activity
20192023
most citedHow to Protect Your Privacy? A Framework for Counter-Adversarial Decision Making

3 citations · 9 across the 8 of their papers we have counts for

collaborators

8 papers

eess.SY2023★ 3 cited

A Biologically-Inspired Computational Model of Time Perception

Inês Lourenço, Robert Mattila, Rodrigo Ventura +1

Time perception - how humans and animals perceive the passage of time - forms the basis for important cognitive skills such as decision-making, planning, and communication. In this…

cs.RO2023

Diagnosing and Augmenting Feature Representations in Correctional Inverse Reinforcement Learning

Inês Lourenço, Andreea Bobu, Cristian R. Rojas +1

Robots have been increasingly better at doing tasks for humans by learning from their feedback, but still often suffer from model misalignment due to missing or incorrectly learned…

cs.LG2023

Optimal Transport for Correctional Learning

Rebecka Winqvist, Inês Lourenco, Francesco Quinzan +2

The contribution of this paper is a generalized formulation of correctional learning using optimal transport, which is about how to optimally transport one mass distribution to ano…

cs.LG2021

A Teacher-Student Markov Decision Process-based Framework for Online Correctional Learning

Inês Lourenço, Rebecka Winqvist, Cristian R. Rojas +1

A classical learning setting typically concerns an agent/student who collects data, or observations, from a system in order to estimate a certain property of interest. Correctional…

eess.SY2020

Cooperative System Identification via Correctional Learning

Inês Lourenço, Robert Mattila, Cristian R. Rojas +1

We consider a cooperative system identification scenario in which an expert agent (teacher) knows a correct, or at least a good, model of the system and aims to assist a learner-ag…

eess.SY2020★ 3 cited

How to Protect Your Privacy? A Framework for Counter-Adversarial Decision Making

Inês Lourenço, Robert Mattila, Cristian R. Rojas +1

We consider a counter-adversarial sequential decision-making problem where an agent computes its private belief (posterior distribution) of the current state of the world, by filte…

eess.SY2019★ 1 cited

Teaching robots to perceive time -- A reinforcement learning approach (Extended version)

Inês Lourenço, Bo Wahlberg, Rodrigo Ventura

Time perception is the phenomenological experience of time by an individual. In this paper, we study how to replicate neural mechanisms involved in time perception, allowing robots…

eess.SY2019★ 2 cited

What Did Your Adversary Believe? Optimal Filtering and Smoothing in Counter-Adversarial Autonomous Systems

Robert Mattila, Inês Lourenço, Vikram Krishnamurthy +2

We consider fixed-interval smoothing problems for counter-adversarial autonomous systems. An adversary deploys an autonomous filtering and control system that i) measures our curre…