Information Extraction for Clinical Data Mining: A Mammography Case Study. - Houssam Nassif; Ryan W. Woods; Elizabeth S. Burnside; Mehmet Ayvaci; Jude W. Shavlik; David Page (ICDM Workshops, 2009)

An Inductive Logic Programming Approach to Validate Hexose Binding Biochemical Knowledge. - Houssam Nassif; Hassan Al-Ali; Sawsan Khuri; Walid Keirouz; David Page (ILP, 2009)

Uncovering age-specific invasive and DCIS breast cancer rules using inductive logic programming. - Houssam Nassif; David Page; Mehmet Ayvaci; Jude W. Shavlik; Elizabeth S. Burnside (IHI, 2010)

Logical Differential Prediction Bayes Net, improving breast cancer diagnosis for older women. - Houssam Nassif; Yirong Wu; David Page; Elizabeth S. Burnside (AMIA, 2012)

Extracting BI-RADS features from Portuguese clinical texts. - Houssam Nassif; Filipe Cunha; Inês C. Moreira; Ricardo Cruz-Correia; Eliana Sousa; David Page; Elizabeth S. Burnside; Inês de Castro Dutra (BIBM, 2012)

Relational Differential Prediction. - Houssam Nassif; Vítor Santos Costa; Elizabeth S. Burnside; David Page (ECML/PKDD (1), 2012)

Automated identification of protein-ligand interaction features using Inductive Logic Programming: a hexose binding case study. - José Carlos Almeida Santos; Houssam Nassif; David Page; Stephen H. Muggleton; Michael J. E. Sternberg (BMC Bioinform., 2012)

Automatic classification of mammography reports by BI-RADS breast tissue composition class. - Bethany Percha; Houssam Nassif; Jafi A. Lipson; Elizabeth S. Burnside; Daniel L. Rubin (J. Am. Medical Informatics Assoc., 2012)

Genetic Variants Improve Breast Cancer Risk Prediction on Mammograms. - Jie Liu 006; David Page; Houssam Nassif; Jude W. Shavlik; Peggy L. Peissig; Catherine A. McCarty; Adedayo A. Onitilo; Elizabeth S. Burnside (AMIA, 2013)

Using machine learning to identify benign cases with non-definitive biopsy. - Finn Kuusisto; Inês de Castro Dutra; Houssam Nassif; Yirong Wu; Molly E. Klein; Heather B. Neuman; Jude W. Shavlik; Elizabeth S. Burnside (Healthcom, 2013)

Uplift Modeling with ROC: An SRL Case Study. - Houssam Nassif; Finn Kuusisto; Elizabeth S. Burnside; Jude W. Shavlik (ILP (Late Breaking Papers), 2013)

Score As You Lift (SAYL): A Statistical Relational Learning Approach to Uplift Modeling. - Houssam Nassif; Finn Kuusisto; Elizabeth S. Burnside; David Page; Jude W. Shavlik; Vítor Santos Costa (ECML/PKDD (3), 2013)

Support Vector Machines for Differential Prediction. - Finn Kuusisto; Vítor Santos Costa; Houssam Nassif; Elizabeth S. Burnside; David Page; Jude W. Shavlik (ECML/PKDD (2), 2014)

Adaptive, Personalized Diversity for Visual Discovery. - Choon Hui Teo; Houssam Nassif; Daniel N. Hill; Sriram Srinivasan 004; Mitchell Goodman; Vijai Mohan; S. V. N. Vishwanathan (RecSys, 2016)

An Efficient Bandit Algorithm for Realtime Multivariate Optimization. - Daniel N. Hill; Houssam Nassif; Yi Liu 033; Anand Iyer; S. V. N. Vishwanathan (KDD, 2017)

Diversifying Music Recommendations. - Houssam Nassif; Kemal Oral Cansizlar; Mitchell Goodman; S. V. N. Vishwanathan (CoRR, 2018)

Contextual Multi-Armed Bandits for Causal Marketing. - Neela Sawant; Chitti Babu Namballa; Narayanan Sadagopan; Houssam Nassif (CoRR, 2018)

Seeker: Real-Time Interactive Search. - Ari Biswas; Thai T. Pham; Michael Vogelsong; Benjamin Snyder; Houssam Nassif (KDD, 2019)

Deep PQR: Solving Inverse Reinforcement Learning using Anchor Actions. - Sinong Geng; Houssam Nassif; Carlos A. Manzanares; A. Max Reppen; Ronnie Sircar (ICML, 2020)

Decoupling Learning Rates Using Empirical Bayes Priors. - Sareh Nabi; Houssam Nassif; Joseph Hong; Hamed Mamani; Guido Imbens (CoRR, 2020)

Identifying Reward Functions using Anchor Actions. - Sinong Geng; Houssam Nassif; Carlos A. Manzanares; A. Max Reppen; Ronnie Sircar (CoRR, 2020)

Improved Confidence Bounds for the Linear Logistic Model and Applications to Linear Bandits. - Kwang-Sung Jun; Lalit Jain; Houssam Nassif (CoRR, 2020)

Improved Confidence Bounds for the Linear Logistic Model and Applications to Bandits. - Kwang-Sung Jun; Lalit Jain; Houssam Nassif; Blake Mason (ICML, 2021)

Instance-optimal PAC Algorithms for Contextual Bandits. - Zhaoqi Li; Lillian J. Ratliff; Houssam Nassif; Kevin G. Jamieson; Lalit Jain (NeurIPS, 2022)

Adaptive Experimental Design and Counterfactual Inference. - Tanner Fiez; Sergio Gamez; Arick Chen; Houssam Nassif; Lalit Jain (CoRR, 2022)

Bayesian Meta-Prior Learning Using Empirical Bayes. - Sareh Nabi; Houssam Nassif; Joseph Hong; Hamed Mamani; Guido Imbens (Manag. Sci., 2022)

Neural Insights for Digital Marketing Content Design. - Fanjie Kong; Yuan Li 031; Houssam Nassif; Tanner Fiez; Ricardo Henao; Shreya Chakrabarti (KDD, 2023)

Experimental Designs for Heteroskedastic Variance. - Justin Weltz; Tanner Fiez; Alexander Volfovsky; Eric Laber; Blake Mason; Houssam Nassif; Lalit Jain (NeurIPS, 2023)

A Data-Driven State Aggregation Approach for Dynamic Discrete Choice Models. - Sinong Geng; Houssam Nassif; Carlos A. Manzanares (UAI, 2023)

On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models. - Boyao Li; Alexander Thomson; Houssam Nassif; Matthew Engelhard; David Page (NeurIPS, 2024)

Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice. - Tanner Fiez; Houssam Nassif; Yu-Cheng Chen; Sergio Gamez; Lalit Jain (WWW, 2024)

Counterfactual Evaluation of Ads Ranking Models through Domain Adaptation. - Mohamed A. Radwan; Himaghna Bhattacharjee; Quinn Lanners; Jiasheng Zhang; Serkan Karakulak; Houssam Nassif; Murat Ali Bayir (CoRR, 2024)

Factor Learning Portfolio Optimization Informed by Continuous-Time Finance Models. - Sinong Geng; Houssam Nassif; Zhaobin Kuang; Anders Max Reppen; Ronnie Sircar (Trans. Mach. Learn. Res., 2025)