Search Results for author:
Found 8 papers, 3 papers with code
The Elliptical Potential Lemma for General Distributions with an Application to Linear Thompson Sampling
The elliptical potential lemma is a key tool for quantifying uncertainty in estimating parameters of the reward function, but it requires the noise and the prior distributions to be Gaussian.
Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms
We study the structure of regret-minimizing policies in the {\em many-armed} Bayesian multi-armed bandit problem: in particular, with $k$ the number of arms and $T$ the time horizon, we consider the case where $k \geq \sqrt{T}$.
On Frequentist Regret of Linear Thompson Sampling
This paper studies the stochastic linear bandit problem, where a decision-maker chooses actions from possibly time-dependent sets of vectors in $\mathbb{R}^d$ and receives noisy rewards.
The Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms
This finding diverges from the notion of free exploration, which relates to covariate variation, as recently discussed in contextual bandit literature.
A General Theory of the Stochastic Linear Bandit and Its Applications
First, our new notion of optimism in expectation gives rise to a new algorithm, called sieved greedy (SG) that reduces the overexploration problem in OFUL.
Personalizing Many Decisions with High-Dimensional Covariates
We consider the k-armed stochastic contextual bandit problem with d dimensional features, when both k and d can be large.
Multi-scale Embedded CNN for Music Tagging (MsE-CNN)
Convolutional neural networks (CNN) recently gained notable attraction in a variety of machine learning tasks: including music classification and style tagging.
On Low-rank Trace Regression under General Sampling Distribution
In this paper, we study the trace regression when a matrix of parameters B* is estimated via the convex relaxation of a rank-regularized regression or via regularized non-convex optimization.
