AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A novel framework is proposed that decouples operator estimation from value function learning and establishes the smoothness of $Q^*$ through the Bellman equation using the the Holder assumptions on the conditional reward and transition densities.
Offline maximum entropy reinforcement learning seeks to estimate the optimal soft action-value function \(Q^*\) from a fixed dataset without further interaction with the environment. Although \(Q^*\) is characterized by the soft optimal Bellman equation, the corresponding Bellman operator cannot be evaluated directly b...
Gaussian diffusion models offer a practical framework for generative modeling. Denoising L\'{e}vy Probabilistic Models (DLPM) introduce heavy-tailed perturbations with a fixed tail index throughout denoising. We introduce Annealed L\'{e}vy Diffusion (ALD). ALD varies the stability index $\alpha(t)$ to combine heavy-tai...
Effective unsupervised domain adaptation (UDA) requires a representation that captures transferable structure from both the labeled source domain and the unlabeled target domain. We introduce Joint Contrastive-Adversarial Representation Learning (JCAR), a two-stage framework that learns a common representation from the...
In this work, we propose a transfer learning algorithm for covariate shift, integrating sample reweighting with representation learning. A key feature of our approach is the use of a lower-dimensional latent space for modeling, in contrast to conventional methods that operate directly in the high-dimensional ambient sp...
CoxFormer is presented, an approach that learns whole-transcriptome gene representations from biomedical knowledge and co-expression data, thereby enabling spatial omics to predict unmeasured genes, enhance resolution and identify disease-related tissue regions.
This work proposes deep nonparametric Instrumental variable quantile regression (IVQR), a two-stage estimator that combines conditional diffusion modeling with a kernel-smoothed conditional moment formulation and derives end-to-end total variation guarantees for the conditional diffusion model under unbounded support.
Finite-sample bounds are derived that separate the effects of critic estimation, neural drift regression, finite-sample SNIS, diffusion discretization, and inherited actor error on expected average policy suboptimality.
In offline RL, estimating the optimal action-value function $Q^*$ can be formulated as solving the optimal Bellman equation based solely on offline observations. A fundamental challenge is that the reward function and transition kernel are unknown, so the optimal Bellman operator is not directly observable from data. T...
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