Recursive Optimized Certainty Equivalents
The optimized certainty equivalent (OCE) is a family of risk measures that cover important examples such as entropic risk, conditional value-at-risk and mean-variance models.
Square-root regret bounds for continuous-time episodic Markov decision processes
We study reinforcement learning for continuous-time Markov decision processes (MDPs) in the finite-horizon episodic setting.
Logarithmic regret bounds for continuous-time average-reward Markov decision processes
We consider reinforcement learning for continuous-time Markov decision processes (MDPs) in the infinite-horizon, average-reward setting.
Debiasing Samples from Online Learning Using Bootstrap
It has been recently shown in the literature that the sample averages from online learning experiments are biased when used to estimate the mean reward.
Sublinear Regret for Learning POMDPs
We study the model-based undiscounted reinforcement learning for partially observable Markov decision processes (POMDPs).
Breaking Reversibility Accelerates Langevin Dynamics for Non-Convex Optimization
We study two variants that are based on non-reversible Langevin diffusions: the underdamped Langevin dynamics (ULD) and the Langevin dynamics with a non-symmetric drift (NLD).
State-Dependent Temperature Control for Langevin Diffusions
We study the temperature control problem for Langevin diffusions in the context of non-convex optimization.
Decentralized Stochastic Gradient Langevin Dynamics and Hamiltonian Monte Carlo
Stochastic gradient Langevin dynamics (SGLD) and stochastic gradient Hamiltonian Monte Carlo (SGHMC) are two popular Markov Chain Monte Carlo (MCMC) algorithms for Bayesian inference that can scale to large datasets, allowing to sample from the posterior distribution of the parameters of a statistical model given the input data and the prior distribution over the model parameters.
Ciron: a New Benchmark Dataset for Chinese Irony Detection
Automatic Chinese irony detection is a challenging task, and it has a strong impact on linguistic research.
Non-Convex Optimization via Non-Reversible Stochastic Gradient Langevin Dynamics
In this paper, we study the non reversible Stochastic Gradient Langevin Dynamics (NSGLD) which is based on discretization of the non-reversible Langevin diffusion.
Regime Switching Bandits
We study a multi-armed bandit problem where the rewards exhibit regime switching.
Breaking Reversibility Accelerates Langevin Dynamics for Global Non-Convex Optimization
We study two variants that are based on non-reversible Langevin diffusions: the underdamped Langevin dynamics (ULD) and the Langevin dynamics with a non-symmetric drift (NLD).
Global Convergence of Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Stochastic Optimization: Non-Asymptotic Performance Bounds and Momentum-Based Acceleration
We provide finite-time performance bounds for the global convergence of both SGHMC variants for solving stochastic non-convex optimization problems with explicit constants.
Optimal Market Making in the Presence of Latency
This paper studies optimal market making for large-tick assets in the presence of latency.
