Stochastic Processes

Gaussian process nowcasting: application to COVID-19 mortality reporting UAI 2021 Generalized Doubly Reparameterized Gradient Estimators ICML 2021 Generalized Linear Bandits with Local Differential Privacy NeurIPS 2021 Geometric convergence of elliptical slice sampling ICML 2021 GO Hessian for Expectation-Based Objectives AAAI 2021 Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech ICML 2021 Graph reparameterizations for enabling 1000+ Monte Carlo iterations in Bayesian deep neural networks UAI 2021 Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling NeurIPS 2021 Heavy Ball Neural Ordinary Differential Equations NeurIPS 2021 Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks NeurIPS 2021 Hidden Cost of Randomized Smoothing AISTATS 2021 Hierarchical Autoregressive Modeling for Neural Video Compression ICLR 2021 Hierarchical Inducing Point Gaussian Process for Inter-domian Observations AISTATS 2021 High-Dimensional Gaussian Process Inference with Derivatives ICML 2021 Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processes NeurIPS 2021 High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm JMLR 2021 Hoeffding's Inequality for General Markov Chains and Its Applications to Statistical Learning JMLR 2021 How Certain is Your Transformer? EACL 2021 How Helpful is Inverse Reinforcement Learning for Table-to-Text Generation? ACL 2021 How is BERT surprised? Layerwise detection of linguistic anomalies ACL 2021 Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels ICLR 2021 Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity NeurIPS 2021 Implicit Langevin Algorithms for Sampling From Log-concave Densities JMLR 2021 Improved Contrastive Divergence Training of Energy-Based Models ICML 2021 Improving Robustness and Uncertainty Modelling in Neural Ordinary Differential Equations WACV 2021