arXiv research

Model predicts alternating market dominance for two competing firms.

Lower bounds on MALA and HMC for well-conditioned distributions.

WALNUTS improves sampling efficiency and robustness for multi-scale distributions.

HMC with leapfrog integrator mixes faster than MALA under certain smoothness conditions.

We introduce a recent symplectic integration scheme derived for solving physically motivated systems with non-separable Hamiltonians. We show its relevance to Riemannian manifold Hamiltonian Monte Carlo (RMHMC) and provide an alternative to the currently used generalised leapfrog symplectic integrator, which relies on …

A new clustering method improves recovery guarantees by re-embedding data.

Hamiltonian Monte Carlo on ReLU networks is inefficient due to large local error.

Hamiltonian Monte Carlo (HMC) is a state-of-the-art Markov chain Monte Carlo sampling algorithm for drawing samples from smooth probability densities over continuous spaces. We study the variant most widely used in practice, Metropolized HMC with the Störmer-Verlet or leapfrog integrator, and make two primary contribut…

We present a novel technique for learning the mass matrices in samplers obtained from discretized dynamics that preserve some energy function. Existing adaptive samplers use Riemannian preconditioning techniques, where the mass matrices are functions of the parameters being sampled. This leads to significant complexiti…

Adaptive HMC improves sampling efficiency by optimizing mass matrix.

Hamiltonian Monte Carlo (HMC) is a widely deployed method to sample from high-dimensional distributions in Statistics and Machine learning. HMC is known to run very efficiently in practice and its popular second-order "leapfrog" implementation has long been conjectured to run in $d^{1/4}$ gradient evaluations. Here we …

The hybrid Monte Carlo algorithm (HMCA) is applied for Bayesian parameter estimation of the realized stochastic volatility (RSV) model. Using the 2nd order minimum norm integrator (2MNI) for the molecular dynamics (MD) simulation in the HMCA, we find that the 2MNI is more efficient than the conventional leapfrog integr…

Hamiltonian Monte Carlo (HMC) exploits Hamiltonian dynamics to construct efficient proposals for Markov chain Monte Carlo (MCMC). In this paper, we present a generalization of HMC which exploits \textit{non-canonical} Hamiltonian dynamics. We refer to this algorithm as magnetic HMC, since in 3 dimensions a subset of th…

GIST adapts HMC by tuning parameters based on position and momentum.

New HMC method uses asymmetrical momentum distributions and improves performance.

This paper analyzes the convergence of dynamic HMC and NUTS methods.

New method improves sampling efficiency for complex distributions.

MALA mixes optimally in κ√d steps for log-concave sampling.

ATLAS adapts HMC step size and trajectory length for complex geometries.

The article detects market regimes from covariance matrices using VLSTAR and clustering models.

The study identifies and analyzes different market regimes in equity markets using advanced signal processing techniques.

Paper improves asset allocation using machine learning for regime detection.

Proposes methods for learning optimal dynamic treatment regimes robust to unconfoundedness violations.

New model identifies regimes in non-stationary data.

Markov regime switching models have been used in numerous empirical studies in economics and finance. However, the asymptotic distribution of the likelihood ratio test statistic for testing the number of regimes in Markov regime switching models has been an unresolved problem. This paper derives the asymptotic distribu…

Algorithm classifies market regimes using time series signatures.

RegimeFolio optimizes portfolios by adapting to changing market regimes.

Paper addresses uncertainty in model generalization under regime shifts.

Enhances portfolio construction with tailored regime forecasts for individual assets.

In order to obtain a reasonable and reliable forecast method for crude oil price volatility, this paper evaluates the forecast performance of single-regime GARCH models (including the standard linear GARCH model and the nonlinear GJR-GARCH and EGARCH models) and the two-regime Markov Regime Switching GARCH (MRS-GARCH) …

Unified formula for training dynamics of linear networks combining lazy and balanced regimes.

Proves convergence of neural networks in a two-timescale regime.

RAMBO optimizes multi-regime problems by discovering and modeling distinct energy basins.

This paper improves risk control for financial markets by calibrating VaR forecasts using conformal methods.

ReCAP adapts to dynamic financial markets by segmenting and combining policy vectors.

New algorithms improve sampling from complex distributions.

This work models market regimes using CTMSTOU and simulates trading policies.

Study improves S&P 500 volatility forecasting through regime-switching methods.

Enhanced regime shifts detection using unstructured text and financial data.

DeRegiME forecasts with regime structure, improving probabilistic predictions across various time series.

A hybrid approach detects financial market regime switches using PCA and k-means.

A new method improves actor-critic RL by integrating HMC, enhancing policy distribution and exploration.

Study optimal liquidation with multiple regimes using BSDEs with singular terminal values.

Develops a new model to better predict corporate bond yields.

Study of two-layer ReLU neural network phase diagram at infinite-width limit.

MARCD uses generative scenarios to improve portfolio decisions during regime shifts.

Investigates JM for reducing downside risk in market regimes.

New algorithm ensures global convergence in deep neural networks beyond NTK regime.