Martin Mladenov

Publication Activity (10 Years)

Years Active: 2008-2024
Publications (10 Years): 36

Top Topics

Recommender Systems

Quadratic Program

Empirical Bayes

Top Venues

Publications

Craig Boutilier, Martin Mladenov, Guy Tennenholtz
Recommender Ecosystems: A Mechanism Design Perspective on Holistic Modeling and Optimization. AAAI (2024)
Guy Tennenholtz, Yinlam Chow, Chih-Wei Hsu, Jihwan Jeong, Lior Shani, Azamat Tulepbergenov, Deepak Ramachandran, Martin Mladenov, Craig Boutilier
Demystifying Embedding Spaces using Large Language Models. ICLR (2024)
Chih-Wei Hsu, Martin Mladenov, Ofer Meshi, James Pine, Hubert Pham, Shane Li, Xujian Liang, Anton Polishko, Li Yang, Ben Scheetz, Craig Boutilier
Minimizing Live Experiments in Recommender Systems: User Simulation to Evaluate Preference Elicitation Policies. SIGIR (2024)
Abhishek Naik, Bo Chang, Alexandros Karatzoglou, Martin Mladenov, Ed H. Chi, Minmin Chen
Investigating Action-Space Generalization in Reinforcement Learning for Recommendation Systems. WWW (Companion Volume) (2023)
Guy Tennenholtz, Yinlam Chow, Chih-Wei Hsu, Jihwan Jeong, Lior Shani, Azamat Tulepbergenov, Deepak Ramachandran, Martin Mladenov, Craig Boutilier
Demystifying Embedding Spaces using Large Language Models. CoRR (2023)
Guy Tennenholtz, Martin Mladenov, Nadav Merlis, Craig Boutilier
Ranking with Popularity Bias: User Welfare under Self-Amplification Dynamics. CoRR (2023)
Guy Tennenholtz, Nadav Merlis, Lior Shani, Martin Mladenov, Craig Boutilier
Reinforcement Learning with History-Dependent Dynamic Contexts. CoRR (2023)
Siddharth Prasad, Martin Mladenov, Craig Boutilier
Content Prompting: Modeling Content Provider Dynamics to Improve User Welfare in Recommender Ecosystems. CoRR (2023)
Craig Boutilier, Martin Mladenov, Guy Tennenholtz
Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models. CoRR (2023)
Guy Tennenholtz, Nadav Merlis, Lior Shani, Martin Mladenov, Craig Boutilier
Reinforcement Learning with History Dependent Dynamic Contexts. ICML (2023)
Martin Mladenov, Sanjay Ganapathy Subramaniam, Chih-Wei Hsu, Neha Arora, Andrew Tomkins, Craig Boutilier, Carolina Osorio
An adversarial variational inference approach for travel demand calibration of urban traffic simulators. SIGSPATIAL/GIS (2022)
Ruohan Zhan, Konstantina Christakopoulou, Ya Le, Jayden Ooi, Martin Mladenov, Alex Beutel, Craig Boutilier, Ed H. Chi, Minmin Chen
Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities. CoRR (2021)
Branislav Kveton, Mikhail Konobeev, Manzil Zaheer, Chih-Wei Hsu, Martin Mladenov, Craig Boutilier, Csaba Szepesvári
Meta-Thompson Sampling. CoRR (2021)
Ruohan Zhan, Konstantina Christakopoulou, Ya Le, Jayden Ooi, Martin Mladenov, Alex Beutel, Craig Boutilier, Ed Chi, Minmin Chen
Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities. WWW (2021)
Branislav Kveton, Mikhail Konobeev, Manzil Zaheer, Chih-Wei Hsu, Martin Mladenov, Craig Boutilier, Csaba Szepesvári
Meta-Thompson Sampling. ICML (2021)
Martin Mladenov, Chih-Wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, Craig Boutilier
RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems. CoRR (2021)
Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard S. Zemel, Craig Boutilier
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach. CoRR (2020)
Martin Mladenov, Chih-Wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, Craig Boutilier
Demonstrating Principled Uncertainty Modeling for Recommender Ecosystems with RecSim NG. RecSys (2020)
Craig Boutilier, Chih-Wei Hsu, Branislav Kveton, Martin Mladenov, Csaba Szepesvári, Manzil Zaheer
Differentiable Meta-Learning of Bandit Policies. NeurIPS (2020)
Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard S. Zemel, Craig Boutilier
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach. ICML (2020)
Branislav Kveton, Martin Mladenov, Chih-Wei Hsu, Manzil Zaheer, Csaba Szepesvári, Craig Boutilier
Differentiable Meta-Learning in Contextual Bandits. CoRR (2020)
Craig Boutilier, Chih-Wei Hsu, Branislav Kveton, Martin Mladenov, Csaba Szepesvári, Manzil Zaheer
Differentiable Bandit Exploration. CoRR (2020)
Eugene Ie, Chih-Wei Hsu, Martin Mladenov, Vihan Jain, Sanmit Narvekar, Jing Wang, Rui Wu, Craig Boutilier
RecSim: A Configurable Simulation Platform for Recommender Systems. CoRR (2019)
Chih-Wei Hsu, Branislav Kveton, Ofer Meshi, Martin Mladenov, Csaba Szepesvári
