Search Results for author:
Found 25 papers, 5 papers with code
Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition
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.
Inverting Grice's Maxims to Learn Rules from Natural Language Extractions
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.
Collective Graphical Models
This paper introduces Collective Graphical Models---a framework for modeling and probabilistic inference that operates directly on the sufficient statistics of the individual model.
Gaussian Approximation of Collective Graphical Models
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.
Sequential Feature Explanations for Anomaly Detection
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.
HC-Search for Structured Prediction in Computer Vision
The mainstream approach to structured prediction problems in computer vision is to learn an energy function such that the solution minimizes that function.
Transductive Optimization of Top k Precision
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.
Factoring Exogenous State for Model-Free Monte Carlo
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
SMAC is applied to find the optimal policy in this class for the reward functions of each of the stakeholder constituencies.
Incorporating Feedback into Tree-based Anomaly Detection
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.
Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning
Exogenous state variables and rewards can slow down reinforcement learning by injecting uncontrolled variation into the reward signal.
Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations
Then we propose a new dataset called Icons-50 which opens research on a new kind of robustness, surface variation robustness.
Open Category Detection with PAC Guarantees
Further, while there are algorithms for open category detection, there are few empirical results that directly report alien detection rates.
Anomaly Detection in the Presence of Missing Values
We recommend proportional distribution for IF, MAP imputation for LODA, and marginalization for EGMM.
Learning Scripts as Hidden Markov Models
Scripts have been proposed to model the stereotypical event sequences found in narratives.
Robust Artificial Intelligence and Robust Human Organizations
Every AI system is deployed by a human organization.
A Unifying Review of Deep and Shallow Anomaly Detection
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.
Three-quarter Sibling Regression for Denoising Observational Data
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.
Confidence Calibration for Domain Generalization under Covariate Shift
Existing calibration algorithms address the problem of covariate shift via unsupervised domain adaptation.
Deep Convolution for Irregularly Sampled Temporal Point Clouds
We consider the problem of modeling the dynamics of continuous spatial-temporal processes represented by irregular samples through both space and time.
Hidden Heterogeneity: When to Choose Similarity-Based Calibration
However, these methods are unable to detect subpopulations where calibration could also improve prediction accuracy.
The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods
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.
Conformal Prediction Intervals for Markov Decision Process Trajectories
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).
Will My Robot Achieve My Goals? Predicting the Probability that an MDP Policy Reaches a User-Specified Behavior Target
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.
Reinforcement Learning with Exogenous States and Rewards
Exogenous state variables and rewards can slow reinforcement learning by injecting uncontrolled variation into the reward signal.
