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Martin Mladenov
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2020 – today
- 2024
- [c33]Craig Boutilier, Martin Mladenov, Guy Tennenholtz:
Recommender Ecosystems: A Mechanism Design Perspective on Holistic Modeling and Optimization. AAAI 2024: 22575-22583 - [c32]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 - [c31]Chih-Wei Hsu
, Martin Mladenov
Minimizing Live Experiments in Recommender Systems: User Simulation to Evaluate Preference Elicitation Policies. SIGIR 2024: 2925-2929 - [i20]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. CoRR abs/2409.17436 (2024) - 2023
- [c30]Guy Tennenholtz, Nadav Merlis, Lior Shani, Martin Mladenov, Craig Boutilier:
Reinforcement Learning with History Dependent Dynamic Contexts. ICML 2023: 34011-34053 - [c29]Abhishek Naik
Investigating Action-Space Generalization in Reinforcement Learning for Recommendation Systems. WWW (Companion Volume) 2023: 966-972 - [i19]Guy Tennenholtz, Nadav Merlis, Lior Shani, Martin Mladenov, Craig Boutilier:
Reinforcement Learning with History-Dependent Dynamic Contexts. CoRR abs/2302.02061 (2023) - [i18]Guy Tennenholtz, Martin Mladenov, Nadav Merlis, Craig Boutilier:
Ranking with Popularity Bias: User Welfare under Self-Amplification Dynamics. CoRR abs/2305.18333 (2023) - [i17]Siddharth Prasad, Martin Mladenov, Craig Boutilier:
Content Prompting: Modeling Content Provider Dynamics to Improve User Welfare in Recommender Ecosystems. CoRR abs/2309.00940 (2023) - [i16]Craig Boutilier, Martin Mladenov, Guy Tennenholtz:
Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models. CoRR abs/2309.06375 (2023) - [i15]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 abs/2310.04475 (2023) - 2022
- [c28]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: 6:1-6:4 - 2021
- [c27]Branislav Kveton, Mikhail Konobeev, Manzil Zaheer, Chih-Wei Hsu, Martin Mladenov, Craig Boutilier, Csaba Szepesvári:
Meta-Thompson Sampling. ICML 2021: 5884-5893 - [c26]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. WWW 2021: 3872-3883 - [i14]Branislav Kveton, Mikhail Konobeev, Manzil Zaheer, Chih-Wei Hsu, Martin Mladenov, Craig Boutilier, Csaba Szepesvári:
Meta-Thompson Sampling. CoRR abs/2102.06129 (2021) - [i13]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 abs/2103.08057 (2021) - [i12]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 abs/2105.02377 (2021) - 2020
- [c25]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: 6987-6998 - [c24]Craig Boutilier, Chih-Wei Hsu, Branislav Kveton, Martin Mladenov, Csaba Szepesvári, Manzil Zaheer:
Differentiable Meta-Learning of Bandit Policies. NeurIPS 2020 - [c23]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: 591-593 - [i11]Craig Boutilier, Chih-Wei Hsu, Branislav Kveton, Martin Mladenov, Csaba Szepesvári, Manzil Zaheer:
Differentiable Bandit Exploration. CoRR abs/2002.06772 (2020) - [i10]Branislav Kveton, Martin Mladenov, Chih-Wei Hsu, Manzil Zaheer, Csaba Szepesvári, Craig Boutilier:
Differentiable Meta-Learning in Contextual Bandits. CoRR abs/2006.05094 (2020) - [i9]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 abs/2008.00104 (2020)
2010 – 2019
- 2019
- [c22]Martin Mladenov, Ofer Meshi, Jayden Ooi, Dale Schuurmans, Craig Boutilier:
Advantage Amplification in Slowly Evolving Latent-State Environments. IJCAI 2019: 3165-3172 - [i8]Chih-Wei Hsu, Branislav Kveton, Ofer Meshi, Martin Mladenov, Csaba Szepesvári:
Empirical Bayes Regret Minimization. CoRR abs/1904.02664 (2019) - [i7]Martin Mladenov, Ofer Meshi, Jayden Ooi, Dale Schuurmans, Craig Boutilier:
Advantage Amplification in Slowly Evolving Latent-State Environments. CoRR abs/1905.13559 (2019) - [i6]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 abs/1909.04847 (2019) - 2018
- [c21]Craig Boutilier, Alon Cohen, Avinatan Hassidim, Yishay Mansour, Ofer Meshi, Martin Mladenov, Dale Schuurmans:
