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.
This work proposes a scalable framework for trusted multi-party computations using blockchain.
The paper investigates model misspecification in Bayesian inference using neural networks.
Paper fine-tunes a simulation-driven estimator to reduce out-of-distribution errors.
Simulates DeLend Platform behavior to optimize operational parameters.
We propose a simulation method for multidimensional Hawkes processes based on superposition theory of point processes. This formulation allows us to design efficient simulations for Hawkes processes with differing exponentially decaying intensities. We demonstrate that inter-arrival times can be decomposed into simpler…
In this paper, we firstly introduce a method to efficiently implement large-scale high-dimensional convolution with realistic memristor-based circuit components. An experiment verified simulator is adapted for accurate prediction of analog crossbar behavior. An improved conversion algorithm is developed to convert conv…
Method verifies if observed data fits Lévy-Driven Ornstein-Uhlenbeck process.
This work shows how to efficiently simulate parts of quantum landscapes using classical computers.
ML models predict water table depth more accurately than PB models, especially in data-scarce regions.
Locally-verifiable conditions ensure exactness of spline discrete de Rham complex.
This paper proposes a new simulation-based VaR estimation method for complex portfolios.
We propose an approach to reduce the bias of ridge regression and regularization kernel network. When applied to a single data set the new algorithms have comparable learning performance with the original ones. When applied to incremental learning with block wise streaming data the new algorithms are more efficient due…
This paper presents a description of the mechanical operations of banking as used in modern banking systems regulated under the Basel Accords, in order to provide support for a verifiable and complete description of the banking system suitable for computer simulation. Feedback is requested on the contents of this docum…
Paper proposes a method to test and verify control systems with machine learning components.
Consider two networks on overlapping, non-identical vertex sets. Given vertices of interest in the first network, we seek to identify the corresponding vertices, if any exist, in the second network. While in moderately sized networks graph matching methods can be applied directly to recover the missing correspondences,…
It has been suggested that marked point processes might be good candidates for the modelling of financial high-frequency data. A special class of point processes, Hawkes processes, has been the subject of various investigations in the financial community. In this paper, we propose to enhance a basic zero-intelligence o…
Study parameter sensitivities in bond pricing models with jumps.
Paper proposes verifier engineering for improving foundation models.
Develops a new model for controllable and realistic traffic simulation.
We present a novel modulation level classification (MLC) method based on probability distribution distance functions. The proposed method uses modified Kuiper and Kolmogorov-Smirnov distances to achieve low computational complexity and outperforms the state of the art methods based on cumulants and goodness-of-fit test…
A machine-learning method speeds up RIS design by predicting reflection coefficients.
This paper proposes a new algorithmic framework, predictor-verifier training, to train neural networks that are verifiable, i.e., networks that provably satisfy some desired input-output properties. The key idea is to simultaneously train two networks: a predictor network that performs the task at hand,e.g., predicting…
Study shows current simulations are insufficient for optimal neural network training in cosmology.
ESRLCM clusters similar responses, more broadly than traditional models.
Quantum method prices options by evolving a state in imaginary time.
Paper introduces a new deep-learning method for quantum mechanics.
The purpose of this paper is to indicate that the recently proposed Momentum fractional least mean squares (mFLMS) algorithm has some serious flaws in its design and analysis. Our apprehensions are based on the evidence we found in the derivation and analysis in the paper titled: \textquotedblleft \textit{Momentum frac…
In this study we prove the existence of statistical arbitrage opportunities in the Black-Scholes framework by considering trading strategies that consists of borrowing from the risk free rate and taking a long position in the stock until it hits a deterministic barrier level. We derive analytical formulas for the expec…
DEKF maintains stability in LSTM learning with bounded perturbations.
The infinitesimal jackknife (IJ) has recently been applied to the random forest to estimate its prediction variance. These theorems were verified under a traditional random forest framework which uses classification and regression trees (CART) and bootstrap resampling. However, random forests using conditional inferenc…
Efficiently estimates Weingarten maps and curvatures from manifold data.
Fast, reliable, and error-bounded option pricing with neural networks
Fast pricing of American-style options has been a difficult problem since it was first introduced to financial markets in 1970s, especially when the underlying stocks' prices follow some jump-diffusion processes. In this paper, we propose a new algorithm to generate tight upper bounds on the Bermudan option price witho…
We propose LOCO, an algorithm for large-scale ridge regression which distributes the features across workers on a cluster. Important dependencies between variables are preserved using structured random projections which are cheap to compute and must only be communicated once. We show that LOCO obtains a solution which …
In this paper, the optimal mean-reverting portfolio (MRP) design problem is considered, which plays an important role for the statistical arbitrage (a.k.a. pairs trading) strategy in financial markets. The target of the optimal MRP design is to construct a portfolio from the underlying assets that can exhibit a satisfa…
In this paper, we consider linear state-space models with compressible innovations and convergent transition matrices in order to model spatiotemporally sparse transient events. We perform parameter and state estimation using a dynamic compressed sensing framework and develop an efficient solution consisting of two nes…
Study on TVL computation in DeFi protocols, proposing verifiable metrics.
In this paper we present nonparametric estimators for coefficients in stochastic differential equation if the data are described by independent, identically distributed random variables. The problem is formulated as a nonlinear ill-posed operator equation with a deterministic forward operator described by the Fokker-Pl…
MixTrain improves verifiable robustness of neural networks without sacrificing efficiency.
Verify spiral minimal product structure using Takahashi Theorem.
We consider the traffic data reconstruction problem. Suppose we have the traffic data of an entire city that are incomplete because some road data are unobserved. The problem is to reconstruct the unobserved parts of the data. In this paper, we propose a new method to reconstruct incomplete traffic data collected from …
Random Forests provide interpretable prediction intervals with theoretical guarantees.
We develop a simple theoretical framework for the evolution of weighted networks that is consistent with a number of stylized features of real-world data. In our framework, the Barabasi-Albert model of network evolution is extended by assuming that link weights evolve according to a geometric Brownian motion. Our model…
Deep linear networks can closely approximate interpolants without improving risk.
TASID learns policies in high-dimensional settings with abstract simulator knowledge.
IBP-R improves verified adversarial robustness with simple, effective interval bound propagation.
New method for factor analysis using nuclear and norms.
Efficient RNN algorithm guarantees convergence in online learning.