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Tag: dependent

  • Gap-Dependent Bounds for Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation

    Gap-Dependent Bounds for Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation arXiv:2602.20297v1 Announce Type: new Abstract: We study gap-dependent performance guarantees for nearly minimax-optimal algorithms in reinforcement learning with linear function approximation. While prior works have established gap-dependent regret bounds in this setting, existing analyses do not apply to algorithms that achieve the nearly…

  • Time-dependent density estimation using binary classifiers

    Time-dependent density estimation using binary classifiers arXiv:2506.15505v1 Announce Type: new Abstract: We propose a data-driven method to learn the time-dependent probability density of a multivariate stochastic process from sample paths, assuming that the initial probability density is known and can be evaluated. Our method uses a novel time-dependent binary classifier trained using a contrastive estimation-based…

  • Dependent Randomized Rounding for Budget Constrained Experimental Design

    Dependent Randomized Rounding for Budget Constrained Experimental Design arXiv:2506.12677v1 Announce Type: new Abstract: Policymakers in resource-constrained settings require experimental designs that satisfy strict budget limits while ensuring precise estimation of treatment effects. We propose a framework that applies a dependent randomized rounding procedure to convert assignment probabilities into binary treatment decisions. Our proposed solution preserves…

  • Covariate-dependent Graphical Model Estimation via Neural Networks with Statistical Guarantees

    Covariate-dependent Graphical Model Estimation via Neural Networks with Statistical Guarantees arXiv:2504.16356v1 Announce Type: new Abstract: Graphical models are widely used in diverse application domains to model the conditional dependencies amongst a collection of random variables. In this paper, we consider settings where the graph structure is covariate-dependent, and investigate a deep neural network-based approach to…

  • Gap-Dependent Bounds for Federated $Q$-learning

    Gap-Dependent Bounds for Federated $Q$-learning arXiv:2502.02859v1 Announce Type: new Abstract: We present the first gap-dependent analysis of regret and communication cost for on-policy federated $Q$-Learning in tabular episodic finite-horizon Markov decision processes (MDPs). Existing FRL methods focus on worst-case scenarios, leading to $sqrt{T}$-type regret bounds and communication cost bounds with a $log T$ term scaling…