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Found 21 papers, 10 papers with code
Finite-Time Logarithmic Bayes Regret Upper Bounds
In a multi-armed bandit, we obtain $O(c_\Delta \log n)$ and $O(c_h \log^2 n)$ upper bounds for an upper confidence bound algorithm, where $c_h$ and $c_\Delta$ are constants depending on the prior distribution and the gaps of bandit instances sampled from it, respectively.
Selective Uncertainty Propagation in Offline RL
We consider the finite-horizon offline reinforcement learning (RL) setting, and are motivated by the challenge of learning the policy at any step h in dynamic programming (DP) algorithms.
Multi-Task Off-Policy Learning from Bandit Feedback
We prove per-task bounds on the suboptimality of the learned policies, which show a clear improvement over not using the hierarchical model.
Bayesian Fixed-Budget Best-Arm Identification
We also provide a lower bound on the probability of misidentification in a $2$-armed Bayesian bandit and show that our upper bound (almost) matches it for any budget.
Mixed-Effect Thompson Sampling
The regret bound has two terms, one for learning the action parameters and the other for learning the shared effect parameters.
Meta-Learning for Simple Regret Minimization
The Bayesian algorithm has access to a prior distribution over the meta-parameters and its meta simple regret over $m$ bandit tasks with horizon $n$ is mere $\tilde{O}(m / \sqrt{n})$.
Task-Agnostic Graph Explanations
They are also unable to provide explanations in cases where the GNN is trained in a self-supervised manner, and the resulting representations are used in future downstream tasks.
Deep Hierarchy in Bandits
We use this exact posterior to analyze the Bayes regret of HierTS in Gaussian bandits.
Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods
We propose Cold Brew, a teacher-student distillation approach to address the SCS and noisy-neighbor challenges for GNNs.
Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs
Current approaches employ spatial geometries such as boxes to learn query representations that encompass the answer entities and model the logical operations of projection and intersection.
Task-Agnostic Graph Neural Explanations
TAGE enables the explanation of GNN embedding models without downstream tasks and allows efficient explanation of multitask models.
Pure Exploration with Structured Preference Feedback
We consider the problem of pure exploration with subset-wise preference feedback, which contains $N$ arms with features.
Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge Graphs
Promising approaches to tackle this problem include embedding the KG units (e. g., entities and relations) in a Euclidean space such that the query embedding contains the information relevant to its results.
Robust Outlier Arm Identification
We study the problem of Robust Outlier Arm Identification (ROAI), where the goal is to identify arms whose expected rewards deviate substantially from the majority, by adaptively sampling from their reward distributions.
MaxGap Bandit: Adaptive Algorithms for Approximate Ranking
This paper studies the problem of adaptively sampling from K distributions (arms) in order to identify the largest gap between any two adjacent means.
Conservative Exploration using Interleaving
In many practical problems, a learning agent may want to learn the best action in hindsight without ever taking a bad action, which is significantly worse than the default production action.
Adaptive Sampling for Coarse Ranking
We consider the problem of active coarse ranking, where the goal is to sort items according to their means into clusters of pre-specified sizes, by adaptively sampling from their reward distributions.
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.
DCM Bandits: Learning to Rank with Multiple Clicks
This work presents the first practical and regret-optimal online algorithm for learning to rank with multiple clicks in a cascade-like click model.
Sparse Dueling Bandits
We prove that in the absence of structural assumptions, the sample complexity of this problem is proportional to the sum of the inverse squared gaps between the Borda scores of each suboptimal arm and the best arm.
