Demo Data from EDBT 2026 / reviewers in the wild / expert
Fletcher Lu
dblp:99/4830
· DBLP profile ↗
7ranked-venue papers
6as first author
0since 2021 · last 2014
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 3 first-authorDatabases, data management, data science and information retrieval · 2 · 2 first-authorApplied, interdisciplinary, general and emerging computing · 2 · 2 first-authorHuman-computer interaction and ubiquitous computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Reinforcement learning · 75% Probabilistic and Bayesian machine learning · 25% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › parameter estimation
maximum likelihood estimation |
0.0 | 1 | 2002 | Investigating the Maximum Likelihood Alternative to TD(lambda) · ICML 2002 |
Machine learning › Reinforcement learning › temporal difference learning › eligibility traces
TD(lambda) |
0.0 | 1 | 2002 | Investigating the Maximum Likelihood Alternative to TD(lambda) · ICML 2002 |
Machine learning › Reinforcement learning
temporal difference learning |
0.0 | 1 | 2002 | Investigating the Maximum Likelihood Alternative to TD(lambda) · ICML 2002 |
Machine learning › Reinforcement learning
value function estimation |
0.0 | 1 | 2002 | Investigating the Maximum Likelihood Alternative to TD(lambda) · ICML 2002 |
Methods — techniques the papers use, named apart from their topics
maximum likelihood · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | Reducing adolescent obesity with a social networking mobile fitness applicationabstractWe present the results of the third phase in a three part study developing an application to reduce obesity levels in adolescents with a mobile fitness application that encourages youth to engage in fitness activity by providing social networking tools to facilitate motivation. In the first phase, we studied effecting positive attitudinal changes towards fitness. The second phase studied physical improvements with the application in our target group. This third phase tests the efficacy of the socialization tools in our application for motivating both positive attitudinal changes and physical improvements towards fitness. The application contained thirteen distinct exercises that subjects could engage in using sensors on the mobile devices to unbiasedly measure progress. The socialization tools to encourage fitness engagement allowed for such activities as friending, sharing progress and collaboratively exercising with friends either in person or remotely. The study participants were adolescents age 14 to 15 with 20 subjects in an experimental group that used the fitness application and 15 control subjects that did not. The study ran for eight weeks with the results indicating both positive attitude changes towards fitness exercises and body-mass index (BMI) improvements in fitness levels correlated to socialization interests/activities for the subjects using the application, while the control group showed no such correlation of fitness level improvement. Fletcher Lu, Manon Lemonde |
Healthcom | 1 |
| 2013 | Reducing adolescent obesity with a mobile fitness application: Study results of youth age 15 to 17abstractWe present the results of the second phase of a multiphase study into the feasibility of combating growing adolescent obesity rates with a fitness game deployed on mobile devices. The fitness application includes tracking software to measure and count exercises performed and socialization features to compete and collaborate with friends. In the first phase of our study we tested producing positive changes in test subjects' attitudes towards fitness as a precursor to producing sustained fitness interest. This second phase study tests the application on its efficacy for improving physical fitness levels in adolescents. This study included 19 subjects aged 15 to 17 using the fitness application over a six week period. Students were categorized into underweight, healthy, and overweight/obese categories according to the United States' Centres for Disease Control's 2000 published charts on body-mass index (BMI) percentile charts. Test results showed an average decrease in BMI of 0.51 for overweight/obese subjects, while healthy subjects maintained their healthy BMI levels with an average BMI change of 0.02. Only one subject was classified as underweight and had a BMI increase of 0.32. Fletcher Lu, Kei Turner, Bernadette Murphy |
Healthcom | 1 |
| 2007 | Uncovering Fraud in Direct Marketing Data with a Fraud Auditing Case Builder
Fletcher Lu |
PKDD | 1 |
| 2005 | Detecting Fraud in Health Insurance Data: Learning to Model Incomplete Benford's Law Distributions
Fletcher Lu, J. Efrim Boritz |
ECML | 1 |
| 2003 | Monte Carlo Matrix Inversion Policy Evaluation
Fletcher Lu, Dale Schuurmans |
UAI | 1 |
| 2002 | Investigating the Maximum Likelihood Alternative to TD(lambda)
Fletcher Lu, Relu Patrascu, Dale Schuurmans |
ICML | 1 |
| 1998 | What is Initiative?
Robin Cohen, Coralee Allaby, Christian Cumbaa, Mark Fitzgerald, Kinson Ho, Bowen Hui, Celine Latulipe, Fletcher Lu, Nancy Moussa, David Pooley, Alex Qian, Saheem Siddiqi |
User Model. User Adapt. Interact. | 8 |