Daniele Calandriello | DeepAI

DeepAI AI Chat

Featured Co-authors

Masashi Sugiyama
222 publications
Ang Li
174 publications
Gang Niu
93 publications
Remi Munos
89 publications
Lorenzo Rosasco
88 publications
Michal Valko
73 publications
Alessandro Lazaric
66 publications
Eric Moulines
63 publications
Alessandro Rudi
50 publications
Daniele Pucci
47 publications
Charles Blundell
45 publications

∙ 05/02/2023

Unlocking the Power of Representations in Long-term Novelty-based Exploration

We introduce Robust Exploration via Clustering-based Online Density Esti...

0 Alaa Saade, et al. ∙

∙ 03/14/2023

Fast Rates for Maximum Entropy Exploration

We consider the reinforcement learning (RL) setting, in which the agent ...

0 Daniil Tiapkin, et al. ∙

∙ 12/06/2022

Understanding Self-Predictive Learning for Reinforcement Learning

We study the learning dynamics of self-predictive learning for reinforce...

0 Yunhao Tang, et al. ∙

∙ 09/28/2022

Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees

We consider reinforcement learning in an environment modeled by an episo...

0 Daniil Tiapkin, et al. ∙

∙ 06/16/2022

BYOL-Explore: Exploration by Bootstrapped Prediction

We present BYOL-Explore, a conceptually simple yet general approach for ...

0 Zhaohan Daniel Guo, et al. ∙

∙ 04/10/2022

Information-theoretic Online Memory Selection for Continual Learning

A challenging problem in task-free continual learning is the online sele...

0 Shengyang Sun, et al. ∙

∙ 01/30/2022

Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times

Computing a Gaussian process (GP) posterior has a computational cost cub...

0 Daniele Calandriello, et al. ∙

∙ 11/03/2021

One Pass ImageNet

We present the One Pass ImageNet (OPIN) problem, which aims to study the...

0 Huiyi Hu, et al. ∙

∙ 06/23/2021

ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions

We introduce ParK, a new large-scale solver for kernel ridge regression....

0 Luigi Carratino, et al. ∙

∙ 04/29/2021

On the Emergence of Whole-body Strategies from Humanoid Robot Push-recovery Learning

Balancing and push-recovery are essential capabilities enabling humanoid...

10 Diego Ferigo, et al. ∙

∙ 06/30/2020

Sampling from a k-DPP without looking at all items

Determinantal point processes (DPPs) are a useful probabilistic model fo...

5 Daniele Calandriello, et al. ∙

∙ 02/23/2020

Near-linear Time Gaussian Process Optimization with Adaptive Batching and Resparsification

Gaussian processes (GP) are one of the most successful frameworks to mod...

2 Daniele Calandriello, et al. ∙

∙ 08/27/2019

Statistical and Computational Trade-Offs in Kernel K-Means

We investigate the efficiency of k-means in terms of both statistical an...

8 Daniele Calandriello, et al. ∙

∙ 05/31/2019

Exact sampling of determinantal point processes with sublinear time preprocessing

We study the complexity of sampling from a distribution over all index s...

0 Michał Dereziński, et al. ∙

∙ 03/13/2019

Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret

Gaussian processes (GP) are a popular Bayesian approach for the optimiza...

4 Daniele Calandriello, et al. ∙

∙ 10/31/2018

On Fast Leverage Score Sampling and Optimal Learning

Leverage score sampling provides an appealing way to perform approximate...

24 Alessandro Rudi, et al. ∙

∙ 03/27/2018

Distributed Adaptive Sampling for Kernel Matrix Approximation

Most kernel-based methods, such as kernel or Gaussian process regression...

0 Daniele Calandriello, et al. ∙

∙ 06/15/2017

Second-Order Kernel Online Convex Optimization with Adaptive Sketching

Kernel online convex optimization (KOCO) is a framework combining the ex...

0 Daniele Calandriello, et al. ∙

∙ 09/13/2016

Analysis of Kelner and Levin graph sparsification algorithm for a streaming setting

We derive a new proof to show that the incremental resparsification algo...

0 Daniele Calandriello, et al. ∙

∙ 01/21/2016

Incremental Spectral Sparsification for Large-Scale Graph-Based Semi-Supervised Learning

While the harmonic function solution performs well in many semi-supervis...

0 Daniele Calandriello, et al. ∙

∙ 04/30/2013

Semi-Supervised Information-Maximization Clustering

Semi-supervised clustering aims to introduce prior knowledge in the deci...

0 Daniele Calandriello, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world