8 citations · 11 across the 6 of their papers we have counts for
6 papers
Better-than-KL PAC-Bayes Bounds
Ilja Kuzborskij, Kwang-Sung Jun, Yulian Wu +2
Let be a sequence of random elements, where is a fixed scalar function, are independent random variables (data), and i…
Graph Sparsifications using Neural Network Assisted Monte Carlo Tree Search
Alvin Chiu, Mithun Ghosh, Reyan Ahmed +3
Graph neural networks have been successful for machine learning, as well as for combinatorial and graph problems such as the Subgraph Isomorphism Problem and the Traveling Salesman…
Nearly Optimal Steiner Trees using Graph Neural Network Assisted Monte Carlo Tree Search
Reyan Ahmed, Mithun Ghosh, Kwang-Sung Jun +1
Graph neural networks are useful for learning problems, as well as for combinatorial and graph problems such as the Subgraph Isomorphism Problem and the Traveling Salesman Problem.…
Tighter PAC-Bayes Bounds Through Coin-Betting
Kyoungseok Jang, Kwang-Sung Jun, Ilja Kuzborskij +1
We consider the problem of estimating the mean of a sequence of random elements where is a fixed scalar function, ar…
Norm-Agnostic Linear Bandits
Spencer, Gales, Sunder Sethuraman +1
Linear bandits have a wide variety of applications including recommendation systems yet they make one strong assumption: the algorithms must know an upper bound on the norm of…
Graph-Based Active Learning: A New Look at Expected Error Minimization
Kwang-Sung Jun, Robert Nowak
In graph-based active learning, algorithms based on expected error minimization (EEM) have been popular and yield good empirical performance. The exact computation of EEM optimally…