75 citations · 820 across the 81 of their papers we have counts for
19 papers · 1 filter
Pack only the essentials: Adaptive dictionary learning for kernel ridge regression
Daniele Calandriello, Alessandro Lazaric, Michal Valko
One of the major limits of kernel ridge regression (KRR) is that storing and manipulating the kernel matrix K_n for n samples requires O(n^2) space, which rapidly becomes unfeasibl…
A single algorithm for both restless and rested rotting bandits
Julien Seznec, Pierre Ménard, Alessandro Lazaric +1
In many application domains (e.g., recommender systems, intelligent tutoring systems), the rewards associated to the actions tend to decrease over time. This decay is either caused…
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric +2
Computing a Gaussian process (GP) posterior has a computational cost cubical in the number of historical points. A reformulation of the same GP posterior highlights that this compl…
Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation
Marc Abeille, Alessandro Lazaric
We study the exploration-exploitation dilemma in the linear quadratic regulator (LQR) setting. Inspired by the extended value iteration algorithm used in optimistic algorithms for…
Meta-learning with Stochastic Linear Bandits
Leonardo Cella, Alessandro Lazaric, Massimiliano Pontil
We investigate meta-learning procedures in the setting of stochastic linear bandits tasks. The goal is to select a learning algorithm which works well on average over a class of ba…
Active Model Estimation in Markov Decision Processes
Jean Tarbouriech, Shubhanshu Shekhar, Matteo Pirotta +2
We study the problem of efficient exploration in order to learn an accurate model of an environment, modeled as a Markov decision process (MDP). Efficient exploration in this probl…
Near-linear Time Gaussian Process Optimization with Adaptive Batching and Resparsification
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric +2
Gaussian processes (GP) are one of the most successful frameworks to model uncertainty. However, GP optimization (e.g., GP-UCB) suffers from major scalability issues. Experimental…
No-Regret Exploration in Goal-Oriented Reinforcement Learning
Jean Tarbouriech, Evrard Garcelon, Michal Valko +2
Many popular reinforcement learning problems (e.g., navigation in a maze, some Atari games, mountain car) are instances of the episodic setting under its stochastic shortest path (…
Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric +2
Gaussian processes (GP) are a well studied Bayesian approach for the optimization of black-box functions. Despite their effectiveness in simple problems, GP-based algorithms hardly…
Active Exploration in Markov Decision Processes
Jean Tarbouriech, Alessandro Lazaric
We introduce the active exploration problem in Markov decision processes (MDPs). Each state of the MDP is characterized by a random value and the learner should gather samples to e…
Distributed Adaptive Sampling for Kernel Matrix Approximation
Daniele Calandriello, Alessandro Lazaric, Michal Valko
Most kernel-based methods, such as kernel or Gaussian process regression, kernel PCA, ICA, or -means clustering, do not scale to large datasets, because constructing and storing…
Second-Order Kernel Online Convex Optimization with Adaptive Sketching
Daniele Calandriello, Alessandro Lazaric, Michal Valko
Kernel online convex optimization (KOCO) is a framework combining the expressiveness of non-parametric kernel models with the regret guarantees of online learning. First-order KOCO…
Thompson Sampling for Linear-Quadratic Control Problems
Marc Abeille, Alessandro Lazaric
We consider the exploration-exploitation tradeoff in linear quadratic (LQ) control problems, where the state dynamics is linear and the cost function is quadratic in states and con…
Active Learning for Accurate Estimation of Linear Models
Carlos Riquelme, Mohammad Ghavamzadeh, Alessandro Lazaric
We explore the sequential decision making problem where the goal is to estimate uniformly well a number of linear models, given a shared budget of random contexts independently sam…
Analysis of Kelner and Levin graph sparsification algorithm for a streaming setting
Daniele Calandriello, Alessandro Lazaric, Michal Valko
We derive a new proof to show that the incremental resparsification algorithm proposed by Kelner and Levin (2013) produces a spectral sparsifier in high probability. We rigorously…
Incremental Spectral Sparsification for Large-Scale Graph-Based Semi-Supervised Learning
Daniele Calandriello, Alessandro Lazaric, Michal Valko +1
While the harmonic function solution performs well in many semi-supervised learning (SSL) tasks, it is known to scale poorly with the number of samples. Recent successful and scala…
Online Stochastic Optimization under Correlated Bandit Feedback
Mohammad Gheshlaghi Azar, Alessandro Lazaric, Emma Brunskill
In this paper we consider the problem of online stochastic optimization of a locally smooth function under bandit feedback. We introduce the high-confidence tree (HCT) algorithm, a…
Sequential Transfer in Multi-armed Bandit with Finite Set of Models
Mohammad Gheshlaghi Azar, Alessandro Lazaric, Emma Brunskill
Learning from prior tasks and transferring that experience to improve future performance is critical for building lifelong learning agents. Although results in supervised and reinf…
Regret Bounds for Reinforcement Learning with Policy Advice
Mohammad Gheshlaghi Azar, Alessandro Lazaric, Emma Brunskill
In some reinforcement learning problems an agent may be provided with a set of input policies, perhaps learned from prior experience or provided by advisors. We present a reinforce…