Surrogate Functions for Maximizing Precision at the Top

26 May 2015

Papers citing "Surrogate Functions for Maximizing Precision at the Top"

10 / 10 papers shown

Title
A Recommender System for Scientific Datasets and Analysis Pipelines M. Mazaheri Greg Kiar Tristan Glatard AI4TS 21 3 0 20 Aug 2021
Implicit Rate-Constrained Optimization of Non-decomposable Objectives Abhishek Kumar Harikrishna Narasimhan Andrew Cotter 13 10 0 23 Jul 2021
General Framework for Binary Classification on Top Samples Lukáš Adam V. Mácha Václav Smídl Tomás Pevný 29 5 0 25 Feb 2020
Optimizing Black-box Metrics with Adaptive Surrogates Qijia Jiang Olaoluwa Adigun Harikrishna Narasimhan M. M. Fard Maya R. Gupta 14 16 0 20 Feb 2020
Rich-Item Recommendations for Rich-Users: Exploiting Dynamic and Static Side Information A. Budhiraja G. Hiranandani Darshak Chhatbar Aditya Sinha Navya Yarrabelly Ayush Choure Oluwasanmi Koyejo Prateek Jain 25 4 0 28 Jan 2020
Efficient Algorithms for Smooth Minimax Optimization K. K. Thekumparampil Prateek Jain Praneeth Netrapalli Sewoong Oh 22 190 0 02 Jul 2019
Backdrop: Stochastic Backpropagation Siavash Golkar Kyle Cranmer 19 2 0 04 Jun 2018
A plug-in approach to maximising precision at the top and recall at the top Dirk Tasche 16 7 0 09 Apr 2018
Constrained Classification and Ranking via Quantiles Alan Mackey Xiyang Luo Elad Eban 14 6 0 28 Feb 2018
Optimizing Non-decomposable Measures with Deep Networks Amartya Sanyal Pawan Kumar Purushottam Kar S. Chawla Fabrizio Sebastiani 20 26 0 31 Jan 2018