April 1, 2020

3194 words 15 mins read

Paper Group ANR 412

Ising-based Consensus Clustering on Specialized Hardware. Restore from Restored: Single Image Denoising with Pseudo Clean Image. Advaita: Bug Duplicity Detection System. Algebraic and Analytic Approaches for Parameter Learning in Mixture Models. Beyond without Forgetting: Multi-Task Learning for Classification with Disjoint Datasets. A machine lear …

Ising-based Consensus Clustering on Specialized Hardware


Title	Ising-based Consensus Clustering on Specialized Hardware
Authors	Eldan Cohen, Avradip Mandal, Hayato Ushijima-Mwesigwa, Arnab Roy
Abstract	The emergence of specialized optimization hardware such as CMOS annealers and adiabatic quantum computers carries the promise of solving hard combinatorial optimization problems more efficiently in hardware. Recent work has focused on formulating different combinatorial optimization problems as Ising models, the core mathematical abstraction used by a large number of these hardware platforms, and evaluating the performance of these models when solved on specialized hardware. An interesting area of application is data mining, where combinatorial optimization problems underlie many core tasks. In this work, we focus on consensus clustering (clustering aggregation), an important combinatorial problem that has received much attention over the last two decades. We present two Ising models for consensus clustering and evaluate them using the Fujitsu Digital Annealer, a quantum-inspired CMOS annealer. Our empirical evaluation shows that our approach outperforms existing techniques and is a promising direction for future research.
Tasks	Combinatorial Optimization
Published	2020-03-04
URL	https://arxiv.org/abs/2003.01887v1
PDF	https://arxiv.org/pdf/2003.01887v1.pdf
PWC	https://paperswithcode.com/paper/ising-based-consensus-clustering-on
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Restore from Restored: Single Image Denoising with Pseudo Clean Image


Title	Restore from Restored: Single Image Denoising with Pseudo Clean Image
Authors	Seunghwan Lee, Donghyeon Cho, Jiwon Kim, Tae Hyun Kim
Abstract	Under certain statistical assumptions of noise (e.g., zero-mean noise), recent self-supervised approaches for denoising have been introduced to learn network parameters without ground-truth clean images, and these methods can restore an image by exploiting information available from the given input (i.e., internal statistics) at test time. However, self-supervised methods are not yet properly combined with conventional supervised denoising methods which train the denoising networks with a large number of external training images. Thus, we propose a new denoising approach that can greatly outperform the state-of-the-art supervised denoising methods by adapting (fine-tuning) their network parameters to the given specific input through self-supervision without changing the fully original network architectures. We demonstrate that the proposed method can be easily employed with state-of-the-art denoising networks without additional parameters, and achieve state-of-the-art performance on numerous denoising benchmark datasets.
Tasks	Denoising, Image Denoising
Published	2020-03-09
URL	https://arxiv.org/abs/2003.04721v1
PDF	https://arxiv.org/pdf/2003.04721v1.pdf
PWC	https://paperswithcode.com/paper/restore-from-restored-single-image-denoising
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Advaita: Bug Duplicity Detection System


Title	Advaita: Bug Duplicity Detection System
Authors	Amit Kumar, Manohar Madanu, Hari Prakash, Lalitha Jonnavithula, Srinivasa Rao Aravilli
Abstract	Bugs are prevalent in software development. To improve software quality, bugs are filed using a bug tracking system. Properties of a reported bug would consist of a headline, description, project, product, component that is affected by the bug and the severity of the bug. Duplicate bugs rate (% of duplicate bugs) are in the range from single digit (1 to 9%) to double digits (40%) based on the product maturity , size of the code and number of engineers working on the project. Duplicate bugs range are between 9% to 39% in some of the open source projects like Eclipse, Firefox etc. Detection of duplicity deals with identifying whether any two bugs convey the same meaning. This detection of duplicates helps in de-duplication. Detecting duplicate bugs help reduce triaging efforts and saves time for developers in fixing the issues. Traditional natural language processing techniques are less accurate in identifying similarity between sentences. Using the bug data present in a bug tracking system, various approaches were explored including several machine learning algorithms, to obtain a viable approach that can identify duplicate bugs, given a pair of sentences(i.e. the respective bug descriptions). This approach considers multiple sets of features viz. basic text statistical features, semantic features and contextual features. These features are extracted from the headline, description and component and are subsequently used to train a classification algorithm.
Tasks
Published	2020-01-24
URL	https://arxiv.org/abs/2001.10376v1
PDF	https://arxiv.org/pdf/2001.10376v1.pdf
PWC	https://paperswithcode.com/paper/advaita-bug-duplicity-detection-system
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Algebraic and Analytic Approaches for Parameter Learning in Mixture Models


Title	Algebraic and Analytic Approaches for Parameter Learning in Mixture Models
Authors	Akshay Krishnamurthy, Arya Mazumdar, Andrew McGregor, Soumyabrata Pal
Abstract	We present two different approaches for parameter learning in several mixture models in one dimension. Our first approach uses complex-analytic methods and applies to Gaussian mixtures with shared variance, binomial mixtures with shared success probability, and Poisson mixtures, among others. An example result is that $\exp(O(N^{1/3}))$ samples suffice to exactly learn a mixture of $k<N$ Poisson distributions, each with integral rate parameters bounded by $N$. Our second approach uses algebraic and combinatorial tools and applies to binomial mixtures with shared trial parameter $N$ and differing success parameters, as well as to mixtures of geometric distributions. Again, as an example, for binomial mixtures with $k$ components and success parameters discretized to resolution $\epsilon$, $O(k^2(N/\epsilon)^{8/\sqrt{\epsilon}})$ samples suffice to exactly recover the parameters. For some of these distributions, our results represent the first guarantees for parameter estimation.
Tasks
Published	2020-01-19
URL	https://arxiv.org/abs/2001.06776v1
PDF	https://arxiv.org/pdf/2001.06776v1.pdf
PWC	https://paperswithcode.com/paper/algebraic-and-analytic-approaches-for
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Beyond without Forgetting: Multi-Task Learning for Classification with Disjoint Datasets


