Martin Mladenov

According to our database, Martin Mladenov authored at least 46 papers between 2008 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Minimizing Live Experiments in Recommender Systems: User Simulation to Evaluate Preference Elicitation Policies.

Chih-Wei Hsu, Martin Mladenov, Ofer Meshi, James Pine, Hubert Pham, Shane Li, Xujian Liang, Anton Polishko, Li Yang, Ben Scheetz, Craig Boutilier

Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2024

Demystifying Embedding Spaces using Large Language Models.

Guy Tennenholtz, Yinlam Chow, Chih-Wei Hsu, Jihwan Jeong, Lior Shani, Azamat Tulepbergenov, Deepak Ramachandran, Martin Mladenov, Craig Boutilier

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Recommender Ecosystems: A Mechanism Design Perspective on Holistic Modeling and Optimization.

Craig Boutilier, Martin Mladenov, Guy Tennenholtz

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models.

Craig Boutilier, Martin Mladenov, Guy Tennenholtz

CoRR, 2023

Content Prompting: Modeling Content Provider Dynamics to Improve User Welfare in Recommender Ecosystems.

Siddharth Prasad, Martin Mladenov, Craig Boutilier

CoRR, 2023

Ranking with Popularity Bias: User Welfare under Self-Amplification Dynamics.

Guy Tennenholtz, Martin Mladenov, Nadav Merlis, Craig Boutilier

CoRR, 2023

Investigating Action-Space Generalization in Reinforcement Learning for Recommendation Systems.

Abhishek Naik, Bo Chang, Alexandros Karatzoglou, Martin Mladenov, Ed H. Chi, Minmin Chen

Proceedings of the Companion Proceedings of the ACM Web Conference 2023, 2023

Reinforcement Learning with History Dependent Dynamic Contexts.

Guy Tennenholtz, Nadav Merlis, Lior Shani, Martin Mladenov, Craig Boutilier

Proceedings of the International Conference on Machine Learning, 2023

2022

An adversarial variational inference approach for travel demand calibration of urban traffic simulators.

Martin Mladenov, Sanjay Ganapathy Subramaniam, Chih-Wei Hsu, Neha Arora, Andrew Tomkins, Craig Boutilier, Carolina Osorio

Proceedings of the 30th International Conference on Advances in Geographic Information Systems, 2022

2021

RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems.

Martin Mladenov, Chih-Wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, Craig Boutilier

CoRR, 2021

Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities.

Ruohan Zhan, Konstantina Christakopoulou, Ya Le, Jayden Ooi, Martin Mladenov, Alex Beutel, Craig Boutilier, Ed H. Chi, Minmin Chen

Proceedings of the WWW '21: The Web Conference 2021, 2021

Meta-Thompson Sampling.

Branislav Kveton, Mikhail Konobeev, Manzil Zaheer, Chih-Wei Hsu, Martin Mladenov, Craig Boutilier, Csaba Szepesvári

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Differentiable Meta-Learning in Contextual Bandits.

Branislav Kveton, Martin Mladenov, Chih-Wei Hsu, Manzil Zaheer, Csaba Szepesvári, Craig Boutilier

CoRR, 2020

Differentiable Bandit Exploration.

Craig Boutilier, Chih-Wei Hsu, Branislav Kveton, Martin Mladenov, Csaba Szepesvári, Manzil Zaheer

CoRR, 2020

Demonstrating Principled Uncertainty Modeling for Recommender Ecosystems with RecSim NG.

Martin Mladenov, Chih-Wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, Craig Boutilier

Proceedings of the RecSys 2020: Fourteenth ACM Conference on Recommender Systems, 2020

Differentiable Meta-Learning of Bandit Policies.

Craig Boutilier, Chih-Wei Hsu, Branislav Kveton, Martin Mladenov, Csaba Szepesvári, Manzil Zaheer

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach.

Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard S. Zemel, Craig Boutilier

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

RecSim: A Configurable Simulation Platform for Recommender Systems.

Eugene Ie, Chih-Wei Hsu, Martin Mladenov, Vihan Jain, Sanmit Narvekar, Jing Wang, Rui Wu, Craig Boutilier

CoRR, 2019

Empirical Bayes Regret Minimization.

Chih-Wei Hsu, Branislav Kveton, Ofer Meshi, Martin Mladenov, Csaba Szepesvári

CoRR, 2019

Advantage Amplification in Slowly Evolving Latent-State Environments.

Martin Mladenov, Ofer Meshi, Jayden Ooi, Dale Schuurmans, Craig Boutilier

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

2018

Planning and Learning with Stochastic Action Sets.

