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Control of Memory, Active Perception, and Action in Minecraft

Control of Memory, Active Perception, and Action in Minecraft

30 May 2016
Michael T. Lash
Valliappa Chockalingam
Ashwin Balakrishnan
Honglak Lee
ArXiv (abs)PDFHTML

Papers citing "Control of Memory, Active Perception, and Action in Minecraft"

17 / 17 papers shown
Title
Sparse and Faithful Explanations Without Sparse Models
Sparse and Faithful Explanations Without Sparse Models
Yiyang Sun
Zhi Chen
Vittorio Orlandi
Tong Wang
Cynthia Rudin
76
3
0
15 Feb 2024
Counterfactual Explanation for Regression via Disentanglement in Latent
  Space
Counterfactual Explanation for Regression via Disentanglement in Latent Space
Xuan Zhao
Klaus Broelemann
Gjergji Kasneci
OODCMLBDL
33
1
0
14 Nov 2023
Counterfactual Explanation via Search in Gaussian Mixture Distributed
  Latent Space
Counterfactual Explanation via Search in Gaussian Mixture Distributed Latent Space
Xuan Zhao
Klaus Broelemann
Gjergji Kasneci
CML
30
0
0
25 Jul 2023
HEX: Human-in-the-loop Explainability via Deep Reinforcement Learning
HEX: Human-in-the-loop Explainability via Deep Reinforcement Learning
Michael T. Lash
86
0
0
02 Jun 2022
Counterfactual Explanations via Latent Space Projection and
  Interpolation
Counterfactual Explanations via Latent Space Projection and Interpolation
Brian Barr
Matthew R. Harrington
Samuel Sharpe
Capital One
BDL
70
10
0
02 Dec 2021
Latent-CF: A Simple Baseline for Reverse Counterfactual Explanations
Latent-CF: A Simple Baseline for Reverse Counterfactual Explanations
R. Balasubramanian
Samuel Sharpe
Brian Barr
J. Wittenbach
C. Bayan Bruss
BDL
55
18
0
16 Dec 2020
Personalized Cardiovascular Disease Risk Mitigation via Longitudinal
  Inverse Classification
Personalized Cardiovascular Disease Risk Mitigation via Longitudinal Inverse Classification
Michael T. Lash
W. Street
11
3
0
16 Nov 2020
A Survey on the Explainability of Supervised Machine Learning
A Survey on the Explainability of Supervised Machine Learning
Nadia Burkart
Marco F. Huber
FaMLXAI
77
781
0
16 Nov 2020
A survey of algorithmic recourse: definitions, formulations, solutions,
  and prospects
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
Amir-Hossein Karimi
Gilles Barthe
Bernhard Schölkopf
Isabel Valera
FaML
70
172
0
08 Oct 2020
Inverse Classification with Limited Budget and Maximum Number of
  Perturbed Samples
Inverse Classification with Limited Budget and Maximum Number of Perturbed Samples
Jaehoon Koo
Diego Klabjan
J. Utke
57
2
0
29 Sep 2020
Optimal Sepsis Patient Treatment using Human-in-the-loop Artificial
  Intelligence
Optimal Sepsis Patient Treatment using Human-in-the-loop Artificial Intelligence
Akash Gupta
Michael T. Lash
S. Nachimuthu
20
12
0
16 Sep 2020
Explaining Data-Driven Decisions made by AI Systems: The Counterfactual
  Approach
Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach
Carlos Fernandez
F. Provost
Xintian Han
CML
72
72
0
21 Jan 2020
Treant: Training Evasion-Aware Decision Trees
Treant: Training Evasion-Aware Decision Trees
Stefano Calzavara
Claudio Lucchese
Gabriele Tolomei
S. Abebe
S. Orlando
AAML
75
41
0
02 Jul 2019
Prophit: Causal inverse classification for multiple continuously valued
  treatment policies
Prophit: Causal inverse classification for multiple continuously valued treatment policies
Michael T. Lash
Qihang Lin
W. Street
CML
35
3
0
14 Feb 2018
Realistic risk-mitigating recommendations via inverse classification
Realistic risk-mitigating recommendations via inverse classification
Michael T. Lash
W. Street
16
1
0
13 Nov 2016
Generalized Inverse Classification
Generalized Inverse Classification
Michael T. Lash
Qihang Lin
W. Street
Jennifer G. Robinson
Jeffrey W. Ohlmann
75
62
0
05 Oct 2016
Early Predictions of Movie Success: the Who, What, and When of
  Profitability
Early Predictions of Movie Success: the Who, What, and When of Profitability
Michael T. Lash
K. Zhao
39
160
0
17 Jun 2015
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