Planning and Learning with Stochastic Action Sets. IJCAI 2018: 4674-4682 - [c20]Samuel Kolb, Martin Mladenov, Scott Sanner, Vaishak Belle, Kristian Kersting:
Efficient Symbolic Integration for Probabilistic Inference. IJCAI 2018: 5031-5037 - [i5]Craig Boutilier, Alon Cohen, Amit Daniely, Avinatan Hassidim, Yishay Mansour, Ofer Meshi, Martin Mladenov, Dale Schuurmans:
Planning and Learning with Stochastic Action Sets. CoRR abs/1805.02363 (2018) - 2017
- [j3]Kristian Kersting, Martin Mladenov, Pavel Tokmakov:
Relational linear programming. Artif. Intell. 244: 188-216 (2017) - [c19]Martin Mladenov, Vaishak Belle, Kristian Kersting:
The Symbolic Interior Point Method. AAAI 2017: 1199-1205 - [c18]Martin Mladenov, Leonard Kleinhans, Kristian Kersting:
Lifted Inference for Convex Quadratic Programs. AAAI 2017: 2350-2356 - [c17]Martin Mladenov, Craig Boutilier, Dale Schuurmans, Ofer Meshi, Gal Elidan, Tyler Lu:
Logistic Markov Decision Processes. IJCAI 2017: 2486-2493 - 2016
- [c16]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 - [i4]Martin Mladenov, Vaishak Belle, Kristian Kersting:
The Symbolic Interior Point Method. CoRR abs/1605.08187 (2016) - [i3]Martin Mladenov, Leonard Kleinhans, Kristian Kersting:
Lifted Convex Quadratic Programming. CoRR abs/1606.04486 (2016) - 2015
- [c15]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: 171-177 - [c14]Martin Mladenov, Kristian Kersting:
Equitable Partitions of Concave Free Energies. UAI 2015: 602-611 - 2014
- [c13]Udi Apsel, Kristian Kersting, Martin Mladenov:
Lifting Relational MAP-LPs using Cluster Signatures. StarAI@AAAI 2014 - [c12]Kristian Kersting, Martin Mladenov, Roman Garnett, Martin Grohe:
Power Iterated Color Refinement. AAAI 2014: 1904-1910 - [c11]Udi Apsel, Kristian Kersting, Martin Mladenov:
Lifting Relational MAP-LPs Using Cluster Signatures. AAAI 2014: 2403-2409 - [c10]Martin Mladenov, Kristian Kersting, Amir Globerson:
Efficient Lifting of MAP LP Relaxations Using k-Locality. AISTATS 2014: 623-632 - [c9]Martin Grohe
Dimension Reduction via Colour Refinement. ESA 2014: 505-516 - [c8]Martin Mladenov, Amir Globerson, Kristian Kersting:
Lifted Message Passing as Reparametrization of Graphical Models. UAI 2014: 603-612 - [i2]Kristian Kersting, Martin Mladenov, Pavel Tokmakov:
Relational Linear Programs. CoRR abs/1410.3125 (2014) - 2013
- [j2]Babak Ahmadi, Kristian Kersting, Martin Mladenov, Sriraam Natarajan:
Exploiting symmetries for scaling loopy belief propagation and relational training. Mach. Learn. 92(1): 91-132 (2013) - [c7]Martin Mladenov, Kristian Kersting:
Lifted Inference via k-Locality. StarAI@AAAI 2013 - [i1]Martin Grohe, Kristian Kersting, Martin Mladenov, Erkal Selman:
Dimension Reduction via Colour Refinement. CoRR abs/1307.5697 (2013) - 2012
- [j1]Gennady L. Andrienko
Identifying Place Histories from Activity Traces with an Eye to Parameter Impact. IEEE Trans. Vis. Comput. Graph. 18(5): 675-688 (2012) - [c6]Daan Fierens, Kristian Kersting, Jesse Davis
Pairwise Markov Logic. ILP 2012: 58-73 - [c5]Martin Mladenov, Babak Ahmadi, Kristian Kersting:
Lifted Linear Programming. AISTATS 2012: 788-797 - 2011
- [c4]Babak Ahmadi, Martin Mladenov, Kristian Kersting, Scott Sanner:
On Lifted PageRank, Kalman Filter and Towards Lifted Linear Program Solving. LWA 2011: 35-42 - 2010
- [c3]Gennady L. Andrienko
Discovering bits of place histories from people's activity traces. IEEE VAST 2010: 59-66 - [c2]Gennady L. Andrienko
Extracting Events from Spatial Time Series. IV 2010: 48-53
2000 – 2009
- 2008
- [c1]Martin Mladenov, Michael Mock, Karl-Erwin Grosspietsch:
Fault monitoring and correction in a walking robot using LMS filters. WISES 2008: 1-10
Coauthor Index
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last updated on 2024-10-18 19:32 CEST by the dblp team
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