Stochastic Processes

Double Explore-then-Commit: Asymptotic Optimality and Beyond COLT 2021 Doubly infinite residual neural networks: a diffusion process approach JMLR 2021 Dynamic Analysis of Higher-Order Coordination in Neuronal Assemblies via De-Sparsified Orthogonal Matching Pursuit NeurIPS 2021 Dynamics of Stochastic Momentum Methods on Large-scale, Quadratic Models NeurIPS 2021 Efficient and Accurate Gradients for Neural SDEs NeurIPS 2021 Efficient Inference of Flexible Interaction in Spiking-neuron Networks ICLR 2021 Efficient sampling from the Bingham distribution ALT 2021 Elastic Consistency: A Practical Consistency Model for Distributed Stochastic Gradient Descent AAAI 2021 Emphatic Algorithms for Deep Reinforcement Learning ICML 2021 Entropic gradient descent algorithms and wide flat minima ICLR 2021 Equilibrium and non-Equilibrium regimes in the learning of Restricted Boltzmann Machines NeurIPS 2021 Estimating High Order Gradients of the Data Distribution by Denoising NeurIPS 2021 Estimating Uncertainty Intervals from Collaborating Networks JMLR 2021 Expected Eligibility Traces AAAI 2021 Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features AISTATS 2021 Exponential Reduction in Sample Complexity with Learning of Ising Model Dynamics ICML 2021 Factored Policy Gradients: Leveraging Structure for Efficient Learning in MOMDPs NeurIPS 2021 Factorising Meaning and Form for Intent-Preserving Paraphrasing ACL 2021 Fast and Scalable Adversarial Training of Kernel SVM via Doubly Stochastic Gradients AAAI 2021 Fast Doubly-Adaptive MCMC to Estimate the Gibbs Partition Function with Weak Mixing Time Bounds NeurIPS 2021 Featurized density ratio estimation UAI 2021 Federated stochastic gradient Langevin dynamics UAI 2021 Forecasting Reservoir Inflow via Recurrent Neural ODEs AAAI 2021 Future Moment Assessment for Action Query WACV 2021 Gaussian Process-Based Real-Time Learning for Safety Critical Applications ICML 2021