Empirical Bayes Regret Minimization. CoRR (2019)
Martin Mladenov, Ofer Meshi, Jayden Ooi, Dale Schuurmans, Craig Boutilier
Advantage Amplification in Slowly Evolving Latent-State Environments. CoRR (2019)
Martin Mladenov, Ofer Meshi, Jayden Ooi, Dale Schuurmans, Craig Boutilier
Advantage Amplification in Slowly Evolving Latent-State Environments. IJCAI (2019)
Craig Boutilier, Alon Cohen, Amit Daniely, Avinatan Hassidim, Yishay Mansour, Ofer Meshi, Martin Mladenov, Dale Schuurmans
Planning and Learning with Stochastic Action Sets. CoRR (2018)
Craig Boutilier, Alon Cohen, Avinatan Hassidim, Yishay Mansour, Ofer Meshi, Martin Mladenov, Dale Schuurmans
Planning and Learning with Stochastic Action Sets. IJCAI (2018)
Samuel Kolb, Martin Mladenov, Scott Sanner, Vaishak Belle, Kristian Kersting
Efficient Symbolic Integration for Probabilistic Inference. IJCAI (2018)
Martin Mladenov, Vaishak Belle, Kristian Kersting
The Symbolic Interior Point Method. AAAI (2017)
Martin Mladenov, Craig Boutilier, Dale Schuurmans, Ofer Meshi, Gal Elidan, Tyler Lu
Logistic Markov Decision Processes. IJCAI (2017)
Martin Mladenov, Leonard Kleinhans, Kristian Kersting
Lifted Inference for Convex Quadratic Programs. AAAI (2017)
Kristian Kersting, Martin Mladenov, Pavel Tokmakov
Relational linear programming. Artif. Intell. 244 (2017)
Martin Mladenov, Leonard Kleinhans, Kristian Kersting
Lifted Convex Quadratic Programming. CoRR (2016)
Martin Mladenov, Danny Heinrich, Leonard Kleinhans, Felix Gonsior, Kristian Kersting
RELOOP: A Python-Embedded Declarative Language for Relational Optimization. AAAI Workshop: Declarative Learning Based Programming (2016)
Martin Mladenov, Vaishak Belle, Kristian Kersting
The Symbolic Interior Point Method. CoRR (2016)
Fabian Hadiji, Martin Mladenov, Christian Bauckhage, Kristian Kersting
Computer Science on the Move: Inferring Migration Regularities from the Web via Compressed Label Propagation. IJCAI (2015)
Martin Mladenov, Kristian Kersting
Equitable Partitions of Concave Free Energies. UAI (2015)
Martin Mladenov, Amir Globerson, Kristian Kersting
Lifted Message Passing as Reparametrization of Graphical Models. UAI (2014)
Kristian Kersting, Martin Mladenov, Roman Garnett, Martin Grohe
Power Iterated Color Refinement. AAAI (2014)
Martin Mladenov, Kristian Kersting, Amir Globerson
Efficient Lifting of MAP LP Relaxations Using k-Locality. AISTATS (2014)
Kristian Kersting, Martin Mladenov, Pavel Tokmakov
Relational Linear Programs. CoRR (2014)
Martin Grohe, Kristian Kersting, Martin Mladenov, Erkal Selman
Dimension Reduction via Colour Refinement. ESA (2014)
Udi Apsel, Kristian Kersting, Martin Mladenov
Lifting Relational MAP-LPs using Cluster Signatures. AAAI Workshop: Statistical Relational Artificial Intelligence (2014)
Udi Apsel, Kristian Kersting, Martin Mladenov
Lifting Relational MAP-LPs Using Cluster Signatures. AAAI (2014)
Martin Grohe, Kristian Kersting, Martin Mladenov, Erkal Selman
Dimension Reduction via Colour Refinement. CoRR (2013)
Babak Ahmadi, Kristian Kersting, Martin Mladenov, Sriraam Natarajan
Exploiting symmetries for scaling loopy belief propagation and relational training. Mach. Learn. 92 (1) (2013)
Martin Mladenov, Kristian Kersting
Lifted Inference via k-Locality. AAAI Workshop: Statistical Relational Artificial Intelligence (2013)
Daan Fierens, Kristian Kersting, Jesse Davis, Jian Chen, Martin Mladenov
Pairwise Markov Logic. ILP (2012)
Gennady L. Andrienko, Natalia V. Andrienko, Martin Mladenov, Michael Mock, Christian Pölitz
Identifying Place Histories from Activity Traces with an Eye to Parameter Impact. IEEE Trans. Vis. Comput. Graph. 18 (5) (2012)
Martin Mladenov, Babak Ahmadi, Kristian Kersting
Lifted Linear Programming. AISTATS (2012)
Babak Ahmadi, Martin Mladenov, Kristian Kersting, Scott Sanner
On Lifted PageRank, Kalman Filter and Towards Lifted Linear Program Solving. LWA (2011)
Gennady L. Andrienko, Natalia V. Andrienko, Martin Mladenov, Michael Mock, Christian Pölitz
Discovering bits of place histories from people's activity traces. IEEE VAST (2010)
Gennady L. Andrienko, Natalia V. Andrienko, Martin Mladenov, Michael Mock, Christian Pölitz
Extracting Events from Spatial Time Series. IV (2010)
Martin Mladenov, Michael Mock, Karl-Erwin Grosspietsch
Fault monitoring and correction in a walking robot using LMS filters. WISES (2008)