Paper Group ANR 452
REMIX: Automated Exploration for Interactive Outlier Detection. Provable Self-Representation Based Outlier Detection in a Union of Subspaces. A hybrid spatial data mining approach based on fuzzy topological relations and MOSES evolutionary algorithm. Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to P …
REMIX: Automated Exploration for Interactive Outlier Detection
| Title | REMIX: Automated Exploration for Interactive Outlier Detection |
| Authors | Yanjie Fu, Charu Aggarwal, Srinivasan Parthasarathy, Deepak S. Turaga, Hui Xiong |
| Abstract | Outlier detection is the identification of points in a dataset that do not conform to the norm. Outlier detection is highly sensitive to the choice of the detection algorithm and the feature subspace used by the algorithm. Extracting domain-relevant insights from outliers needs systematic exploration of these choices since diverse outlier sets could lead to complementary insights. This challenge is especially acute in an interactive setting, where the choices must be explored in a time-constrained manner. In this work, we present REMIX, the first system to address the problem of outlier detection in an interactive setting. REMIX uses a novel mixed integer programming (MIP) formulation for automatically selecting and executing a diverse set of outlier detectors within a time limit. This formulation incorporates multiple aspects such as (i) an upper limit on the total execution time of detectors (ii) diversity in the space of algorithms and features, and (iii) meta-learning for evaluating the cost and utility of detectors. REMIX provides two distinct ways for the analyst to consume its results: (i) a partitioning of the detectors explored by REMIX into perspectives through low-rank non-negative matrix factorization; each perspective can be easily visualized as an intuitive heatmap of experiments versus outliers, and (ii) an ensembled set of outliers which combines outlier scores from all detectors. We demonstrate the benefits of REMIX through extensive empirical validation on real-world data. |
| Tasks | Meta-Learning, Outlier Detection |
| Published | 2017-05-17 |
| URL | http://arxiv.org/abs/1705.05986v1 |
| http://arxiv.org/pdf/1705.05986v1.pdf | |
| PWC | https://paperswithcode.com/paper/remix-automated-exploration-for-interactive |
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Provable Self-Representation Based Outlier Detection in a Union of Subspaces
| Title | Provable Self-Representation Based Outlier Detection in a Union of Subspaces |
| Authors | Chong You, Daniel P. Robinson, René Vidal |
| Abstract | Many computer vision tasks involve processing large amounts of data contaminated by outliers, which need to be detected and rejected. While outlier detection methods based on robust statistics have existed for decades, only recently have methods based on sparse and low-rank representation been developed along with guarantees of correct outlier detection when the inliers lie in one or more low-dimensional subspaces. This paper proposes a new outlier detection method that combines tools from sparse representation with random walks on a graph. By exploiting the property that data points can be expressed as sparse linear combinations of each other, we obtain an asymmetric affinity matrix among data points, which we use to construct a weighted directed graph. By defining a suitable Markov Chain from this graph, we establish a connection between inliers/outliers and essential/inessential states of the Markov chain, which allows us to detect outliers by using random walks. We provide a theoretical analysis that justifies the correctness of our method under geometric and connectivity assumptions. Experimental results on image databases demonstrate its superiority with respect to state-of-the-art sparse and low-rank outlier detection methods. |
| Tasks | Outlier Detection |
| Published | 2017-04-12 |
| URL | http://arxiv.org/abs/1704.03925v1 |
| http://arxiv.org/pdf/1704.03925v1.pdf | |
| PWC | https://paperswithcode.com/paper/provable-self-representation-based-outlier |
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A hybrid spatial data mining approach based on fuzzy topological relations and MOSES evolutionary algorithm
| Title | A hybrid spatial data mining approach based on fuzzy topological relations and MOSES evolutionary algorithm |
| Authors | Amir Hossein Goudarzi, Nasser Ghadiri |
