Variance Reduced Training with Stratified Sampling for Forecasting
Models

Variance Reduced Training with Stratified Sampling for Forecasting Models

2 March 2021

Christopher De Sa

Dean Phillips Foster

Papers citing "Variance Reduced Training with Stratified Sampling for Forecasting Models"

10 / 10 papers shown

Title
Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models Tanmay Gautam Youngsuk Park Hao Zhou Parameswaran Raman Wooseok Ha 43 11 0 11 Apr 2024
Adaptive Sampling for Probabilistic Forecasting under Distribution Shift Luca Masserano Syama Sundar Rangapuram Shubham Kapoor Rajbir-Singh Nirwan Youngsuk Park Michael Bohlke-Schneider TTA AI4TS 8 2 0 23 Feb 2023
Coordinating Distributed Example Orders for Provably Accelerated Training A. Feder Cooper Wentao Guo Khiem Pham Tiancheng Yuan Charlie F. Ruan Yucheng Lu Chris De Sa 24 6 0 02 Feb 2023
A Survey of Dataset Refinement for Problems in Computer Vision Datasets Zhijing Wan Zhixiang Wang CheukTing Chung Zheng Wang 23 7 0 21 Oct 2022
GraB: Finding Provably Better Data Permutations than Random Reshuffling Yucheng Lu Wentao Guo Christopher De Sa FedML 11 16 0 22 May 2022
Robust Probabilistic Time Series Forecasting Taeho Yoon Youngsuk Park Ernest K. Ryu Yuyang Wang AAML AI4TS 6 18 0 24 Feb 2022
Multivariate Quantile Function Forecaster Kelvin K. Kan Franccois-Xavier Aubet Tim Januschowski Youngsuk Park Konstantinos Benidis Lars Ruthotto Jan Gasthaus AI4TS 25 22 0 23 Feb 2022
Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting Youngsuk Park Danielle C. Maddix Franccois-Xavier Aubet Kelvin K. Kan Jan Gasthaus Yuyang Wang UQCV AI4TS 84 37 0 12 Nov 2021
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization Samuel Horváth Lihua Lei Peter Richtárik Michael I. Jordan 52 30 0 13 Feb 2020
Improving fairness in machine learning systems: What do industry practitioners need? Kenneth Holstein Jennifer Wortman Vaughan Hal Daumé Miroslav Dudík Hanna M. Wallach FaML HAI 192 730 0 13 Dec 2018