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.
A new method for estimating large-scale linear models with improved precision.
This paper studies statistical estimation in optional regression models.
Synthesizes robust estimators for domain adaptation.
Sparse linear regression, which entails finding a sparse solution to an underdetermined system of linear equations, can formally be expressed as an -constrained least-squares problem. The Orthogonal Least-Squares (OLS) algorithm sequentially selects the features (i.e., columns of the coefficient matrix) to greedil…
The accuracy of least squares calibration using option premiums and particle filtering of price data to find model parameters is determined. Derivative models using exponential Lévy processes are calibrated using regularized weighted least squares with respect to the minimal entropy martingale measure. Sequential impor…
Given a sequential learning algorithm and a target model, sequential machine teaching aims to find the shortest training sequence to drive the learning algorithm to the target model. We present the first principled way to find such shortest training sequences. Our key insight is to formulate sequential machine teaching…
New method identifies network dynamics and noise structure.
The log-periodic power law (LPPL) is a model of asset prices during endogenous bubbles. If the on-going development of a bubble is suspected, asset prices can be fit numerically to the LPPL law. The best solutions can then indicate whether a bubble is in progress and, if so, the bubble critical time (i.e., when the bub…
Dynamic pricing policy converges to Nash equilibrium with low regret.
Novel Bayesian approach for non-stationary linear contextual bandits.
Improves Bayesian optimisation for engineering design problems with many variables.
Optimal hashing embeddings reduce linear least squares solving time.
Unified framework SVAM learns GLMs robustly to adversarial label corruption.
This work improves SINDy-type algorithms for system identification using score-guided dictionary selection.
Expectile regression is a nice tool for investigating conditional distributions beyond the conditional mean. It is well-known that expectiles can be described with the help of the asymmetric least square loss function, and this link makes it possible to estimate expectiles in a non-parametric framework by a support vec…
RL agent outperforms model-based approach in detecting price manipulation.
Paper proposes a debiased estimator for adaptive linear regression.
New method designs experiments robustly for nonlinear estimation, improving parameter knowledge.
This paper achieves first-order regret bounds in reinforcement learning with large state spaces.
CD converges linearly for MCP/SCAD penalized least squares.
When optimizing over-parameterized models, such as deep neural networks, a large set of parameters can achieve zero training error. In such cases, the choice of the optimization algorithm and its respective hyper-parameters introduces biases that will lead to convergence to specific minimizers of the objective. Consequ…
This paper is concerned with sequential filtering based stochastic optimization (FSO) approaches that leverage a probabilistic perspective to implement the incremental proximity method (IPM). The present FSO methods are derived based on the Kalman filter (KF) and the extended KF (EKF). In contrast with typical methods …
We introduce a new method to price American-style options on underlying investments governed by stochastic volatility (SV) models. The method does not require the volatility process to be observed. Instead, it exploits the fact that the optimal decision functions in the corresponding dynamic programming problem can be …
Optimal algorithm for high-dimensional stochastic linear bandits with sparse parameters.
ORFit trains models on streaming data with one pass, minimizing memory and computational costs.
Illustrates interleaved learning with Kalman Filter for linear least squares.
Combines control variates and adaptive importance sampling for Monte Carlo integration.
Stochastic optimization algorithms update models with cheap per-iteration costs sequentially, which makes them amenable for large-scale data analysis. Such algorithms have been widely studied for structured sparse models where the sparsity information is very specific, e.g., convex sparsity-inducing norms or -n…
We study randomized sketching methods for approximately solving least-squares problem with a general convex constraint. The quality of a least-squares approximation can be assessed in different ways: either in terms of the value of the quadratic objective function (cost approximation), or in terms of some distance meas…
We compare the risk of ridge regression to a simple variant of ordinary least squares, in which one simply projects the data onto a finite dimensional subspace (as specified by a Principal Component Analysis) and then performs an ordinary (un-regularized) least squares regression in this subspace. This note shows that …
Cross validation residuals are well known for the ordinary least squares model. Here leave-M-out cross validation is extended to generalised least squares. The relationship between cross validation residuals and Cook's distance is demonstrated, in terms of an approximation to the difference in the generalised residual …
New binary AA methods improve on existing techniques.
The paper improves Kaczmarz algorithm with momentum for linear least squares.
New algorithm improves online binary classification with constant time complexity.
Reduced-rank method improves least-squares regression under output regularity.
We develop a general theory for the goodness-of-fit test to non-linear models. In particular, we assume that the observations are noisy samples of a submanifold defined by a \yao{sufficiently smooth non-linear map}. The observation noise is additive Gaussian. Our main result shows that the "residual" of the model fit, …
New framework optimizes forecasting and decision-making in dynamic systems.
We propose and study a new model for reinforcement learning with rich observations, generalizing contextual bandits to sequential decision making. These models require an agent to take actions based on observations (features) with the goal of achieving long-term performance competitive with a large set of policies. To …
Proposes a partitioned least squares model for feature grouping.
ESNs trained with Tikhonov least squares approximate ergodic dynamical systems in L2(μ) norm.
Develops a new screening method called Newton screening for faster and more accurate sparse learning.
A new algorithm solves nonnegative least squares faster with nonnegative data.
Directly estimates Fisher score for likelihood maximization.
The paper proposes a least squares method for binary compressive sampling with low intrinsic dimension signals.
This paper studies an unsupervised deep learning-based numerical approach for solving partial differential equations (PDEs). The approach makes use of the deep neural network to approximate solutions of PDEs through the compositional construction and employs least-squares functionals as loss functions to determine para…
In this work, we highlight a connection between the incremental proximal method and stochastic filters. We begin by showing that the proximal operators coincide, and hence can be realized with, Bayes updates. We give the explicit form of the updates for the linear regression problem and show that there is a one-to-one …
We propose a new forward-backward stochastic differential equation solver for high-dimensional derivatives pricing problems by combining deep learning solver with least square regression technique widely used in the least square Monte Carlo method for the valuation of American options. Our numerical experiments demonst…
New method speeds up solving L0-regularized least-squares problems.