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.
Enhances DGPs with adaptive RKHS Fourier features for better non-stationary pattern modeling.
problem Capturing complex non-stationary patterns in non-linear dynamical systems.
method Integrates ODE-based RKHS Fourier features into DGPs using convolution operations for adaptive amplitude and phase modulation. Uses a doubly stochastic variational inference framework.
result Improved predictive performance across various regression tasks.
A new convolutional spectral kernel network learns hierarchical and local features.
problem Lack of deep learning in non-stationary spectral kernels.
method Introduces convolutional filters and deep architectures into non-stationary spectral kernels, derives generalization error bounds, and introduces regularizers.
result Validated the effectiveness of the convolutional spectral kernel network on real-world datasets.
Estimates and infers multi-stage stationary treatment policies with variable selection.
problem Valid inference for multi-stage stationary treatment policies with high-dimensional feature variables.
method Estimate the value function using augmented inverse probability weighted estimator, apply penalty for variable selection, construct one-step improvements for valid inference.
result Improved estimators are asymptotically normal, valid inference for policy parameters demonstrated.
Study causal financial signals for non-stationary markets, improving short-term forecasts.
problem Short-term forecasting in non-stationary financial markets under causal constraints.
method Construct causal signals from heterogeneous micro-features using causal centering, linear aggregation, Kalman filter, and forward-like operator.
result Causally constructed observables can exhibit substantial economic relevance in specific regimes but degrade under regime shifts.
This paper proposes a hierarchical feature extractor for non-stationary streaming time series based on the concept of switching observable Markov chain models. The slow time-scale non-stationary behaviors are considered to be a mixture of quasi-stationary fast time-scale segments that are exhibited by complex dynamical…
A defining feature of non-stationary systems is the time dependence of their statistical parameters. Measured time series may exhibit Gaussian statistics on short time horizons, due to the central limit theorem. The sample statistics for long time horizons, however, averages over the time-dependent parameters. To model…
We consider an online decision making setting known as contextual bandit problem, and propose an approach for improving contextual bandit performance by using an adaptive feature extraction (representation learning) based on online clustering. Our approach starts with an off-line pre-training on unlabeled history of co…
DSSCN improves lifelong learning of non-stationary data streams through adaptive network construction.
problem Lifelong learning of non-stationary data streams with efficient and adaptive models.
method Deep stacked stochastic configuration network (DSSCN) with self-constructing deep stacked network structure and adaptive hidden unit parameters.
result DSSCN outperforms existing data stream algorithms in continual learning of non-stationary data streams.
The rollout of new versions of a feature in modern applications is a manual multi-stage process, as the feature is released to ever larger groups of users, while its performance is carefully monitored. This kind of A/B testing is ubiquitous, but suboptimal, as the monitoring requires heavy human intervention, is not gu…
Financial markets are prominent examples for highly non-stationary systems. Sample averaged observables such as variances and correlation coefficients strongly depend on the time window in which they are evaluated. This implies severe limitations for approaches in the spirit of standard equilibrium statistical mechanic…
Bayesian optimization with Gaussian processes speeds up searches for stationary points.
problem Accelerating searches for stationary points on potential energy surfaces.
method Unified Bayesian optimization view using Gaussian process regression with derivative observations, inverse-distance kernels, and active learning.
result Surrogates can reduce the number of expensive electronic structure evaluations by an order of magnitude.
Presents STRIPE model for probabilistic forecasting of non-stationary time series.
problem Probabilistic forecasting of non-stationary time series.
method STRIPE model representing structured diversity based on shape and time features, with diversification mechanism using determinantal point processes (DPP).
result STRIPE significantly outperforms baseline methods for representing diversity while maintaining forecasting accuracy.
This paper is the third of a series on Hamiltonian stationary Lagrangian surfaces. We present here the most general theory, valid for any Hermitian symmetric target space. Using well-chosen moving frame formalism, we show that the equations are equivalent to an integrable system, generalizing the C^2 subcase analyzed i…