50 citations · 108 across the 18 of their papers we have counts for
6 papers · 1 filter
Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models
Craig Boutilier, Martin Mladenov, Guy Tennenholtz
Modern recommender systems lie at the heart of complex ecosystems that couple the behavior of users, content providers, advertisers, and other actors. Despite this, the focus of th…
pyRDDLGym: From RDDL to Gym Environments
Ayal Taitler, Michael Gimelfarb, Jihwan Jeong +4
We present pyRDDLGym, a Python framework for auto-generation of OpenAI Gym environments from RDDL declerative description. The discrete time step evolution of variables in RDDL is…
Planning and Learning with Stochastic Action Sets
Craig Boutilier, Alon Cohen, Amit Daniely +5
In many practical uses of reinforcement learning (RL) the set of actions available at a given state is a random variable, with realizations governed by an exogenous stochastic proc…
Lifted Convex Quadratic Programming
Martin Mladenov, Leonard Kleinhans, Kristian Kersting
Symmetry is the essential element of lifted inference that has recently demon- strated the possibility to perform very efficient inference in highly-connected, but symmetric probab…
The Symbolic Interior Point Method
Martin Mladenov, Vaishak Belle, Kristian Kersting
A recent trend in probabilistic inference emphasizes the codification of models in a formal syntax, with suitable high-level features such as individuals, relations, and connective…
Relational Linear Programs
Kristian Kersting, Martin Mladenov, Pavel Tokmakov
We propose relational linear programming, a simple framework for combing linear programs (LPs) and logic programs. A relational linear program (RLP) is a declarative LP template de…