35 citations · 36 across the 17 of their papers we have counts for
17 papers
Langevin for Nonconvex Optimization: Exact, Inexact and Zeroth-Order
Emanuele Naldi, Marco Rando, Lorenzo Rosasco +1
We study Langevin-based methods for non-convex optimization under smoothness and dissipativity assumptions, focusing on non-asymptotic expected excess-risk bounds rather than sampl…
Clock-state olfactory search in turbulent flows using Q-learning: The geometry of plume recovery
Marco Rando, Robin A. Heinonen, Yujia Qi +1
Finding an odor source in a turbulent flow requires effectively leveraging the history of olfactory observations into a robust navigation strategy. In this work, we use tabular Q-l…
On the Hardness of Junking LLMs
Marco Rando, Samuel Vaiter
Large language models (LLMs) are known to be vulnerable to jailbreak attacks, which typically rely on carefully designed prompts containing explicit semantic structure. These attac…
Smart strategies to navigate turbulent odor plumes reorienting to local wind
Lorenzo Piro, Maurizio Carbone, Luca Biferale +4
Olfactory search in turbulent environments is a sensorimotor problem that many animals solve with remarkable efficiency, yet replicating this ability in artificial systems is an en…
ZOBA: An Efficient Single-loop Zeroth-order Bilevel Optimization Algorithm
Marco Rando, Samuel Vaiter
Bilevel optimization problems consist of minimizing a value function whose evaluation depends on the solution of an inner optimization problem. These problems are typically tackled…
A Proximal Stochastic Variance Reduced Zeroth-order Algorithm using Structured Finite-Differences
Marco Rando, Cheik Traoré, Cesare Molinari +2
Minimizing finite sums of functions is a central problem in optimization, arising in numerous practical applications. Such problems are commonly addressed using first-order optimiz…
A Structured Tour of Optimization with Finite Differences
Marco Rando, Cesare Molinari, Lorenzo Rosasco +1
Finite-difference methods are widely used for zeroth-order optimization in settings where gradient information is unavailable or expensive to compute. These procedures mimic first-…
Automatic Gain Tuning for Humanoid Robots Walking Architectures Using Gradient-Free Optimization Techniques
Carlotta Sartore, Marco Rando, Giulio Romualdi +3
Developing sophisticated control architectures has endowed robots, particularly humanoid robots, with numerous capabilities. However, tuning these architectures remains a challengi…
A New Formulation for Zeroth-Order Optimization of Adversarial EXEmples in Malware Detection
Marco Rando, Luca Demetrio, Lorenzo Rosasco +1
Machine learning malware detectors are vulnerable to adversarial EXEmples, i.e., carefully-crafted Windows programs tailored to evade detection. Unlike other adversarial problems,…
Q-learning with temporal memory to navigate turbulence
Marco Rando, Martin James, Alessandro Verri +2
We consider the problem of olfactory searches in a turbulent environment. We focus on agents that respond solely to odor stimuli, with no access to spatial perception nor prior inf…
An Optimal Structured Zeroth-order Algorithm for Non-smooth Optimization
Marco Rando, Cesare Molinari, Lorenzo Rosasco +1
Finite-difference methods are a class of algorithms designed to solve black-box optimization problems by approximating a gradient of the target function on a set of directions. In…
Fast kernel methods for Data Quality Monitoring as a goodness-of-fit test
Gaia Grosso, Nicolò Lai, Marco Letizia +5
We here propose a machine learning approach for monitoring particle detectors in real-time. The goal is to assess the compatibility of incoming experimental data with a reference d…
A fast and flexible machine learning approach to data quality monitoring
Gaia Grosso, Nicolò Lai, Marco Letizia +4
We present a machine learning based approach for real-time monitoring of particle detectors. The proposed strategy evaluates the compatibility between incoming batches of experimen…
Efficient Unsupervised Learning for Plankton Images
Paolo Didier Alfano, Marco Rando, Marco Letizia +3
Monitoring plankton populations in situ is fundamental to preserve the aquatic ecosystem. Plankton microorganisms are in fact susceptible of minor environmental perturbations, that…
Stochastic Zeroth order Descent with Structured Directions
Marco Rando, Cesare Molinari, Silvia Villa +1
We introduce and analyze Structured Stochastic Zeroth order Descent (S-SZD), a finite difference approach that approximates a stochastic gradient on a set of orthogonal d…
Learning new physics efficiently with nonparametric methods
Marco Letizia, Gianvito Losapio, Marco Rando +5
We present a machine learning approach for model-independent new physics searches. The corresponding algorithm is powered by recent large-scale implementations of kernel methods, n…
Ada-BKB: Scalable Gaussian Process Optimization on Continuous Domains by Adaptive Discretization
Marco Rando, Luigi Carratino, Silvia Villa +1
Gaussian process optimization is a successful class of algorithms(e.g. GP-UCB) to optimize a black-box function through sequential evaluations. However, for functions with continuo…