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ORCIDHoussam Nassif
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2020 – today
- 2024
[c21]Tanner Fiez
, Houssam Nassif, Yu-Cheng Chen, Sergio Gamez, Lalit Jain:
Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice. WWW 2024: 3586-3597- [i15]Tanner Fiez, Houssam Nassif, Yu-Cheng Chen, Sergio Gamez, Lalit Jain:
Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice. CoRR abs/2402.10870 (2024) - [i14]Mohamed A. Radwan, Himaghna Bhattacharjee, Quinn Lanners, Jiasheng Zhang, Serkan Karakulak, Houssam Nassif, Murat Ali Bayir:
Counterfactual Evaluation of Ads Ranking Models through Domain Adaptation. CoRR abs/2409.19824 (2024) - 2023
- [c20]Fanjie Kong, Yuan Li, Houssam Nassif, Tanner Fiez, Ricardo Henao, Shreya Chakrabarti:
Neural Insights for Digital Marketing Content Design. KDD 2023: 4320-4332 - [c19]Justin Weltz, Tanner Fiez, Alexander Volfovsky, Eric Laber, Blake Mason, Houssam Nassif, Lalit Jain:
Experimental Designs for Heteroskedastic Variance. NeurIPS 2023 - [c18]Sinong Geng, Houssam Nassif, Carlos A. Manzanares:
A Data-Driven State Aggregation Approach for Dynamic Discrete Choice Models. UAI 2023: 647-657 - [i13]Fanjie Kong, Yuan Li, Tanner Fiez, Shreya Chakrabarti, Ricardo Henao, Houssam Nassif:
Neural Insights for Digital Marketing Content Design. CoRR abs/2302.01416 (2023) - [i12]Sinong Geng, Houssam Nassif, Carlos A. Manzanares:
A Data-Driven State Aggregation Approach for Dynamic Discrete Choice Models. CoRR abs/2304.04916 (2023) - 2022
- [j3]Sareh Nabi, Houssam Nassif, Joseph Hong, Hamed Mamani, Guido Imbens:
Bayesian Meta-Prior Learning Using Empirical Bayes. Manag. Sci. 68(3): 1737-1755 (2022) - [c17]Zhaoqi Li, Lillian J. Ratliff, Houssam Nassif, Kevin G. Jamieson, Lalit Jain:
Instance-optimal PAC Algorithms for Contextual Bandits. NeurIPS 2022 - [i11]Zhaoqi Li, Lillian J. Ratliff, Houssam Nassif, Kevin G. Jamieson, Lalit Jain:
Instance-optimal PAC Algorithms for Contextual Bandits. CoRR abs/2207.02357 (2022) - [i10]Tanner Fiez, Sergio Gamez, Arick Chen, Houssam Nassif, Lalit Jain:
Adaptive Experimental Design and Counterfactual Inference. CoRR abs/2210.14369 (2022) - 2021
- [c16]Kwang-Sung Jun, Lalit Jain, Houssam Nassif, Blake Mason:
Improved Confidence Bounds for the Linear Logistic Model and Applications to Bandits. ICML 2021: 5148-5157 - 2020
- [c15]Sinong Geng, Houssam Nassif, Carlos A. Manzanares, A. Max Reppen, Ronnie Sircar:
Deep PQR: Solving Inverse Reinforcement Learning using Anchor Actions. ICML 2020: 3431-3441 - [i9]Sareh Nabi, Houssam Nassif, Joseph Hong, Hamed Mamani, Guido Imbens:
Decoupling Learning Rates Using Empirical Bayes Priors. CoRR abs/2002.01129 (2020) - [i8]Sinong Geng, Houssam Nassif, Carlos A. Manzanares, A. Max Reppen, Ronnie Sircar:
Identifying Reward Functions using Anchor Actions. CoRR abs/2007.07443 (2020) - [i7]Kwang-Sung Jun, Lalit Jain, Houssam Nassif:
Improved Confidence Bounds for the Linear Logistic Model and Applications to Linear Bandits. CoRR abs/2011.11222 (2020)
2010 – 2019
- 2019
- [c14]Ari Biswas, Thai T. Pham, Michael Vogelsong, Benjamin Snyder, Houssam Nassif:
Seeker: Real-Time Interactive Search. KDD 2019: 2867-2875 - [i6]Ari Biswas, Thai T. Pham, Michael Vogelsong, Benjamin Snyder, Houssam Nassif:
Seeker: Real-Time Interactive Search. CoRR abs/1905.13125 (2019) - 2018
- [i5]Choon Hui Teo, Houssam Nassif, Daniel N. Hill, Sriram Srinivasan, Mitchell Goodman, Vijai Mohan, S. V. N. Vishwanathan:
Adaptive, Personalized Diversity for Visual Discovery. CoRR abs/1810.01477 (2018) - [i4]Houssam Nassif, Kemal Oral Cansizlar, Mitchell Goodman, S. V. N. Vishwanathan:
