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Found 17 papers, 1 papers with code
Estimating the history of a random recursive tree
This paper studies the problem of estimating the order of arrival of the vertices in a random recursive tree.
On the quality of randomized approximations of Tukey's depth
Tukey's depth (or halfspace depth) is a widely used measure of centrality for multivariate data.
Online-to-PAC Conversions: Generalization Bounds via Regret Analysis
We establish a connection between the online and statistical learning setting by showing that the existence of an online learning algorithm with bounded regret in this game implies a bound on the generalization error of the statistical learning algorithm, up to a martingale concentration term that is independent of the complexity of the statistical learning method.
Archaeology of random recursive dags and Cooper-Frieze random networks
We study the problem of finding the root vertex in large growing networks.
Generalization Bounds via Convex Analysis
Since the celebrated works of Russo and Zou (2016, 2019) and Xu and Raginsky (2017), it has been well known that the generalization error of supervised learning algorithms can be bounded in terms of the mutual information between their input and the output, given that the loss of any fixed hypothesis has a subgaussian tail.
Bandit problems with fidelity rewards
The fidelity bandits problem is a variant of the $K$-armed bandit problem in which the reward of each arm is augmented by a fidelity reward that provides the player with an additional payoff depending on how 'loyal' the player has been to that arm in the past.
Learning to maximize global influence from local observations
The goal of the decision maker is to select the sequence of agents in a way that the total number of influenced nodes in the network.
Benign Overfitting in Linear Regression
Motivated by this phenomenon, we consider when a perfect fit to training data in linear regression is compatible with accurate prediction.
Learning partial correlation graphs and graphical models by covariance queries
We study the problem of recovering the structure underlying large Gaussian graphical models or, more generally, partial correlation graphs.
Online Influence Maximization with Local Observations
We consider an online influence maximization problem in which a decision maker selects a node among a large number of possibilities and places a piece of information at the node.
On the Hardness of Inventory Management with Censored Demand Data
Furthermore, we modify the proposed policy in order to perform well in terms of the tracking regret, that is, using as benchmark the best sequence of inventory decisions that switches a limited number of times.
Boltzmann Exploration Done Right
Boltzmann exploration is a classic strategy for sequential decision-making under uncertainty, and is one of the most standard tools in Reinforcement Learning (RL).
Algorithmic stability and hypothesis complexity
The bounds are based on martingale inequalities in the Banach space to which the hypotheses belong.
An Improved Parametrization and Analysis of the EXP3++ Algorithm for Stochastic and Adversarial Bandits
In the adversarial regime regret guarantee remains unchanged.
Sub-Gaussian estimators of the mean of a random vector
We study the problem of estimating the mean of a random vector $X$ given a sample of $N$ independent, identically distributed points.
Regularization, sparse recovery, and median-of-means tournaments
A regularized risk minimization procedure for regression function estimation is introduced that achieves near optimal accuracy and confidence under general conditions, including heavy-tailed predictor and response variables.
Regret in Online Combinatorial Optimization
We also recover the optimal bounds for the full information setting.
