84 citations · 295 across the 18 of their papers we have counts for
19 papers
Threshold-aware Learning to Generate Feasible Solutions for Mixed Integer Programs
Taehyun Yoon, Jinwon Choi, Hyokun Yun +1
Finding a high-quality feasible solution to a combinatorial optimization (CO) problem in a limited time is challenging due to its discrete nature. Recently, there has been an incre…
Bag of Tricks for In-Distribution Calibration of Pretrained Transformers
Jaeyoung Kim, Dongbin Na, Sungchul Choi +1
While pre-trained language models (PLMs) have become a de-facto standard promoting the accuracy of text classification tasks, recent studies find that PLMs often predict over-confi…
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with Drone
Aigerim Bogyrbayeva, Taehyun Yoon, Hanbum Ko +3
Reinforcement learning has recently shown promise in learning quality solutions in many combinatorial optimization problems. In particular, the attention-based encoder-decoder mode…
Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards
Kyungjae Lee, Hongjun Yang, Sungbin Lim +1
In this paper, we consider stochastic multi-armed bandits (MABs) with heavy-tailed rewards, whose -th moment is bounded by a constant for . First, we propose a…
Neural Bootstrapper
Minsuk Shin, Hyungjoo Cho, Hyun-seok Min +1
Bootstrapping has been a primary tool for ensemble and uncertainty quantification in machine learning and statistics. However, due to its nature of multiple training and resampling…
AutoCLINT: The Winning Method in AutoCV Challenge 2019
Woonhyuk Baek, Ildoo Kim, Sungwoong Kim +1
NeurIPS 2019 AutoDL challenge is a series of six automated machine learning competitions. Particularly, AutoCV challenges mainly focused on classification tasks on visual domain. I…