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Found 12 papers, 1 papers with code
A Risk-Averse Framework for Non-Stationary Stochastic Multi-Armed Bandits
In a typical stochastic multi-armed bandit problem, the objective is often to maximize the expected sum of rewards over some time horizon $T$.
Deep Reinforcement Learning Algorithms for Hybrid V2X Communication: A Benchmarking Study
In today's era, autonomous vehicles demand a safety level on par with aircraft.
Beyond Log-Concavity: Theory and Algorithm for Sum-Log-Concave Optimization
In particular, we show that such functions are in general not convex but still satisfy generalized convexity inequalities.
A Nested Matrix-Tensor Model for Noisy Multi-view Clustering
In order to study the theoretical performance of this approach, we characterize the behavior of this best rank-one approximation in terms of the alignments of the obtained component vectors with the hidden model parameter vectors, in the large-dimensional regime.
One-Step Distributional Reinforcement Learning
Reinforcement learning (RL) allows an agent interacting sequentially with an environment to maximize its long-term expected return.
Distributional Reinforcement Learning
reinforcement-learning
+1
Robustness and risk management via distributional dynamic programming
In dynamic programming (DP) and reinforcement learning (RL), an agent learns to act optimally in terms of expected long-term return by sequentially interacting with its environment modeled by a Markov decision process (MDP).
Weighted Empirical Risk Minimization: Sample Selection Bias Correction based on Importance Sampling
We consider statistical learning problems, when the distribution $P'$ of the training observations $Z'_1,\; \ldots,\; Z'_n$ differs from the distribution $P$ involved in the risk one seeks to minimize (referred to as the test distribution) but is still defined on the same measurable space as $P$ and dominates it.
Weighted Empirical Risk Minimization: Transfer Learning based on Importance Sampling
We consider statistical learning problems, when the distribution $P'$ of the training observations $Z'_1,\; \ldots,\; Z'_n$ differs from the distribution $P$ involved in the risk one seeks to minimize (referred to as the \textit{test distribution}) but is still defined on the same measurable space as $P$ and dominates it.
Dimensionality Reduction and (Bucket) Ranking: a Mass Transportation Approach
Whereas most dimensionality reduction techniques (e. g. PCA, ICA, NMF) for multivariate data essentially rely on linear algebra to a certain extent, summarizing ranking data, viewed as realizations of a random permutation $\Sigma$ on a set of items indexed by $i\in \{1,\ldots,\; n\}$, is a great statistical challenge, due to the absence of vector space structure for the set of permutations $\mathfrak{S}_n$.
Profitable Bandits
We adapt and study three well-known strategies in this purpose, that were proved to be most efficient in other settings: kl-UCB, Bayes-UCB and Thompson Sampling.
Ranking Data with Continuous Labels through Oriented Recursive Partitions
This problem generalizes bi/multi-partite ranking to a certain extent and the task of finding optimal scoring functions s(x) can be naturally cast as optimization of a dedicated functional criterion, called the IROC curve here, or as maximization of the Kendall ${\tau}$ related to the pair (s(X), Y ).
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.
