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Binary classifier calibration using a Bayesian non-parametric approach

Binary classifier calibration using a Bayesian non-parametri...

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SIAM International Conference on Data Mining 2015, SDM 2015

作者： Naeini, Mahdi Pakdaman Cooper, Gregory F. Hauskrecht, Milos Intelligent Systems Program University of Pittsburgh United States Department of Biomedical Informatics University of Pittsburgh United States Computer Science Department University of Pittsburgh United States

ISBN: (纸本)9781510811522

Learning probabilistic predictive models that are well calibrated is critical for many prediction and decisionmaking tasks in Data mining. This paper presents two new non-parametric methods for calibrating outputs of binary classification models: a method based on the Bayes optimal selection and a method based on the Bayesian model averaging. The advantage of these methods is that they are independent of the algorithm used to learn a predictive model, and they can be applied in a post-processing step, after the model is learned. This makes them applicable to a wide variety of machine learning models and methods. These calibration methods, as well as other methods, are tested on a variety of datasets in terms of both discrimination and calibration performance. The results show the methods either outperform or are comparable in performance to the state-of-the-art calibration methods. Copyright © SIAM.

关键词： Calibration

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EXPLANATION BY PROGRESSIVE EXAGGERATION 8

EXPLANATION BY PROGRESSIVE EXAGGERATION

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8th International Conference on Learning Representations, ICLR 2020

作者： Singla, Sumedha Pollack, Brian Chen, Junxiang Batmanghelich, Kayhan Department of Computer Science University of Pittsburgh United States Department of Biomedical Informatics University of Pittsburgh United States Intelligent Systems Program University of Pittsburgh United States

As machine learning methods see greater adoption and implementation in high stakes applications such as medical image diagnosis, the need for model interpretability and explanation has become more critical. Classical approaches that assess feature importance (e.g., saliency maps) do not explain how and why a particular region of an image is relevant to the prediction. We propose a method that explains the outcome of a classification black-box by gradually exaggerating the semantic effect of a given class. Given a query input to a classifier, our method produces a progressive set of plausible variations of that query, which gradually changes the posterior probability from its original class to its negation. These counter-factually generated samples preserve features unrelated to the classification decision, such that a user can employ our method as a "tuning knob" to traverse a data manifold while crossing the decision boundary. Our method is model agnostic and only requires the output value and gradient of the predictor with respect to its input. © 2020 8th International Conference on Learning Representations, ICLR 2020. All rights reserved.

关键词： Semantics

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Linear program approximations for factored continuous-state Markov decision processes 03

Linear program approximations for factored continuous-state ...

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Proceedings of the 17th International Conference on Neural Information Processing systems

作者： Milos Hauskrecht Branislav Kveton Department of Computer Science and Intelligent Systems Program University of Pittsburgh

Approximate linear programming (ALP) has emerged recently as one of the most promising methods for solving complex factored MDPs with finite state spaces. In this work we show that ALP solutions are not limited only to MDPs with finite state spaces, but that they can also be applied successfully to factored continuous-state MDPs (CMDPs). We show how one can build an ALP-based approximation for such a model and contrast it to existing solution methods. We argue that this approach offers a robust alternative for solving high dimensional continuous-state space problems. The point is supported by experiments on three CMDP problems with 24-25 continuous state factors.

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An Approach to Detect Abnormal Submissions for CodeWorkout Dataset 8

An Approach to Detect Abnormal Submissions for CodeWorkout D...

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8th Educational Data Mining in computer science Education Workshop, CSEDM 2024

作者： Hicks, Alex Shi, Yang Lekshmi-Narayanan, Arun-Balajiee Yan, Wei Marwan, Samiha Dept of Computer Science Virginia Tech Blacksburg VA Dept of Computer Science Utah State University Logan UT Intelligent Systems Program University of Pittsburgh Pittsburgh PA School of Informatics Computing and Cyber Systems North Arizona University Flagstaff AZ Dept. of Computer Science University of Virginia wCharlottesville VA

Students’ interactions while solving problems in learning environments (i.e. log data) are often used to support students’ learning. For example, researchers use log data to develop systems that can provide students with personalized problem recommendations based on their knowledge level. However, anomalies in the students’ log data, such as cheating to solve programming problems, could introduce a hidden bias in the log data. As a result, these systems may provide inaccurate problem recommendations, and therefore, defeat their purpose. Classical cheating detection methods, such as MOSS, can be used to detect code plagiarism. However, these methods cannot detect other abnormal events such as a student gaming a system with multiple attempts of similar solutions to a particular programming problem. This paper presents a preliminary study to analyze log data with anomalies. The goal of our work is to overcome the abnormal instances when modeling personalizable recommendations in programming learning environments. © 2024 Copyright for this paper by its authors.

