Viliam Lisý | DeepAI

DeepAI

Featured Co-authors

Bo An
51 publications
Michael Bowling
39 publications
Tomáš Pevný
25 publications
Neil Burch
16 publications
Martin Schmid
14 publications
Dustin Morrill
14 publications
Branislav Bosansky
9 publications
Vojtěch Kovařík
9 publications
Kevin Waugh
7 publications
Karel Horák
7 publications
Matej Moravčík
6 publications

∙ 09/06/2022

Avast-CTU Public CAPE Dataset

There is a limited amount of publicly available data to support research...

Branislav Bosansky, et al. ∙

∙ 08/04/2022

Explaining Classifiers Trained on Raw Hierarchical Multiple-Instance Data

Learning from raw data input, thus limiting the need for feature enginee...

Tomáš Pevný, et al. ∙

∙ 12/23/2021

Continual Depth-limited Responses for Computing Counter-strategies in Extensive-form Games

In real-world applications, game-theoretic algorithms often interact wit...

David Milec, et al. ∙

∙ 12/20/2021

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

Many recent results in imperfect information games were only formulated ...

Vojtěch Kovařík, et al. ∙

∙ 10/22/2021

Improving Robustness of Malware Classifiers using Adversarial Strings Generated from Perturbed Latent Representations

In malware behavioral analysis, the list of accessed and created files v...

Marek Galovic, et al. ∙

∙ 05/19/2021

Mill.jl and JsonGrinder.jl: automated differentiable feature extraction for learning from raw JSON data

Learning from raw data input, thus limiting the need for manual feature ...

Šimon Mandlík, et al. ∙

∙ 02/27/2021

Multi-agent Reinforcement Learning in OpenSpiel: A Reproduction Report

In this report, we present results reproductions for several core algori...

Michael Walton, et al. ∙

∙ 09/30/2020

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

Solution concepts of traditional game theory assume entirely rational pl...

David Milec, et al. ∙

∙ 09/25/2020

Symbolic Relational Deep Reinforcement Learning based on Graph Neural Networks

We present a novel deep reinforcement learning framework for solving rel...

Jaromír Janisch, et al. ∙

∙ 04/22/2020

Discovering Imperfectly Observable Adversarial Actions using Anomaly Detection

Anomaly detection is a method for discovering unusual and suspicious beh...

Olga Petrova, et al. ∙

∙ 11/20/2019

Deep Reinforcement Learning with Explicitly Represented Knowledge and Variable State and Action Spaces

We focus on a class of real-world domains, where gathering hierarchical ...

Jaromír Janisch, et al. ∙

∙ 09/05/2019

Classification with Costly Features as a Sequential Decision-Making Problem

This work focuses on a specific classification problem, where the inform...

Jaromír Janisch, et al. ∙

∙ 06/26/2019

Rethinking Formal Models of Partially Observable Multiagent Decision Making

Multiagent decision-making problems in partially observable environments...

Vojtěch Kovařík, et al. ∙

∙ 06/14/2019

Problems with the EFG formalism: a solution attempt using observations

We argue that the extensive-form game (EFG) model isn't powerful enough ...

Vojtěch Kovařík, et al. ∙

∙ 05/31/2019

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

Depth-limited look-ahead search is an essential tool for agents playing ...

Vojtěch Kovařík, et al. ∙

∙ 12/18/2018

Monte Carlo Continual Resolving for Online Strategy Computation in Imperfect Information Games

Online game playing algorithms produce high-quality strategies with a fr...

Michal Šustr, et al. ∙

∙ 04/23/2018

Analysis of Hannan Consistent Selection for Monte Carlo Tree Search in Simultaneous Move Games

Hannan consistency, or no external regret, is a key concept for learning...

Vojtěch Kovařík, et al. ∙

∙ 03/14/2018

Constructing Imperfect Recall Abstractions to Solve Large Extensive-Form Games

Extensive-form games are an important model of finite sequential interac...

Jiri Cermak, et al. ∙

∙ 11/20/2017

Classification with Costly Features using Deep Reinforcement Learning

We study a classification problem where each feature can be acquired for...

Jaromír Janisch, et al. ∙

∙ 01/06/2017

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

Artificial intelligence has seen several breakthroughs in recent years, ...

Matej Moravčík, et al. ∙