#estimators — Public Fediverse posts

'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

'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

'Non-Asymptotic Guarantees for Robust Statistical Learning under Infinite Variance Assumption', by Lihu Xu, Fang Yao, Qiuran Yao, Huiming Zhang.

http://jmlr.org/papers/v24/22-0034.html

#estimators #estimator #estimations

'Neural Estimation of Statistical Divergences', by Sreejith Sreekumar, Ziv Goldfeld.

http://jmlr.org/papers/v23/21-1212.html

#divergences #estimation #estimators

@MarkRubin

i keep saying...there are no #unbiased #estimators