40 citations · 101 across the 14 of their papers we have counts for
15 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…
MICO: Selective Search with Mutual Information Co-training
Zhanyu Wang, Xiao Zhang, Hyokun Yun +2
In contrast to traditional exhaustive search, selective search first clusters documents into several groups before all the documents are searched exhaustively by a query, to limit…
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…
Tiering as a Stochastic Submodular Optimization Problem
Hyokun Yun, Michael Froh, Roshan Makhijani +3
Tiering is an essential technique for building large-scale information retrieval systems. While the selection of documents for high priority tiers critically impacts the efficiency…
Robustness to Capitalization Errors in Named Entity Recognition
Sravan Bodapati, Hyokun Yun, Yaser Al-Onaizan
Robustness to capitalization errors is a highly desirable characteristic of named entity recognizers, yet we find standard models for the task are surprisingly brittle to such nois…
Deep Active Learning for Named Entity Recognition
Yanyao Shen, Hyokun Yun, Zachary C. Lipton +2
Deep learning has yielded state-of-the-art performance on many natural language processing tasks including named entity recognition (NER). However, this typically requires large am…
DS-MLR: Exploiting Double Separability for Scaling up Distributed Multinomial Logistic Regression
Parameswaran Raman, Sriram Srinivasan, Shin Matsushima +3
Scaling multinomial logistic regression to datasets with very large number of data points and classes is challenging. This is primarily because one needs to compute the log-partiti…
WordRank: Learning Word Embeddings via Robust Ranking
Shihao Ji, Hyokun Yun, Pinar Yanardag +2
Embedding words in a vector space has gained a lot of attention in recent years. While state-of-the-art methods provide efficient computation of word similarities via a low-dimensi…
A Scalable Asynchronous Distributed Algorithm for Topic Modeling
Hsiang-Fu Yu, Cho-Jui Hsieh, Hyokun Yun +2
Learning meaningful topic models with massive document collections which contain millions of documents and billions of tokens is challenging because of two reasons: First, one need…
Distributed Stochastic Optimization of the Regularized Risk
Shin Matsushima, Hyokun Yun, Xinhua Zhang +1
Many machine learning algorithms minimize a regularized risk, and stochastic optimization is widely used for this task. When working with massive data, it is desirable to perform s…
Ranking via Robust Binary Classification and Parallel Parameter Estimation in Large-Scale Data
Hyokun Yun, Parameswaran Raman, S. V. N. Vishwanathan
We propose RoBiRank, a ranking algorithm that is motivated by observing a close connection between evaluation metrics for learning to rank and loss functions for robust classificat…
NOMAD: Non-locking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion
Hyokun Yun, Hsiang-Fu Yu, Cho-Jui Hsieh +2
We develop an efficient parallel distributed algorithm for matrix completion, named NOMAD (Non-locking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix…
Efficiently Sampling Multiplicative Attribute Graphs Using a Ball-Dropping Process
Hyokun Yun, S. V. N. Vishwanathan
We introduce a novel and efficient sampling algorithm for the Multiplicative Attribute Graph Model (MAGM - Kim and Leskovec (2010)}). Our algorithm is \emph{strictly} more efficien…
Quilting Stochastic Kronecker Product Graphs to Generate Multiplicative Attribute Graphs
Hyokun Yun, S. V. N. Vishwanathan
We describe the first sub-quadratic sampling algorithm for the Multiplicative Attribute Graph Model (MAGM) of Kim and Leskovec (2010). We exploit the close connection between MAGM…
Using Logistic Regression to Analyze the Balance of a Game: The Case of StarCraft II
Hyokun Yun
Recently, the market size of online game has been increasing astonishingly fast, and so does the importance of good game design. In online games, usually a human user competes with…