Martin Mladenov | DeepAI

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∙ 09/08/2023

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

Modern recommender systems lie at the heart of complex ecosystems that c...

0 Craig Boutilier, et al. ∙

∙ 09/02/2023

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

Users derive value from a recommender system (RS) only to the extent tha...

0 Siddharth Prasad, et al. ∙

∙ 05/24/2023

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

While popularity bias is recognized to play a role in recommmender (and ...

0 Guy Tennenholtz, et al. ∙

∙ 02/04/2023

Reinforcement Learning with History-Dependent Dynamic Contexts

We introduce Dynamic Contextual Markov Decision Processes (DCMDPs), a no...

0 Guy Tennenholtz, et al. ∙

∙ 03/14/2021

RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems

The development of recommender systems that optimize multi-turn interact...

0 Martin Mladenov, et al. ∙

∙ 02/11/2021

Meta-Thompson Sampling

Efficient exploration in multi-armed bandits is a fundamental online lea...

0 Branislav Kveton, et al. ∙

∙ 07/31/2020

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

Most recommender systems (RS) research assumes that a user's utility can...

4 Martin Mladenov, et al. ∙

∙ 06/09/2020

Differentiable Meta-Learning in Contextual Bandits

We study a contextual bandit setting where the learning agent has access...

0 Branislav Kveton, et al. ∙

∙ 02/17/2020

Differentiable Bandit Exploration

We learn bandit policies that maximize the average reward over bandit in...

22 Craig Boutilier, et al. ∙

∙ 09/11/2019

RecSim: A Configurable Simulation Platform for Recommender Systems

We propose RecSim, a configurable platform for authoring simulation envi...

7 Eugene Ie, et al. ∙

∙ 05/29/2019

Advantage Amplification in Slowly Evolving Latent-State Environments

Latent-state environments with long horizons, such as those faced by rec...

0 Martin Mladenov, et al. ∙

∙ 04/04/2019

Empirical Bayes Regret Minimization

The prevalent approach to bandit algorithm design is to have a low-regre...

6 Chih-Wei Hsu, et al. ∙

∙ 05/07/2018

Planning and Learning with Stochastic Action Sets

In many practical uses of reinforcement learning (RL) the set of actions...

0 Craig Boutilier, et al. ∙

∙ 06/14/2016

Lifted Convex Quadratic Programming

Symmetry is the essential element of lifted inference that has recently ...

0 Martin Mladenov, et al. ∙

∙ 05/26/2016

The Symbolic Interior Point Method

A recent trend in probabilistic inference emphasizes the codification of...

0 Martin Mladenov, et al. ∙

∙ 10/12/2014

Relational Linear Programs

We propose relational linear programming, a simple framework for combing...

0 Kristian Kersting, et al. ∙

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