11 citations · 18 across the 14 of their papers we have counts for
6 papers · 1 filter
Hyperparameter-Free Approach for Faster Minimum Bayes Risk Decoding
Yuu Jinnai, Kaito Ariu
Minimum Bayes-Risk (MBR) decoding is shown to be a powerful alternative to beam search decoding for a wide range of text generation tasks. However, MBR requires a huge amount of ti…
Model-Based Minimum Bayes Risk Decoding for Text Generation
Yuu Jinnai, Tetsuro Morimura, Ukyo Honda +2
Minimum Bayes Risk (MBR) decoding has been shown to be a powerful alternative to beam search decoding in a variety of text generation tasks. MBR decoding selects a hypothesis from…
Discovering Options for Exploration by Minimizing Cover Time
Yuu Jinnai, Jee Won Park, David Abel +1
One of the main challenges in reinforcement learning is solving tasks with sparse reward. We show that the difficulty of discovering a distant rewarding state in an MDP is bounded…
Finding Options that Minimize Planning Time
Yuu Jinnai, David Abel, D Ellis Hershkowitz +2
We formalize the problem of selecting the optimal set of options for planning as that of computing the smallest set of options so that planning converges in less than a given maxim…
A Survey of Parallel A*
Alex Fukunaga, Adi Botea, Yuu Jinnai +1
A* is a best-first search algorithm for finding optimal-cost paths in graphs. A* benefits significantly from parallelism because in many applications, A* is limited by memory usage…
On Hash-Based Work Distribution Methods for Parallel Best-First Search
Yuu Jinnai, Alex Fukunaga
Parallel best-first search algorithms such as Hash Distributed A* (HDA*) distribute work among the processes using a global hash function. We analyze the search and communication o…