This paper optimizes DC pension plan investments using O-U process and loan.
problem Optimizing investment strategy for DC pension plans under specific market conditions.
method Dynamic programming and Hamilton-Jacobi-Bellman equation to derive optimal investment strategy.
result Explicit expression for optimal investment strategy derived.
We investigate properties of estimators obtained by minimization of U-processes with the Lasso penalty in high-dimensional settings. Our attention is focused on the ranking problem that is popular in machine learning. It is related to guessing the ordering between objects on the basis of their observed predictors. We p…
MSRL learns a representation maximizing mutual info with response variables.
problem Learning sufficient representations for complex, multi-dimensional data.
method Variational mutual information, deep neural networks, generalized Dudley's inequality.
result MSRL achieves consistent and accurate representation learning.
The paper proposes tree-based methods for automatically learning similarity measures.
problem Automatically learning similarity measures in feature spaces.
method Formulates similarity learning as a pairwise bipartite ranking problem and uses recursive tree-based ROC optimization.
result Validates iterative partitioning procedures for similarity learning and proposes efficient algorithms.
The paper finds optimal levels for traders in mean-reverting markets.
problem Determining optimal levels for traders in mean-reverting markets.
method Analytical framework using heat potentials.
result Developed an analytical solution for optimal levels.
This paper approximates SA iterates using Gaussian distributions for tail bounds.
problem Characterizing the distribution of stochastic approximation iterates in finite time.
method Approximating pre-limit distributions of SA iterates by Gaussian sequences with recursively defined covariances.
result Explicit bounds on the Wasserstein-1 distance between rescaled iterates and Gaussians.
Entropy regularized OT test assesses independence between samples.
problem Testing independence between two samples.
method Entropy regularized optimal transport.
result Non-asymptotic bounds for test statistic established.
GCQRF predicts survival quantiles without linearity assumptions.
problem Survival analysis with right censoring and nonlinearity.
method Global Censored Quantile Random Forest (GCQRF) for complex relationships.
result GCQRF outperforms existing methods in predictive accuracy.
In a wide range of statistical learning problems such as ranking, clustering or metric learning among others, the risk is accurately estimated by U-statistics of degree d≥1, i.e. functionals of the training data with low variance that take the form of averages over k-tuples. From a computational perspective, …
Pairwise quantile regression tackles similarity scoring in biometric systems.
problem Analyzing errors in similarity scoring for facial recognition.
method Established theoretical guarantees for pairwise quantile regression solutions, leveraging sharp concentration results for U-processes. result Proved generalization bounds and identified conditions for fast learning rates.
Proposes a new random forest weighted local Fréchet regression method.
problem Complex metric space valued responses and curse of dimensionality in Fréchet regression.
method Locally adaptive kernel generated by random forests for local average and local linear Fréchet regression.
result Significantly improves existing Fréchet regression methods with theoretical guarantees.
Pac-Bayes bounds are among the most accurate generalization bounds for classifiers learned from independently and identically distributed (IID) data, and it is particularly so for margin classifiers: there have been recent contributions showing how practical these bounds can be either to perform model selection (Ambrol…