#quantile — Public Fediverse posts

'Deep Nonparametric Quantile Regression under Covariate Shift', by Xingdong Feng, Xin He, Yuling Jiao, Lican Kang, Caixing Wang.

http://jmlr.org/papers/v25/24-0906.html

#quantile #nonparametric #reweighted

'Value-Distributional Model-Based Reinforcement Learning', by Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, Felix Berkenkamp, Jan Peters.

http://jmlr.org/papers/v25/23-0913.html

#reinforcement #quantile #learns

'Value-Distributional Model-Based Reinforcement Learning', by Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, Felix Berkenkamp, Jan Peters.

http://jmlr.org/papers/v25/23-0913.html

#reinforcement #quantile #learns

'Value-Distributional Model-Based Reinforcement Learning', by Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, Felix Berkenkamp, Jan Peters.

http://jmlr.org/papers/v25/23-0913.html

#reinforcement #quantile #learns

'Value-Distributional Model-Based Reinforcement Learning', by Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, Felix Berkenkamp, Jan Peters.

http://jmlr.org/papers/v25/23-0913.html

#reinforcement #quantile #learns

'Value-Distributional Model-Based Reinforcement Learning', by Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, Felix Berkenkamp, Jan Peters.

http://jmlr.org/papers/v25/23-0913.html

#reinforcement #quantile #learns

'Continuous Prediction with Experts' Advice', by Nicholas J. A. Harvey, Christopher Liaw, Victor S. Portella.

http://jmlr.org/papers/v25/22-0803.html

#stochastic #prediction #quantile

'An Analysis of Quantile Temporal-Difference Learning', by Mark Rowland et al.

http://jmlr.org/papers/v25/23-0154.html

#quantile #reinforcement #stochastic

'Nonparametric Estimation of Non-Crossing Quantile Regression Process with Deep ReQU Neural Networks', by Guohao Shen, Yuling Jiao, Yuanyuan Lin, Joel L. Horowitz, Jian Huang.

http://jmlr.org/papers/v25/22-0488.html

#quantile #nonparametric #estimation

'Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond', by Nathan Kallus, Xiaojie Mao, Masatoshi Uehara.

http://jmlr.org/papers/v25/23-0661.html

#quantile #inference #estimation

'Confidence Intervals and Hypothesis Testing for High-dimensional Quantile Regression: Convolution Smoothing and Debiasing', by Yibo Yan, Xiaozhou Wang, Riquan Zhang.

http://jmlr.org/papers/v24/22-1217.html

#quantile #lasso #regression

'Flexible Model Aggregation for Quantile Regression', by Rasool Fakoor, Taesup Kim, Jonas Mueller, Alexander J. Smola, Ryan J. Tibshirani.

http://jmlr.org/papers/v24/22-0799.html

#quantile #quantiles #ensembles

'Flexible Model Aggregation for Quantile Regression', by Rasool Fakoor, Taesup Kim, Jonas Mueller, Alexander J. Smola, Ryan J. Tibshirani.

http://jmlr.org/papers/v24/22-0799.html

#quantile #quantiles #ensembles

'Flexible Model Aggregation for Quantile Regression', by Rasool Fakoor, Taesup Kim, Jonas Mueller, Alexander J. Smola, Ryan J. Tibshirani.

http://jmlr.org/papers/v24/22-0799.html

#quantile #quantiles #ensembles

'Flexible Model Aggregation for Quantile Regression', by Rasool Fakoor, Taesup Kim, Jonas Mueller, Alexander J. Smola, Ryan J. Tibshirani.

http://jmlr.org/papers/v24/22-0799.html

#quantile #quantiles #ensembles

'Flexible Model Aggregation for Quantile Regression', by Rasool Fakoor, Taesup Kim, Jonas Mueller, Alexander J. Smola, Ryan J. Tibshirani.

http://jmlr.org/papers/v24/22-0799.html

#quantile #quantiles #ensembles

Bounded Space Differentially Private Quantiles

Daniel Alabi, Omri Ben-Eliezer, Anamay Chaturvedi

Action editor: Gautam Kamath.

https://openreview.net/forum?id=sixOD8YVvM

#quantiles #quantile #privacy

'Recursive Quantile Estimation: Non-Asymptotic Confidence Bounds', by Likai Chen, Georg Keilbar, Wei Biao Wu.

http://jmlr.org/papers/v24/22-0021.html

#bandit #quantile #estimation

'Calibrated Multiple-Output Quantile Regression with Representation Learning', by Shai Feldman, Stephen Bates, Yaniv Romano.

http://jmlr.org/papers/v24/21-1280.html

#quantile #prediction #generative

#Quantile is a lossless numeric compression scheme for #Rust.

Quantile compresses integers and floats with very high compression ratio. Quantile uses ranges and offsets which are learned from a dataset and used to compress an input. Compressed data can be written directly to a file that can then be read and decompressed without any accessory data. Quantile preserves order and NaN values.

Website 🔗️: https://github.com/mwlon/quantile-compression

#free #opensource #foss #fossmendations #programming