Search Results for author: Thomas G. Dietterich

Found 25 papers, 5 papers with code

Reinforcement Learning with Exogenous States and Rewards

no code implementations22 Mar 2023 George Trimponias, Thomas G. Dietterich

Exogenous state variables and rewards can slow reinforcement learning by injecting uncontrolled variation into the reward signal.


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

no code implementations29 Nov 2022 Alexander Guyer, Thomas G. Dietterich

By inverting PCQR, we obtain guarantees for the probability that the cumulative reward of an autonomous system will fall below a threshold sampled from the marginal distribution of the response variable (i. e., a calibrated CDF estimate) that we employ to predict coverage probabilities for user-specified target intervals.

Conformal Prediction Prediction Intervals +1

Conformal Prediction Intervals for Markov Decision Process Trajectories

no code implementations10 Jun 2022 Thomas G. Dietterich, Jesse Hostetler

This paper extends previous work on conformal prediction for functional data and conformalized quantile regression to provide conformal prediction intervals over the future behavior of an autonomous system executing a fixed control policy on a Markov Decision Process (MDP).

Conformal Prediction Management +2

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

no code implementations4 Mar 2022 Thomas G. Dietterich, Alexander Guyer

In many object recognition applications, the set of possible categories is an open set, and the deployed recognition system will encounter novel objects belonging to categories unseen during training.

Anomaly Detection Object +3

Hidden Heterogeneity: When to Choose Similarity-Based Calibration

1 code implementation3 Feb 2022 Kiri L. Wagstaff, Thomas G. Dietterich

However, these methods are unable to detect subpopulations where calibration could also improve prediction accuracy.

Classifier calibration Decision Making

Deep Convolution for Irregularly Sampled Temporal Point Clouds

no code implementations1 May 2021 Erich Merrill, Stefan Lee, Li Fuxin, Thomas G. Dietterich, Alan Fern

We consider the problem of modeling the dynamics of continuous spatial-temporal processes represented by irregular samples through both space and time.

Starcraft Starcraft II

Three-quarter Sibling Regression for Denoising Observational Data

no code implementations31 Dec 2020 Shiv Shankar, Daniel Sheldon, Tao Sun, John Pickering, Thomas G. Dietterich

However, it will remove intrinsic variability if the variables are dependent, and therefore does not apply to many situations, including modeling of species counts that are controlled by common causes.

Denoising regression

A Unifying Review of Deep and Shallow Anomaly Detection

no code implementations24 Sep 2020 Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, Klaus-Robert Müller

Deep learning approaches to anomaly detection have recently improved the state of the art in detection performance on complex datasets such as large collections of images or text.

One-Class Classification

Learning Scripts as Hidden Markov Models

no code implementations11 Sep 2018 J. Walker Orr, Prasad Tadepalli, Janardhan Rao Doppa, Xiaoli Fern, Thomas G. Dietterich

Scripts have been proposed to model the stereotypical event sequences found in narratives.


Anomaly Detection in the Presence of Missing Values

no code implementations5 Sep 2018 Thomas G. Dietterich, Tadesse Zemicheal

We recommend proportional distribution for IF, MAP imputation for LODA, and marginalization for EGMM.

Anomaly Detection Imputation

Open Category Detection with PAC Guarantees

1 code implementation ICML 2018 Si Liu, Risheek Garrepalli, Thomas G. Dietterich, Alan Fern, Dan Hendrycks

Further, while there are algorithms for open category detection, there are few empirical results that directly report alien detection rates.

Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations

2 code implementations ICLR 2019 Dan Hendrycks, Thomas G. Dietterich

Then we propose a new dataset called Icons-50 which opens research on a new kind of robustness, surface variation robustness.

Adversarial Defense Benchmarking

Incorporating Feedback into Tree-based Anomaly Detection

2 code implementations30 Aug 2017 Shubhomoy Das, Weng-Keen Wong, Alan Fern, Thomas G. Dietterich, Md Amran Siddiqui

Unfortunately, in realworld applications, this process can be exceedingly difficult for the analyst since a large fraction of high-ranking anomalies are false positives and not interesting from the application perspective.

Anomaly Detection

Factoring Exogenous State for Model-Free Monte Carlo

no code implementations28 Mar 2017 Sean McGregor, Rachel Houtman, Claire Montgomery, Ronald Metoyer, Thomas G. Dietterich

One visualization approach is to invoke the simulator to generate on-policy trajectories and then visualize those trajectories.


Fast Optimization of Wildfire Suppression Policies with SMAC

no code implementations28 Mar 2017 Sean McGregor, Rachel Houtman, Claire Montgomery, Ronald Metoyer, Thomas G. Dietterich

SMAC is applied to find the optimal policy in this class for the reward functions of each of the stakeholder constituencies.

Management SMAC+

Transductive Optimization of Top k Precision

no code implementations20 Oct 2015 Li-Ping Liu, Thomas G. Dietterich, Nan Li, Zhi-Hua Zhou

This paper introduces a new approach, Transductive Top K (TTK), that seeks to minimize the hinge loss over all training instances under the constraint that exactly $k$ test instances are predicted as positive.

Binary Classification Information Retrieval +1

HC-Search for Structured Prediction in Computer Vision

no code implementations CVPR 2015 Michael Lam, Janardhan Rao Doppa, Sinisa Todorovic, Thomas G. Dietterich

The mainstream approach to structured prediction problems in computer vision is to learn an energy function such that the solution minimizes that function.

Monocular Depth Estimation object-detection +3

Sequential Feature Explanations for Anomaly Detection

no code implementations28 Feb 2015 Md Amran Siddiqui, Alan Fern, Thomas G. Dietterich, Weng-Keen Wong

An SFE of an anomaly is a sequence of features, which are presented to the analyst one at a time (in order) until the information contained in the highlighted features is enough for the analyst to make a confident judgement about the anomaly.

Anomaly Detection

Gaussian Approximation of Collective Graphical Models

no code implementations20 May 2014 Li-Ping Liu, Daniel Sheldon, Thomas G. Dietterich

The Collective Graphical Model (CGM) models a population of independent and identically distributed individuals when only collective statistics (i. e., counts of individuals) are observed.

Collective Graphical Models

no code implementations NeurIPS 2011 Daniel R. Sheldon, Thomas G. Dietterich

This paper introduces Collective Graphical Models---a framework for modeling and probabilistic inference that operates directly on the sufficient statistics of the individual model.

Inverting Grice's Maxims to Learn Rules from Natural Language Extractions

no code implementations NeurIPS 2011 Mohammad S. Sorower, Janardhan R. Doppa, Walker Orr, Prasad Tadepalli, Thomas G. Dietterich, Xiaoli Z. Fern

However, unlike standard approaches to missing data, in this setting we know that facts are more likely to be missing from the text in cases where the reader can infer them from the facts that are mentioned combined with the domain knowledge.

Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition

5 code implementations21 May 1999 Thomas G. Dietterich

The paper presents an online model-free learning algorithm, MAXQ-Q, and proves that it converges wih probability 1 to a kind of locally-optimal policy known as a recursively optimal policy, even in the presence of the five kinds of state abstraction.

Hierarchical Reinforcement Learning Q-Learning +2

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