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.
Divides state space into regions with identical term structure shapes.
Structured State-Space Duality connects SSMs to masked attention.
Generalizes bits back coding for time-series models with latent Markov structures.
A new method learns complex dynamical systems from data efficiently.
Stanza models complex time series with balance between traditional and deep learning approaches.
Structured state space models improve ECG classification and reveal new insights.
State-space systems generate probabilistic dependencies between inputs and outputs.
This work studies learning dynamics in SSMs, linking them to deep linear networks.
Gaussian state space models have been used for decades as generative models of sequential data. They admit an intuitive probabilistic interpretation, have a simple functional form, and enjoy widespread adoption. We introduce a unified algorithm to efficiently learn a broad class of linear and non-linear state space mod…
Implements SSSD for missing value imputation and forecasting in time series data.
The problem of combined state and input estimation of linear structural systems based on measured responses and a priori knowledge of structural model is considered. A novel methodology using Gaussian process latent force models is proposed to tackle the problem in a stochastic setting. Gaussian process latent force mo…
We consider the problem of learning low-dimensional representations for large-scale Markov chains. We formulate the task of representation learning as that of mapping the state space of the model to a low-dimensional state space, called the kernel space. The kernel space contains a set of meta states which are desired …
Method infers causal structure from system behaviors using RKHS and kernel -machines.
How can we efficiently propagate uncertainty in a latent state representation with recurrent neural networks? This paper introduces stochastic recurrent neural networks which glue a deterministic recurrent neural network and a state space model together to form a stochastic and sequential neural generative model. The c…
Our article considers a Gaussian variational approximation of the posterior density in a high-dimensional state space model. The variational parameters to be optimized are the mean vector and the covariance matrix of the approximation. The number of parameters in the covariance matrix grows as the square of the number …
The method approximates stationary distributions of Markov models by truncating irrelevant states.
New model preserves symmetry in multivariate time series, improving performance.
ROAD-EnKFs use learned low-dimensional models to improve state reconstruction and forecasting.
The paper stabilizes PD term structures under forecast uncertainty using a Kalman filter with an anchored observation model.
New model captures state-dependent variability in partially observed systems.
We propose a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learning and evolutionary algorithms. Our approach uses a sequential model-based optimization (SMBO) strategy, in which we search for structures i…
This primer explains diffusion models in general state spaces.
Gaussian processes allow for flexible specification of prior assumptions of unknown dynamics in state space models. We present a procedure for efficient Bayesian learning in Gaussian process state space models, where the representation is formed by projecting the problem onto a set of approximate eigenfunctions derived…
New method improves deep learning model robustness and accuracy for long sequences.
The Gaussian process state space model (GPSSM) is a non-linear dynamical system, where unknown transition and/or measurement mappings are described by GPs. Most research in GPSSMs has focussed on the state estimation problem, i.e., computing a posterior of the latent state given the model. However, the key challenge in…
Structured prediction tasks pose a fundamental trade-off between the need for model complexity to increase predictive power and the limited computational resources for inference in the exponentially-sized output spaces such models require. We formulate and develop the Structured Prediction Cascade architecture: a seque…
Generates synthetic ECGs conditioned on clinical statements.
Paper introduces OMD for ordered state transitions in SSMs.
In many scientific fields, such as economics and neuroscience, we are often faced with nonstationary time series, and concerned with both finding causal relations and forecasting the values of variables of interest, both of which are particularly challenging in such nonstationary environments. In this paper, we study c…
Paper introduces untangling number to quantify 3-periodic tangle complexity.
Algorithm estimates human decision-making in high-dimensional states with finite-time guarantees.
A new diffusion model improves cryo-EM structure sampling.
Develops state-space deep Gaussian processes for irregular signals.
EBMs trained on discrete data using heat equations on graph structures.
A new method for estimating uncertainty in deep neural networks.
We introduce a variational approach to learning and inference of temporally hierarchical structure and representation for sequential data. We propose the Variational Temporal Abstraction (VTA), a hierarchical recurrent state space model that can infer the latent temporal structure and thus perform the stochastic state …
Leveraging an equivalence property in the state-space of a Markov Decision Process (MDP) has been investigated in several studies. This paper studies equivalence structure in the reinforcement learning (RL) setup, where transition distributions are no longer assumed to be known. We present a notion of similarity betwee…
We develop a normative framework for hierarchical model-based policy optimization based on applying second-order methods in the space of all possible state-action paths. The resulting natural path gradient performs policy updates in a manner which is sensitive to the long-range correlational structure of the induced st…
It is known that knot homologies admit a physical description as spaces of open BPS states. We study operators and algebras acting on these spaces. This leads to a very rich story, which involves wall crossing phenomena, algebras of closed BPS states acting on spaces of open BPS states, and deformations of Landau-Ginzb…
Approaches to learning Bayesian networks from data typically combine a scoring function with a heuristic search procedure. Given a Bayesian network structure, many of the scoring functions derived in the literature return a score for the entire equivalence class to which the structure belongs. When using such a scoring…
Foams have Lie algebra symmetries that simplify web state spaces.
The paper introduces Causal Neural Operators to approximate operators in stochastic analysis.
Adaptive discretization improves model-based RL in large spaces.
A toy model shows how locality can emerge in the universe's Hamiltonian and initial state.
A fundamental problem in geophysical modeling is related to the identification and approximation of causal structures among physical processes. However, resolving the bidirectional mappings between physical parameters and model state variables (i.e., solving the forward and inverse problems) is challenging, especially …
This paper explores and develops alternative statistical representations and estimation approaches for dynamic mortality models. The framework we adopt is to reinterpret popular mortality models such as the Lee-Carter class of models in a general state-space modelling methodology, which allows modelling, estimation and…
A new method for state estimation on complex networks.
New PG samplers improve inference in coupled state-space models.