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.
RER improves sample complexity by updating in reverse order.
Current recommender systems exploit user and item similarities by collaborative filtering. Some advanced methods also consider the temporal evolution of item ratings as a global background process. However, all prior methods disregard the individual evolution of a user's experience level and how this is expressed in th…
The paper analyzes and improves the learning rates of distributed kernel ridge regression.
Paper proposes a surrogate model for efficient experience rating in large insurance portfolios.
Approximates bond option volatilities using affine short-rate models.
Paper improves learning rates for SGD and NAG.
Recommendation systems and computing advertisements have gradually entered the field of academic research from the field of commercial applications. Click-through rate prediction is one of the core research issues because the prediction accuracy affects the user experience and the revenue of merchants and platforms. Fe…
T-Rex selector selects variables fast and controls FDR in high-dimensional data.
Developed unbiased estimators for Heston model with stochastic interest rates.
The paper improves evolutionary computation by optimizing selection rates.
SALR improves deep learning generalization by dynamically adjusting learning rates.
D-Adaptation automatically sets optimal learning rates without manual tuning.
We prove optimal bounds for the convergence rate of ordinal embedding (also known as non-metric multidimensional scaling) in the 1-dimensional case. The examples witnessing optimality of our bounds arise from a result in additive number theory on sets of integers with no three-term arithmetic progressions. We also carr…
This paper demonstrates dynamic hyper-parameter setting, for deep neural network training, using Mutual Information (MI). The specific hyper-parameter studied in this paper is the learning rate. MI between the output layer and true outcomes is used to dynamically set the learning rate of the network through the trainin…
Sparse PCA selects variables with FDR control for improved performance.
Cyclical learning rates improve DRL performance without manual tuning.
This paper introduces a Bayesian framework for optimizing online experiments to maximize profit.
Large learning rates improve generalization, but optimal ranges are narrower than previously thought.
Mixed likelihood GPs improve model performance in human-in-the-loop experiments.
We propose a stochastic optimization method for minimizing loss functions, expressed as an expected value, that adaptively controls the batch size used in the computation of gradient approximations and the step size used to move along such directions, eliminating the need for the user to tune the learning rate. The pro…
The paper analyzes insurance risks using stochastic models.
We propose a new model for pricing Quanto CDS and risky bonds. The model operates with four stochastic factors, namely: hazard rate, foreign exchange rate, domestic interest rate, and foreign interest rate, and also allows for jumps-at-default in the FX and foreign interest rates. Corresponding systems of PDEs are deri…
nGPT learns to transfer learning rates across model dimensions and token horizons.
Paper generalizes extragradient methods for solving equations and inclusions with improved convergence rates.
Random learning rate improves neural network training without extra cost.
Fractal learning rate schedules accelerate vanilla gradient descent.
The study proposes algorithms to minimize rating discordance in missing data.
GradaGrad adapts learning rate non-monotonically, overcoming AdaGrad's step size decrease.
SALSA automatically adjusts learning rates in stochastic gradient methods.
Arguably the biggest challenge in applying neural networks is tuning the hyperparameters, in particular the learning rate. The sensitivity to the learning rate is due to the reliance on backpropagation to train the network. In this paper we present the first application of Implicit Stochastic Gradient Descent (ISGD) to…
This paper studies the complexity of the stochastic gradient algorithm for PCA when the data are observed in a streaming setting. We also propose an online approach for selecting the learning rate. Simulation experiments confirm the practical relevance of the plain stochastic gradient approach and that drastic improvem…
We study two time-scale linear stochastic approximation algorithms, which can be used to model well-known reinforcement learning algorithms such as GTD, GTD2, and TDC. We present finite-time performance bounds for the case where the learning rate is fixed. The key idea in obtaining these bounds is to use a Lyapunov fun…
Optimizing deep neural networks is largely thought to be an empirical process, requiring manual tuning of several hyper-parameters, such as learning rate, weight decay, and dropout rate. Arguably, the learning rate is the most important of these to tune, and this has gained more attention in recent works. In this paper…
The learning rate is one of the most important hyper-parameters for model training and generalization. However, current hand-designed parametric learning rate schedules offer limited flexibility and the predefined schedule may not match the training dynamics of high dimensional and non-convex optimization problems. In …
AutoGD automatically adjusts learning rates for gradient descent.
Cyclical learning rate improves neural machine translation performance.
We present a novel method for the numerical pricing of American options based on Monte Carlo simulation and the optimization of exercise strategies. Previous solutions to this problem either explicitly or implicitly determine so-called optimal exercise regions, which consist of points in time and space at which a given…
Attribute-aware CF models aims at rating prediction given not only the historical rating from users to items, but also the information associated with users (e.g. age), items (e.g. price), or even ratings (e.g. rating time). This paper surveys works in the past decade developing attribute-aware CF systems, and discover…
We present a general framework for solving a large class of learning problems with non-linear functions of classification rates. This includes problems where one wishes to optimize a non-decomposable performance metric such as the F-measure or G-mean, and constrained training problems where the classifier needs to sati…
Study proves convergence of interest rate model approximations.
We improve private training accuracy with learning rate schedules and matrix factorizations.
New methods for inferring, predicting, and estimating continuous-time, discrete-event processes.
Hamiltonian Monte Carlo on ReLU networks is inefficient due to large local error.
Common event-triggered state estimation (ETSE) algorithms save communication in networked control systems by predicting agents' behavior, and transmitting updates only when the predictions deviate significantly. The effectiveness in reducing communication thus heavily depends on the quality of the dynamics models used …
This paper reviews methods for constructing confidence intervals for error rates in 1:1 matching tasks.
We consider rate swaps which pay a fixed rate against a floating rate in presence of bid-ask spread costs. Even for simple models of bid-ask spread costs, there is no explicit strategy optimizing an expected function of the hedging error. We here propose an efficient algorithm based on the stochastic gradient method to…
Stochastic gradient descent with a large initial learning rate is widely used for training modern neural net architectures. Although a small initial learning rate allows for faster training and better test performance initially, the large learning rate achieves better generalization soon after the learning rate is anne…
This paper presents empirical evidence using recently developed techniques in econophysics suggesting that the degree of long-range dependence in interest rates depends on the conduct of monetary policy. We study the term structure of interest rates for the US and find evidence that global Hurst exponents change dramat…