Craig Boutilier, Alon Cohen, Amit Daniely, Avinatan Hassidim, Yishay Mansour, Ofer Meshi, Martin Mladenov, Dale Schuurmans

CoRR, 2018

Efficient Symbolic Integration for Probabilistic Inference.

Samuel Kolb, Martin Mladenov, Scott Sanner, Vaishak Belle, Kristian Kersting

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Planning and Learning with Stochastic Action Sets.

Craig Boutilier, Alon Cohen, Avinatan Hassidim, Yishay Mansour, Ofer Meshi, Martin Mladenov, Dale Schuurmans

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

2017

Relational linear programming.

Kristian Kersting, Martin Mladenov, Pavel Tokmakov

Artif. Intell., 2017

Logistic Markov Decision Processes.

Martin Mladenov, Craig Boutilier, Dale Schuurmans, Ofer Meshi, Gal Elidan, Tyler Lu

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Lifted Inference for Convex Quadratic Programs.

Martin Mladenov, Leonard Kleinhans, Kristian Kersting

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

The Symbolic Interior Point Method.

Martin Mladenov, Vaishak Belle, Kristian Kersting

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Lifted Convex Quadratic Programming.

Martin Mladenov, Leonard Kleinhans, Kristian Kersting

CoRR, 2016

RELOOP: A Python-Embedded Declarative Language for Relational Optimization.

Martin Mladenov, Danny Heinrich, Leonard Kleinhans, Felix Gonsior, Kristian Kersting

Proceedings of the Declarative Learning Based Programming, 2016

2015

Equitable Partitions of Concave Free Energies.

Martin Mladenov, Kristian Kersting

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Computer Science on the Move: Inferring Migration Regularities from the Web via Compressed Label Propagation.

Fabian Hadiji, Martin Mladenov, Christian Bauckhage, Kristian Kersting

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

2014

Relational Linear Programs.

Kristian Kersting, Martin Mladenov, Pavel Tokmakov

CoRR, 2014

Lifted Message Passing as Reparametrization of Graphical Models.

Martin Mladenov, Amir Globerson, Kristian Kersting

Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Dimension Reduction via Colour Refinement.

Martin Grohe, Kristian Kersting, Martin Mladenov, Erkal Selman

Proceedings of the Algorithms - ESA 2014, 2014

Efficient Lifting of MAP LP Relaxations Using k-Locality.

Martin Mladenov, Kristian Kersting, Amir Globerson

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

Power Iterated Color Refinement.

Kristian Kersting, Martin Mladenov, Roman Garnett, Martin Grohe

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

Lifting Relational MAP-LPs using Cluster Signatures.

Udi Apsel, Kristian Kersting, Martin Mladenov

Proceedings of the Statistical Relational Artificial Intelligence, 2014

2013

Exploiting symmetries for scaling loopy belief propagation and relational training.

Babak Ahmadi, Kristian Kersting, Martin Mladenov, Sriraam Natarajan

Mach. Learn., 2013

Lifted Inference via k-Locality.

Martin Mladenov, Kristian Kersting

Proceedings of the Statistical Relational Artificial Intelligence, 2013

2012

Identifying Place Histories from Activity Traces with an Eye to Parameter Impact.

Gennady L. Andrienko, Natalia V. Andrienko, Martin Mladenov, Michael Mock, Christian Pölitz

IEEE Trans. Vis. Comput. Graph., 2012

Lifted Linear Programming.

Martin Mladenov, Babak Ahmadi, Kristian Kersting

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Pairwise Markov Logic.

Daan Fierens, Kristian Kersting, Jesse Davis, Jian Chen, Martin Mladenov

Proceedings of the Inductive Logic Programming - 22nd International Conference, 2012

2011

On Lifted PageRank, Kalman Filter and Towards Lifted Linear Program Solving.

Babak Ahmadi, Martin Mladenov, Kristian Kersting, Scott Sanner

Proceedings of the Report of the symposium "Lernen, 2011

2010

Extracting Events from Spatial Time Series.

Gennady L. Andrienko, Natalia V. Andrienko, Martin Mladenov, Michael Mock, Christian Pölitz

Proceedings of the 14th International Conference on Information Visualisation, 2010

Discovering bits of place histories from people's activity traces.

Gennady L. Andrienko, Natalia V. Andrienko, Martin Mladenov, Michael Mock, Christian Pölitz

Proceedings of the 5th IEEE Conference on Visual Analytics Science and Technology, 2010

2008

Fault monitoring and correction in a walking robot using LMS filters.

Martin Mladenov, Michael Mock, Karl-Erwin Grosspietsch

Proceedings of the International Workshop on Intelligent Solutions in Embedded Systems, 2008