23 citations · 66 across the 10 of their papers we have counts for
10 papers
One Pass ImageNet
Huiyi Hu, Ang Li, Daniele Calandriello +1
We present the One Pass ImageNet (OPIN) problem, which aims to study the effectiveness of deep learning in a streaming setting. ImageNet is a widely known benchmark dataset that ha…
On the Emergence of Whole-body Strategies from Humanoid Robot Push-recovery Learning
Diego Ferigo, Raffaello Camoriano, Paolo Maria Viceconte +4
Balancing and push-recovery are essential capabilities enabling humanoid robots to solve complex locomotion tasks. In this context, classical control systems tend to be based on si…
Sampling from a -DPP without looking at all items
Daniele Calandriello, Michał Dereziński, Michal Valko
Determinantal point processes (DPPs) are a useful probabilistic model for selecting a small diverse subset out of a large collection of items, with applications in summarization, s…
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…
Statistical and Computational Trade-Offs in Kernel K-Means
Daniele Calandriello, Lorenzo Rosasco
We investigate the efficiency of k-means in terms of both statistical and computational requirements. More precisely, we study a Nyström approach to kernel k-means. We analyze the…
Exact sampling of determinantal point processes with sublinear time preprocessing
Michał Dereziński, Daniele Calandriello, Michal Valko
We study the complexity of sampling from a distribution over all index subsets of the set with the probability of a subset proportional to the determinant of the…