Thomas G. Dietterich | DeepAI

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∙ 03/22/2023

Reinforcement Learning with Exogenous States and Rewards

Exogenous state variables and rewards can slow reinforcement learning by...

0 George Trimponias, et al. ∙

∙ 11/29/2022

Will My Robot Achieve My Goals? Predicting the Probability that an MDP Policy Reaches a User-Specified Behavior Target

As an autonomous system performs a task, it should maintain a calibrated...

0 Alexander Guyer, et al. ∙

∙ 09/22/2022

Oracle Analysis of Representations for Deep Open Set Detection

The problem of detecting a novel class at run time is known as Open Set ...

4 Risheek Garrepalli, et al. ∙

∙ 06/10/2022

Conformal Prediction Intervals for Markov Decision Process Trajectories

Before delegating a task to an autonomous system, a human operator may w...

8 Thomas G. Dietterich, et al. ∙

∙ 03/04/2022

The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods

In many object recognition applications, the set of possible categories ...

7 Thomas G. Dietterich, et al. ∙

∙ 02/03/2022

Hidden Heterogeneity: When to Choose Similarity-Based Calibration

Trustworthy classifiers are essential to the adoption of machine learnin...

7 Kiri L. Wagstaff, et al. ∙

∙ 05/01/2021

Deep Convolution for Irregularly Sampled Temporal Point Clouds

We consider the problem of modeling the dynamics of continuous spatial-t...

11 Erich Merrill, et al. ∙

∙ 12/31/2020

Three-quarter Sibling Regression for Denoising Observational Data

Many ecological studies and conservation policies are based on field obs...

1 Shiv Shankar, et al. ∙

∙ 09/24/2020

A Unifying Review of Deep and Shallow Anomaly Detection

Deep learning approaches to anomaly detection have recently improved the...

49 Lukas Ruff, et al. ∙

∙ 12/11/2018

Deep Anomaly Detection with Outlier Exposure

It is important to detect and handle anomalous inputs when deploying mac...

8 Dan Hendrycks, et al. ∙

∙ 11/27/2018

Robust Artificial Intelligence and Robust Human Organizations

Every AI system is deployed by a human organization. In high risk applic...

0 Thomas G. Dietterich, et al. ∙

∙ 09/11/2018

Learning Scripts as Hidden Markov Models

Scripts have been proposed to model the stereotypical event sequences fo...

0 J. Walker Orr, et al. ∙

∙ 09/05/2018

Anomaly Detection in the Presence of Missing Values

Standard methods for anomaly detection assume that all features are obse...

0 Thomas G. Dietterich, et al. ∙

∙ 08/01/2018

Open Category Detection with PAC Guarantees

Open category detection is the problem of detecting "alien" test instanc...

4 Si Liu, et al. ∙

∙ 07/04/2018

Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations

In this paper we establish rigorous benchmarks for image classifier robu...

0 Dan Hendrycks, et al. ∙

∙ 06/05/2018

Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning

Exogenous state variables and rewards can slow down reinforcement learni...

6 Thomas G. Dietterich, et al. ∙

∙ 08/30/2017

Incorporating Feedback into Tree-based Anomaly Detection

Anomaly detectors are often used to produce a ranked list of statistical...

0 Shubhomoy Das, et al. ∙

∙ 03/28/2017

Fast Optimization of Wildfire Suppression Policies with SMAC

Managers of US National Forests must decide what policy to apply for dea...

0 Sean McGregor, et al. ∙

∙ 03/28/2017

Factoring Exogenous State for Model-Free Monte Carlo

Policy analysts wish to visualize a range of policies for large simulato...

0 Sean McGregor, et al. ∙

∙ 02/28/2015

Sequential Feature Explanations for Anomaly Detection

In many applications, an anomaly detection system presents the most anom...

1 Md Amran Siddiqui, et al. ∙

∙ 05/20/2014

Gaussian Approximation of Collective Graphical Models

The Collective Graphical Model (CGM) models a population of independent ...

0 Li-Ping Liu, et al. ∙

∙ 10/16/2012

Inferring Strategies from Limited Reconnaissance in Real-time Strategy Games

In typical real-time strategy (RTS) games, enemy units are visible only ...

0 Jesse Hostetler, et al. ∙

∙ 10/16/2012

Active Imitation Learning via Reduction to I.I.D. Active Learning

In standard passive imitation learning, the goal is to learn a target po...

0 Kshitij Judah, et al. ∙

∙ 07/04/2012

Learning from Sparse Data by Exploiting Monotonicity Constraints

When training data is sparse, more domain knowledge must be incorporated...

0 Eric E. Altendorf, et al. ∙

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