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.
Grover search for optimal portfolios based on Sharpe ratio.
We analyze general model selection procedures using penalized empirical loss minimization under computational constraints. While classical model selection approaches do not consider computational aspects of performing model selection, we argue that any practical model selection procedure must not only trade off estimat…
We presented Bayesian portfolio selection strategy, via the factor asset pricing model. If the market is information efficient, the proposed strategy will mimic the market; otherwise, the strategy will outperform the market. The strategy depends on the selection of a portfolio via Bayesian multiple testing methodol…
New algorithm reduces super-arm selection complexity exponentially.
New method improves CATE model selection with optimal regret rates.
We introduce an efficient algorithmic framework for model selection in online learning, also known as parameter-free online learning. Departing from previous work, which has focused on highly structured function classes such as nested balls in Hilbert space, we propose a generic meta-algorithm framework that achieves o…
The key issue in Dynamic Ensemble Selection (DES) is defining a suitable criterion for calculating the classifiers' competence. There are several criteria available to measure the level of competence of base classifiers, such as local accuracy estimates and ranking. However, using only one criterion may lead to a poor …
New GIC improves model selection for structured sparse models.
Bayesian-guided method selects optimal design from large candidate pool.
In this article, we investigate large sample properties of model selection procedures in a general Bayesian framework when a closed form expression of the marginal likelihood function is not available or a local asymptotic quadratic approximation of the log-likelihood function does not exist. Under appropriate identifi…
High throughput genetic sequencing arrays with thousands of measurements per sample and a great amount of related censored clinical data have increased demanding need for better measurement specific model selection. In this paper we establish strong oracle properties of nonconcave penalized methods for nonpolynomial (N…
ADML combines debiased learning with data-driven model selection for efficient inference.
Audio source separation is the process of separating a mixture (e.g. a pop band recording) into isolated sounds from individual sources (e.g. just the lead vocals). Deep learning models are the state-of-the-art in source separation, given that the mixture to be separated is similar to the mixtures the deep model was tr…
SoQal uses selective oracle questioning to improve active learning of cardiac signals.
Oracle-efficient algorithms reduce combinatorial semi-bandit regret to logarithmic time.
Proposes RDIV for IV estimation avoiding limitations of existing methods.
SoQal reduces oracle label requests in active learning by up to 35%.
RPI combines imitation and reinforcement learning to improve policies efficiently.
New algorithm finds best subset in high-dimensional data models.
While model selection is a well-studied topic in parametric and nonparametric regression or density estimation, selection of possibly high-dimensional nuisance parameters in semiparametric problems is far less developed. In this paper, we propose a selective machine learning framework for making inferences about a fini…
The paper studies the benefits of curriculum learning in linear regression tasks.
Oracle inequalities and variable selection properties for the Lasso in linear models have been established under a variety of different assumptions on the design matrix. We show in this paper how the different conditions and concepts relate to each other. The restricted eigenvalue condition (Bickel et al., 2009) or the…
New algorithm reduces high-dimensional data processing costs and achieves true sparsity.
Dynamic Ensemble Selection (DES) techniques aim to select locally competent classifiers for the classification of each new test sample. Most DES techniques estimate the competence of classifiers using a given criterion over the region of competence of the test sample (its the nearest neighbors in the validation set). T…
Financial markets are complex environments that produce enormous amounts of noisy and non-stationary data. One fundamental problem is online portfolio selection, the goal of which is to exploit this data to sequentially select portfolios of assets to achieve positive investment outcomes while managing risks. Various al…
New analysis reveals gaps in selective classifiers, guiding improvements.
Often times, in many design problems, there is a need to select a small set of informative or representative elements from a large ground set of entities in an optimal fashion. Submodular optimization that provides for a formal way to solve such problems, has recently received significant attention from the controls co…
The lasso and related sparsity inducing algorithms have been the target of substantial theoretical and applied research. Correspondingly, many results are known about their behavior for a fixed or optimally chosen tuning parameter specified up to unknown constants. In practice, however, this oracle tuning parameter is …
This paper investigates tradeoffs among optimization errors, statistical rates of convergence and the effect of heavy-tailed errors for high-dimensional robust regression with nonconvex regularization. When the additive errors in linear models have only bounded second moment, we show that iteratively reweighted $\ell_1…
Study finds on-chain data can proxy off-chain cryptocurrency pricing.
We address the new problem of estimating a piece-wise constant signal with the purpose of detecting its change points and the levels of clusters. Our approach is to model it as a nonparametric penalized least square model selection on a family of models indexed over the collection of partitions of the design points and…
Study merges datasets to improve AI model performance.
Two important goals of high-dimensional modeling are prediction and variable selection. In this article, we consider regularization with combined and concave penalties, and study the sampling properties of the global optimum of the suggested method in ultra-high dimensional settings. The -penalty provides th…
High-dimensional data analysis has motivated a spectrum of regularization methods for variable selection and sparse modeling, with two popular classes of convex ones and concave ones. A long debate has been on whether one class dominates the other, an important question both in theory and to practitioners. In this pape…
Propose an XMSE-aware mixed estimator for EB that interpolates between ML and EB shrinkage.
This paper establishes non-asymptotic oracle inequalities for the prediction error and estimation accuracy of the LASSO in stationary vector autoregressive models. These inequalities are used to establish consistency of the LASSO even when the number of parameters is of a much larger order of magnitude than the sample …
New model handles complex non-linear relationships with hidden graph structures.
A method for finding most influential sets reduces a complex problem to a sequence of simpler top- problems.
In the regression setting, given a set of hyper-parameters, a model-estimation procedure constructs a model from training data. The optimal hyper-parameters that minimize generalization error of the model are usually unknown. In practice they are often estimated using split-sample validation. Up to now, there is an ope…
New active learning method for kernel selection improves efficiency and accuracy.
A new method for dynamic feature selection outperforms existing approaches.
New algorithms adapt to friendly environments in online learning.
Efficient algorithms recover two sparse models from a mix of linear queries.
This paper studies simultaneous feature selection and extraction in supervised and unsupervised learning. We propose and investigate selective reduced rank regression for constructing optimal explanatory factors from a parsimonious subset of input features. The proposed estimators enjoy sharp oracle inequalities, and w…
MAPS and MAPS-SE learn from multiple suboptimal experts to improve policies efficiently.
Active learning selects most informative unlabeled samples for labeling.
Sufficient supervised information is crucial for any machine learning models to boost performance. However, labeling data is expensive and sometimes difficult to obtain. Active learning is an approach to acquire annotations for data from a human oracle by selecting informative samples with a high probability to enhance…
ARL-GEN adapts to the smallest model class in nested families for RL with improved regret.