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Found 6 papers, 3 papers with code
Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors
Interactive recommender systems have emerged as a promising paradigm to overcome the limitations of the primitive user feedback used by traditional recommender systems (e. g., clicks, item consumption, ratings).
ConQUR: Mitigating Delusional Bias in Deep Q-learning
Delusional bias is a fundamental source of error in approximate Q-learning.
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.
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.
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.
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.