| Abstract | Making high-quality decisions in strategic spatial planning is heavily dependent on extracting knowledge from vast amounts of data. Although many decision-making problems like developing urban areas require such perception and reasoning, existing methods in this field usually neglect the deep knowledge mined from geographic databases and are based on pure statistical methods. Due to the large volume of data gathered in spatial databases, and the uncertainty of spatial objects, mining association rules for high-level knowledge representation is a challenging task. Few algorithms manage geographical and non-geographical data using topological relations. In this paper, a novel approach for spatial data mining based on the MOSES evolutionary framework is presented which improves the classic genetic programming approach. A hybrid architecture called GGeo is proposed to apply the MOSES mining rules considering fuzzy topological relations from spatial data. The uncertainty and fuzziness aspects are addressed using an enriched model of topological relations by fuzzy region connection calculus. Moreover, to overcome the problem of time-consuming fuzzy topological relationships calculations, this a novel data pre-processing method is offered. GGeo analyses and learns from geographical and non-geographical data and uses topological and distance parameters, and returns a series of arithmetic-spatial formulas as classification rules. The proposed approach is resistant to noisy data, and all its stages run in parallel to increase speed. This approach may be used in different spatial data classification problems as well as representing an appropriate method of data analysis and economic policy making. |
| Tasks | Decision Making |
| Published | 2017-04-21 |
| URL | http://arxiv.org/abs/1704.06621v1 |
| http://arxiv.org/pdf/1704.06621v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-hybrid-spatial-data-mining-approach-based |
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Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games
| Title | Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games |
| Authors | Peng Peng, Ying Wen, Yaodong Yang, Quan Yuan, Zhenkun Tang, Haitao Long, Jun Wang |
| Abstract | Many artificial intelligence (AI) applications often require multiple intelligent agents to work in a collaborative effort. Efficient learning for intra-agent communication and coordination is an indispensable step towards general AI. In this paper, we take StarCraft combat game as a case study, where the task is to coordinate multiple agents as a team to defeat their enemies. To maintain a scalable yet effective communication protocol, we introduce a Multiagent Bidirectionally-Coordinated Network (BiCNet [‘bIknet]) with a vectorised extension of actor-critic formulation. We show that BiCNet can handle different types of combats with arbitrary numbers of AI agents for both sides. Our analysis demonstrates that without any supervisions such as human demonstrations or labelled data, BiCNet could learn various types of advanced coordination strategies that have been commonly used by experienced game players. In our experiments, we evaluate our approach against multiple baselines under different scenarios; it shows state-of-the-art performance, and possesses potential values for large-scale real-world applications. |
| Tasks | Starcraft |
| Published | 2017-03-29 |
| URL | http://arxiv.org/abs/1703.10069v4 |
| http://arxiv.org/pdf/1703.10069v4.pdf | |
| PWC | https://paperswithcode.com/paper/multiagent-bidirectionally-coordinated-nets |
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Adaptive active queue management controller for TCP communication networks using PSO-RBF models
| Title | Adaptive active queue management controller for TCP communication networks using PSO-RBF models |
| Authors | Mansour Sheikhan, Reza Shahnazi, Ehasn Hemmati |
| Abstract | Addressing performance degradations in end-to-end congestion control has been one of the most active research areas in the last decade. Active queue management (AQM) aims to improve the overall network throughput, while providing lower delay and reduce packet loss and improving network. The basic idea is to actively trigger packet dropping (or marking provided by explicit congestion notification (ECN)) before buffer overflow. Radial bias function (RBF)-based AQM controller is proposed in this paper. RBF controller is suitable as an AQM scheme to control congestion in TCP communication networks since it is nonlinear. Particle swarm optimization (PSO) algorithm is also employed to derive RBF parameters such that the integrated-absolute error (IAE) is minimized. Furthermore, in order to improve the robustness of RBF controller, an error-integral term is added to RBF equation. The results of the comparison with Drop Tail, adaptive random early detection (ARED), random exponential marking (REM), and proportional-integral (PI) controllers are presented. Integral-RBF has better performance not only in comparison with RBF but also with ARED, REM and PI controllers in the case of link utilization while packet loss rate is small. |
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| Published | 2017-11-16 |
| URL | http://arxiv.org/abs/1711.06356v1 |
| http://arxiv.org/pdf/1711.06356v1.pdf | |
| PWC | https://paperswithcode.com/paper/adaptive-active-queue-management-controller |
