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.
A new autoregressive model learns the order of graph generation tasks.
Optimized variable orderings improve autoregressive model performance.
Paper proposes AXE loss for non-autoregressive machine translation, improving performance.
PARD generates graphs efficiently and invariantly to node ordering.
New model handles missing data effectively in autoregressive models.
Neural autoregressive models are explicit density estimators that achieve state-of-the-art likelihoods for generative modeling. The D-dimensional data distribution is factorized into an autoregressive product of one-dimensional conditional distributions according to the chain rule. Data completion is a more involved ta…
Autoregressive flow models can perform causal discovery and inference tasks.
The purpose of this paper is to provide a sharp analysis on the asymptotic behavior of the Durbin-Watson statistic. We focus our attention on the first-order autoregressive process where the driven noise is also given by a first-order autoregressive process. We establish the almost sure convergence and the asymptotic n…
Non-autoregressive method speeds up protein folding prediction 23 times.
We present the Insertion Transformer, an iterative, partially autoregressive model for sequence generation based on insertion operations. Unlike typical autoregressive models which rely on a fixed, often left-to-right ordering of the output, our approach accommodates arbitrary orderings by allowing for tokens to be ins…
A new online learning setting for autoregressive processes with sublinear regret.
Causal autoregressive flows enable accurate causal inference and prediction.
Improved simulation of phase transitions using hierarchical autoregressive networks.
Linear autoregressive models serve as basic representations of discrete time stochastic processes. Different attempts have been made to provide non-linear versions of the basic autoregressive process, including different versions based on kernel methods. Motivated by the powerful framework of Hilbert space embeddings o…
We describe an optimal adversarial attack formulation against autoregressive time series forecast using Linear Quadratic Regulator (LQR). In this threat model, the environment evolves according to a dynamical system; an autoregressive model observes the current environment state and predicts its future values; an attac…
LMConv improves autoregressive models for image generation and completion.
Due to the unparallelizable nature of the autoregressive factorization, AutoRegressive Translation (ART) models have to generate tokens sequentially during decoding and thus suffer from high inference latency. Non-AutoRegressive Translation (NART) models were proposed to reduce the inference time, but could only achiev…
We propose a new class of models specifically tailored for spatio-temporal data analysis. To this end, we generalize the spatial autoregressive model with autoregressive and heteroskedastic disturbances, i.e. SARAR(1,1), by exploiting the recent advancements in Score Driven (SD) models typically used in time series eco…
A new clustering method for vector time series using autoregressive dynamics.
Paper proposes a self-supervised method to denoise autoregressive signals with heavy-tailed noise.
EventFlow forecasts event sequences without autoregression, improving accuracy.
Improved lattice field theory simulations with local-Autoregressive Conditional Normalizing Flow.
Modified asymmetric hidden Markov models for time series with autoregressive components.
New method makes machine learning approximations unbiased and efficient.
We introduce a new criterion to determine the order of an autoregressive model fitted to time series data. It has the benefits of the two well-known model selection techniques, the Akaike information criterion and the Bayesian information criterion. When the data is generated from a finite order autoregression, the Bay…
Autoregressive networks can achieve promising performance in many sequence modeling tasks with short-range dependence. However, when handling high-dimensional inputs and outputs, the huge amount of parameters in the network lead to expensive computational cost and low learning efficiency. The problem can be alleviated …
New model generates graphs with tighter likelihood bounds and better quality.
We present Sequential Neural Likelihood (SNL), a new method for Bayesian inference in simulator models, where the likelihood is intractable but simulating data from the model is possible. SNL trains an autoregressive flow on simulated data in order to learn a model of the likelihood in the region of high posterior dens…
The paper introduces reservoir computing models for complex systems.
MIC improves VAR order selection accuracy.
Normalising flows (NFS) map two density functions via a differentiable bijection whose Jacobian determinant can be computed efficiently. Recently, as an alternative to hand-crafted bijections, Huang et al. (2018) proposed neural autoregressive flow (NAF) which is a universal approximator for density functions. Their fl…
Generative model predicts financial market order flow with high accuracy.
Paper proposes a new sparse VAR model for high-dimensional time series.
BiGG model efficiently generates sparse graphs with reduced complexity.
This paper explores how Transformers predict next tokens in autoregressive tasks.
We demonstrate the use of conditional autoregressive generative models (van den Oord et al., 2016a) over a discrete latent space (van den Oord et al., 2017b) for forward planning with MCTS. In order to test this method, we introduce a new environment featuring varying difficulty levels, along with moving goals and obst…
Sparse Tucker decomposition with graph regularization improves time series forecasting accuracy.
ARDMs are a new model class for autoregressive diffusion that generalize existing models and can compress data efficiently.
Study local sensitivity of HDD and CDD temperature derivatives prices.
Efficient method classifies locally stationary time series based on second-order characteristics.
In this work, we consider the class of multi-state autoregressive processes that can be used to model non-stationary time-series of interest. In order to capture different autoregressive (AR) states underlying an observed time series, it is crucial to select the appropriate number of states. We propose a new model sele…
The purpose of this paper is to propose a time-varying vector autoregressive model (TV-VAR) for forecasting multivariate time series. The model is casted into a state-space form that allows flexible description and analysis. The volatility covariance matrix of the time series is modelled via inverted Wishart and singul…
This work is the first part of a project dealing with an in-depth study of effective techniques used in econometrics in order to make accurate forecasts in the concrete framework of one of the major economies of the most productive Italian area, namely the province of Verona. In particular, we develop an approach mainl…
Thermalizer stabilizes autoregressive models for long-term predictions in chaotic systems.
In order to disentangle the internal dynamics from exogenous factors within the Autoregressive Conditional Duration (ACD) model, we present an effective measure of endogeneity. Inspired from the Hawkes model, this measure is defined as the average fraction of events that are triggered due to internal feedback mechanism…
LOB-Bench benchmarks generative AI for financial data, outperforming traditional models.
Vector autoregression (VAR) is a fundamental tool for modeling multivariate time series. However, as the number of component series is increased, the VAR model becomes overparameterized. Several authors have addressed this issue by incorporating regularized approaches, such as the lasso in VAR estimation. Traditional a…
Bayesian causal inference method improves accuracy over traditional approaches.