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Found 13 papers, 3 papers with code
Distributional Off-Policy Evaluation for Slate Recommendations
Prior work has developed estimators that leverage the structure in slates to estimate the expected off-policy performance, but the estimation of the entire performance distribution remains elusive.
FigCaps-HF: A Figure-to-Caption Generative Framework and Benchmark with Human Feedback
Captions are crucial for understanding scientific visualizations and documents.
Local Policy Improvement for Recommender Systems
The conventional way to address this problem is through importance sampling correction, but this comes with practical limitations.
Control Variates for Slate Off-Policy Evaluation
We study the problem of off-policy evaluation from batched contextual bandit data with multidimensional actions, often termed slates.
Off-Policy Evaluation of Slate Policies under Bayes Risk
We study the problem of off-policy evaluation for slate bandits, for the typical case in which the logging policy factorizes over the slots of the slate.
More Efficient Off-Policy Evaluation through Regularized Targeted Learning
We study the problem of off-policy evaluation (OPE) in Reinforcement Learning (RL), where the aim is to estimate the performance of a new policy given historical data that may have been generated by a different policy, or policies.
Scalar Posterior Sampling with Applications
Our algorithm termed deterministic schedule PSRL (DS-PSRL) is efficient in terms of time, sample, and space complexity.
Optimizing over a Restricted Policy Class in Markov Decision Processes
However, under a condition that is akin to the occupancy measures of the base policies having large overlap, we show that there exists an efficient algorithm that finds a policy that is almost as good as the best convex combination of the base policies.
Posterior Sampling for Large Scale Reinforcement Learning
Our algorithm termed deterministic schedule PSRL (DS-PSRL) is efficient in terms of time, sample, and space complexity.
Does Weather Matter? Causal Analysis of TV Logs
To the best of our knowledge, this is the first large-scale causal study of the impact of weather on TV watching patterns.
A posteriori error bounds for joint matrix decomposition problems
Joint matrix triangularization is often used for estimating the joint eigenstructure of a set M of matrices, with applications in signal processing and machine learning.
Low-dimensional Data Embedding via Robust Ranking
We describe a new method called t-ETE for finding a low-dimensional embedding of a set of objects in Euclidean space.
Approximate Joint Matrix Triangularization
The a priori bounds are theoretical inequalities that involve functions of the ground-truth matrices and noise matrices, whereas the a posteriori bounds are given in terms of observable quantities that can be computed from the input matrices.
