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Found 5 papers, 0 papers with code
Pessimistic Off-Policy Multi-Objective Optimization
The pessimistic estimator can be optimized by policy gradients and performs well in all of our experiments.
Linear Bandits with Feature Feedback
In comparison, the regret of traditional linear bandits is $d\sqrt{T}$, where $d$ is the total number of (relevant and irrelevant) features, so the improvement can be dramatic if $k\ll d$.
Scalable Generalized Linear Bandits: Online Computation and Hashing
Second, for the case where the number $N$ of arms is very large, we propose new algorithms in which each next arm is selected via an inner product search.
Active Algorithms For Preference Learning Problems with Multiple Populations
In this paper we model the problem of learning preferences of a population as an active learning problem.
Neural Reconstruction with Approximate Message Passing (NeuRAMP)
Many functional descriptions of spiking neurons assume a cascade structure where inputs are passed through an initial linear filtering stage that produces a low-dimensional signal that drives subsequent nonlinear stages.