关键词： CS1 Dataset Cleaning Dataset Standards Educational Data Mining Introductory programming

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Designing engaging camera based mobile games for implicit heart rate monitoring

Designing engaging camera based mobile games for implicit he...

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32nd Annual ACM Conference on Human Factors in Computing systems, CHI EA 2014

作者： Han, Teng Canny, John Shi, Lanfei Wang, Jingtao Xiao, Xiang Intelligent Systems Program University of Pittsburgh Pittsburgh PA 15260 United States Computer Science Division UC Berkeley Berkeley CA 94720 United States Computer Science University of Pittsburgh Pittsburgh PA 15260 United States

ISBN: (纸本)9781450324748

Heart rate monitoring is widely used in clinical care, fitness training, and stress management. However, tracking individuals' heart rate faces two major challenges, namely equipment availability and user motivation. In this paper, we present a novel technique, LivePulse Games (LPG), to measure users' heart rate in real time by having them play casual games on unmodified mobile phones. With LPG, heart rate is calculated by detecting changes in transparency of users' fingertips via a mobile device's built-in camera. More importantly, LPG integrate users' camera lens covering actions as an essential control mechanism for game play, and detect heart rate implicitly from intermittent lens covering actions. We explore the design space and trade-offs of LPG through three rounds of interactive design and report the preliminary results from a 12-subject user study.

关键词： Serious games

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Building a Reinforcement Learning Environment from Limited Data to Optimize Teachable Robot Interventions 15

Building a Reinforcement Learning Environment from Limited D...

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15th International Conference on Educational Data Mining, EDM 2022

作者： Maidment, Tristan Yu, Mingzhi Lobczowski, Nikki Kovashka, Adriana Walker, Erin Litman, Diane Nokes-Malach, Timothy Intelligent Systems Program University of Pittsburgh United States Computer Science Department University of Pittsburgh United States Learning Research and Development Center University of Pittsburgh United States

ISBN: (纸本)9781733673631

Working collaboratively in groups can positively impact performance and student engagement. intelligent social agents can provide a source of personalized support for students, and their benefits likely extend to collaborative settings, but it is difficult to determine how these agents should interact with students. Reinforcement learning (RL) offers an opportunity for adapting the interactions between the social agent and the students to better support collaboration and learning. However, using RL in education with social agents typically involves training using real students. In this work, we train an RL agent in a high-quality simulated environment to learn how to improve students’ collaboration. Data was collected during a pilot study with dyads of students who worked together to tutor an intelligent teachable robot. We explore the process of building an environment from the data, training a policy, and the impact of the policy on different students, compared to various baselines. © 2022 Copyright is held by the author(s).

关键词： Reinforcement learning

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Application of Geometric Invariants in the Problem of Trajectory Tracking for UAVs

Application of Geometric Invariants in the Problem of Trajec...

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2022 International Siberian Conference on Control and Communications, SIBCON 2022

作者： Khachumov, Mikhail Ailamazyan Program Systems Institute of RAS Intelligent Control Laboratory Pereslavl-Zalessky Russia Federal Research Center 'Computer Science and Control' Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9781665476287

The study discusses approaches and an apply example of using geometric invariants in problems of control theory. An analytical review of modern papers in the field of trajectory tracking and path following control problems for unmanned aerial vehicles (UAVs) has been carried out. To increase the autonomy and ability to cope with uncertainties, it is proposed to use the theory of intelligent-geometric control. The problem of the trajectory tracking is considered in the framework of the pursuit-evasion differential game. Control invariants are considered in the form of ellipsoids, that can be used, for example, in pursuit-evasion problems. The task of determining the parameters of the pursuer's motion to meet the evader on an ellipsoid is solved. The dependence of the pursuer's speed on the parameters of the ellipse is shown. A modeling scheme is proposed aimed at joint application of optimal and intelligent control methods to solve trajectory tracking problem in difficult conditions. Simulations carried out in a disturbed environment on controlling the motion of a UAV group showed prospects of the proposed approach. © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Generalized evidence pre-propagated importance sampling for hybrid bayesian networks

Generalized evidence pre-propagated importance sampling for ...

