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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...
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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...
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05/25/2023
DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models
Learning from human feedback has been shown to improve text-to-image mod...
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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 ...
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02/23/2023
Aligning Text-to-Image Models using Human Feedback
Deep generative models have shown impressive results in text-to-image sy...
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02/21/2023
Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management
Reinforcement learning (RL) has shown great promise for developing dialo...
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02/04/2023
Reinforcement Learning with History-Dependent Dynamic Contexts
We introduce Dynamic Contextual Markov Decision Processes (DCMDPs), a no...
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10/27/2022
Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report
In September 2021, the "One Hundred Year Study on Artificial Intelligenc...
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07/25/2022
Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning
Despite recent advances in natural language understanding and generation...
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07/20/2022
Building Human Values into Recommender Systems: An Interdisciplinary Synthesis
Recommender systems are the algorithms which select, filter, and persona...
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05/31/2022
A Mixture-of-Expert Approach to RL-based Dialogue Management
Despite recent advancements in language models (LMs), their application ...
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02/06/2022
Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors
Interactive recommender systems (RSs) allow users to express intent, pre...
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01/24/2022
IMO^3: Interactive Multi-Objective Off-Policy Optimization
Most real-world optimization problems have multiple objectives. A system...
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06/10/2021
Thompson Sampling with a Mixture Prior
We study Thompson sampling (TS) in online decision-making problems where...
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03/14/2021
RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems
The development of recommender systems that optimize multi-turn interact...
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02/11/2021
Meta-Thompson Sampling
Efficient exploration in multi-armed bandits is a fundamental online lea...
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12/01/2020
Non-Stationary Latent Bandits
Users of recommender systems often behave in a non-stationary fashion, d...
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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...
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06/15/2020
Latent Bandits Revisited
A latent bandit problem is one in which the learning agent knows the arm...
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06/09/2020
Differentiable Meta-Learning in Contextual Bandits
We study a contextual bandit setting where the learning agent has access...
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02/27/2020
ConQUR: Mitigating Delusional Bias in Deep Q-learning
Delusional bias is a fundamental source of error in approximate Q-learni...
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02/17/2020
Differentiable Bandit Exploration
We learn bandit policies that maximize the average reward over bandit in...
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02/12/2020
Data Efficient Training for Reinforcement Learning with Adaptive Behavior Policy Sharing
Deep Reinforcement Learning (RL) is proven powerful for decision making ...
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02/08/2020
BRPO: Batch Residual Policy Optimization
In batch reinforcement learning (RL), one often constrains a learned pol...
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11/20/2019
Gradient-based Optimization for Bayesian Preference Elicitation
Effective techniques for eliciting user preferences have taken on added ...
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09/26/2019
CAQL: Continuous Action Q-Learning
Value-based reinforcement learning (RL) methods like Q-learning have sho...
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09/11/2019
RecSim: A Configurable Simulation Platform for Recommender Systems
We propose RecSim, a configurable platform for authoring simulation envi...
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06/21/2019
Randomized Exploration in Generalized Linear Bandits
We study two randomized algorithms for generalized linear bandits, GLM-T...
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05/29/2019
Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology
Most practical recommender systems focus on estimating immediate user en...
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05/29/2019
Advantage Amplification in Slowly Evolving Latent-State Environments
Latent-state environments with long horizons, such as those faced by rec...
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03/21/2019
Perturbed-History Exploration in Stochastic Linear Bandits
We propose a new online algorithm for minimizing the cumulative regret i...
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02/26/2019
Perturbed-History Exploration in Stochastic Multi-Armed Bandits
We propose an online algorithm for cumulative regret minimization in a s...
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10/04/2018
Seq2Slate: Re-ranking and Slate Optimization with RNNs
Ranking is a central task in machine learning and information retrieval....
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05/07/2018
Planning and Learning with Stochastic Action Sets
In many practical uses of reinforcement learning (RL) the set of actions...
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04/13/2013
Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence (2000)
This is the Proceedings of the Sixteenth Conference on Uncertainty in Ar...
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03/13/2013
Modal Logics for Qualitative Possibility and Beliefs
Possibilistic logic has been proposed as a numerical formalism for reaso...
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03/06/2013
The Probability of a Possibility: Adding Uncertainty to Default Rules
We present a semantics for adding uncertainty to conditional logics for ...
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02/27/2013
Integrating Planning and Execution in Stochastic Domains
We investigate planning in time-critical domains represented as Markov D...
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02/13/2013
Context-Specific Independence in Bayesian Networks
Bayesian networks provide a language for qualitatively representing the ...
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02/06/2013
Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks
We present an algorithm for arc reversal in Bayesian networks with tree-...
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02/06/2013
Correlated Action Effects in Decision Theoretic Regression
Much recent research in decision theoretic planning has adopted Markov d...
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01/30/2013
Hierarchical Solution of Markov Decision Processes using Macro-actions
We investigate the use of temporally abstract actions, or macro-actions,...
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01/30/2013
Structured Reachability Analysis for Markov Decision Processes
Recent research in decision theoretic planning has focussed on making th...
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01/23/2013
SPUDD: Stochastic Planning using Decision Diagrams
Markov decisions processes (MDPs) are becoming increasing popular as mod...
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01/23/2013
Continuous Value Function Approximation for Sequential Bidding Policies
Market-based mechanisms such as auctions are being studied as an appropr...
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01/23/2013
Reasoning With Conditional Ceteris Paribus Preference Statem
In many domains it is desirable to assess the preferences of users in a ...
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01/16/2013
Value-Directed Belief State Approximation for POMDPs
We consider the problem belief-state monitoring for the purposes of impl...
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01/16/2013
Approximately Optimal Monitoring of Plan Preconditions
Monitoring plan preconditions can allow for replanning when a preconditi...
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01/10/2013
Value-Directed Sampling Methods for POMDPs
We consider the problem of approximate belief-state monitoring using par...
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01/10/2013
