17 citations · 48 across the 26 of their papers we have counts for
20 papers · 1 filter
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…
Data-driven simulator of multi-animal behavior with unknown dynamics via offline and online reinforcement learning
Keisuke Fujii, Kazushi Tsutsui, Yu Teshima +6
Simulators of animal movements play a valuable role in studying behavior. Advances in imitation learning for robotics have expanded possibilities for reproducing human and animal m…
Wavy Transformer
Satoshi Noguchi, Yoshinobu Kawahara
Transformers have achieved remarkable success across natural language processing (NLP) and computer vision (CV). However, deep transformer models often suffer from an over-smoothin…
Learning Stochastic Nonlinear Dynamics with Embedded Latent Transfer Operators
Naichang Ke, Ryogo Tanaka, Yoshinobu Kawahara
We consider an operator-based latent Markov representation of a stochastic nonlinear dynamical system, where the stochastic evolution of the latent state embedded in a reproducing…
SiT: Symmetry-Invariant Transformers for Generalisation in Reinforcement Learning
Matthias Weissenbacher, Rishabh Agarwal, Yoshinobu Kawahara
An open challenge in reinforcement learning (RL) is the effective deployment of a trained policy to new or slightly different situations as well as semantically-similar environment…
The Disappearance of Timestep Embedding in Modern Time-Dependent Neural Networks
Bum Jun Kim, Yoshinobu Kawahara, Sang Woo Kim
Dynamical systems are often time-varying, whose modeling requires a function that evolves with respect to time. Recent studies such as the neural ordinary differential equation pro…
Glocal Hypergradient Estimation with Koopman Operator
Ryuichiro Hataya, Yoshinobu Kawahara
Gradient-based hyperparameter optimization methods update hyperparameters using hypergradients, gradients of a meta criterion with respect to hyperparameters. Previous research use…
Learning interaction rules from multi-animal trajectories via augmented behavioral models
Keisuke Fujii, Naoya Takeishi, Kazushi Tsutsui +10
Extracting the interaction rules of biological agents from movement sequences pose challenges in various domains. Granger causality is a practical framework for analyzing the inter…
A Quadratic Actor Network for Model-Free Reinforcement Learning
Matthias Weissenbacher, Yoshinobu Kawahara
In this work we discuss the incorporation of quadratic neurons into policy networks in the context of model-free actor-critic reinforcement learning. Quadratic neurons admit an exp…
Discriminant Dynamic Mode Decomposition for Labeled Spatio-Temporal Data Collections
Naoya Takeishi, Keisuke Fujii, Koh Takeuchi +1
Extracting coherent patterns is one of the standard approaches towards understanding spatio-temporal data. Dynamic mode decomposition (DMD) is a powerful tool for extracting cohere…
Decentralized policy learning with partial observation and mechanical constraints for multiperson modeling
Keisuke Fujii, Naoya Takeishi, Yoshinobu Kawahara +1
Extracting the rules of real-world multi-agent behaviors is a current challenge in various scientific and engineering fields. Biological agents independently have limited observati…
A Characteristic Function for Shapley-Value-Based Attribution of Anomaly Scores
Naoya Takeishi, Yoshinobu Kawahara
In anomaly detection, the degree of irregularity is often summarized as a real-valued anomaly score. We address the problem of attributing such anomaly scores to input features for…