Near-Optimal Discrete Optimization for Experimental Design: A Regret Minimization Approach

14 November 2017

Aarti Singh

Papers citing "Near-Optimal Discrete Optimization for Experimental Design: A Regret Minimization Approach"

25 / 25 papers shown

Title
$Breaking the $\log(1/\Delta_2)$ Barrier: Better Batched Best Arm Identification with Adaptive Grids$ Breaking the $\log(1/\Delta_2)$ Barrier: Better Batched Best Arm Identification with Adaptive Grids Tianyuan Jin Qin Zhang Dongruo Zhou 120 0 0 29 Jan 2025
Experimental Design Using Interlacing Polynomials L. Lau Robert Wang Hong Zhou 22 0 0 15 Oct 2024
Robust Offline Active Learning on Graphs Yuanchen Wu Yubai Yuan OffRL 48 0 0 15 Aug 2024
Sketchy Moment Matching: Toward Fast and Provable Data Selection for Finetuning Yijun Dong Hoang Phan Xiang Pan Qi Lei 148 6 0 08 Jul 2024
Experimental Designs for Heteroskedastic Variance Justin Weltz Tanner Fiez Alex Volfovsky Eric B. Laber Blake Mason Houssam Nassif Lalit P. Jain 76 5 0 06 Oct 2023
Additive Causal Bandits with Unknown Graph Alan Malek Virginia Aglietti Silvia Chiappa CML 59 9 0 13 Jun 2023
Experimental Design for Any $p$ -Norm L. Lau Robert Wang Hong Zhou 12 1 0 03 May 2023
Composable Coresets for Constrained Determinant Maximization and Beyond S. Mahabadi T. Vuong 51 1 0 01 Nov 2022
Partition-Based Active Learning for Graph Neural Networks Jiaqi Ma Ziqiao Ma Joyce Chai Qiaozhu Mei 62 18 0 23 Jan 2022
Collaborative Pure Exploration in Kernel Bandit Yihan Du Wei Chen Yuko Kuroki Longbo Huang 104 12 0 29 Oct 2021
Near Instance Optimal Model Selection for Pure Exploration Linear Bandits Yinglun Zhu Julian Katz-Samuels Robert D. Nowak 55 7 0 10 Sep 2021
Pure Exploration in Kernel and Neural Bandits Yinglun Zhu Dongruo Zhou Ruoxi Jiang Quanquan Gu Rebecca Willett Robert D. Nowak 57 16 0 22 Jun 2021
Experimental Design for Regret Minimization in Linear Bandits Andrew Wagenmaker Julian Katz-Samuels Kevin Jamieson 114 16 0 01 Nov 2020
A Local Search Framework for Experimental Design L. Lau Hong Zhou 27 9 0 29 Oct 2020
Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Design Yufei Ruan Jiaqi Yang Yuanshuo Zhou OffRL 168 52 0 04 Jul 2020
An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits Julian Katz-Samuels Lalit P. Jain Zohar Karnin Kevin Jamieson 176 68 0 21 Jun 2020
$λ$ -Regularized A-Optimal Design and its Approximation by $λ$ -Regularized Proportional Volume Sampling U. Tantipongpipat 18 0 0 19 Jun 2020
Maximizing Determinants under Matroid Constraints V. Madan Aleksandar Nikolov Mohit Singh U. Tantipongpipat 49 8 0 16 Apr 2020
Active Learning for Graph Neural Networks via Node Feature Propagation Yuexin Wu Yichong Xu Aarti Singh Yiming Yang A. Dubrawski GNN AI4CE 101 65 0 16 Oct 2019
Sequential Experimental Design for Transductive Linear Bandits Tanner Fiez Lalit P. Jain Kevin Jamieson Lillian J. Ratliff 77 109 0 20 Jun 2019
Bandit Principal Component Analysis W. Kotłowski Gergely Neu 48 17 0 08 Feb 2019
Composable Core-sets for Determinant Maximization Problems via Spectral Spanners Piotr Indyk S. Mahabadi S. Gharan A. Rezaei 50 20 0 31 Jul 2018
Proportional Volume Sampling and Approximation Algorithms for A-Optimal Design Aleksandar Nikolov Mohit Singh U. Tantipongpipat 96 48 0 22 Feb 2018
Gaussian Process Landmarking on Manifolds Tingran Gao S. Kovalsky Ingrid Daubechies 124 39 0 09 Feb 2018
Active Regression via Linear-Sample Sparsification Xue Chen Eric Price 127 62 0 27 Nov 2017