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Yuchen Jiao

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  1. arXiv

    Robust Q-learning drops the generative model: one Markovian trajectory suffices, with near-minimax sample complexity

    For robust Markov decision processes with total-variation uncertainty sets, a model-free asynchronous Q-learning algorithm relies only on a single Markovian trajectory from a behavior policy, without a generative model or queries to arbitrary state-action pairs. It estimates robust Bellman updates through a resampled surrogate next state, uses $|S||A|$ space, and reaches entrywise ϵ\epsilon accuracy with a sample complexity of order ∣S∣∣A∣tmixϵ2μmin⁡\frac{|S||A| t_{\mathrm{mix}}}{\epsilon^2 \mu_{\min}} up to logarithmic factors, carrying the extra factor inherent to Q-learning.

    Chinese University of Hong Kong
    Yuchen Jiao（First author）
    Q-learningSample ComplexityDistributionally Robust Optimization
  2. arXiv

    Stochastic diffusion sampling drops the higher-order detour: F-DDPM evaluates at a cleaner lookahead point and lowers FID at matched NFE

    Stochastic diffusion samplers have lagged deterministic ones, and raising approximation order does not necessarily improve their strong convergence order. F-DDPM evaluates the data-prediction model at a cleaner lookahead point built from the previous prediction and the previously injected noise, still with one model evaluation per iteration and a first-order strong convergence guarantee. On CIFAR-10, ImageNet, FFHQ and LSUN it reaches lower FID than the evaluated stochastic baselines at matched NFE budgets, and with 1.5 times the NFE it approaches or beats deterministic baselines in many settings.

    Yuchen Jiao（First author）
    Forward-Value DiscretizationForward DDPMForward DPMSolverConvergence AnalysisDiffusion Models

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