arXiv research

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

The paper proposes methods to estimate positive examples and learn classifiers from mixed data.

Applications such as weather forecasting and personalized medicine demand models that output calibrated probability estimates---those representative of the true likelihood of a prediction. Most models are not calibrated out of the box but are recalibrated by post-processing model outputs. We find in this work that popu…

New methods reduce bias in estimating calibration error.

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

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…

Paper analyzes ECE bias and provides bounds for its estimation.

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

Plots show miscalibration directly as slopes of secant lines.

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

Isotonic regression binning affects calibration statistics of machine learning models.

Histogram binning method proven with guarantees without splitting data.

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

The objective of this work is to take advantage of deep neural networks in order to make next day crime count predictions in a fine-grain city partition. We make predictions using Chicago and Portland crime data, which is augmented with additional datasets covering weather, census data, and public transportation. The c…

For various applications, the relations between the dependent and independent variables are highly nonlinear. Consequently, for large scale complex problems, neural networks and regression trees are commonly preferred over linear models such as Lasso. This work proposes learning the feature nonlinearities by binning fe…

Deep clustering is the first method to handle general audio separation scenarios with multiple sources of the same type and an arbitrary number of sources, performing impressively in speaker-independent speech separation tasks. However, little is known about its effectiveness in other challenging situations such as mus…

Bayesian inference engines improve density estimation accuracy and scalability.

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…

A new estimator for asynchronous tick data shows better correlation estimates.

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…

AdaCat improves density estimation and planning in autoregressive models.

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

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…

TCE measures calibration error with a test-based approach.

New method reduces bias in estimating causal effects from discretized variables.

New method uses machine learning to estimate sensitivity without binning.

Paper uses conformal prediction for solar power forecasting in electricity markets.

Improved binning technique boosts nUV measure performance.

A new survival analysis method eliminates hyperparameter tuning.

We present sparse tree-based and list-based density estimation methods for binary/categorical data. Our density estimation models are higher dimensional analogies to variable bin width histograms. In each leaf of the tree (or list), the density is constant, similar to the flat density within the bin of a histogram. His…

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…

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…

Enhances conformal prediction for better uncertainty estimates in armed conflict fatalities.

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

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 …

Given discrete time observations over a fixed time interval, we study a nonparametric Bayesian approach to estimation of the volatility coefficient of a stochastic differential equation. We postulate a histogram-type prior on the volatility with piecewise constant realisations on bins forming a partition of the time in…

The optimal binning is the optimal discretization of a variable into bins given a discrete or continuous numeric target. We present a rigorous and extensible mathematical programming formulation for solving the optimal binning problem for a binary, continuous and multi-class target type, incorporating constraints not p…

BMTI method estimates densities without bins, outperforming traditional estimators.

Paper introduces RPWithPrior for efficient label differential privacy in regression.

A new method detects and displays pairwise dependence between variates.

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

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…

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

Accurate calibration of probabilistic predictive models learned is critical for many practical prediction and decision-making tasks. There are two main categories of methods for building calibrated classifiers. One approach is to develop methods for learning probabilistic models that are well-calibrated, ab initio. The…

Estimates sample size for subgroup analysis in randomized experiments.

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

C-OPH improves One Permutation Hashing by using a shorter circulant permutation.

New method calibrates machine learning models with theoretical guarantees.