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.
ANN predicts methane heat transfer in rocket engines efficiently.
In this paper, we investigate the cooling-off effect (opposite to the magnet effect) from two aspects. Firstly, from the viewpoint of dynamics, we study the existence of the cooling-off effect by following the dynamical evolution of some financial variables over a period of time before the stock price hits its limit. S…
L-Cool improves image and language translation by cooling low-density samples.
Singapore's cooling measures did not increase housing wealth overall.
The study optimizes simulated annealing's cooling schedule for better performance.
A new estimator for state values in reinforcement learning reduces complexity and improves convergence.
Proposes a new framework for image generation using classification latent space representations.
ReGEN-TAD detects anomalies in financial time series with interpretable models.
Study local sensitivity of HDD and CDD temperature derivatives prices.
New method reduces Gibbs partition function estimation complexity.
The Viterbi process can be extended indefinitely in a pairwise Markov model.
In this paper, we present an online reinforcement learning algorithm, called Renewal Monte Carlo (RMC), for infinite horizon Markov decision processes with a designated start state. RMC is a Monte Carlo algorithm and retains the advantages of Monte Carlo methods including low bias, simplicity, and ease of implementatio…
We study an online multi-task learning setting, in which instances of related tasks arrive sequentially, and are handled by task-specific online learners. We consider an algorithmic framework to model the relationship of these tasks via a set of convex constraints. To exploit this relationship, we design a novel algori…
New priors improve Bayesian neural networks without cooling.
Deep learning detects cloud changes due to human aerosols.
Deep learning model predicts material microstructures from processing methods.
Cost-aware BO minimizes function evaluations with varying costs.
C3 compresses images and videos with low complexity and high performance.
We demonstrate that graphs embedded on surfaces are a powerful and practical tool to generate, characterize and simulate networks with a broad range of properties. Remarkably, the study of topologically embedded graphs is non-restrictive because any network can be embedded on a surface with sufficiently high genus. The…
Online social networks (OSN) contain extensive amount of information about the underlying society that is yet to be explored. One of the most feasible technique to fetch information from OSN, crawling through Application Programming Interface (API) requests, poses serious concerns over the the guarantees of the estimat…
Liouville domains have become central objects in symplectic and contact geometry. However, the auxiliary data they involve --- namely, Liouville forms --- and the non-compactness of their completions generate some inconvenience. The notion of ideal Liouville domains is designed to suppress these awkward aspects and to …
Price limit trading rules are adopted in some stock markets (especially emerging markets) trying to cool off traders' short-term trading mania on individual stocks and increase market efficiency. Under such a microstructure, stocks may hit their up-limits and down-limits from time to time. However, the behaviors of pri…
New framework for 3D spatial topology enumeration and identification.
Model predicts road traffic using high-dimensional time-series with L1-penalization.
Using ultra-high-frequency data extracted from the order flows of 23 stocks traded on the Shenzhen Stock Exchange, we study the empirical regularities of order placement in the opening call auction, cool period and continuous auction. The distributions of relative logarithmic prices against reference prices in the thre…
CityTFT models urban building energy using a data-driven approach.
In this paper, we propose an information-theoretic exploration strategy for stochastic, discrete multi-armed bandits that achieves optimal regret. Our strategy is based on the value of information criterion. This criterion measures the trade-off between policy information and obtainable rewards. High amounts of policy …
Bayesian Optimization (BO) has become a core method for solving expensive black-box optimization problems. While much research focussed on the choice of the acquisition function, we focus on online length-scale adaption and the choice of kernel function. Instead of choosing hyperparameters in view of maximum likelihood…
We develop robust Markov Decision Processes with risk measures for uncertain environments.
We give the first rigorous proof of the convergence of Riemannian Hamiltonian Monte Carlo, a general (and practical) method for sampling Gibbs distributions. Our analysis shows that the rate of convergence is bounded in terms of natural smoothness parameters of an associated Riemannian manifold. We then apply the metho…
Variational inference (VI) combined with data subsampling enables approximate posterior inference over large data sets, but suffers from poor local optima. We first formulate a deterministic annealing approach for the generic class of conditionally conjugate exponential family models. This approach uses a decreasing te…
Improved loss functions adapt to weight-space anisotropy, outperforming isotropic counterparts.
We develop importance sampling based efficient simulation techniques for three commonly encountered rare event probabilities associated with random walks having i.i.d. regularly varying increments; namely, 1) the large deviation probabilities, 2) the level crossing probabilities, and 3) the level crossing probabilities…
Deep learning predicts tissue properties from cell-laden hydrogels.
We consider dynamics of financial markets as dynamics of expectations and discuss such a dynamics from the point of view of phenomenological thermodynamics. We describe a financial Carnot cycle and the financial analogue of a heat machine. We see, that while in physics a perpetuum mobile is absolutely impossible, in ec…
Two new regularization methods improve neural network performance and complexity control.
SGD converges almost surely in non-convex problems, avoiding saddle points and accelerating convergence.
We study platforms in the sharing economy and discuss the need for incentivizing users to explore options that otherwise would not be chosen. For instance, rental platforms such as Airbnb typically rely on customer reviews to provide users with relevant information about different options. Yet, often a large fraction o…
CoolMomentum combines momentum and Simulated Annealing for deep learning optimization.
The paper extends gradient flow and relaxation studies to non-flat Riemannian manifolds.
We introduce a novel Entropy-driven Monte Carlo (EdMC) strategy to efficiently sample solutions of random Constraint Satisfaction Problems (CSPs). First, we extend a recent result that, using a large-deviation analysis, shows that the geometry of the space of solutions of the Binary Perceptron Learning Problem (a proto…
Gibbs sampling is a workhorse for Bayesian inference but has several limitations when used for parameter estimation, and is often much slower than non-sampling inference methods. SAME (State Augmentation for Marginal Estimation) \cite{Doucet99,Doucet02} is an approach to MAP parameter estimation which gives improved pa…
New method solves supercooled Stefan problem, proving minimal solutions are physical.
Semi-supervised method predicts faults in systems with multiple operation modes.
Deep learning improves classification and characterization of amorphous materials.
The study uses DPGMM to analyze pulsar families in parameter space.
Bayesian neural networks improve performance at finite temperature.
Novel probabilistic models forecast residential heating and electricity demand at hourly resolution.