activity
20142026
most citedMove Evaluation in Go Using Deep Convolutional Neural Networks

93 citations · 177 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2026★ 2 cited

Advancing Mathematics Research with AI-Driven Formal Proof Search

George Tsoukalas, Anton Kovsharov, Sergey Shirobokov +18

Large language models (LLMs) increasingly excel at mathematical reasoning, but their unreliability limits their utility in mathematics research. A mitigation is using LLMs to gener…

stat.ML2026

An Efficient Algorithm for Thresholding Monte Carlo Tree Search

Shoma Nameki, Atsuyoshi Nakamura, Junpei Komiyama +1

We introduce the Thresholding Monte Carlo Tree Search problem, in which, given a tree and a threshold , a player must answer whether the root node value of $\mathc…

cs.LG2023★ 5 cited

AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning

Michaël Mathieu, Sherjil Ozair, Srivatsan Srinivasan +21

StarCraft II is one of the most challenging simulated reinforcement learning environments; it is partially observable, stochastic, multi-agent, and mastering StarCraft II requires…

cs.LG2018★ 77 cited

Bayesian Optimization in AlphaGo

Yutian Chen, Aja Huang, Ziyu Wang +4

During the development of AlphaGo, its many hyper-parameters were tuned with Bayesian optimization multiple times. This automatic tuning process resulted in substantial improvement…

cs.LG2014★ 93 cited

Move Evaluation in Go Using Deep Convolutional Neural Networks

Chris J. Maddison, Aja Huang, Ilya Sutskever +1

The game of Go is more challenging than other board games, due to the difficulty of constructing a position or move evaluation function. In this paper we investigate whether deep c…