arXiv research

Optimal binning method for numeric targets using mathematical programming.

Bin Packing problems have been widely studied because of their broad applications in different domains. Known as a set of NP-hard problems, they have different vari- ations and many heuristics have been proposed for obtaining approximate solutions. Specifically, for the 1D variable sized bin packing problem, the two ke…

Improved binning technique boosts nUV measure performance.

Balls-and-Bins sampling improves DP-SGD privacy and utility.

The MAP-Elites algorithm produces a set of high-performing solutions that vary according to features defined by the user. This technique has the potential to be a powerful tool for design space exploration, but is limited by the need for numerous evaluations. The Surrogate-Assisted Illumination algorithm (SAIL), introd…

A method for non-parametric conditional distribution estimation using CRPS-optimal binning.

This paper improves multi-class calibration methods using mutual information maximization-based binning.

Paper analyzes ECE bias and provides bounds for its estimation.

Many datasets are in the form of tables of binned data. Performing regression on these data usually involves either reading off bin heights, ignoring data from neighbouring bins or interpolating between bins thus over or underestimating the true bin integrals. In this paper we propose an elegant method for performing G…

Paper analyzes nonconvex bandit problems with improved adaptive methods.

A new survival analysis method eliminates hyperparameter tuning.

This paper introduces minimum-risk recalibration for probabilistic classifiers, improving their reliability and accuracy.

Histogram binning method proven with guarantees without splitting data.

Isotonic regression binning affects calibration statistics of machine learning models.

TCE measures calibration error with a test-based approach.

In subgroup discovery, also known as supervised pattern mining, discovering high quality one-dimensional subgroups and refinements of these is a crucial task. For nominal attributes, this is relatively straightforward, as we can consider individual attribute values as binary features. For numerical attributes, the task…

Improved online algorithm for convex losses with near-optimal swap regret.

New methods reduce bias in estimating calibration error.

Improved kernel ridge regression for large datasets using weighted random binning.

Overconfidence and underconfidence in machine learning classifiers is measured by calibration: the degree to which the probabilities predicted for each class match the accuracy of the classifier on that prediction. How one measures calibration remains a challenge: expected calibration error, the most popular metric, ha…

In the framework of Multifractal Diffusion Entropy Analysis we propose a method for choosing an optimal bin-width in histograms generated from underlying probability distributions of interest. The method presented uses techniques of Rényi's entropy and the mean squared error analysis to discuss the conditions under whi…

New methods improve estimation of nonhomogeneous Poisson processes from limited data.

Unified framework for PDF estimation using MDL-based binning and tensor factorization.

A new method detects and displays pairwise dependence between variates.

We consider the non-parametric regression problem under Huber's $ε$-contamination model, in which an $ε$ fraction of observations are subject to arbitrary adversarial noise. We first show that a simple local binning median step can effectively remove the adversary noise and this median estimator is minimax optimal up t…

AdaCat improves density estimation and planning in autoregressive models.

A new method improves quantile regression for high-dimensional data.

New bin-wise scaling methods improve prediction uncertainty calibration for machine learning.

Heuristic algorithms such as simulated annealing, Concorde, and METIS are effective and widely used approaches to find solutions to combinatorial optimization problems. However, they are limited by the high sample complexity required to reach a reasonable solution from a cold-start. In this paper, we introduce a novel …

We consider the problem of inferring the values of an arbitrary set of variables (e.g., risk of diseases) given other observed variables (e.g., symptoms and diagnosed diseases) and high-dimensional signals (e.g., MRI images or EEG). This is a common problem in healthcare since variables of interest often differ for dif…

Solves online 3D bin packing with deep reinforcement learning under constraints.

Paper introduces a differentiable STFT for continuous window length optimization.

In this work, we present a method to compute the Kantorovich-Wasserstein distance of order one between a pair of two-dimensional histograms. Recent works in Computer Vision and Machine Learning have shown the benefits of measuring Wasserstein distances of order one between histograms with $n$ bins, by solving a classic…

Probability Density Estimation (PDE) is a multivariate discrimination technique based on sampling signal and background densities defined by event samples from data or Monte-Carlo (MC) simulations in a multi-dimensional phase space. In this paper, we present a modification of the PDE method that uses a self-adapting bi…

Paper introduces RPWithPrior for efficient label differential privacy in regression.

Study three types of uncertainty quantification for binary classification without distributional assumptions.

Modeling default contagion and systemic risk using a balls-and-bins approach.

Object detection in streaming images is a major step in different detection-based applications, such as object tracking, action recognition, robot navigation, and visual surveillance applications. In mostcases, image quality is noisy and biased, and as a result, the data distributions are disturbed and imbalanced. Most…

The fast demographic growth, together with the concentration of the population in cities and the increasing amount of daily waste, are factors that push to the limit the ability of waste assimilation by Nature. Therefore, we need technological means to make an optimal management of the waste collection process, which r…

Earlier studies have shown that stock market distributions can be well described by distributions derived from Tsallis entropy, which is a generalization of Shannon entropy to non-extensive systems. In this paper, Tsallis relative entropy (TRE), which is the generalization of Kullback-Leibler relative entropy (KLRE) to…

BaNk-UCB tackles batched nonparametric bandits with k-NN regression and UCB.

New algorithm optimizes smooth functions with Hölder exponent > 1.

This study examines how discretization improves neural forecasting models.

The goal of lossy data compression is to reduce the storage cost of a data set $X$ while retaining as much information as possible about something ($Y$) that you care about. For example, what aspects of an image $X$ contain the most information about whether it depicts a cat? Mathematically, this corresponds to finding…

New method unfolds distribution moments directly from data without binning.

In this paper we perform a statistical analysis over the returns and relative prices of the CAC $40$ and the S\&P $500$ with the purpose of analyzing the intra-day seasonalities of single and cross-sectional stock dynamics. In order to do that, we characterized the dynamics of a stock (or a set of stocks) by the evolut…

A new DP algorithm improves privacy in hashing and sampling for search and learning.

Cheap permutation tests speed up distribution testing without sacrificing accuracy.