arXiv research

This work extends identifiability analysis to sequential latent variable models, focusing on Switching Dynamical Systems.

New algorithm learns switching dynamics from multiple neural signals.

As a metric to measure the performance of an online method, dynamic regret with switching cost has drawn much attention for online decision making problems. Although the sublinear regret has been provided in many previous researches, we still have little knowledge about the relation between the dynamic regret and the s…

Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switching linear dynamical system (SLDS) and the switching vector autoregressive (VAR) process. Our Bayesian nonparametric approach utilizes a hiera…

Framework models multiscale dynamics with Bayesian learning for regime changes.

Many natural systems, such as neurons firing in the brain or basketball teams traversing a court, give rise to time series data with complex, nonlinear dynamics. We can gain insight into these systems by decomposing the data into segments that are each explained by simpler dynamic units. Building on switching linear dy…

New algorithms improve sampling from complex distributions.

This work addresses identifiability in sequential data with switching dynamics, introducing a new estimator.

Breaks down complex nonlinear dynamics into simpler components.

The stochastic knapsack has been used as a model in wide ranging applications from dynamic resource allocation to admission control in telecommunication. In recent years, a variation of the model has become a basic tool in studying problems that arise in revenue management and dynamic/flexible pricing; and it is in thi…

SCaLE tackles dynamic regret in noisy bandit feedback with switching costs.

Using movement primitive libraries is an effective means to enable robots to solve more complex tasks. In order to build these movement libraries, current algorithms require a prior segmentation of the demonstration trajectories. A promising approach is to model the trajectory as being generated by a set of Switching L…

Proposes a new model for better speech segmentation.

We introduce a machine learning approach for extracting fine-grained representations of protein evolution from molecular dynamics datasets. Metastable switching linear dynamical systems extend standard switching models with a physically-inspired stability constraint. This constraint enables the learning of nuanced repr…

PCGS-TF uses a Transformer to adaptively control expert switching in non-stationary environments.

Optimal switching regret for all segmentations in online convex optimisation.

The paper improves competitive and dynamic regret bounds for smoothed online learning.

We study the problem of dynamically trading futures in a regime-switching market. Modeling the underlying asset price as a Markov-modulated diffusion process, we present a utility maximization approach to determine the optimal futures trading strategy. This leads to the analysis of the associated system of Hamilton-Jac…

ITF improves DSR but inflates curvature, while marginal likelihood reduces it, affecting QoIs.

Paper proposes variational inference for piecewise-linear systems.

New algorithm tackles non-stationary combinatorial semi-bandit problems with optimal regret bounds.

Model interest rates and energy futures with regime-switching dynamics.

Develops a method to model neural dynamics with flexible yet interpretable latent states.

Bayesian method clusters time series with varying dynamics.

The problem of optimal switching between nonlinear autonomous subsystems is investigated in this study where the objective is not only bringing the states to close to the desired point, but also adjusting the switching pattern, in the sense of penalizing switching occurrences and assigning different preferences to util…

New algorithm reduces decision switching in dynamic environments.

Study optimal investment and reinsurance for insurance companies in a dynamic market model.

New algorithm for continuous-time switching systems using variational inference.

OMGD algorithm optimizes online convex optimization with switching costs and delayed gradients.

We propose an efficient inference method for switching nonlinear dynamical systems. The key idea is to learn an inference network which can be used as a proposal distribution for the continuous latent variables, while performing exact marginalization of the discrete latent variables. This allows us to use the reparamet…

We consider an impulse control problem in infinite horizon applied with switching technology. We suppose that the firm decides at certain moments (impulse moments) to switch technology, leading to a jump of the firm value. We show that the value function for such problems satisfies a dynamic programming principle versi…

In this paper we consider the problem of calculating the quantiles of a risky position, the dynamic of which is described as a continuous time regime-switching jump-diffusion, by using Fourier Transform methods. Furthermore, we study a classical option-based portfolio strategy which minimizes the Value-at-Risk of the h…

New algorithm minimizes cumulative loss in dynamic linear bandits without prior knowledge of comparator switches.

Estimates hybrid dynamical systems with polynomial expansions and Markovian switching.

SALT models combine ARHMM and SLDS for efficient, interpretable time-series analysis.

This paper proposes a multi-scale Markov-Switching GARCH model for EUR/USD volatility.

Researchers develop a method to control nonlinear systems with Koopman operator regression.

ADVISOR dynamically balances imitation and reinforcement learning to overcome the imitation gap.

Patients with epilepsy can manifest short, sub-clinical epileptic "bursts" in addition to full-blown clinical seizures. We believe the relationship between these two classes of events---something not previously studied quantitatively---could yield important insights into the nature and intrinsic dynamics of seizures. A…

Unified framework detects change-points and estimates parameters in nonlinear systems with regime switching.

Optimized portfolio management with dynamic market regimes using RL and OC learning.

This paper studies the impact of limited switches on resource-constrained dynamic pricing with demand learning. We focus on the classical price-based blind network revenue management problem and extend our results to the bandits with knapsacks problem. In both settings, a decision maker faces stochastic and distributio…

Near-logarithmic regret per switch achieved for mixable/exp-concave losses.

This paper studies the optimal VIX futures trading problems under a regime-switching model. We consider the VIX as mean reversion dynamics with dependence on the regime that switches among a finite number of states. For the trading strategies, we analyze the timings and sequences of the investor's market participation,…

New framework optimizes deep learning training by deferring large batch sizes to late stages.

Latent force models (LFMs) are hybrid models combining mechanistic principles with non-parametric components. In this article, we shall show how LFMs can be equivalently formulated and solved using the state variable approach. We shall also show how the Gaussian process prior used in LFMs can be equivalently formulated…

SSL framework identifies non-linear systems without labeled data.

Study optimal portfolios in a non-Markovian regime-switching model with random time horizon.