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arXiv research

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.

168,695 papers · 148 categories

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8162331 · May 202619922001200920172026
48 results for external correctors

HERMES model predicts nonstationary fashion trends using social media data.

problem Forecasting nonstationary fashion time series for optimal inventory decisions.
method Hybrid model combining parametric models, seasonal components, and recurrent neural networks with external signals.
result State-of-the-art results on fashion dataset and M4 competition time series.

Neural CDEs correct errors in learned time-series models for better forecasting.

problem Error accumulation in multi-step forecasts of learned time-series models.
method Predictor-Corrector framework with a neural controlled differential equation.
result The proposed framework consistently improves forecasting performance across various models.

GADD accelerates uniform-rate discrete diffusion models by 2 orders of magnitude.

problem Slow sampling in uniform-rate discrete diffusion models.
method Gibbs-based corrector (GADD) that constructs Gibbs posterior likelihoods directly from the concrete score function.
result Achieves an overall sampling complexity of O(polylog(ε1))\mathcal{O}(\mathrm{polylog} (\varepsilon^{-1})).

This paper improves SGMs by using a predictor-corrector scheme to converge faster.

problem Theoretical and practical limitations of existing SGMs when T1oT_1 o \infty.
method Integrates a predictor-corrector scheme after the forward process to converge in finite time.
result Convergence guarantees for SGMs require only a fixed finite time T1T_1.

We present a predictor-corrector framework, called PicCoLO, that can transform a first-order model-free reinforcement or imitation learning algorithm into a new hybrid method that leverages predictive models to accelerate policy learning. The new "PicCoLOed" algorithm optimizes a policy by recursively repeating two ste…

2018-10-15abs ↗pdf ↗

DPC uses physics and neural nets to solve SDEs.

problem Solving stochastic differential equations with missing physics.
method Physics-data fusion with conditional maximum mean discrepancy (CMMD) loss.
result DPC achieves highly accurate solutions on benchmark examples.

Polynomial convergence proved for SGM, improving over previous methods.

problem Learning probability distributions from data and generating samples efficiently.
method Proved polynomial convergence for SGM using accurate score estimates.
result First polynomial convergence guarantees for SGM, independent of dimensionality.

This paper provides a neural approach to represent option implied information.

problem Link between implied density and volatility for arbitrage-free modeling.
method Minimalist perspective on implied volatility, neural representation with arbitrage constraints.
result Shallow feedforward network with a single hidden layer effectively approximates implied density and volatility.

Paper develops an efficient method for conformal prediction in sparse linear models.

problem Computing conformal prediction sets for sparse linear models is computationally infeasible.
method Numerical continuation techniques to approximate the solution path efficiently.
result The method accurately approximates conformal prediction sets for sparse linear models.

In this paper we investigate the strict convexity and the differentiability properties of the stable norm, which corresponds to the homogenized surface tension for a periodic perimeter homogenization problem (in a regular and uniformly elliptic case). We prove that it is always differentiable in totally irrational dire…

2012-05-07abs ↗pdf ↗

The purpose of this paper is to derive the anisotropic averaged Euler equations and to study their geometric and analytic properties. These new equations involve the evolution of a mean velocity field and an advected symmetric tensor that captures the fluctuation effects. Besides the derivation of these equations, the …

2000-05-03abs ↗pdf ↗

Neural dynamical systems are dynamical systems that are described at least in part by neural networks. The class of continuous-time neural dynamical systems must, however, be numerically integrated for simulation and learning. Here, we present a compact neural circuit for two common numerical integrators: the explicit …

2019-11-23abs ↗pdf ↗

In high-dimensional data analysis, penalized likelihood estimators are shown to provide superior results in both variable selection and parameter estimation. A new algorithm, APPLE, is proposed for calculating the Approximate Path for Penalized Likelihood Estimators. Both the convex penalty (such as LASSO) and the nonc…

2012-11-02abs ↗pdf ↗

The Hull-White one factor model is used to price interest rate options. The parameters of the model are often calibrated to simple liquid instruments, in particular European swaptions. It is therefore very important to have very efficient pricing formula for simple instruments. Such a formula is proposed here for Europ…

2009-01-13abs ↗pdf ↗

Bayesian inference reconstructs external potentials in DFT for many-particle systems.

problem Reconstructing external potentials in classical density-functional theory (DFT) for many-particle systems.
method Combines Bayesian inference with classical DFT to probabilistically reconstruct external potentials.
result Accurately infers external potentials and density profiles with uncertainty quantification.

Proposes a method to use external machine-learning predictions in multinomial logistic regression.

problem Improving statistical inference using summary-level external machine-learning predictions.
method Empirical-likelihood framework incorporating moment constraints from external nonparametric machine-learning predictions.
result Fused estimator achieves strict efficiency gain over primary-only estimator under mild conditions.

