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Found 16 papers, 3 papers with code
A/B/n Testing with Control in the Presence of Subpopulations
At every time step, a subpopulation is sampled and an arm is chosen: the resulting observation is an independent draw from the arm conditioned on the subpopulation.
Fast Rate Learning in Stochastic First Price Bidding
First-price auctions have largely replaced traditional bidding approaches based on Vickrey auctions in programmatic advertising.
On Limited-Memory Subsampling Strategies for Bandits
There has been a recent surge of interest in nonparametric bandit algorithms based on subsampling.
Efficient Algorithms for Stochastic Repeated Second-price Auctions
We further provide the first parametric lower bound for this problem that applies to generic UCB-like strategies.
Self-Concordant Analysis of Generalized Linear Bandits with Forgetting
Contextual sequential decision problems with categorical or numerical observations are ubiquitous and Generalized Linear Bandits (GLB) offer a solid theoretical framework to address them.
A Comparative Study of Gamma Markov Chains for Temporal Non-Negative Matrix Factorization
Non-negative matrix factorization (NMF) has become a well-established class of methods for the analysis of non-negative data.
Algorithms for Non-Stationary Generalized Linear Bandits
The statistical framework of Generalized Linear Models (GLM) can be applied to sequential problems involving categorical or ordinal rewards associated, for instance, with clicks, likes or ratings.
Weighted Linear Bandits for Non-Stationary Environments
To address this problem, we propose D-LinUCB, a novel optimistic algorithm based on discounted linear regression, where exponential weights are used to smoothly forget the past.
Stochastic Bandit Models for Delayed Conversions
We assume that the probability of conversion associated with each action is unknown while the distribution of the conversion delay is known, distinguishing between the (idealized) case where the conversion events may be observed whatever their delay and the more realistic setting in which late conversions are censored.
Multiple-Play Bandits in the Position-Based Model
Sequentially learning to place items in multi-position displays or lists is a task that can be cast into the multiple-play semi-bandit setting.
Sequential ranking under random semi-bandit feedback
In many web applications, a recommendation is not a single item suggested to a user but a list of possibly interesting contents that may be ranked in some contexts.
Learning From Missing Data Using Selection Bias in Movie Recommendation
Recommending items to users is a challenging task due to the large amount of missing information.
On the Complexity of Best Arm Identification in Multi-Armed Bandit Models
The stochastic multi-armed bandit model is a simple abstraction that has proven useful in many different contexts in statistics and machine learning.
On the Complexity of A/B Testing
A/B testing refers to the task of determining the best option among two alternatives that yield random outcomes.
The KL-UCB Algorithm for Bounded Stochastic Bandits and Beyond
This paper presents a finite-time analysis of the KL-UCB algorithm, an online, horizon-free index policy for stochastic bandit problems.
Online EM Algorithm for Latent Data Models
The resulting algorithm is usually simpler and is shown to achieve convergence to the stationary points of the Kullback-Leibler divergence between the marginal distribution of the observation and the model distribution at the optimal rate, i. e., that of the maximum likelihood estimator.