Diversifying Music Recommendations. CoRR abs/1810.01482 (2018) - [i3]Neela Sawant, Chitti Babu Namballa, Narayanan Sadagopan, Houssam Nassif:
Contextual Multi-Armed Bandits for Causal Marketing. CoRR abs/1810.01859 (2018) - [i2]Houssam Nassif, Hassan Al-Ali, Sawsan Khuri, Walid Keirouz, David Page:
An Inductive Logic Programming Approach to Validate Hexose Binding Biochemical Knowledge. CoRR abs/1810.04707 (2018) - [i1]Daniel N. Hill, Houssam Nassif, Yi Liu, Anand Iyer, S. V. N. Vishwanathan:
An Efficient Bandit Algorithm for Realtime Multivariate Optimization. CoRR abs/1810.09558 (2018) - 2017
- [c13]Daniel N. Hill, Houssam Nassif, Yi Liu, Anand Iyer, S. V. N. Vishwanathan:
An Efficient Bandit Algorithm for Realtime Multivariate Optimization. KDD 2017: 1813-1821 - 2016
- [c12]Choon Hui Teo, Houssam Nassif, Daniel N. Hill, Sriram Srinivasan, Mitchell Goodman, Vijai Mohan, S. V. N. Vishwanathan:
Adaptive, Personalized Diversity for Visual Discovery. RecSys 2016: 35-38 - 2014
- [c11]Finn Kuusisto, Vítor Santos Costa, Houssam Nassif, Elizabeth S. Burnside, David Page, Jude W. Shavlik:
Support Vector Machines for Differential Prediction. ECML/PKDD (2) 2014: 50-65 - 2013
- [c10]Jie Liu, David Page, Houssam Nassif, Jude W. Shavlik, Peggy L. Peissig, Catherine A. McCarty, Adedayo A. Onitilo, Elizabeth S. Burnside:
Genetic Variants Improve Breast Cancer Risk Prediction on Mammograms. AMIA 2013 - [c9]Finn Kuusisto, Inês de Castro Dutra, Houssam Nassif, Yirong Wu, Molly E. Klein, Heather B. Neuman, Jude W. Shavlik, Elizabeth S. Burnside:
Using machine learning to identify benign cases with non-definitive biopsy. Healthcom 2013: 283-285 - [c8]Houssam Nassif, Finn Kuusisto, Elizabeth S. Burnside, Jude W. Shavlik:
Uplift Modeling with ROC: An SRL Case Study. ILP (Late Breaking Papers) 2013: 40-45 - [c7]Houssam Nassif, Finn Kuusisto, Elizabeth S. Burnside, David Page, Jude W. Shavlik, Vítor Santos Costa:
Score As You Lift (SAYL): A Statistical Relational Learning Approach to Uplift Modeling. ECML/PKDD (3) 2013: 595-611 - 2012
- [j2]José Carlos Almeida Santos, Houssam Nassif, David Page, Stephen H. Muggleton, Michael J. E. Sternberg:
Automated identification of protein-ligand interaction features using Inductive Logic Programming: a hexose binding case study. BMC Bioinform. 13: 162 (2012) - [j1]Bethany Percha, Houssam Nassif, Jafi A. Lipson, Elizabeth S. Burnside, Daniel L. Rubin:
Automatic classification of mammography reports by BI-RADS breast tissue composition class. J. Am. Medical Informatics Assoc. 19(5): 913-916 (2012) - [c6]Houssam Nassif, Yirong Wu, David Page, Elizabeth S. Burnside:
Logical Differential Prediction Bayes Net, improving breast cancer diagnosis for older women. AMIA 2012 - [c5]Houssam Nassif, Filipe Cunha, Inês C. Moreira, Ricardo Cruz-Correia, Eliana Sousa, David Page, Elizabeth S. Burnside, Inês de Castro Dutra:
Extracting BI-RADS features from Portuguese clinical texts. BIBM 2012: 1-4 - [c4]Houssam Nassif, Vítor Santos Costa, Elizabeth S. Burnside, David Page:
Relational Differential Prediction. ECML/PKDD (1) 2012: 617-632 - 2010
- [c3]Houssam Nassif, David Page, Mehmet Ayvaci, Jude W. Shavlik, Elizabeth S. Burnside:
Uncovering age-specific invasive and DCIS breast cancer rules using inductive logic programming. IHI 2010: 76-82
2000 – 2009
- 2009
- [c2]Houssam Nassif, Ryan W. Woods, Elizabeth S. Burnside, Mehmet Ayvaci, Jude W. Shavlik, David Page:
Information Extraction for Clinical Data Mining: A Mammography Case Study. ICDM Workshops 2009: 37-42 - [c1]Houssam Nassif, Hassan Al-Ali, Sawsan Khuri, Walid Keirouz, David Page:
An Inductive Logic Programming Approach to Validate Hexose Binding Biochemical Knowledge. ILP 2009: 149-165
Coauthor Index
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