8 citations · 48 across the 17 of their papers we have counts for
17 papers
LPML: LLM-Prompting Markup Language for Mathematical Reasoning
Ryutaro Yamauchi, Sho Sonoda, Akiyoshi Sannai +1
In utilizing large language models (LLMs) for mathematical reasoning, addressing the errors in the reasoning and calculation present in the generated text by LLMs is a crucial chal…
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice
Toshinori Kitamura, Tadashi Kozuno, Yunhao Tang +12
Mirror descent value iteration (MDVI), an abstraction of Kullback-Leibler (KL) and entropy-regularized reinforcement learning (RL), has served as the basis for recent high-performi…
Langevin Autoencoders for Learning Deep Latent Variable Models
Shohei Taniguchi, Yusuke Iwasawa, Wataru Kumagai +1
Markov chain Monte Carlo (MCMC), such as Langevin dynamics, is valid for approximating intractable distributions. However, its usage is limited in the context of deep latent variab…
Equivariant and Invariant Reynolds Networks
Akiyoshi Sannai, Makoto Kawano, Wataru Kumagai
Invariant and equivariant networks are useful in learning data with symmetry, including images, sets, point clouds, and graphs. In this paper, we consider invariant and equivariant…
Group Equivariant Conditional Neural Processes
Makoto Kawano, Wataru Kumagai, Akiyoshi Sannai +2
We present the group equivariant conditional neural process (EquivCNP), a meta-learning method with permutation invariance in a data set as in conventional conditional neural proce…
Universal Approximation Theorem for Equivariant Maps by Group CNNs
Wataru Kumagai, Akiyoshi Sannai
Group symmetry is inherent in a wide variety of data distributions. Data processing that preserves symmetry is described as an equivariant map and often effective in achieving high…
Variable Selection for Nonparametric Learning with Power Series Kernels
Kota Matsui, Wataru Kumagai, Kenta Kanamori +2
In this paper, we propose a variable selection method for general nonparametric kernel-based estimation. The proposed method consists of two-stage estimation: (1) construct a consi…
Regret Analysis for Continuous Dueling Bandit
Wataru Kumagai
The dueling bandit is a learning framework wherein the feedback information in the learning process is restricted to a noisy comparison between a pair of actions. In this research,…
Learning Bound for Parameter Transfer Learning
Wataru Kumagai
We consider a transfer-learning problem by using the parameter transfer approach, where a suitable parameter of feature mapping is learned through one task and applied to another o…
Asymptotic Compatibility between LOCC Conversion and Recovery
Kosuke Ito, Wataru Kumagai, Masahito Hayashi
Recently, entanglement concentration was explicitly shown to be irreversible. However, it is still not clear what kind of states can be reversibly converted in the asymptotic setti…
Parallel Distributed Block Coordinate Descent Methods based on Pairwise Comparison Oracle
Kota Matsui, Wataru Kumagai, Takafumi Kanamori
This paper provides a block coordinate descent algorithm to solve unconstrained optimization problems. In our algorithm, computation of function values or gradients is not required…
Random Number Conversion and LOCC Conversion via Restricted Storage
Wataru Kumagai, Masahito Hayashi
We consider random number conversion (RNC) through random number storage with restricted size. We clarify the relation between the performance of RNC and the size of storage in the…
Second-Order Asymptotics of Conversions of Distributions and Entangled States Based on Rayleigh-Normal Probability Distributions
Wataru Kumagai, Masahito Hayashi
We discuss the asymptotic behavior of conversions between two independent and identical distributions up to the second-order conversion rate when the conversion is produced by a de…
Trade-off between Performance and Reversibility of Entanglement Concentration for Pure Entangled State
Wataru Kumagai, Masahito Hayashi
In quantum information theory, it is widely believed that entanglement concentration for bipartite pure states is asymptotically reversible. In order to examine this, we give a pre…
Second Order Asymptotics for Random Number Generation
Wataru Kumagai, Masahito Hayashi
We treat a random number generation from an i.i.d. probability distribution of to that of . When or is a uniform distribution, the problems have been well-known as t…
Irreversibility of Entanglement Concentration for Pure State
Wataru Kumagai, Masahito Hayashi
For a pure state on a composite system , both the entanglement cost and the distillable entanglement coincide with the von…
Quantum hypothesis testing for quantum Gaussian states: Quantum analogues of chi-square, t and F tests
Wataru Kumagai, Masahito Hayashi
We treat quantum counterparts of testing problems whose optimal tests are given by chi-square, t and F tests. These quantum counterparts are formulated as quantum hypothesis testin…