Search Results for author: Guillermo A. Pérez

Found 10 papers, 2 papers with code

Distillation of RL Policies with Formal Guarantees via Variational Abstraction of Markov Decision Processes (Technical Report)

1 code implementation17 Dec 2021 Florent Delgrange, Ann Nowé, Guillermo A. Pérez

Finally, we show how one can use a policy obtained via state-of-the-art RL to efficiently train a variational autoencoder that yields a discrete latent model with provably approximately correct bisimulation guarantees.

Continuous One-Counter Automata

no code implementations28 Jan 2021 Michael Blondin, Tim Leys, Filip Mazowiecki, Philip Oftermatt, Guillermo A. Pérez

Our three main results are as follows: (1) We prove that the reachability problem for COCA with global upper and lower bound tests is in NC2; (2) that, in general, the problem is decidable in polynomial time; and (3) that it is decidable in the polynomial hierarchy for COCA with parametric counter updates and bound tests.

Formal Languages and Automata Theory Logic in Computer Science

Safe Learning for Near Optimal Scheduling

no code implementations19 May 2020 Damien Busatto-Gaston, Debraj Chakraborty, Shibashis Guha, Guillermo A. Pérez, Jean-François Raskin

In this paper, we investigate the combination of synthesis, model-based learning, and online sampling techniques to obtain safe and near-optimal schedulers for a preemptible task scheduling problem.


Robustness Verification for Classifier Ensembles

no code implementations12 May 2020 Dennis Gross, Nils Jansen, Guillermo A. Pérez, Stephan Raaijmakers

The robustness-checking problem consists of assessing, given a set of classifiers and a labelled data set, whether there exists a randomized attack that induces a certain expected loss against all classifiers.

Image Classification

Revisiting Parameter Synthesis for One-Counter Automata

no code implementations3 May 2020 Guillermo A. Pérez, Ritam Raha

We study the (parameter) synthesis problem for one-counter automata with parameters.

Logic in Computer Science Formal Languages and Automata Theory

Let's Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Framework

no code implementations6 Apr 2020 Floris Geerts, Filip Mazowiecki, Guillermo A. Pérez

In this paper we cast neural networks defined on graphs as message-passing neural networks (MPNNs) in order to study the distinguishing power of different classes of such models.

The Impatient May Use Limited Optimism to Minimize Regret

no code implementations17 Nov 2018 Michaël Cadilhac, Guillermo A. Pérez, Marie van den Bogaard

Discounted-sum games provide a formal model for the study of reinforcement learning, where the agent is enticed to get rewards early since later rewards are discounted.

On the Complexity of Value Iteration

no code implementations13 Jul 2018 Nikhil Balaji, Stefan Kiefer, Petr Novotný, Guillermo A. Pérez, Mahsa Shirmohammadi

We show that, given a horizon $n$ in binary and an MDP, computing an optimal policy is EXP-complete, thus resolving an open problem that goes back to the seminal 1987 paper on the complexity of MDPs by Papadimitriou and Tsitsiklis.

Learning-Based Mean-Payoff Optimization in an Unknown MDP under Omega-Regular Constraints

no code implementations24 Apr 2018 Jan Křetínský, Guillermo A. Pérez, Jean-François Raskin

Assuming the support of the unknown transition function and a lower bound on the minimal transition probability are known in advance, we show that in MDPs consisting of a single end component, two combinations of guarantees on the parity and mean-payoff objectives can be achieved depending on how much memory one is willing to use.

online learning

Optimizing Expectation with Guarantees in POMDPs (Technical Report)

1 code implementation26 Nov 2016 Krishnendu Chatterjee, Petr Novotný, Guillermo A. Pérez, Jean-François Raskin, Đorđe Žikelić

In this work we go beyond both the "expectation" and "threshold" approaches and consider a "guaranteed payoff optimization (GPO)" problem for POMDPs, where we are given a threshold $t$ and the objective is to find a policy $\sigma$ such that a) each possible outcome of $\sigma$ yields a discounted-sum payoff of at least $t$, and b) the expected discounted-sum payoff of $\sigma$ is optimal (or near-optimal) among all policies satisfying a).

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