Academic Path

• 2017 - PhD in CS, Sapienza University
• 2014/15 - IRIF, Paris
• 2016, 2018  - Simons Institute for the Theory of Computing
• 2017-2018 - Max-Planck Institute for Informatics
• 2019 - COATI, INRIA d'Université Côte d'Azur

Research Interests

Best PhD + Young Resercher Prizes by It. Ch. EATCS

Computational Dynamics

Neural Network Pruning

Blalock et al. (2020): iterated magnitude pruning still SOTA compression technique.

The Lottery Ticket Hypothesis

The Strong LTH

Proving the SLTH

SLTH and the Random Subset-Sum Problem

Find combination of random weights close to $w$:

RSSP. For which $n$ does the following holds?

Given $X_1,...,X_n$ i.i.d. random variables, with prob. $1-\epsilon$ for each $z\in [-1,1]$ there is $S\subseteq\{1,...,n\}$ such that $$z-\epsilon\leq\sum_{i\in S} X_i \leq z+\epsilon.$$

Deep connection with integer linear programs

[Dyer & Frieze '89,
Borst et al. '22]

SLTH for Convolutional Neural Networks

Theorem (da Cunha et al., ICLR 2022).

Given $\epsilon,\delta>0$, any CNN with $k$ parameters and $\ell$ layers, and kernels with $\ell_1$ norm at most 1, can be approximated within error $\epsilon$ by pruning a random CNN with $O\bigl(k\log \frac{k\ell}{\min\{\epsilon,\delta\}}\bigr)$ parameters and $2\ell$ layers with probability at least $1-\delta$.

Thank you