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Phylogenetics of Indo-European Language families via an Algebro-Geometric Analysis of their Syntactic Structures
| Title | Phylogenetics of Indo-European Language families via an Algebro-Geometric Analysis of their Syntactic Structures |
| Authors | Kevin Shu, Andrew Ortegaray, Robert Berwick, Matilde Marcolli |
| Abstract | Using Phylogenetic Algebraic Geometry, we analyze computationally the phylogenetic tree of subfamilies of the Indo-European language family, using data of syntactic structures. The two main sources of syntactic data are the SSWL database and Longobardi’s recent data of syntactic parameters. We compute phylogenetic invariants and likelihood functions for two sets of Germanic languages, a set of Romance languages, a set of Slavic languages and a set of early Indo-European languages, and we compare the results with what is known through historical linguistics. |
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| Published | 2017-12-05 |
| URL | https://arxiv.org/abs/1712.01719v2 |
| https://arxiv.org/pdf/1712.01719v2.pdf | |
| PWC | https://paperswithcode.com/paper/phylogenetics-of-indo-european-language |
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Generalised Discount Functions applied to a Monte-Carlo AImu Implementation
| Title | Generalised Discount Functions applied to a Monte-Carlo AImu Implementation |
| Authors | Sean Lamont, John Aslanides, Jan Leike, Marcus Hutter |
| Abstract | In recent years, work has been done to develop the theory of General Reinforcement Learning (GRL). However, there are few examples demonstrating these results in a concrete way. In particular, there are no examples demonstrating the known results regarding gener- alised discounting. We have added to the GRL simulation platform AIXIjs the functionality to assign an agent arbitrary discount functions, and an environment which can be used to determine the effect of discounting on an agent’s policy. Using this, we investigate how geometric, hyperbolic and power discounting affect an informed agent in a simple MDP. We experimentally reproduce a number of theoretical results, and discuss some related subtleties. It was found that the agent’s behaviour followed what is expected theoretically, assuming appropriate parameters were chosen for the Monte-Carlo Tree Search (MCTS) planning algorithm. |
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| Published | 2017-03-03 |
| URL | http://arxiv.org/abs/1703.01358v1 |
| http://arxiv.org/pdf/1703.01358v1.pdf | |
| PWC | https://paperswithcode.com/paper/generalised-discount-functions-applied-to-a |
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Structured Learning of Tree Potentials in CRF for Image Segmentation
| Title | Structured Learning of Tree Potentials in CRF for Image Segmentation |
| Authors | Fayao Liu, Guosheng Lin, Ruizhi Qiao, Chunhua Shen |
| Abstract | We propose a new approach to image segmentation, which exploits the advantages of both conditional random fields (CRFs) and decision trees. In the literature, the potential functions of CRFs are mostly defined as a linear combination of some pre-defined parametric models, and then methods like structured support vector machines (SSVMs) are applied to learn those linear coefficients. We instead formulate the unary and pairwise potentials as nonparametric forests—ensembles of decision trees, and learn the ensemble parameters and the trees in a unified optimization problem within the large-margin framework. In this fashion, we easily achieve nonlinear learning of potential functions on both unary and pairwise terms in CRFs. Moreover, we learn class-wise decision trees for each object that appears in the image. Due to the rich structure and flexibility of decision trees, our approach is powerful in modelling complex data likelihoods and label relationships. The resulting optimization problem is very challenging because it can have exponentially many variables and constraints. We show that this challenging optimization can be efficiently solved by combining a modified column generation and cutting-planes techniques. Experimental results on both binary (Graz-02, Weizmann horse, Oxford flower) and multi-class (MSRC-21, PASCAL VOC 2012) segmentation datasets demonstrate the power of the learned nonlinear nonparametric potentials. |
| Tasks | Semantic Segmentation |
| Published | 2017-03-26 |
| URL | http://arxiv.org/abs/1703.08764v1 |
| http://arxiv.org/pdf/1703.08764v1.pdf | |
| PWC | https://paperswithcode.com/paper/structured-learning-of-tree-potentials-in-crf |
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Predicting protein-protein interactions based on rotation of proteins in 3D-space
| Title | Predicting protein-protein interactions based on rotation of proteins in 3D-space |
| Authors | Samaneh Aghajanbaglo, Sobhan Moosavi, Maseud Rahgozar, Amir Rahimi |
