| Nome |
# |
|
Factual and Counterfactual Explanations for Black Box Decision Making, file e0d6c930-727a-fcf8-e053-d805fe0aa794
|
815
|
|
Clustering individual transactional data for masses of users, file e0d6c930-92da-fcf8-e053-d805fe0aa794
|
457
|
|
PRUDEnce: A system for assessing privacy risk vs utility in data sharing ecosystems, file e0d6c92b-3bd0-fcf8-e053-d805fe0aa794
|
270
|
|
MyWay: Location prediction via mobility profiling, file e0d6c92e-c562-fcf8-e053-d805fe0aa794
|
225
|
|
null, file e0d6c929-ec91-fcf8-e053-d805fe0aa794
|
213
|
|
A survey of methods for explaining black box models, file e0d6c92b-20c0-fcf8-e053-d805fe0aa794
|
204
|
|
Learning data mining, file e0d6c92e-f687-fcf8-e053-d805fe0aa794
|
170
|
|
Investigating Neighborhood Generation Methods for Explanations of Obscure Image Classifiers, file e0d6c92e-d358-fcf8-e053-d805fe0aa794
|
163
|
|
Analyzing Privacy Risk in Human Mobility Data, file e0d6c92f-e679-fcf8-e053-d805fe0aa794
|
160
|
|
Big Data Research in Italy: A Perspective, file e0d6c928-5c4e-fcf8-e053-d805fe0aa794
|
142
|
|
null, file e0d6c92e-a739-fcf8-e053-d805fe0aa794
|
134
|
|
Unveiling mobility complexity through complex network analysis, file e0d6c928-00b3-fcf8-e053-d805fe0aa794
|
124
|
|
Discovering temporal regularities in retail customers’ shopping behavior, file e0d6c92c-68ed-fcf8-e053-d805fe0aa794
|
108
|
|
Privacy risk for individual basket patterns, file e0d6c92e-a360-fcf8-e053-d805fe0aa794
|
106
|
|
Gastroesophageal reflux symptoms among Italian university students: Epidemiology and dietary correlates using automatically recorded transactions, file e0d6c92b-6090-fcf8-e053-d805fe0aa794
|
94
|
|
A Data Mining Approach to Assess Privacy Risk in Human Mobility Data, file e0d6c92e-a9fa-fcf8-e053-d805fe0aa794
|
91
|
|
Opening the black box: a primer for anti-discrimination, file e0d6c930-a6bd-fcf8-e053-d805fe0aa794
|
84
|
|
Explaining Any Time Series Classifier, file e0d6c930-1958-fcf8-e053-d805fe0aa794
|
83
|
|
Meaningful Explanations of Black Box AI Decision Systems, file e0d6c92f-adb5-fcf8-e053-d805fe0aa794
|
74
|
|
Occlusion-based explanations in deep recurrent models for biomedical signals, file e0d6c931-c8ec-fcf8-e053-d805fe0aa794
|
18
|
|
An ethico-legal framework for social data science, file e0d6c930-4aae-fcf8-e053-d805fe0aa794
|
16
|
|
A Survey on Privacy in Human Mobility, file e985e2aa-8d68-4a9b-a19f-2944cc5a4712
|
14
|
|
Deriving a Single Interpretable Model by Merging Tree-Based Classifiers, file e0d6c932-306e-fcf8-e053-d805fe0aa794
|
10
|
|
Privacy Preserving Multidimensional Profiling, file e0d6c92c-82f5-fcf8-e053-d805fe0aa794
|
7
|
|
How Data Mining and Machine Learning Evolved from Relational Data Base to Data Science, file d05975e9-a94c-410a-ad4b-a24614b02d27
|
5
|
|
Black Box Explanation by Learning Image Exemplars in the Latent Feature Space, file e0d6c92e-a31b-fcf8-e053-d805fe0aa794
|
5
|
|
Privacy Risk of Global Explainers, file 08312f06-8637-4d1b-be7b-d0a396be89cb
|
4
|
|
XAI.it 2022 - Preface to the Third Italian Workshop on Explainable Artificial Intelligence, file 74733cd4-dbd7-411e-8dec-268b7856fa78
|
4
|
|
Global Explanations with Local Scoring, file e0d6c92e-a289-fcf8-e053-d805fe0aa794
|
4
|
|
Privacy by Design for Mobility Data Analytics, file e0d6c92b-4a5f-fcf8-e053-d805fe0aa794
|
3
|
|
PRIMULE: Privacy risk mitigation for user profiles, file e0d6c92e-2a18-fcf8-e053-d805fe0aa794
|
3
|
|
Local Rule-Based Explanations of Black Box Decision Systems, file e0d6c92e-e63a-fcf8-e053-d805fe0aa794
|
3
|
|
MyWay: Location prediction via mobility profiling, file e0d6c930-48a3-fcf8-e053-d805fe0aa794
|
3
|
|
Give more data, awareness and control to individual citizens, and they will help COVID-19 containment, file e0d6c931-99fe-fcf8-e053-d805fe0aa794
|
3
|
|
Monitoring Fairness in HOLDA, file bed01e3f-6b6c-4bf3-bd7d-76a64ee58262
|
2
|
|
Uncovering Student Temporal Learning Patterns, file d456981b-d1fb-4bc9-9f0d-3c6a3cac1cda
|
2
|
|
Predicting and Explaining Privacy Risk Exposure in Mobility Data, file e0d6c932-3068-fcf8-e053-d805fe0aa794
|
2
|
|
From depression to suicidal discourse on Reddit, file 30780eb0-f78a-4804-8061-36f28115dfbc
|
1
|
|
TRIPLEx: Triple Extraction for Explanation, file 505c3b32-7836-4633-b080-f17e73cbb014
|
1
|
|
Analysis of exposome and genetic variability suggests stress as a major contributor for development of pancreatic ductal adenocarcinoma, file cde6f785-81cb-439b-bd5f-461804eebfe9
|
1
|
|
Detecting Addiction, Anxiety, and Depression by Users Psychometric Profiles, file d0a015fa-d74b-4c7f-8a1b-5215dc539767
|
1
|
|
Partition-based clustering using constraint optimization, file e0d6c929-6526-fcf8-e053-d805fe0aa794
|
1
|
|
Explaining Multi-label Black-Box Classifiers for Health Applications, file e0d6c92e-f685-fcf8-e053-d805fe0aa794
|
1
|
|
Authenticated Outlier Mining for Outsourced Databases, file e0d6c930-4334-fcf8-e053-d805fe0aa794
|
1
|
|
Privacy Risk Assessment of Individual Psychometric Profiles, file e0d6c931-c3e7-fcf8-e053-d805fe0aa794
|
1
|
|
Trustworthy AI at KDD Lab, file e4d1d0fc-2149-4c01-b81e-2e806877e88b
|
1
|
|
Time-on-Task Estimation by data-driven Outlier Detection based on Learning Activities, file eb6c5f90-c186-4655-ad42-8f98138a4282
|
1
|
| Totale |
3.835 |