heavy-tailed distribution

Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data ICML 2022 Left Heavy Tails and the Effectiveness of the Policy and Value Networks in DNN-based best-first search for Sokoban Planning NeurIPS 2022 Marginal Tail-Adaptive Normalizing Flows ICML 2022 Optimal Mean Estimation without a Variance COLT 2022 Outlier-Robust Sparse Mean Estimation for Heavy-Tailed Distributions NeurIPS 2022 Spectral risk-based learning using unbounded losses AISTATS 2022 Taming Fat-Tailed (“Heavier-Tailed” with Potentially Infinite Variance) Noise in Federated Learning NeurIPS 2022 Fractional moment-preserving initialization schemes for training deep neural networks AISTATS 2021 Graphical Models in Heavy-Tailed Markets NeurIPS 2021 Handling Long-tailed Feature Distribution in AdderNets NeurIPS 2021 Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks NeurIPS 2021 Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning JMLR 2021 Learning to Stop with Surprisingly Few Samples COLT 2021 Learning with risk-averse feedback under potentially heavy tails AISTATS 2021 Multiplicative Noise and Heavy Tails in Stochastic Optimization ICML 2021 No-Regret Reinforcement Learning with Heavy-Tailed Rewards AISTATS 2021 On Proximal Policy Optimization’s Heavy-tailed Gradients ICML 2021 Optimal Best-Arm Identification Methods for Tail-Risk Measures NeurIPS 2021 Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions ICML 2021 Regret Minimization in Heavy-Tailed Bandits COLT 2021 Robust Inference for High-Dimensional Linear Models via Residual Randomization ICML 2021 Robustness and scalability under heavy tails, without strong convexity AISTATS 2021 Scaling-Up Robust Gradient Descent Techniques AAAI 2021 Taming heavy-tailed features by shrinkage AISTATS 2021 A Fast Spectral Algorithm for Mean Estimation with Sub-Gaussian Rates COLT 2020