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Found 41 papers, 8 papers with code
DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models
These techniques first learn a reward function that captures what humans care about in the task and then improve the models based on the learned reward function.
Ranking with Popularity Bias: User Welfare under Self-Amplification Dynamics
While popularity bias is recognized to play a role in recommmender (and other ranking-based) systems, detailed analyses of its impact on user welfare have largely been lacking.
Aligning Text-to-Image Models using Human Feedback
Our results demonstrate the potential for learning from human feedback to significantly improve text-to-image models.
Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management
By exploiting MoE-LM structure, our methods significantly reduce the size of the action space and improve the efficacy of RL-based DM.
Reinforcement Learning with History-Dependent Dynamic Contexts
We introduce Dynamic Contextual Markov Decision Processes (DCMDPs), a novel reinforcement learning framework for history-dependent environments that generalizes the contextual MDP framework to handle non-Markov environments, where contexts change over time.
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 Intelligence" project (AI100) issued the second report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society.
Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning
Despite recent advances in natural language understanding and generation, and decades of research on the development of conversational bots, building automated agents that can carry on rich open-ended conversations with humans "in the wild" remains a formidable challenge.
Building Human Values into Recommender Systems: An Interdisciplinary Synthesis
We collect a set of values that seem most relevant to recommender systems operating across different domains, then examine them from the perspectives of current industry practice, measurement, product design, and policy approaches.
A Mixture-of-Expert Approach to RL-based Dialogue Management
Despite recent advancements in language models (LMs), their application to dialogue management (DM) problems and ability to carry on rich conversations remain a challenge.
Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors
Interactive recommender systems (RSs) allow users to express intent, preferences and contexts in a rich fashion, often using natural language.
IMO$^3$: Interactive Multi-Objective Off-Policy Optimization
This problem has been studied extensively in the setting of known objective functions.
Thompson Sampling with a Mixture Prior
We study Thompson sampling (TS) in online decision making, where the uncertain environment is sampled from a mixture distribution.
Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities
We then build a REINFORCE recommender agent, coined EcoAgent, to optimize a joint objective of user utility and the counterfactual utility lift of the provider associated with the recommended content, which we show to be equivalent to maximizing overall user utility and the utilities of all providers on the platform under some mild assumptions.
RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems
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 the recommender ecosystem have drawn increasing attention in recent years.
Non-Stationary Latent Bandits
The key idea is to frame this problem as a latent bandit, where the prototypical models of user behavior are learned offline and the latent state of the user is inferred online from its interactions with the models.
Differentiable Meta-Learning of Bandit Policies
Exploration policies in Bayesian bandits maximize the average reward over problem instances drawn from some distribution P. In this work, we learn such policies for an unknown distribution P using samples from P. Our approach is a form of meta-learning and exploits properties of P without making strong assumptions about its form.
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach
We develop several scalable techniques to solve the matching problem, and also draw connections to various notions of user regret and fairness, arguing that these outcomes are fairer in a utilitarian sense.
Latent Bandits Revisited
A latent bandit problem is one in which the learning agent knows the arm reward distributions conditioned on an unknown discrete latent state.
Meta-Learning Bandit Policies by Gradient Ascent
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 a prior distribution over environment parameters.
ConQUR: Mitigating Delusional Bias in Deep Q-learning
Delusional bias is a fundamental source of error in approximate Q-learning.
Differentiable Bandit Exploration
In this work, we learn such policies for an unknown distribution $\mathcal{P}$ using samples from $\mathcal{P}$.
Data Efficient Training for Reinforcement Learning with Adaptive Behavior Policy Sharing
Deep Reinforcement Learning (RL) is proven powerful for decision making in simulated environments.
BRPO: Batch Residual Policy Optimization
In batch reinforcement learning (RL), one often constrains a learned policy to be close to the behavior (data-generating) policy, e. g., by constraining the learned action distribution to differ from the behavior policy by some maximum degree that is the same at each state.
Gradient-based Optimization for Bayesian Preference Elicitation
Effective techniques for eliciting user preferences have taken on added importance as recommender systems (RSs) become increasingly interactive and conversational.
CAQL: Continuous Action Q-Learning
Value-based reinforcement learning (RL) methods like Q-learning have shown success in a variety of domains.
RecSim: A Configurable Simulation Platform for Recommender Systems
We propose RecSim, a configurable platform for authoring simulation environments for recommender systems (RSs) that naturally supports sequential interaction with users.
Randomized Exploration in Generalized Linear Bandits
GLM-TSL samples a generalized linear model (GLM) from the Laplace approximation to the posterior distribution.
Advantage Amplification in Slowly Evolving Latent-State Environments
Latent-state environments with long horizons, such as those faced by recommender systems, pose significant challenges for reinforcement learning (RL).
Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology
(i) We develop SLATEQ, a decomposition of value-based temporal-difference and Q-learning that renders RL tractable with slates.
Perturbed-History Exploration in Stochastic Linear Bandits
Our algorithm, perturbed-history exploration in a linear bandit (LinPHE), estimates a linear model from its perturbed history and pulls the arm with the highest value under that model.
Perturbed-History Exploration in Stochastic Multi-Armed Bandits
Finally, we empirically evaluate PHE and show that it is competitive with state-of-the-art baselines.
Non-delusional Q-learning and value-iteration
We identify a fundamental source of error in Q-learning and other forms of dynamic programming with function approximation.
Data center cooling using model-predictive control
Despite impressive recent advances in reinforcement learning (RL), its deployment in real-world physical systems is often complicated by unexpected events, limited data, and the potential for expensive failures.
Seq2Slate: Re-ranking and Slate Optimization with RNNs
Ranking is a central task in machine learning and information retrieval.
Planning and Learning with Stochastic Action Sets
From an RL perspective, we show that Q-learning with sampled action sets is sound.
Safe Exploration for Identifying Linear Systems via Robust Optimization
Motivated by the cooling of Google's data centers, we study how one can safely identify the parameters of a system model with a desired accuracy and confidence level.
Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence (2000)
This is the Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, which was held in San Francisco, CA, June 30 - July 3, 2000
Context-Specific Independence in Bayesian Networks
Bayesian networks provide a language for qualitatively representing the conditional independence properties of a distribution.
Structured Reachability Analysis for Markov Decision Processes
Another contribution is the illustration of how the compact representation of reachability constraints can be exploited by several existing (exact and approximate) abstraction algorithms for MDPs.
Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Sets
We show that, under very general assumptions, the optimal choice query w. r. t.\ EVOI coincides with \emph{optimal recommendation set}, that is, a set maximizing expected utility of the user selection.
