no code implementations • 21 May 2024 • Taihei Oki, Shinsaku Sakaue
For the stochastic bandit setting, we present $O(T^{-1/2})$-simple regret and $O(T^{2/3})$-regret algorithms under $T$ times access to unbiased noisy value oracles of M${}^{\natural}$-concave functions.
no code implementations • 13 Feb 2024 • Shinsaku Sakaue, Han Bao, Taira Tsuchiya, Taihei Oki
We extend the exploit-the-surrogate-gap framework to online structured prediction with \emph{Fenchel--Young losses}, a large family of surrogate losses including the logistic loss for multiclass classification, obtaining finite surrogate regret bounds in various structured prediction problems.
no code implementations • 1 Sep 2023 • Shinsaku Sakaue, Taihei Oki
Experiments demonstrate that learning projection matrices from data is indeed beneficial: it leads to significantly higher solution quality than the existing random projection while greatly reducing the time for solving LPs.
no code implementations • 2 Feb 2023 • Shinsaku Sakaue, Taihei Oki
The main technical difficulty lies in learning predictions that are provably close to sets of all optimal solutions, for which we present an online-gradient-descent-based method.
no code implementations • 17 Sep 2022 • Shinsaku Sakaue, Taihei Oki
Specifically, for rank-$k$ approximation using an $m \times n$ learned sketching matrix with $s$ non-zeros in each column, they proved an $\tilde{\mathrm{O}}(nsm)$ bound on the \emph{fat shattering dimension} ($\tilde{\mathrm{O}}$ hides logarithmic factors).
1 code implementation • 13 Jun 2022 • Shinichi Hemmi, Taihei Oki, Shinsaku Sakaue, Kaito Fujii, Satoru Iwata
One classical and practical method is the lazy greedy algorithm, which is applicable to general submodular function maximization, while a recent fast greedy algorithm based on the Cholesky factorization is more efficient for DPP MAP inference.
no code implementations • 20 May 2022 • Shinsaku Sakaue, Taihei Oki
Motivated by this emerging approach, we study the sample complexity of learning heuristic functions for GBFS and A*.
no code implementations • 20 May 2022 • Shinsaku Sakaue, Taihei Oki
Augmenting algorithms with learned predictions is a promising approach for going beyond worst-case bounds.
no code implementations • 21 Dec 2017 • Takashi Kurokawa, Taihei Oki, Hiromichi Nagao
The proposed methods are applicable to a wide variety of data that can be regarded as signals on Cartesian product graphs.