arXiv research

Improves test set performance and reduces out-of-sample disappointment for unstable models.

The paper explores how investors make decisions under disappointment aversion, finding that they prefer not to invest.

The optimization of the variance supplemented by a budget constraint and an asymmetric $\ell_1$ regularizer is carried out analytically by the replica method borrowed from the theory of disordered systems. The asymmetric regularizer allows us to penalize short and long positions differently, so the present treatment in…

This paper develops a model of reference-dependent assessment of subjective beliefs in which loss-averse people optimally choose the expectation as the reference point to balance the current felicity from the optimistic anticipation and the future disappointment from the realisation. The choice of over-optimism or over…

Paper analyzes high-dimensional portfolio risks and finds empirical out-of-sample relative loss is more reliable.

This paper proposes an out-of-sample extension framework for a global manifold learning algorithm (Isomap) that uses temporal information in out-of-sample points in order to make the embedding more robust to noise and artifacts. Given a set of noise-free training data and its embedding, the proposed framework extends t…

We consider the problem of vertex classification for graphs constructed from the latent position model. It was shown previously that the approach of embedding the graphs into some Euclidean space followed by classification in that space can yields a universally consistent vertex classifier. However, a major technical d…

New method for estimating out-of-sample R² from gene expression data.

Graph embeddings, a class of dimensionality reduction techniques designed for relational data, have proven useful in exploring and modeling network structure. Most dimensionality reduction methods allow out-of-sample extensions, by which an embedding can be applied to observations not present in the training set. Appli…

Paper introduces DOO models to outperform SAA out-of-sample.

Many popular dimensionality reduction procedures have out-of-sample extensions, which allow a practitioner to apply a learned embedding to observations not seen in the initial training sample. In this work, we consider the problem of obtaining an out-of-sample extension for the adjacency spectral embedding, a procedure…

OTSL improves structure learning accuracy with out-of-sample and resampling strategies.

In this paper we consider backward stochastic differential equations with time-delayed generators of a moving average type. The classical framework with linear generators depending on $(Y(t),Z(t))$ is extended and we investigate linear generators depending on $(\frac{1}{t}\int_0^tY(s)ds, \frac{1}{t}\int_0^tZ(s)ds)$. We…

Dimensionality reduction methods are very common in the field of high dimensional data analysis. Typically, algorithms for dimensionality reduction are computationally expensive. Therefore, their applications for the analysis of massive amounts of data are impractical. For example, repeated computations due to accumula…

The paper critiques UBI as ineffective for addressing technological unemployment.

Paper proposes a diagnostic tool for evaluating model performance out-of-sample.

Financial global crisis has devastating impacts to economies since early XX century and continues to impose increasing collateral damages for governments, enterprises, and society in general. Up to now, all efforts to obtain efficient methods to predict these events have been disappointing. However, the quest for a rob…

We identify and validate a model for PCR in high dimensions, improving prediction guarantees.

Study the impact of overfitting on linear predictive models' performance.

New framework for learning policies that converge in out-of-sample regions.

Optimizes decisions without knowing the true distribution using historical data.

Bayesian neural networks show good correlation between out-of-sample performance and Bayesian evidence.

Paper develops a method to predict spatial point processes with guarantees.

The paper analyzes LOCV for high-dimensional risk estimation, proving error bounds.

Bayesian optimisation outperforms standard ML-II in small samples.

Paper presents a new way to analyze machine learning generalization without probabilistic assumptions.

Under the framework of spectral clustering, the key of subspace clustering is building a similarity graph which describes the neighborhood relations among data points. Some recent works build the graph using sparse, low-rank, and $\ell_2$-norm-based representation, and have achieved state-of-the-art performance. Howeve…

Diffusion maps are a nonlinear manifold learning technique based on harmonic analysis of a diffusion process over the data. Out-of-sample extensions with computational complexity $\mathcal{O}(N)$, where $N$ is the number of points comprising the manifold, frustrate applications to online learning applications requiring…

Enhances supervised visualization for unseen data using autoencoders and random forest.

The paper uses machine learning to forecast macroeconomic outcomes with high-dimensional data.

Proposes a new model to maximize out-of-sample Sharpe ratios by forecasting tangency portfolios.

Let $X=\mathbf{X}\cup\mathbf{Z}$ be a data set in $\mathbb{R}^D$, where $\mathbf{X}$ is the training set and $\mathbf{Z}$ is the test one. Many unsupervised learning algorithms based on kernel methods have been developed to provide dimensionality reduction (DR) embedding for a given training set $Φ: \mathbf{X} \to \mat…

Understanding if classifiers generalize to out-of-sample datasets is a central problem in machine learning. Microscopy images provide a standardized way to measure the generalization capacity of image classifiers, as we can image the same classes of objects under increasingly divergent, but controlled factors of variat…

EB improves asset pricing by mining large strategies without lookahead bias.

This research extends the Pareto/NBD model using neural networks for better out-of-sample predictions.

Optimal data-driven formulations are found for learning and decision-making with historical data.

Several popular graph embedding techniques for representation learning and dimensionality reduction rely on performing computationally expensive eigendecompositions to derive a nonlinear transformation of the input data space. The resulting eigenvectors encode the embedding coordinates for the training samples only, an…

We propose a Genetic Programming architecture for the generation of foreign exchange trading strategies. The system's principal features are the evolution of free-form strategies which do not rely on any prior models and the utilization of price series from multiple instruments as input data. This latter feature consti…

Estimates error for robust M-estimators with convex penalties.

New model optimizes portfolios over multiple periods using predictive control.

Random feature mapping (RFM) is a popular method for speeding up kernel methods at the cost of losing a little accuracy. We study kernel ridge regression with random feature mapping (RFM-KRR) and establish novel out-of-sample error upper and lower bounds. While out-of-sample bounds for RFM-KRR have been established by …

Endogenous randomness emerges from adversarial market learning.

RandALO speeds up risk estimation for large datasets.

A new deep learning model improves asset pricing predictions.

Non-linear manifold learning enables high-dimensional data analysis, but requires out-of-sample-extension methods to process new data points. In this paper, we propose a manifold learning algorithm based on deep learning to create an encoder, which maps a high-dimensional dataset and its low-dimensional embedding, and …

Validates policies using past observational data with guarantees about out-of-sample performance.

This tutorial evaluates machine learning for healthcare applications.

Paper proposes a method to improve prediction intervals for neural networks.