50 citations · 108 across the 18 of their papers we have counts for
21 papers
Minimizing Live Experiments in Recommender Systems: User Simulation to Evaluate Preference Elicitation Policies
Chih-Wei Hsu, Martin Mladenov, Ofer Meshi +8
Evaluation of policies in recommender systems typically involves A/B testing using live experiments on real users to assess a new policy's impact on relevant metrics. This ``gold s…
Demystifying Embedding Spaces using Large Language Models
Guy Tennenholtz, Yinlam Chow, Chih-Wei Hsu +6
Embeddings have become a pivotal means to represent complex, multi-faceted information about entities, concepts, and relationships in a condensed and useful format. Nevertheless, t…
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…
Content Prompting: Modeling Content Provider Dynamics to Improve User Welfare in Recommender Ecosystems
Siddharth Prasad, Martin Mladenov, Craig Boutilier
Users derive value from a recommender system (RS) only to the extent that it is able to surface content (or items) that meet their needs/preferences. While RSs often have a compreh…
Ranking with Popularity Bias: User Welfare under Self-Amplification Dynamics
Guy Tennenholtz, Martin Mladenov, Nadav Merlis +2
While popularity bias is recognized to play a crucial role in recommmender (and other ranking-based) systems, detailed analysis of its impact on collective user welfare has largely…
Reinforcement Learning with History-Dependent Dynamic Contexts
Guy Tennenholtz, Nadav Merlis, Lior Shani +2
We introduce Dynamic Contextual Markov Decision Processes (DCMDPs), a novel reinforcement learning framework for history-dependent environments that generalizes the contextual MDP…
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…
Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities
Ruohan Zhan, Konstantina Christakopoulou, Ya Le +6
Most existing recommender systems focus primarily on matching users to content which maximizes user satisfaction on the platform. It is increasingly obvious, however, that content…
RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems
Martin Mladenov, Chih-Wei Hsu, Vihan Jain +7
The development of recommender systems that optimize multi-turn interaction with users, and model the interactions of different agents (e.g., users, content providers, vendors) in…
Meta-Thompson Sampling
Branislav Kveton, Mikhail Konobeev, Manzil Zaheer +4
Efficient exploration in bandits is a fundamental online learning problem. We propose a variant of Thompson sampling that learns to explore better as it interacts with bandit insta…
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach
Martin Mladenov, Elliot Creager, Omer Ben-Porat +3
Most recommender systems (RS) research assumes that a user's utility can be maximized independently of the utility of the other agents (e.g., other users, content providers). In re…
Meta-Learning Bandit Policies by Gradient Ascent
Branislav Kveton, Martin Mladenov, Chih-Wei Hsu +3
Most bandit policies are designed to either minimize regret in any problem instance, making very few assumptions about the underlying environment, or in a Bayesian sense, assuming…
Differentiable Bandit Exploration
Craig Boutilier, Chih-Wei Hsu, Branislav Kveton +3
Exploration policies in Bayesian bandits maximize the average reward over problem instances drawn from some distribution . In this work, we learn such policies for an…
RecSim: A Configurable Simulation Platform for Recommender Systems
Eugene Ie, Chih-wei Hsu, Martin Mladenov +5
We propose RecSim, a configurable platform for authoring simulation environments for recommender systems (RSs) that naturally supports sequential interaction with users. RecSim all…
Advantage Amplification in Slowly Evolving Latent-State Environments
Martin Mladenov, Ofer Meshi, Jayden Ooi +2
Latent-state environments with long horizons, such as those faced by recommender systems, pose significant challenges for reinforcement learning (RL). In this work, we identify and…
Empirical Bayes Regret Minimization
Chih-Wei Hsu, Branislav Kveton, Ofer Meshi +2
Most bandit algorithm designs are purely theoretical. Therefore, they have strong regret guarantees, but also are often too conservative in practice. In this work, we pioneer the i…
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…
Dimension Reduction via Colour Refinement
Martin Grohe, Kristian Kersting, Martin Mladenov +1
Colour refinement is a basic algorithmic routine for graph isomorphism testing, appearing as a subroutine in almost all practical isomorphism solvers. It partitions the vertices of…