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AAAI-07/IAAI-07 Proceedings: 22nd AAAI Conference on Artificial Intelligence and the 19th Innovative Applications of Artificial Intelligence Conference

作者： Yuan, Changhe Druzdzel, Marek J. Department of Computer Science and Engineering Mississippi State University Mississippi State MS 39762 Intelligent Systems Program School of Information Sciences University of Pittsburgh Pittsburgh PA 15260

ISBN: (纸本)1577353234

In this paper, we first provide a new theoretical understanding of the Evidence Pre-propagated Importance Sampling algorithm (EPIS-BN) (Yuan & Druzdzel 2003;2006b) and show that its importance function minimizes the KL-divergence between the function itself and the exact posterior probability distribution in Polytrees. We then generalize the method to deal with inference in general hybrid Bayesian networks consisting of deterministic equations and arbitrary probability distributions. Using a novel technique called soft arc reversal, the new algorithm can also handle evidential reasoning with observed deterministic variables. Copyright © 2007, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Bayesian networks

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Plan Management Issues for Cognitive Robotics: Project Overview

Plan Management Issues for Cognitive Robotics: Project Overv...

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1998 AAAI Fall Symposium

作者： Horty, John F. Pollack, Martha E. Philosophy Department Institute for Advanced Computer Studies University of Maryland College ParkMD20742 United States Department of Computer Science Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States

Progress in cognitive robotics can comeeither fromefforts to provide physical robots with more autonomy, or from efforts to make AI techniques more generally applicable to the needs of robotic agents. Weare conducting research on dynamic plan management, whichis an instance of the latter type of effort. In this paper, we provide an overview of our research project. Plan managementis the process (or set of related processes) by which a goal-directed agent coordinates, updates, and monitors its plans in response to ongoing changes in its environment. We discuss the main challenges in automating plan management, and then illustrate our approach by focusing on one plan management task: evaluating options for action in the context of existing plans. We also briefly describe the Plan Management Agent (PMA), a system weare building as a testbed for our plan management theories. Although PMA is not a robotic system, weare interested in also applying dynamicplan managementechniques to mobile robots, whichis whyweare participating in this symposium. © 1998, AAAI (***). All rights reserved.

关键词： Robotics

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The Model of Managing a Group of UAVs by a Single Operator for Tasks Requiring Increased Attention

The Model of Managing a Group of UAVs by a Single Operator f...

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2023 International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2023

作者： Khachumov, Mikhail Khachumov, Vyacheslav Intelligent Control Laboratory Ailamazyan Program Systems Institute of Ras Pereslavl-Zalessky Russia Rudn University Moscow Russia Federal Research Center Computer Science and Control of Ras Moscow Russia

ISBN: (纸本)9781665475952

The problem of optimizing the load on an operator of unmanned aerial vehicles (UAVs), which performs real-time tasks of researching and monitoring territories in an unstable environment is considered. Working load depends on the intensity of the information exchange between onboard and ground control stations and is determined by the agreed limit that leads to a limitation in the number of managed vehicles. As a mathematical model of the multi-functional control system, we use a closed-loop queuing system, which allows to establish coordination among its major elements (onboard computing complex, ground control station and operator) and reduce the loss of service requests. The latter is one of the main requirements to functioning of the system during emergencies. Of considerable interest are optimization statements for the problems of distribution of functions between elements and choosing the load modes. We gave formulations of some optimization problems in a general form and made appropriate conclusions. It is assumed that as an applied task, the problem of monitoring fire sites in forest areas can be considered, which requires increased attention from the operator to process information coming to the ground control station. © 2023 IEEE.

关键词： Distribution functions

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