Nassif, Houssam

11 publications

TMLR 2025 Factor Learning Portfolio Optimization Informed by Continuous-Time Finance Models Sinong Geng, Houssam Nassif, Zhaobin Kuang, Anders Max Reppen, K. Ronnie Sircar

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

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

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

ICMLW 2023 Factor Learning Portfolio Optimization Informed by Continuous-Time Finance Models Sinong Geng, Houssam Nassif, Zhaobin Kuang, Anders Max Reppen, K. Ronnie Sircar

NeurIPS 2022 Instance-Optimal PAC Algorithms for Contextual Bandits Zhaoqi Li, Lillian Ratliff, Houssam Nassif, Kevin G. Jamieson, Lalit Jain

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

ICML 2020 Deep PQR: Solving Inverse Reinforcement Learning Using Anchor Actions Sinong Geng, Houssam Nassif, Carlos Manzanares, Max Reppen, Ronnie Sircar

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

ECML-PKDD 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 2012 Relational Differential Prediction Houssam Nassif, Vítor Santos Costa, Elizabeth S. Burnside, David Page