| Abstract | Protein-Protein Interactions (PPIs) perform essential roles in biological functions. Although some experimental techniques have been developed to detect PPIs, they suffer from high false positive and high false negative rates. Consequently, efforts have been devoted during recent years to develop computational approaches to predict the interactions utilizing various sources of information. Therefore, a unique category of prediction approaches has been devised which is based on the protein sequence information. However, finding an appropriate feature encoding to characterize the sequence of proteins is a major challenge in such methods. In presented work, a sequence based method is proposed to predict protein-protein interactions using N-Gram encoding approaches to describe amino acids and a Relaxed Variable Kernel Density Estimator (RVKDE) as a machine learning tool. Moreover, since proteins can rotate in 3D-space, amino acid compositions have been considered with “undirected” property which leads to reduce dimensions of the vector space. The results show that our proposed method achieves the superiority of prediction performance with improving an F-measure of 2.5% on Human Protein Reference Dataset (HPRD). |
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| Published | 2017-12-22 |
| URL | http://arxiv.org/abs/1712.09332v1 |
| http://arxiv.org/pdf/1712.09332v1.pdf | |
| PWC | https://paperswithcode.com/paper/predicting-protein-protein-interactions-based |
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Prediction of Kidney Function from Biopsy Images Using Convolutional Neural Networks
| Title | Prediction of Kidney Function from Biopsy Images Using Convolutional Neural Networks |
| Authors | David Ledbetter, Long Ho, Kevin V Lemley |
| Abstract | A Convolutional Neural Network was used to predict kidney function in patients with chronic kidney disease from high-resolution digital pathology scans of their kidney biopsies. Kidney biopsies were taken from participants of the NEPTUNE study, a longitudinal cohort study whose goal is to set up infrastructure for observing the evolution of 3 forms of idiopathic nephrotic syndrome, including developing predictors for progression of kidney disease. The knowledge of future kidney function is desirable as it can identify high-risk patients and influence treatment decisions, reducing the likelihood of irreversible kidney decline. |
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| Published | 2017-02-06 |
| URL | http://arxiv.org/abs/1702.01816v1 |
| http://arxiv.org/pdf/1702.01816v1.pdf | |
| PWC | https://paperswithcode.com/paper/prediction-of-kidney-function-from-biopsy |
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Reliable Clustering of Bernoulli Mixture Models
| Title | Reliable Clustering of Bernoulli Mixture Models |
| Authors | Amir Najafi, Abolfazl Motahari, Hamid R. Rabiee |
| Abstract | A Bernoulli Mixture Model (BMM) is a finite mixture of random binary vectors with independent dimensions. The problem of clustering BMM data arises in a variety of real-world applications, ranging from population genetics to activity analysis in social networks. In this paper, we analyze the clusterability of BMMs from a theoretical perspective, when the number of clusters is unknown. In particular, we stipulate a set of conditions on the sample complexity and dimension of the model in order to guarantee the Probably Approximately Correct (PAC)-clusterability of a dataset. To the best of our knowledge, these findings are the first non-asymptotic bounds on the sample complexity of learning or clustering BMMs. |
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| Published | 2017-10-05 |
| URL | https://arxiv.org/abs/1710.02101v3 |
| https://arxiv.org/pdf/1710.02101v3.pdf | |
| PWC | https://paperswithcode.com/paper/reliable-clustering-of-bernoulli-mixture |
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Training Gaussian Mixture Models at Scale via Coresets
| Title | Training Gaussian Mixture Models at Scale via Coresets |
| Authors | Mario Lucic, Matthew Faulkner, Andreas Krause, Dan Feldman |
| Abstract | How can we train a statistical mixture model on a massive data set? In this work we show how to construct coresets for mixtures of Gaussians. A coreset is a weighted subset of the data, which guarantees that models fitting the coreset also provide a good fit for the original data set. We show that, perhaps surprisingly, Gaussian mixtures admit coresets of size polynomial in dimension and the number of mixture components, while being independent of the data set size. Hence, one can harness computationally intensive algorithms to compute a good approximation on a significantly smaller data set. More importantly, such coresets can be efficiently constructed both in distributed and streaming settings and do not impose restrictions on the data generating process. Our results rely on a novel reduction of statistical estimation to problems in computational geometry and new combinatorial complexity results for mixtures of Gaussians. Empirical evaluation on several real-world datasets suggests that our coreset-based approach enables significant reduction in training-time with negligible approximation error. |
