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Stochastic Processes

2,667 papers

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Papers

Generalized Doubly Reparameterized Gradient Estimators ICML 2021
Scalable Normalizing Flows for Permutation Invariant Densities ICML 2021
Asymmetric Heavy Tails and Implicit Bias in Gaussian Noise Injections ICML 2021
Best Model Identification: A Rested Bandit Formulation ICML 2021
Large-Scale Multi-Agent Deep FBSDEs ICML 2021
Overcoming Catastrophic Forgetting by Bayesian Generative Regularization ICML 2021
SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation ICML 2021
Beyond Variance Reduction: Understanding the True Impact of Baselines on Policy Optimization ICML 2021
Differentiable Particle Filtering via Entropy-Regularized Optimal Transport ICML 2021
Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data ICML 2021
High-Dimensional Gaussian Process Inference with Derivatives ICML 2021
On the Inherent Regularization Effects of Noise Injection During Training ICML 2021
Improved Contrastive Divergence Training of Energy-Based Models ICML 2021
Putting the “Learning" into Learning-Augmented Algorithms for Frequency Estimation ICML 2021
Exponential Reduction in Sample Complexity with Learning of Ising Model Dynamics ICML 2021
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning ICML 2021
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions ICML 2021
The Heavy-Tail Phenomenon in SGD ICML 2021
SPECTRE: defending against backdoor attacks using robust statistics ICML 2021
Multiplicative Noise and Heavy Tails in Stochastic Optimization ICML 2021
STRODE: Stochastic Boundary Ordinary Differential Equation ICML 2021
What Are Bayesian Neural Network Posteriors Really Like? ICML 2021
Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics ICML 2021
Marginalized Stochastic Natural Gradients for Black-Box Variational Inference ICML 2021
Emphatic Algorithms for Deep Reinforcement Learning ICML 2021