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Found 6 papers, 1 papers with code
On semi-supervised estimation using exponential tilt mixture models
Consider a semi-supervised setting with a labeled dataset of binary responses and predictors and an unlabeled dataset with only the predictors.
Persistently Trained, Diffusion-assisted Energy-based Models
Maximum likelihood (ML) learning for energy-based models (EBMs) is challenging, partly due to non-convergence of Markov chain Monte Carlo. Several variations of ML learning have been proposed, but existing methods all fail to achieve both post-training image generation and proper density estimation.
Understanding Accelerated Gradient Methods: Lyapunov Analyses and Hamiltonian Assisted Interpretations
We formulate two classes of first-order algorithms more general than previously studied for minimizing smooth and strongly convex or, respectively, smooth and convex functions.
Tractable and Near-Optimal Adversarial Algorithms for Robust Estimation in Contaminated Gaussian Models
This is the first time such near-optimal error rates are established for adversarial algorithms with linear discriminators under Huber's contamination model.
Semi-supervised Logistic Learning Based on Exponential Tilt Mixture Models
Consider semi-supervised learning for classification, where both labeled and unlabeled data are available for training.