New estimator improves ATT estimation efficiency with external controls.

problem Reduced efficiency when incorporating external controls into ATT estimation.
method Proposes a novel doubly robust estimator for ATT that maintains higher efficiency than standard approaches.
result Demonstrates improved efficiency of the new estimator compared to standard approaches, even under model misspecification.

Method estimates model performance on external samples from limited statistical characteristics.

problem Limited access to multiple datasets due to privacy and commercial restrictions.
method Search for weights that match external statistics and are closest to uniform, using model performance on weighted internal sample as an estimation.
result Estimated external performance is closer to actual performance than internal performance.

The study assesses external validity by evaluating worst-case treatment effects across subpopulations.

problem Underrepresentation of marginalized groups and limited study populations.
method Develops a semiparametrically efficient estimator for worst-case treatment effects (WTE) and uses cross-fitting to guard against brittle findings.
result The proposed framework guards against invalid findings due to unanticipated population shifts.

A method for logistic regression inference using both internal and external data.

problem Inability to estimate intercept and marginal case proportion in case-control logistic regression.
method Empirical likelihood approach integrating internal and external data.
result Intercept parameter becomes identifiable with external information, and all parameters are estimable consistently.

Study examines remittances in Nepal, linking external demand and domestic monetary conditions.

problem Understanding the dynamics of remittances in Nepal's economy.
method Constructed composite indices via PCA for external demand and domestic monetary conditions. Used ARDL, cointegration, DOLS, ECM, and machine learning for analysis.
result Strong positive long-run effect of external demand on remittances, significant negative impact of tighter domestic monetary conditions.

Framework for estimating treatment effects using external control data.

problem Improving efficiency in estimating average treatment effects (ATE) in hybrid trials.
method Developed a formal causal inference framework based on exchangeability assumptions and graphical criteria. Proposed estimators and efficient doubly-robust methods.
result Established finite-sample performance and demonstrated application to spinal muscular atrophy trial.

Study identifies negative data externalities affecting model performance on specific groups.

problem Negative data externalities on group performance in machine learning models.
method Characterized and detected data-model inefficiencies, focusing on specific types of externalities.
result Negative data externalities can lower model performance on specific sub-groups, even with larger datasets.

Survey of methods to incorporate external knowledge into stock price prediction.

problem Challenges in predicting stock prices due to market volatility and non-linearity.
method Survey of methods for acquiring and incorporating external knowledge into stock price prediction models.
result Systematic synthesis of previous studies on external knowledge types and their application in stock price prediction.

Estimates non-parametric logistic model using case-control data and external summary info.

problem Imbalanced binary data in case-control studies.
method Two-step estimation procedure with deep neural network for functional approximation.
result Proposed estimator achieves optimal convergence rate in non-parametric regression.

The importance of nodes in a network constantly fluctuates based on changes in the network structure as well as changes in external interest. We propose an evolving teleportation adaptation of the PageRank method to capture how changes in external interest influence the importance of a node. This framework seamlessly g…

2012-03-27abs ↗pdf ↗

UAMM uses external market prices to improve AMM efficiency and reduce liquidity provider risk.

problem Traditional AMMs lack consideration of external markets and risk management.
method UAMM calculates prices by incorporating external market prices and impermanent loss, maintaining constant product curve properties.
result UAMM eliminates arbitrage opportunities when external market prices are efficient, reducing liquidity provider risk.

Model shows PoS networks can be captured by external finance, leading to centralization.

problem Long-term centralization of PoS networks under external finance pressures.
method Heterogeneous macroeconomic model with two actor classes: investors and consumers.
result External finance forces PoS networks to centralize, leading to zero internal staking yield.

This paper addresses external validity bias in causal inference.

problem Estimating causal effects in a target population.
method Synthesis of approaches for generalizability and transportability, including tests for heterogeneity of treatment effects and differences between study and target populations.
result Framework for addressing external validity bias in causal inference.

Study finds multifractal cross-correlations between agricultural markets and external uncertainties.

problem Investigating relationships between agricultural spot markets and external uncertainties.
method Multifractal detrending moving-average cross-correlation analysis (MF-X-DMA).
result Maize exhibits intrinsic joint multifractality with all uncertainty proxies.

In this paper, we study the evolution of submannifold moving by mean curvature minus a external force field. We prove that the flow has a long-time smooth solution for all time under almost optimal conditions. Those conditions are that the second fundamental form on the initial submanifolds is not too large, the extern…

2006-11-29abs ↗pdf ↗

The notion of \emph{policy regret} in online learning is a well defined? performance measure for the common scenario of adaptive adversaries, which more traditional quantities such as external regret do not take into account. We revisit the notion of policy regret and first show that there are online learning settings …

2018-11-09abs ↗pdf ↗

Study shows stock price interactions increase during crises due to external stimulus.

problem Understanding stock price interactions during economic crises.
method Granger Causality and recurrence analysis on stock price series.
result External stimulus drives stock price interactions during crises.