'Sampling and Estimation on Manifolds using the Langevin Diffusion', by Karthik Bharath, Alexander Lewis, Akash Sharma, Michael V. Tretyakov.
http://jmlr.org/papers/v26/24-0829.html
#estimation #langevin #estimators
'Sampling and Estimation on Manifolds using the Langevin Diffusion', by Karthik Bharath, Alexander Lewis, Akash Sharma, Michael V. Tretyakov.
http://jmlr.org/papers/v26/24-0829.html
#estimation #langevin #estimators
'Instability, Computational Efficiency and Statistical Accuracy', by Nhat Ho, Koulik Khamaru, Raaz Dwivedi, Martin J. Wainwright, Michael I. Jordan, Bin Yu.
http://jmlr.org/papers/v26/22-0300.html
#estimation #estimators #algorithms
'Error estimation and adaptive tuning for unregularized robust M-estimator', by Pierre C. Bellec, Takuya Koriyama.
http://jmlr.org/papers/v26/24-0060.html
#estimation #estimators #estimator
'Locally Private Causal Inference for Randomized Experiments', by Yuki Ohnishi, Jordan Awan.
http://jmlr.org/papers/v26/23-1401.html
#privacy #private #estimators
'Learning with a linear loss function: excess risk and estimation bound..."', by Guillaume Lecué, Lucie Neirac.
http://jmlr.org/papers/v25/23-1405.html
#adversarial #estimators #regularized
'Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition', by Varun Kanade, Patrick Rebeschini, Tomas Vaskevicius.
http://jmlr.org/papers/v25/23-0063.html
#rademacher #estimators #estimator
'Causal effects of intervening variables in settings with unmeasured confounding', by Lan Wen, Aaron Sarvet, Mats Stensrud.
http://jmlr.org/papers/v25/23-1077.html
#estimates #causal #estimators
'Inference on High-dimensional Single-index Models with Streaming Data', by Dongxiao Han, Jinhan Xie, Jin Liu, Liuquan Sun, Jian Huang, Bei Jiang, Linglong Kong.
http://jmlr.org/papers/v25/22-1124.html
#lasso #semiparametric #estimators
'Stability and L2-penalty in Model Averaging', by Hengkun Zhu, Guohua Zou.
http://jmlr.org/papers/v25/23-0853.html
#averaging #estimators #models
'Nonparametric Regression Using Over-parameterized Shallow ReLU Neural Networks', by Yunfei Yang, Ding-Xuan Zhou.
http://jmlr.org/papers/v25/23-0918.html
#nonparametric #estimators #minimax
'Statistical Optimality of Divide and Conquer Kernel-based Functional Linear Regression', by Jiading Liu, Lei Shi.
http://jmlr.org/papers/v25/22-1326.html
#estimators #regression #prediction
'Adjusted Wasserstein Distributionally Robust Estimator in Statistical Learning', by Yiling Xie, Xiaoming Huo.
http://jmlr.org/papers/v25/23-0379.html
#wasserstein #estimators #robust
'A Complete Characterization of Linear Estimators for Offline Policy Evaluation', by Juan C. Perdomo, Akshay Krishnamurthy, Peter Bartlett, Sham Kakade.
http://jmlr.org/papers/v24/22-0341.html
#reinforcement #policy #estimators
'On the Estimation of Derivatives Using Plug-in Kernel Ridge Regression Estimators', by Zejian Liu, Meng Li.
http://jmlr.org/papers/v24/21-1110.html
#estimation #estimators #nonparametric
'Distributed Algorithms for U-statistics-based Empirical Risk Minimization', by Lanjue Chen, Alan T.K. Wan, Shuyi Zhang, Yong Zhou.
http://jmlr.org/papers/v24/21-0890.html
#empirical #algorithms #estimators
'Unbiased Multilevel Monte Carlo Methods for Intractable Distributions: MLMC Meets MCMC', by Tianze Wang, Guanyang Wang.
http://jmlr.org/papers/v24/22-1468.html
#mlmc #mcmc #estimators
'Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics', by Kamélia Daudel, Joe Benton, Yuyang Shi, Arnaud Doucet.
http://jmlr.org/papers/v24/22-1160.html
#variational #divergence #estimators
Pathwise gradient variance reduction in variational inference via zero-variance control variates
'Contrasting Identifying Assumptions of Average Causal Effects: Robustness and Semiparametric Efficiency', by Tetiana Gorbach, Xavier de Luna, Juha Karvanen, Ingeborg Waernbaum.
http://jmlr.org/papers/v24/21-1392.html
#causal #estimators #estimation
Transfer Learning for High-dimensional Quantile Regression with Statistical Guarantee