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.
Plane Delaunay triangulations are rigid under Luo's discrete conformal change.
Extends Neural ODEs to model discrete changes in continuous systems.
Plane triangulations remain rigid under discrete conformal changes.
Unified framework detects changes in complex system models.
Change-point analysis is a flexible and computationally tractable tool for the analysis of times series data from systems that transition between discrete states and whose observables are corrupted by noise. The change-point algorithm is used to identify the time indices (change points) at which the system transitions …
Bayesian Context Trees improve change-point detection in discrete data.
Our work proves robustness of embedding schemes to discrete changes in text.
The study tackles modeling high-frequency financial data using continuous distributions, finding them inadequate.
New algorithm learns changing discrete distributions with minimal drift error.
Study nondegenerate singularities in mean curvature flow.
The paper proves a new discrete Laplacian for 3D meshes and shows its superiority over primal construction.
Develops deep jump learning for continuous treatment OPE.
This paper proposes a novel model of financial prices where: (i) prices are discrete; (ii) prices change in continuous time; (iii) a high proportion of price changes are reversed in a fraction of a second. Our model is analytically tractable and directly formulated in terms of the calendar time and price impact curve. …
AdaCat improves density estimation and planning in autoregressive models.
Study proposes a new GAN for realistic discrete financial orders.
New algorithm tracks changes in infinite action space rewards.
We present a notion of super Ricci flow for time-dependent finite weighted graphs. A challenging feature is that these flows typically encounter singularities where the underlying graph structure changes. Our notion is robust enough to allow the flow to continue past these singularities. As a crucial tool for this purp…
While normalizing flows have led to significant advances in modeling high-dimensional continuous distributions, their applicability to discrete distributions remains unknown. In this paper, we show that flows can in fact be extended to discrete events---and under a simple change-of-variables formula not requiring log-d…
Despite remarkable successes, Deep Reinforcement Learning (DRL) is not robust to hyperparameterization, implementation details, or small environment changes (Henderson et al. 2017, Zhang et al. 2018). Overcoming such sensitivity is key to making DRL applicable to real world problems. In this paper, we identify sensitiv…
Study examines how slight model changes affect multi-period optimization outcomes.
We consider a square-integrable semimartingale and investigate the convex order relations between its discrete, continuous and predictable quadratic variation. As the main results, we show that if the semimartingale has conditionally independent increments and symmetric jump measure, then its discrete realized variance…
This paper investigates the effects of a price limit change on the volatility of the Korean stock market's (KRX) intraday stock price process. Based on the most recent transaction data from the KRX, which experienced a change in the price limit on June 15, 2015, we examine the change in realized variance after the pric…
Improves change-point detection for high-dimensional time-series.
The telegraph process models a random motion with finite velocity and it is usually proposed as an alternative to diffusion models. The process describes the position of a particle moving on the real line, alternatively with constant velocity or . The changes of direction are governed by an homogeneous Poisso…
Solves utility maximization for delayed informed investors.
This work analyzes discrete diffusion models using stochastic integrals, providing error bounds and insights.
Differentiable segmented models for non-stationary data.
Paper detects gradual changes in cluster structure using MC fusion.
New method reduces discrete flow transitions, improving perplexity estimation.
Develops a new method to measure causal effects in continuous and discrete settings.
We study how the round-off (or discretization) error changes the statistical properties of a Gaussian long memory process. We show that the autocovariance and the spectral density of the discretized process are asymptotically rescaled by a factor smaller than one, and we compute exactly this scaling factor. Consequentl…
We amend the statement of point~(i) in Theorem~1.3 in arxiv:0901.1022 and supply the additional arguments and minor changes for the results that depend on it. We also seize the occasion and generalize to non-finitely generated lattices.
New interpretation of discrete conformality using polyhedral convex hulls.
Paper extends SI method for detecting CPs in complex systems' frequency domain.
We study the problem of learning sparse structure changes between two Markov networks and . Rather than fitting two Markov networks separately to two sets of data and figuring out their differences, a recent work proposed to learn changes \emph{directly} via estimating the ratio between two Markov network models…
Study examines pricing strategies in competitive supply chains with discrete prices.
A new sampler for complex discrete distributions efficiently updates all variables in parallel.
Process Monitoring involves tracking a system's behaviors, evaluating the current state of the system, and discovering interesting events that require immediate actions. In this paper, we consider monitoring temporal system state sequences to help detect the changes of dynamic systems, check the divergence of the syste…
Accelerates convergence in global non-convex optimization with reversible diffusion.
Paper uses TCN with attention to predict UHF stock price changes.
New CUSUM method detects changes in Hawkes networks efficiently.
We unify and establish equivalence between the pathwise and the quasi-sure approaches to robust modelling of financial markets in discrete time. In particular, we prove a Fundamental Theorem of Asset Pricing and a Superhedging Theorem, which encompass the formulations of [Bouchard, B., & Nutz, M. (2015). Arbitrage and …
ContinuousNet generalizes ResNets to continuous dynamical systems.
Spin networks are at the core of quantum gravity. Our aim is to plug the mathematical community at large into the procedures turn to create a finite quantum theory of general relativity. For this, because of the different cultural backgraund, we would like to change the tack: to relate discrete (combinatorial) objects …
Lane change is a challenging task which requires delicate actions to ensure safety and comfort. Some recent studies have attempted to solve the lane-change control problem with Reinforcement Learning (RL), yet the action is confined to discrete action space. To overcome this limitation, we formulate the lane change beh…
The potential function of the optimistic limit of the colored Jones polynomial and the construction of the solution of the hyperbolicity equations were defined in the authors' previous articles. In this article, we define the Reidemeister transformations of the potential function and the solution by the changes of them…
Hybrid RL method optimizes trading by balancing continuous and discrete actions.
In a previous paper, we showed how certain orientations of the edges of a graph G embedded in a closed oriented surface S can be understood as discrete spin structures on S. We then used this correspondence to give a geometric proof of the Pfaffian formula for the partition function of the dimer model on G. In the pres…