17 citations · 48 across the 26 of their papers we have counts for
9 papers · 2 filters
Value-Rectified Distillation for Flow-based Offline Reinforcement Learning
Ke Jiang, Wen Jiang, Yoshinobu Kawahara +1
We aim to address the problem of Out-of-Distribution (OOD) Mode Averaging, a critical problem that emerges when distilling Flow Matching (FM) based behavior policies in offline Rei…
Invariant-Measure Reasoners: Stable Representations for Latent Reasoning
Yuto Inui, Takuya Konishi, Yoshinobu Kawahara
Latent reasoning models repeatedly update a latent state using the same recurrent block. As the recurrent depth increases, the sequence of latent states may converge to a compact s…
Causal Representation Learning with Instantaneous and Lagged Relations via Nonstationarity
Tatsuya Yamada, Hiroshi Morioka, Yoshinobu Kawahara
Causal representation learning for time-series data aims to identify latent states and their causal relations from observations. In this setting, an important challenge is to model…
Robust data-driven discovery of fractional differential equations via weak formulations and Pareto-based subset selection
Pongpisit Thanasutives, Yoshinobu Kawahara
Fractional partial differential equations describe nonlocal dynamics, but discovering them from noisy data is difficult because fractional differentiation amplifies high-frequency…
Dynamics-aware identification of governing equations from sparse and noisy data
Pongpisit Thanasutives, Yoshinobu Kawahara
Sparse identification of nonlinear dynamics (SINDy) and PDE functional identification (PDE-FIND) recover parsimonious ordinary and partial differential equations (ODEs and PDEs) fr…
Structured Noise Adaptation for Sequential Bayesian Filtering with Embedded Latent Transfer Operators
Naichang Ke, Pongpisit Thanasutives, Yoshinobu Kawahara
Kalman filters based on the Embedded Latent Transfer Operators (ELTO) emerge as novel statistical tools for sequential state estimation. However, a critical limitation stems from t…
Deep Spectral Learning of Embedded Latent Transfer Operators for Stochastic Dynamical Systems
Ryogo Tanaka, Yoshinobu Kawahara
We propose a spectral learning method for stochastic nonlinear dynamical systems represented with embedded latent transfer operators in deep feature spaces. We instantiate the meth…
Mesh Field Theory: Port-Hamiltonian Formulation of Mesh-Based Physics
Satoshi Noguchi, Yoshinobu Kawahara
We present Mesh Field Theory (MeshFT) and its neural realization, MeshFT-Net: a structure-preserving framework for mesh-based continuum physics that cleanly separates the physics'…
Dualformer: Time-Frequency Dual Domain Learning for Long-term Time Series Forecasting
Jingjing Bai, Yoshinobu Kawahara
Transformer-based models, despite their promise for long-term time series forecasting (LTSF), suffer from an inherent low-pass filtering effect that limits their effectiveness. Thi…