arXiv research

New algorithms ensure fair selection in combinatorial semi-bandit with unrestricted delays.

A new data-driven model forecasts electricity prices efficiently.

QPass evaluates soccer players' passes based on intrinsic value.

An on-going debate in the energy economics and power market community has raised the question if energy-only power markets are increasingly failing due to growing feed-in shares from subsidized renewable energy sources (RES). The short answer to this is: No, they are not failing. Energy-based power markets are, however…

The paper connects DNN generalization to node SNR using information theory.

New SVRG and SARAH schemes reduce tuning effort for variance reduction.

A novel linear classification method that possesses the merits of both the Support Vector Machine (SVM) and the Distance-weighted Discrimination (DWD) is proposed in this article. The proposed Distance-weighted Support Vector Machine method can be viewed as a hybrid of SVM and DWD that finds the classification directio…

Develops a novel approach for discriminative data analysis.

Introduces partial Jacobi manifolds in convenient spaces.

Proposes a fair RMAB framework ensuring equal exposure to arms.

We introduce a new and highly tractable structural model for spot and derivative prices in electricity markets. Using a stochastic model of the bid stack, we translate the demand for power and the prices of generating fuels into electricity spot prices. The stack structure allows for a range of generator efficiencies p…

We propose directed time series regression, a new approach to estimating parameters of time-series models for use in certainty equivalent model predictive control. The approach combines merits of least squares regression and empirical optimization. Through a computational study involving a stochastic version of a well …

This article considers a one-parameter family of circles F_C, which has the interesting property that the null isocline of the family is the largest member of the family. This family of circles is bounded and we consider the problem of deriving an equation for the envelope of F_C. We provide one standard solution, and …

A feature-weighted mean shift algorithm improves clustering in high-dimensional data.

A new learning-to-rank approach ensures fairness for item providers in dynamic ranking systems.

Paper identifies bias and strategic behavior in crowdsourced performance assessments.

Statistical learning improves reactive power control in distribution systems.

We consider the problem of learning the structure of Ising models (pairwise binary Markov random fields) from i.i.d. samples. While several methods have been proposed to accomplish this task, their relative merits and limitations remain somewhat obscure. By analyzing a number of concrete examples, we show that low-comp…

We provide yet another proof of the existence of calibrated forecasters; it has two merits. First, it is valid for an arbitrary finite number of outcomes. Second, it is short and simple and it follows from a direct application of Blackwell's approachability theorem to carefully chosen vector-valued payoff function and …

This paper proposes an ensemble of Gaussian processes for Bayesian optimization.

A novel graph-regularized CCA approach for datasets with a common source graph.

We consider the problem of learning the structure of Ising models (pairwise binary Markov random fields) from i.i.d. samples. While several methods have been proposed to accomplish this task, their relative merits and limitations remain somewhat obscure. By analyzing a number of concrete examples, we show that low-comp…

We present an algorithm producing a dynamic non-self-financing hedging strategy in an incomplete market corresponding to investor-relevant risk criterion. The optimization is a two stage process that first determines admissible model parameters that correspond to the market price of the option being hedged. The second …

We introduce a family of adaptive estimators on graphs, based on penalizing the $\ell_1$ norm of discrete graph differences. This generalizes the idea of trend filtering [Kim et al. (2009), Tibshirani (2014)], used for univariate nonparametric regression, to graphs. Analogous to the univariate case, graph trend filteri…

A new metric captures shape information in manifold learning.

The paper improves count data regression models for overdispersed data.

We propose a new framework for how to use sequential Monte Carlo (SMC) algorithms for inference in probabilistic graphical models (PGM). Via a sequential decomposition of the PGM we find a sequence of auxiliary distributions defined on a monotonically increasing sequence of probability spaces. By targeting these auxili…

The paper models intraday power prices using fundamental drivers.

New definition for forward rates in multi-state models.

DID measures similarity invariant to diffeomorphisms.

VB approximates posterior mean perfectly in linear Gaussian VAR models.

The study revisits portfolio diversification by relaxing assumptions for skewed, multi-regime, and leptokurtic asset returns.

Previous work on recommender systems mainly focus on fitting the ratings provided by users. However, the response patterns, i.e., some items are rated while others not, are generally ignored. We argue that failing to observe such response patterns can lead to biased parameter estimation and sub-optimal model performanc…

We create synthetic Morse code datasets for machine learning.

We consider the class of convex minimization problems, composed of a self-concordant function, such as the $\log\det$ metric, a convex data fidelity term $h(\cdot)$ and, a regularizing -- possibly non-smooth -- function $g(\cdot)$. This type of problems have recently attracted a great deal of interest, mainly due to th…

Gaussian process (GP) predictors are an important component of many Bayesian approaches to machine learning. However, even a straightforward implementation of Gaussian process regression (GPR) requires O(n^2) space and O(n^3) time for a dataset of n examples. Several approximation methods have been proposed, but there …

This paper proposes a new method for estimating sparse precision matrices in the high dimensional setting. It has been popular to study fast computation and adaptive procedures for this problem. We propose a novel approach, called Sparse Column-wise Inverse Operator, to address these two issues. We analyze an adaptive …

ML models predict stock prices poorly during recessions.

Proposes SGELU for neural networks to improve performance and convergence.

The study identifies influential bioinformatics algorithms for scalable computing.

Novel statistical inference using SGD for large-scale problems.

The study improves VaR forecast accuracy by modeling conditional quantile dynamics.

Investigates projections onto explicit subspaces and their variance effects.

A method uses CG to create efficient channels for ideal observers.

Cubic spline smoothing improves interpolation between irregularly sampled data.

Hybrid approach for large-scale network synchronization using KF and PTP.

New algorithm reduces data access and complexity for big data problems.

This paper aims at achieving a simultaneously sparse and low-rank estimator from the semidefinite population covariance matrices. We first benefit from a convex optimization which develops $l_1$-norm penalty to encourage the sparsity and nuclear norm to favor the low-rank property. For the proposed estimator, we then p…