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Found 20 papers, 6 papers with code
Speed Up the Cold-Start Learning in Two-Sided Bandits with Many Arms
Next, we design two-phase bandit algorithms that first use subsampling and low-rank matrix estimation to obtain a substantially smaller targeted set of products and then apply a UCB procedure on the target products to find the best one.
Thompson Sampling Efficiently Learns to Control Diffusion Processes
To the best of our knowledge, this is the first such result for Thompson sampling in a diffusion process control problem.
Learning to Recommend Using Non-Uniform Data
Utilizing this observation, we introduce a new optimization problem to select a weight matrix that minimizes the upper bound on the prediction error.
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.
Recommendation on a Budget: Column Space Recovery from Partially Observed Entries with Random or Active Sampling
We analyze alternating minimization for column space recovery of a partially observed, approximately low rank matrix with a growing number of columns and a fixed budget of observations per column.
The Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms
We theoretically show that this source of free exploration is deeply connected to the distribution of a tail event for the prior distribution of arm rewards.
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.
Optimal Experimental Design for Staggered Rollouts
Next, we study an adaptive experimental design problem, where both the decision to continue the experiment and treatment assignment decisions are updated after each period's data is collected.
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.
Ensemble Methods for Causal Effects in Panel Data Settings
This paper studies a panel data setting where the goal is to estimate causal effects of an intervention by predicting the counterfactual values of outcomes for treated units, had they not received the treatment.
Matrix Completion Methods for Causal Panel Data Models
In this paper we study methods for estimating causal effects in settings with panel data, where some units are exposed to a treatment during some periods and the goal is estimating counterfactual (untreated) outcomes for the treated unit/period combinations.
Statistics Theory Econometrics Statistics Theory
Mostly Exploration-Free Algorithms for Contextual Bandits
We prove that this algorithm is rate optimal without any additional assumptions on the context distribution or the number of arms.
Scaled Least Squares Estimator for GLMs in Large-Scale Problems
We study the problem of efficiently estimating the coefficients of generalized linear models (GLMs) in the large-scale setting where the number of observations $n$ is much larger than the number of predictors $p$, i. e. $n\gg p \gg 1$.
Scalable Approximations for Generalized Linear Problems
Using this relation, we design an algorithm that achieves the same accuracy as the empirical risk minimizer through iterations that attain up to a cubic convergence rate, and that are cheaper than any batch optimization algorithm by at least a factor of $\mathcal{O}(p)$.
Dynamic Pricing with Demand Covariates
In particular, we assume that the firm knows the expected demand under a particular price from historical data, and in each period, before setting the price, the firm has access to extra information (demand covariates) which may be predictive of the demand.
Estimating LASSO Risk and Noise Level
In this context, we develop new estimators for the $\ell_2$ estimation risk $\|\hat{\theta}-\theta_0\|_2$ and the variance of the noise.
The LASSO risk: asymptotic results and real world examples
We consider the problem of learning a coefficient vector x0 from noisy linear observation y=Ax0+w.
