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Found 41 papers, 10 papers with code
Koopman operators with intrinsic observables in rigged reproducing kernel Hilbert spaces
This paper presents a novel approach for estimating the Koopman operator defined on a reproducing kernel Hilbert space (RKHS) and its spectra.
Glocal Hypergradient Estimation with Koopman Operator
Through numerical experiments of hyperparameter optimization, including optimization of optimizers, we demonstrate the effectiveness of the glocal hypergradient estimation.
Fast, accurate, and interpretable decoding of electrocorticographic signals using dynamic mode decomposition
DMs can improve the accuracy of neural decoding when used with the nonlinear Grassmann kernel, compared to conventional power features.
Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations
In the experiments, using chase-and-escape and football tasks with the different dynamics between the unknown source and target environments, we show that our approach achieved a balance between the reproducibility and the generalization ability compared with the baselines.
Modeling Nonlinear Dynamics in Continuous Time with Inductive Biases on Decay Rates and/or Frequencies
Inductive biases are helpful for training neural networks especially when training data are small.
Data-driven End-to-end Learning of Pole Placement Control for Nonlinear Dynamics via Koopman Invariant Subspaces
With the proposed method, a policy network is trained such that the eigenvalues of a Koopman operator of controlled dynamics are close to the target eigenvalues.
Stable Invariant Models via Koopman Spectra
Weight-tied models have attracted attention in the modern development of neural networks.
Estimating counterfactual treatment outcomes over time in complex multiagent scenarios
Evaluation of intervention in a multiagent system, e. g., when humans should intervene in autonomous driving systems and when a player should pass to teammates for a good shot, is challenging in various engineering and scientific fields.
Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics
The learned policies may then be deployed in real-world settings where interactions are costly or dangerous.
Ranked #1 on
Offline RL
on D4RL
Learning interaction rules from multi-animal trajectories via augmented behavioral models
In this paper, we propose a new framework for learning Granger causality from multi-animal trajectories via augmented theory-based behavioral models with interpretable data-driven models.
Koopman Spectrum Nonlinear Regulator and Provably Efficient Online Learning
In this work, we present a novel paradigm of controlling nonlinear systems via the minimization of the Koopman spectrum cost: a cost over the Koopman operator of the controlled dynamics.
A Quadratic Actor Network for Model-Free Reinforcement Learning
In this work we discuss the incorporation of quadratic neurons into policy networks in the context of model-free actor-critic reinforcement learning.
Discriminant Dynamic Mode Decomposition for Labeled Spatio-Temporal Data Collections
Extracting coherent patterns is one of the standard approaches towards understanding spatio-temporal data.
Meta-Learning for Koopman Spectral Analysis with Short Time-series
With the proposed method, a representation of a given short time-series is obtained by a bidirectional LSTM for extracting its properties.
Reproducing kernel Hilbert C*-module and kernel mean embeddings
Kernel methods have been among the most popular techniques in machine learning, where learning tasks are solved using the property of reproducing kernel Hilbert space (RKHS).
Neural Dynamic Mode Decomposition for End-to-End Modeling of Nonlinear Dynamics
With our proposed method, the forecast error is backpropagated through the neural networks and the spectral decomposition, enabling end-to-end learning of Koopman spectral analysis.
Kernel Mean Embeddings of Von Neumann-Algebra-Valued Measures
Kernel mean embedding (KME) is a powerful tool to analyze probability measures for data, where the measures are conventionally embedded into a reproducing kernel Hilbert space (RKHS).
Decentralized policy learning with partial observation and mechanical constraints for multiperson modeling
Extracting the rules of real-world multi-agent behaviors is a current challenge in various scientific and engineering fields.
Learning Dynamics Models with Stable Invariant Sets
Invariance and stability are essential notions in dynamical systems study, and thus it is of great interest to learn a dynamics model with a stable invariant set.
