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Found 23 papers, 5 papers with code
Non-Stationary Bandits with Intermediate Observations
In fact, if the timescale of the change is comparable to the delay, it is impossible to learn about the environment, since the available observations are already obsolete.
Prior-Dependent Allocations for Bayesian Fixed-Budget Best-Arm Identification in Structured Bandits
We study the problem of Bayesian fixed-budget best-arm identification (BAI) in structured bandits.
Beyond Average Return in Markov Decision Processes
What are the functionals of the reward that can be computed and optimized exactly in Markov Decision Processes? In the finite-horizon, undiscounted setting, Dynamic Programming (DP) can only handle these operations efficiently for certain classes of statistics.
POMRL: No-Regret Learning-to-Plan with Increasing Horizons
We study the problem of planning under model uncertainty in an online meta-reinforcement learning (RL) setting where an agent is presented with a sequence of related tasks with limited interactions per task.
Non-stationary Bandits and Meta-Learning with a Small Set of Optimal Arms
We study a sequential decision problem where the learner faces a sequence of $K$-armed bandit tasks.
EigenGame Unloaded: When playing games is better than optimizing
We build on the recently proposed EigenGame that views eigendecomposition as a competitive game.
Asymptotically Optimal Information-Directed Sampling
We introduce a simple and efficient algorithm for stochastic linear bandits with finitely many actions that is asymptotically optimal and (nearly) worst-case optimal in finite time.
The Elliptical Potential Lemma Revisited
This note proposes a new proof and new perspectives on the so-called Elliptical Potential Lemma.
EigenGame: PCA as a Nash Equilibrium
We present a novel view on principal component analysis (PCA) as a competitive game in which each approximate eigenvector is controlled by a player whose goal is to maximize their own utility function.
Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting
We consider off-policy evaluation in the contextual bandit setting for the purpose of obtaining a robust off-policy selection strategy, where the selection strategy is evaluated based on the value of the chosen policy in a set of proposal (target) policies.
Stochastic bandits with arm-dependent delays
Significant work has been recently dedicated to the stochastic delayed bandit setting because of its relevance in applications.
Non-Stationary Delayed Bandits with Intermediate Observations
In fact, if the timescale of the change is comparable to the delay, it is impossible to learn about the environment, since the available observations are already obsolete.
Solving Bernoulli Rank-One Bandits with Unimodal Thompson Sampling
Stochastic Rank-One Bandits (Katarya et al, (2017a, b)) are a simple framework for regret minimization problems over rank-one matrices of arms.
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.
Linear Bandits with Stochastic Delayed Feedback
Stochastic linear bandits are a natural and well-studied model for structured exploration/exploitation problems and are widely used in applications such as online marketing and recommendation.
Max K-armed bandit: On the ExtremeHunter algorithm and beyond
This paper is devoted to the study of the max K-armed bandit problem, which consists in sequentially allocating resources in order to detect extreme values.
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.
Sparse Stochastic Bandits
In the classical multi-armed bandit problem, d arms are available to the decision maker who pulls them sequentially in order to maximize his cumulative reward.
Bernoulli Rank-$1$ Bandits for Click Feedback
The probability that a user will click a search result depends both on its relevance and its position on the results page.
Stochastic Rank-1 Bandits
The main challenge of the problem is that the individual values of the row and column are unobserved.
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.
