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Found 19 papers, 2 papers with code
BIRDNEST: Bayesian Inference for Ratings-Fraud Detection
To combine these 2 approaches, we formulate our Bayesian Inference for Rating Data (BIRD) model, a flexible Bayesian model of user rating behavior.
FairJudge: Trustworthy User Prediction in Rating Platforms
We propose three metrics: (i) the fairness of a user that quantifies how trustworthy the user is in rating the products, (ii) the reliability of a rating that measures how reliable the rating is, and (iii) the goodness of a product that measures the quality of the product.
Decomposition Strategies for Constructive Preference Elicitation
We propose a decomposition technique for large preference-based decision problems relying exclusively on inference and feedback over partial configurations.
Automating Personnel Rostering by Learning Constraints Using Tensors
Many problems in operations research require that constraints be specified in the model.
Did We Get It Right? Predicting Query Performance in E-commerce Search
In this paper, we address the problem of evaluating whether results served by an e-commerce search engine for a query are good or not.
Intermediate frequency Upgrade design features of NASA D3R Weather Radar System
The NASA dual-frequency, dual-polarization, Doppler radar (D3R) is an important ground validation tool for the global precipitation measurement (GPM) mission dual-frequency precipitation radar (DPR).
Toward Machine-Guided, Human-Initiated Explanatory Interactive Learning
This biases the "narrative" presented by the machine to the user. We address this narrative bias by introducing explanatory guided learning, a novel interactive learning strategy in which: i) the supervisor is in charge of choosing the query instances, while ii) the machine uses global explanations to illustrate its overall behavior and to guide the supervisor toward choosing challenging, informative instances.
Use of adaptive filtering techniques and deconvolution to obtain low range sidelobe samples
In this paper the use of adaptive filtering techniques to obtain better peak sidelobe suppression and integrated sidelobe energy will be discussed with regard to weather radars and obtaining better sensitivity with this technique.
Machine Guides, Human Supervises: Interactive Learning with Global Explanations
Compared to other explanatory interactive learning strategies, which are machine-initiated and rely on local explanations, XGL is designed to be robust against cases in which the explanations supplied by the machine oversell the classifier's quality.
Membership-Mappings for Data Representation Learning: Measure Theoretic Conceptualization
An analytical approach to the variational learning of a membership-mappings based data representation model is considered.
Differentially Private Transferrable Deep Learning with Membership-Mappings
This paper considers the problem of differentially private semi-supervised transfer and multi-task learning.
Information Theoretic Evaluation of Privacy-Leakage, Interpretability, and Transferability for Trustworthy AI
A variational membership-mapping Bayesian model is used for the analytical approximations of the defined information theoretic measures for privacy-leakage, interpretability, and transferability.
Learning Mixed-Integer Linear Programs from Contextual Examples
In this paper, we study the problem of acquiring MILPs from contextual examples, a novel and realistic setting in which examples capture solutions and non-solutions within a specific context.
Few-shot calibration of low-cost air pollution (PM2.5) sensors using meta-learning
Low-cost particulate matter sensors are transforming air quality monitoring because they have lower costs and greater mobility as compared to reference monitors.
Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation
Combinatorial optimisation problems are ubiquitous in artificial intelligence.
Membership-Mappings for Practical Secure Distributed Deep Learning
This study leverages the data representation capability of fuzzy based membership-mappings for practical secure distributed deep learning using fully homomorphic encryption.
On Mitigating the Utility-Loss in Differentially Private Learning: A new Perspective by a Geometrically Inspired Kernel Approach
Privacy-utility tradeoff remains as one of the fundamental issues of differentially private machine learning.
RoboSense At Edge: Detecting Slip, Crumple and Shape of the Object in Robotic Hand for Teleoprations
We proposed ML model will detect the slip, crumple, and shape using the force/torque exerted and the angular positions of the actuators present in the RH.
Comparative Analysis of Transformers for Modeling Tabular Data: A Casestudy using Industry Scale Dataset
We perform a comparative analysis of transformer-based models designed for modeling tabular data, specifically on an industry-scale dataset.
