arXiv research

Study classifies liability insurance policies using machine learning.

Two algorithms achieve optimal regret with limited adaptivity in multinomial logistic bandits.

New approach improves stock policies for paper companies, reducing waste and costs.

Framework improves policy generalizability under biased training data.

City Logistics is characterized by multiple stakeholders that often have different views of such a complex system. From a public policy perspective, identifying stakeholders, issues and trends is a daunting challenge, only partially addressed by traditional observation systems. Nowadays, social media is one of the bigg…

Deep learning solves EV routing with time windows for EV fleets.

New method optimizes policies without assuming known link functions between preferences and rewards.

We consider the problem of learning a policy for a Markov decision process consistent with data captured on the state-actions pairs followed by the policy. We assume that the policy belongs to a class of parameterized policies which are defined using features associated with the state-action pairs. The features are kno…

This work explores adaptations of successful multi-armed bandits policies to the online contextual bandits scenario with binary rewards using binary classification algorithms such as logistic regression as black-box oracles. Some of these adaptations are achieved through bootstrapping or approximate bootstrapping, whil…

Proposes a new reinforcement learning algorithm using Q-function.

We consider sequential decision making problems for binary classification scenario in which the learner takes an active role in repeatedly selecting samples from the action pool and receives the binary label of the selected alternatives. Our problem is motivated by applications where observations are time consuming and…

Probabilistic Bisection Algorithm performs root finding based on knowledge acquired from noisy oracle responses. We consider the generalized PBA setting (G-PBA) where the statistical distribution of the oracle is unknown and location-dependent, so that model inference and Bayesian knowledge updating must be performed s…

ZSPO optimizes RL from unknown link functions using human feedback.

Companies try to maximize their profits by recovering returned products of highly uncertain quality and quantity. In this paper, a reverse logistics network for an Original Equipment Manufacturer (OEM) is presented. Returned products are selected for remanufacturing or scrapping, based on their quality and proportional…

Maximum likelihood estimator performance in logistic regression analyzed.

This paper presents a general theory that aims at explaining timescales observed empirically in technology transitions and predicting those of future transitions. This framework is used further to derive a theory for exploring the dynamics that underlie the complex phenomenon of irreversible and path dependent price or…

Paper finds a lower bound for estimating low-rank matrices in logistic regression.

Revises logistic-softmax likelihood for Bayesian meta-learning in few-shot classification.

Novel bounds for logistic regression coreset construction and feature selection.

Unified framework for sparse logistic regression with nonconvex regularization.

Study explores geometric structure and prior for beta-logistic distribution.

We comment on the fact that gradient ascent for logistic regression has a connection with the perceptron learning algorithm. Logistic learning is the "soft" variant of perceptron learning.

Improved sampling efficiency with Random Reshuffling for Langevin dynamics.

Paper explains learning property of logistic and softmax losses for balanced and imbalanced class data.

Improved sketching for logistic and $\ell_1$ regression with near-linear dimensions.

Safe screening rules reduce computation time in logistic regression with $\ell_0-\ell_2$ regularization.

Proposes logistic-beta process for modeling dependent probabilities with beta marginals.

New machine learning pipeline solves dynamic vehicle routing problems efficiently.

Classification is the most important process in data analysis. However, due to the inherent non-convex and non-smooth structure of the zero-one loss function of the classification model, various convex surrogate loss functions such as hinge loss, squared hinge loss, logistic loss, and exponential loss are introduced. T…

Paper introduces imprecise logistic regression for handling uncertain data.

Improved regret bounds for logistic bandits via novel confidence set construction.

Improved confidence bounds for linear logistic model with applications to bandits.

A new data-oblivious sketch for logistic regression reduces data size while maintaining approximation accuracy.

The l1-regularized logistic regression (or sparse logistic regression) is a widely used method for simultaneous classification and feature selection. Although many recent efforts have been devoted to its efficient implementation, its application to high dimensional data still poses significant challenges. In this paper…

PIANO speeds up multinomial logistic regression solving.

Logistic regression is a widely used method in several fields. When applying logistic regression to imbalanced data, for which majority classes dominate over minority classes, all class labels are estimated as `majority class.' In this article, we use an F-measure optimization method to improve the performance of logis…

Develops first optimal algorithm for logistic bandits.

A new algorithm approximates logistic regression probabilities efficiently.

Paper explores connections between loss functions and consistency in binary classification and regression.

Prevalidated ridge regression simplifies logistic regression for high-dimensional data.

Study shows sample complexity for logistic regression with normal covariates.

Logistic regression gets a new, simpler uniform bound.

The paper reveals the hidden costs of digitizing commodity money and proposes a new stable-coin system.

Estimates future ICU demand using age-structured data and logistic modeling.

SGD converges globally to logistic loss minima for two-layer nets.

The paper proves asymptotic normality for multinomial logistic regression on null covariates.

Gradient descent with logistic loss can make two-layer networks interpolate binary classification data.

Improves logistic regression performance with nonconvex programming.