A Characteristic Function for Shapley-Value-Based Attribution of Anomaly Scores
In anomaly detection, the degree of irregularity is often summarized as a real-valued anomaly score.
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
Analysis via Orthonormal Systems in Reproducing Kernel Hilbert $C^*$-Modules and Applications
Kernel methods have been among the most popular techniques in machine learning, where learning tasks are solved using the property of reproducing kernel Hilbert space (RKHS).
UNIVERSAL MODAL EMBEDDING OF DYNAMICS IN VIDEOS AND ITS APPLICATIONS
Extracting underlying dynamics of objects in image sequences is one of the challenging problems in computer vision.
Krylov Subspace Method for Nonlinear Dynamical Systems with Random Noise
In this paper, we address a lifted representation of nonlinear dynamical systems with random noise based on transfer operators, and develop a novel Krylov subspace method for estimating the operators using finite data, with consideration of the unboundedness of operators.
Metric on random dynamical systems with vector-valued reproducing kernel Hilbert spaces
We empirically illustrate our metric with synthetic data, and evaluate it in the context of the independence test for random processes.
Physically-interpretable classification of biological network dynamics for complex collective motions
A fundamental question addressed here pertains to the classification of collective motion network based on physically-interpretable dynamical properties.
An efficient branch-and-cut algorithm for approximately submodular function maximization
For the problem of maximizing an approximately submodular function (ASFM problem), a greedy algorithm quickly finds good feasible solutions for many instances while guaranteeing ($1-e^{-\gamma}$)-approximation ratio for a given submodular ratio $\gamma$.
Knowledge-Based Regularization in Generative Modeling
Prior domain knowledge can greatly help to learn generative models.
An efficient branch-and-bound algorithm for submodular function maximization
Nemhauser and Wolsey developed an exact algorithm called the constraint generation algorithm that starts from a reduced BIP problem with a small subset of constraints taken from the constraints and repeats solving a reduced BIP problem while adding a new constraint at each iteration.
Dynamic mode decomposition in vector-valued reproducing kernel Hilbert spaces for extracting dynamical structure among observables
In this paper, we formulate Koopman spectral analysis for NLDSs with structures among observables and propose an estimation algorithm for this problem.
Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators
The development of a metric for structural data is a long-term problem in pattern recognition and machine learning.
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition
Spectral decomposition of the Koopman operator is attracting attention as a tool for the analysis of nonlinear dynamical systems.
Dynamic Mode Decomposition with Reproducing Kernels for Koopman Spectral Analysis
In this paper, we consider a spectral analysis of the Koopman operator in a reproducing kernel Hilbert space (RKHS).
Parametric Maxflows for Structured Sparse Learning with Convex Relaxations of Submodular Functions
The proximal problem for structured penalties obtained via convex relaxations of submodular functions is known to be equivalent to minimizing separable convex functions over the corresponding submodular polyhedra.
A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model
Structural equation models and Bayesian networks have been widely used to analyze causal relations between continuous variables.
Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM
Discovering causal relations among observed variables in a given data set is a major objective in studies of statistics and artificial intelligence.
Structured Convex Optimization under Submodular Constraints
A number of discrete and continuous optimization problems in machine learning are related to convex minimization problems under submodular constraints.
Weighted Likelihood Policy Search with Model Selection
Reinforcement learning (RL) methods based on direct policy search (DPS) have been actively discussed to achieve an efficient approach to complicated Markov decision processes (MDPs).
Efficient network-guided multi-locus association mapping with graph cuts
We present SConES, a new efficient method to discover sets of genetic loci that are maximally associated with a phenotype, while being connected in an underlying network.
Minimum Average Cost Clustering
In this paper, we introduce the minimum average cost criterion, and show that the theory of intersecting submodular functions can be used for clustering with submodular objective functions.
Submodularity Cuts and Applications
Several key problems in machine learning, such as feature selection and active learning, can be formulated as submodular set function maximization.
