Search Results for author: Viliam Lisý

Found 10 papers, 5 papers with code

Explaining Classifiers Trained on Raw Hierarchical Multiple-Instance Data

no code implementations4 Aug 2022 Tomáš Pevný, Viliam Lisý, Branislav Bošanský, Petr Somol, Michal Pěchouček

Learning from raw data input, thus limiting the need for feature engineering, is a component of many successful applications of machine learning methods in various domains.

Feature Engineering Multiple Instance Learning

Fast Algorithms for Poker Require Modelling it as a Sequential Bayesian Game

no code implementations20 Dec 2021 Vojtěch Kovařík, David Milec, Michal Šustr, Dominik Seitz, Viliam Lisý

We argue that sequential Bayesian games constitute a natural class of games for generalizing these results.

Complexity and Algorithms for Exploiting Quantal Opponents in Large Two-Player Games

no code implementations30 Sep 2020 David Milec, Jakub Černý, Viliam Lisý, Bo An

This paper aims to analyze and propose scalable algorithms for computing effective and robust strategies against a quantal opponent in normal-form and extensive-form games.

Symbolic Relational Deep Reinforcement Learning based on Graph Neural Networks

1 code implementation25 Sep 2020 Jaromír Janisch, Tomáš Pevný, Viliam Lisý

We present a deep RL framework based on graph neural networks and auto-regressive policy decomposition that naturally works with these problems and is completely domain-independent.


Hierarchical Multiple-Instance Data Classification with Costly Features

1 code implementation20 Nov 2019 Jaromír Janisch, Tomáš Pevný, Viliam Lisý

We motivate our research with a real-world problem of classifying malicious web domains using a remote service that provides various information.

Classification Classification with Costly Features +5

Classification with Costly Features as a Sequential Decision-Making Problem

2 code implementations5 Sep 2019 Jaromír Janisch, Tomáš Pevný, Viliam Lisý

This work focuses on a specific classification problem, where the information about a sample is not readily available, but has to be acquired for a cost, and there is a per-sample budget.

Classification with Costly Features Decision Making +2

Rethinking Formal Models of Partially Observable Multiagent Decision Making

no code implementations26 Jun 2019 Vojtěch Kovařík, Martin Schmid, Neil Burch, Michael Bowling, Viliam Lisý

A second issue is that while EFGs have recently seen significant algorithmic progress, their classical formalization is unsuitable for efficient presentation of the underlying ideas, such as those around decomposition.

Decision Making

Value Functions for Depth-Limited Solving in Zero-Sum Imperfect-Information Games

no code implementations31 May 2019 Vojtěch Kovařík, Dominik Seitz, Viliam Lisý, Jan Rudolf, Shuo Sun, Karel Ha

We provide a formal definition of depth-limited games together with an accessible and rigorous explanation of the underlying concepts, both of which were previously missing in imperfect-information games.

Classification with Costly Features using Deep Reinforcement Learning

1 code implementation20 Nov 2017 Jaromír Janisch, Tomáš Pevný, Viliam Lisý

We study a classification problem where each feature can be acquired for a cost and the goal is to optimize a trade-off between the expected classification error and the feature cost.

Classification Classification with Costly Features +4

DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker

1 code implementation6 Jan 2017 Matej Moravčík, Martin Schmid, Neil Burch, Viliam Lisý, Dustin Morrill, Nolan Bard, Trevor Davis, Kevin Waugh, Michael Johanson, Michael Bowling

Poker is the quintessential game of imperfect information, and a longstanding challenge problem in artificial intelligence.

Game of Poker

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