arXiv research

New algorithm finds best feasible arm in grouped bandits.

The paper tackles identifying Pareto Set with constraints using bandit feedback.

Paper reduces sample complexity for bilinear systems identification to nearly constant.

Wi-Fi signals-based person identification attracts increasing attention in the booming Internet-of-Things era mainly due to its pervasiveness and passiveness. Most previous work applies gaits extracted from WiFi distortions caused by the person walking to achieve the identification. However, to extract useful gait, a p…

A new pricing controller handles resource constraints to infer target prices effectively.

CNN identifies nonlinear human posture control models efficiently.

Algorithm minimizes regret in multi-criteria bandits with constraints.

Improved knowledge gradient (iKG) outperforms the original KG algorithm in best arm identification problems.

We consider the problem of identifying the most profitable product design from a finite set of candidates under unknown consumer preference. A standard approach to this problem follows a two-step strategy: First, estimate the preference of the consumer population, represented as a point in part-worth space, using an ad…

In this paper, we introduce a new gait segmentation method based on accelerometer data and develop a new distance function between two time series, showing novel and effectiveness in simultaneously identifying user and adversary. Comparing with the normally used Neural Network methods, our approaches use geometric feat…

It is often the case that, within an online recommender system, multiple users share a common account. Can such shared accounts be identified solely on the basis of the userprovided ratings? Once a shared account is identified, can the different users sharing it be identified as well? Whenever such user identification …

Paper speeds up topological signal identification and cycle matching.

Method identifies IPS governing equations from particle data efficiently.

Work shows hallucination detection by LLMs is impossible without expert feedback.

This paper establishes that so-called instrumental variables enable the identification and the estimation of a fully nonparametric regression model with Berkson-type measurement error in the regressors. An estimator is proposed and proven to be consistent. Its practical performance and feasibility are investigated via …

Framework identifies population quantities from MNAR feedback using weak shadow variables from pretrained models.

New MCMC method improves sampling efficiency across diverse structural models.

Paper uses machine learning to identify key pathways for c-di-GMP in bacterial genomes.

Study shows ethanol blends and incentives can significantly reduce transportation carbon emissions.

We study the problem of interference source identification, through the lens of recognizing one of 15 different channels that belong to 3 different wireless technologies: Bluetooth, Zigbee, and WiFi. We employ deep learning algorithms trained on received samples taken from a 10 MHz band in the 2.4 GHz ISM Band. We obta…

We study the combinatorial pure exploration problem Best-Set in stochastic multi-armed bandits. In a Best-Set instance, we are given $n$ arms with unknown reward distributions, as well as a family $\mathcal{F}$ of feasible subsets over the arms. Our goal is to identify the feasible subset in $\mathcal{F}$ with the maxi…

RAMEN corrects observational data biases for multiple environments.

Safety is an essential aspect in the facilitation of automated vehicle deployment. Current testing practices are not enough, and going beyond them leads to infeasible testing requirements, such as needing to drive billions of kilometres on public roads. Automated vehicles are exposed to an indefinite number of scenario…

Some statistical models are specified via a data generating process for which the likelihood function cannot be computed in closed form. Standard likelihood-based inference is then not feasible but the model parameters can be inferred by finding the values which yield simulated data that resemble the observed data. Thi…

Crohn's disease, one of two inflammatory bowel diseases (IBD), affects 200,000 people in the UK alone, or roughly one in every 500. We explore the feasibility of deep learning algorithms for identification of terminal ileal Crohn's disease in Magnetic Resonance Enterography images on a small dataset. We show that they …

Data generated by cars is growing at an unprecedented scale. As cars gradually become part of the Internet of Things (IoT) ecosystem, several stakeholders discover the value of in-vehicle network logs containing the measurements of the multitude of sensors deployed within the car. This wealth of data is also expected t…

Designs efficient factorial experiments for product design under budget constraints.

FOSC-X: An extended framework for extracting multiple optimal flat clusterings from hierarchical cluster trees

New method identifies cluster representatives with minimal pulls.

This research tackles backdoor attacks on audio data using a stochastic investment approach.

To construct interpretable explanations that are consistent with the original ML model, counterfactual examples---showing how the model's output changes with small perturbations to the input---have been proposed. This paper extends the work in counterfactual explanations by addressing the challenge of feasibility of su…

Bayesian search optimizes exploration of feasible solutions under expensive constraints.

A cased-based reasoning method predicts rare events on strategic sites using satellite imagery.

Objective: A median of 14.4% of patient undergone at least one adverse event during surgery and a third of them are preventable. The occurrence of adverse events forces surgeons to implement corrective strategies and, thus, deviate from the standard surgical process. Therefore, it is clear that the automatic identifica…

Demonstration-regularized RL reduces sample complexity for policy identification.

Develops a new framework for integrating satellite allocations in small portfolios.

The classical multi-set split feasibility problem seeks a point in the intersection of finitely many closed convex domain constraints, whose image under a linear mapping also lies in the intersection of finitely many closed convex range constraints. Split feasibility generalizes important inverse problems including con…

Enhances OTA FL algorithms by defining inverse feasibility for linear models.

Unified local and global explanations through functional decomposition of low dimensional structures.

Many engineering problems require identifying feasible domains under implicit constraints. One example is finding acceptable car body styling designs based on constraints like aesthetics and functionality. Current active-learning based methods learn feasible domains for bounded input spaces. However, we usually lack pr…

New algorithm exploits curvature of feasible sets for fast online convex optimization.

This paper improves uncertainty quantification in ELM models.

Comonotonic allocations are restored under certain constraints, improving risk-sharing.

Deep learning speeds up real-time emission monitoring.

The paper studies SDP feasibility and sos ranks for specific polynomials.

Paper explores using EEG for better speaker identification, even in noisy environments.

Bayesian framework proves thresholds for multi-graph alignment feasibility.

Stochastic convex optimization problems with expectation constraints (SOECs) are encountered in statistics and machine learning, business, and engineering. In data-rich environments, the SOEC objective and constraints contain expectations defined with respect to large datasets. Therefore, efficient algorithms for solvi…