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Found 13 papers, 0 papers with code
Scaling-up Empirical Risk Minimization: Optimization of Incomplete U-statistics
In a wide range of statistical learning problems such as ranking, clustering or metric learning among others, the risk is accurately estimated by $U$-statistics of degree $d\geq 1$, i. e. functionals of the training data with low variance that take the form of averages over $k$-tuples.
Extending Gossip Algorithms to Distributed Estimation of U-Statistics
Efficient and robust algorithms for decentralized estimation in networks are essential to many distributed systems.
Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions
In decentralized networks (of sensors, connected objects, etc.
Decentralized Topic Modelling with Latent Dirichlet Allocation
Privacy preserving networks can be modelled as decentralized networks (e. g., sensors, connected objects, smartphones), where communication between nodes of the network is not controlled by an all-knowing, central node.
Parallel Contextual Bandits in Wireless Handover Optimization
As cellular networks become denser, a scalable and dynamic tuning of wireless base station parameters can only be achieved through automated optimization.
Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning
For smooth convex and non-convex objective functions, we provide matching lower and upper complexity bounds and show that a naive pipeline parallelization of Nesterov's accelerated gradient descent is optimal.
A Simple and Efficient Smoothing Method for Faster Optimization and Local Exploration
This novel smoothing method is then used to improve first-order non-smooth optimization (both convex and non-convex) by allowing for a local exploration of the search space.
Best Arm Identification in Graphical Bilinear Bandits
We study the best arm identification problem in which the learner wants to find the graph allocation maximizing the sum of the bilinear rewards.
Refined bounds for randomized experimental design
Experimental design is an approach for selecting samples among a given set so as to obtain the best estimator for a given criterion.
An $α$-No-Regret Algorithm For Graphical Bilinear Bandits
We propose the first regret-based approach to the Graphical Bilinear Bandits problem, where $n$ agents in a graph play a stochastic bilinear bandit game with each of their neighbors.
Clustered Multi-Agent Linear Bandits
We address in this paper a particular instance of the multi-agent linear stochastic bandit problem, called clustered multi-agent linear bandits.
Price of Safety in Linear Best Arm Identification
We introduce the safe best-arm identification framework with linear feedback, where the agent is subject to some stage-wise safety constraint that linearly depends on an unknown parameter vector.
Differentially Private Model-Based Offline Reinforcement Learning
We address offline reinforcement learning with privacy guarantees, where the goal is to train a policy that is differentially private with respect to individual trajectories in the dataset.