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| Published | 2017-03-23 |
| URL | http://arxiv.org/abs/1703.08110v2 |
| http://arxiv.org/pdf/1703.08110v2.pdf | |
| PWC | https://paperswithcode.com/paper/training-gaussian-mixture-models-at-scale-via |
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Co-occurrence Filter
| Title | Co-occurrence Filter |
| Authors | Roy J Jevnisek, Shai Avidan |
| Abstract | Co-occurrence Filter (CoF) is a boundary preserving filter. It is based on the Bilateral Filter (BF) but instead of using a Gaussian on the range values to preserve edges it relies on a co-occurrence matrix. Pixel values that co-occur frequently in the image (i.e., inside textured regions) will have a high weight in the co-occurrence matrix. This, in turn, means that such pixel pairs will be averaged and hence smoothed, regardless of their intensity differences. On the other hand, pixel values that rarely co-occur (i.e., across texture boundaries) will have a low weight in the co-occurrence matrix. As a result, they will not be averaged and the boundary between them will be preserved. The CoF therefore extends the BF to deal with boundaries, not just edges. It learns co-occurrences directly from the image. We can achieve various filtering results by directing it to learn the co-occurrence matrix from a part of the image, or a different image. We give the definition of the filter, discuss how to use it with color images and show several use cases. |
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| Published | 2017-03-12 |
| URL | http://arxiv.org/abs/1703.04111v2 |
| http://arxiv.org/pdf/1703.04111v2.pdf | |
| PWC | https://paperswithcode.com/paper/co-occurrence-filter |
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Workflow Complexity for Collaborative Interactions: Where are the Metrics? – A Challenge
| Title | Workflow Complexity for Collaborative Interactions: Where are the Metrics? – A Challenge |
| Authors | Kartik Talamadupula, Biplav Srivastava, Jeffrey O. Kephart |
| Abstract | In this paper, we introduce the problem of denoting and deriving the complexity of workflows (plans, schedules) in collaborative, planner-assisted settings where humans and agents are trying to jointly solve a task. The interactions – and hence the workflows that connect the human and the agents – may differ according to the domain and the kind of agents. We adapt insights from prior work in human-agent teaming and workflow analysis to suggest metrics for workflow complexity. The main motivation behind this work is to highlight metrics for human comprehensibility of plans and schedules. The planning community has seen its fair share of work on the synthesis of plans that take diversity into account – what value do such plans hold if their generation is not guided at least in part by metrics that reflect the ease of engaging with and using those plans? |
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| Published | 2017-09-13 |
| URL | http://arxiv.org/abs/1709.04524v1 |
| http://arxiv.org/pdf/1709.04524v1.pdf | |
| PWC | https://paperswithcode.com/paper/workflow-complexity-for-collaborative |
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Value-Decomposition Networks For Cooperative Multi-Agent Learning
| Title | Value-Decomposition Networks For Cooperative Multi-Agent Learning |
| Authors | Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z. Leibo, Karl Tuyls, Thore Graepel |
| Abstract | We study the problem of cooperative multi-agent reinforcement learning with a single joint reward signal. This class of learning problems is difficult because of the often large combined action and observation spaces. In the fully centralized and decentralized approaches, we find the problem of spurious rewards and a phenomenon we call the “lazy agent” problem, which arises due to partial observability. We address these problems by training individual agents with a novel value decomposition network architecture, which learns to decompose the team value function into agent-wise value functions. We perform an experimental evaluation across a range of partially-observable multi-agent domains and show that learning such value-decompositions leads to superior results, in particular when combined with weight sharing, role information and information channels. |
| Tasks | Multi-agent Reinforcement Learning |
| Published | 2017-06-16 |
| URL | http://arxiv.org/abs/1706.05296v1 |
| http://arxiv.org/pdf/1706.05296v1.pdf | |
| PWC | https://paperswithcode.com/paper/value-decomposition-networks-for-cooperative |
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