A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Method estimates observation functions in state-space models without supervision.
Adversarial attacks on probabilistic state-space models affect latent state and policy decisions.
In nonlinear state-space models, sequential learning about the hidden state can proceed by particle filtering when the density of the observation conditional on the state is available analytically (e.g. Gordon et al., 1993). This condition need not hold in complex environments, such as the incomplete-information equili…
A new method for state estimation in state-space models using incomplete data.
Predictive State Representations (PSRs) are an expressive class of models for controlled stochastic processes. PSRs represent state as a set of predictions of future observable events. Because PSRs are defined entirely in terms of observable data, statistically consistent estimates of PSR parameters can be learned effi…
New model captures state-dependent variability in partially observed systems.
Empirical mode modeling improves state-space analysis of noisy data.
New algorithm infers trajectories from partial observations using optimal transport.
New BED method handles online inference for partially observed dynamical systems.
The Kalman filter (KF) is used in a variety of applications for computing the posterior distribution of latent states in a state space model. The model requires a linear relationship between states and observations. Extensions to the Kalman filter have been proposed that incorporate linear approximations to nonlinear m…
New method for state inference in state-space models with unknown dynamics.
The paper analyzes variational autoencoders for state space models with risk bounds.
Reinforcement learning (RL) in Markov decision processes (MDPs) with large state spaces is a challenging problem. The performance of standard RL algorithms degrades drastically with the dimensionality of state space. However, in practice, these large MDPs typically incorporate a latent or hidden low-dimensional structu…
Efficient RL in large POMDPs with latent determinism and embeddings.
Bayesian model detects altered neural circuits in MCI patients.
A new method for analyzing high-dimensional time-series data using deep neural networks.
Method infers MJPs from noisy observations without prior training.
Efficiently estimates online variational learning using importance sampling.
NCDSSM models irregularly sampled time series with improved imputation and forecasting.
Autoregressive state transitions, where predictions are conditioned on past predictions, are the predominant choice for both deterministic and stochastic sequential models. However, autoregressive feedback exposes the evolution of the hidden state trajectory to potential biases from well-known train-test discrepancies.…
AUCRSS detects change points in partially observed multivariate autocorrelated data.
The paper tackles restless bandits with limited observation, proposing a method to analyze and approximate their optimal strategies.
CVRL tackles complex visual observations in reinforcement learning.
New algorithm for aggregate inference in HMMs with continuous observations.
A new model detects anomalies in time series data efficiently.
This paper addresses the problem of filtering with a state-space model. Standard approaches for filtering assume that a probabilistic model for observations (i.e. the observation model) is given explicitly or at least parametrically. We consider a setting where this assumption is not satisfied; we assume that the knowl…
New method for efficient online variational estimation in streaming data.
A new method learns state and proposal dynamics in state-space models using neural networks.
A new transformer model uses Monte Carlo methods for sequence prediction.
DAC-SSM learns domain-agnostic states for better imitation learning.
dSMC improves parallel processing of state-space models.
Learning a model of dynamics from high-dimensional images can be a core ingredient for success in many applications across different domains, especially in sequential decision making. However, currently prevailing methods based on latent-variable models are limited to working with low resolution images only. In this wo…
Paper unifies subspace identification and DMD for dynamical systems.
This paper uses Factored Latent Analysis (FLA) to learn a factorized, segmental representation for observations of tracked objects over time. Factored Latent Analysis is latent class analysis in which the observation space is subdivided and each aspect of the original space is represented by a separate latent class mod…
We forecast S&P 500 excess returns using a flexible Bayesian econometric state space model with non-Gaussian features at several levels. More precisely, we control for overparameterization via novel global-local shrinkage priors on the state innovation variances as well as the time-invariant part of the state space mod…
This paper addresses the data-driven identification of latent dynamical representations of partially-observed systems, i.e., dynamical systems for which some components are never observed, with an emphasis on forecasting applications, including long-term asymptotic patterns. Whereas state-of-the-art data-driven approac…
New method uncovers small but significant local activities in time-series data.
A nonparametric approach for policy learning for POMDPs is proposed. The approach represents distributions over the states, observations, and actions as embeddings in feature spaces, which are reproducing kernel Hilbert spaces. Distributions over states given the observations are obtained by applying the kernel Bayes' …
Clinical forecasting based on electronic medical records (EMR) can uncover the temporal correlations between patients' conditions and outcomes from sequences of longitudinal clinical measurements. In this work, we propose an intervention-augmented deep state space generative model to capture the interactions among clin…
New method for efficient probabilistic deep state-space models.
New technologies for recording the activity of large neural populations during complex behavior provide exciting opportunities for investigating the neural computations that underlie perception, cognition, and decision-making. Nonlinear state space models provide an interpretable signal processing framework by combinin…
Deep active inference learns policies from sensory inputs.
Many reinforcement learning (RL) tasks provide the agent with high-dimensional observations that can be simplified into low-dimensional continuous states. To formalize this process, we introduce the concept of a DeepMDP, a parameterized latent space model that is trained via the minimization of two tractable losses: pr…
We address tracking and prediction of multiple moving objects in visual data streams as inference and sampling in a disentangled latent state-space model. By encoding objects separately and including explicit position information in the latent state space, we perform tracking via amortized variational Bayesian inferenc…
Poyiadjis et al. (2011) show how particle methods can be used to estimate both the score and the observed information matrix for state space models. These methods either suffer from a computational cost that is quadratic in the number of particles, or produce estimates whose variance increases quadratically with the am…
A new method for state estimation on complex networks.
Model-based reinforcement learning methods typically learn models for high-dimensional state spaces by aiming to reconstruct and predict the original observations. However, drawing inspiration from model-free reinforcement learning, we propose learning a latent dynamics model directly from rewards. In this work, we int…
QATS efficiently decodes HMMs with polylogarithmic complexity.