Title	Beyond without Forgetting: Multi-Task Learning for Classification with Disjoint Datasets
Authors	Yan Hong, Li Niu, Jianfu Zhang, Liqing Zhang
Abstract	Multi-task Learning (MTL) for classification with disjoint datasets aims to explore MTL when one task only has one labeled dataset. In existing methods, for each task, the unlabeled datasets are not fully exploited to facilitate this task. Inspired by semi-supervised learning, we use unlabeled datasets with pseudo labels to facilitate each task. However, there are two major issues: 1) the pseudo labels are very noisy; 2) the unlabeled datasets and the labeled dataset for each task has considerable data distribution mismatch. To address these issues, we propose our MTL with Selective Augmentation (MTL-SA) method to select the training samples in unlabeled datasets with confident pseudo labels and close data distribution to the labeled dataset. Then, we use the selected training samples to add information and use the remaining training samples to preserve information. Extensive experiments on face-centric and human-centric applications demonstrate the effectiveness of our MTL-SA method.
Tasks	Multi-Task Learning
Published	2020-03-15
URL	https://arxiv.org/abs/2003.06746v1
PDF	https://arxiv.org/pdf/2003.06746v1.pdf
PWC	https://paperswithcode.com/paper/beyond-without-forgetting-multi-task-learning
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A machine learning approach to investigate regulatory control circuits in bacterial metabolic pathways


Title	A machine learning approach to investigate regulatory control circuits in bacterial metabolic pathways
Authors	Francesco Bardozzo, Pietro Lio’, Roberto Tagliaferri
Abstract	In this work, a machine learning approach for identifying the multi-omics metabolic regulatory control circuits inside the pathways is described. Therefore, the identification of bacterial metabolic pathways that are more regulated than others in term of their multi-omics follows from the analysis of these circuits . This is a consequence of the alternation of the omic values of codon usage and protein abundance along with the circuits. In this work, the E.Coli’s Glycolysis and its multi-omic circuit features are shown as an example.
Tasks
Published	2020-01-13
URL	https://arxiv.org/abs/2001.04794v1
PDF	https://arxiv.org/pdf/2001.04794v1.pdf
PWC	https://paperswithcode.com/paper/a-machine-learning-approach-to-investigate
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Maximal Closed Set and Half-Space Separations in Finite Closure Systems


Title	Maximal Closed Set and Half-Space Separations in Finite Closure Systems
Authors	Florian Seiffarth, Tamas Horvath, Stefan Wrobel
Abstract	We investigate some algorithmic properties of closed set and half-space separation in abstract closure systems. Assuming that the underlying closure system is finite and given by the corresponding closure operator, we show that the half-space separation problem is NP-complete. In contrast, for the relaxed problem of maximal closed set separation we give a greedy algorithm using linear number of queries (i.e., closure operator calls) and show that this bound is sharp. For a second direction to overcome the negative result above, we consider Kakutani closure systems and prove that they are algorithmically characterized by the greedy algorithm. As one of the major potential application fields, we then focus on Kakutani closure systems over graphs and generalize a fundamental characterization result based on the Pasch axiom to graph structured partitioning of finite sets. In addition, we give a sufficient condition for Kakutani closure systems over graphs in terms of graph minors. For a second application field, we consider closure systems over finite lattices, present an adaptation of the generic greedy algorithm to this kind of closure systems, and consider two potential applications. We show that for the special case of subset lattices over finite ground sets, e.g., for formal concept lattices, its query complexity is only logarithmic in the size of the lattice. The second application is concerned with finite subsumption lattices in inductive logic programming. We show that our method for separating two sets of first-order clauses from each other extends the traditional approach based on least general generalizations of first-order clauses. Though our primary focus is on the generality of the results obtained, we experimentally demonstrate the practical usefulness of the greedy algorithm on binary classification problems in Kakutani and non-Kakutani closure systems.
Tasks
Published	2020-01-13
URL	https://arxiv.org/abs/2001.04417v1
PDF	https://arxiv.org/pdf/2001.04417v1.pdf
PWC	https://paperswithcode.com/paper/maximal-closed-set-and-half-space-separations
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MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection


Title	MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection
Authors	Mi Luo, Fei Chen, Pengxiang Cheng, Zhenhua Dong, Xiuqiang He, Jiashi Feng, Zhenguo Li
Abstract	Recommender systems often face heterogeneous datasets containing highly personalized historical data of users, where no single model could give the best recommendation for every user. We observe this ubiquitous phenomenon on both public and private datasets and address the model selection problem in pursuit of optimizing the quality of recommendation for each user. We propose a meta-learning framework to facilitate user-level adaptive model selection in recommender systems. In this framework, a collection of recommenders is trained with data from all users, on top of which a model selector is trained via meta-learning to select the best single model for each user with the user-specific historical data. We conduct extensive experiments on two public datasets and a real-world production dataset, demonstrating that our proposed framework achieves improvements over single model baselines and sample-level model selector in terms of AUC and LogLoss. In particular, the improvements may lead to huge profit gain when deployed in online recommender systems.
Tasks	Meta-Learning, Model Selection, Recommendation Systems
Published	2020-01-22
URL	https://arxiv.org/abs/2001.10378v2
PDF	https://arxiv.org/pdf/2001.10378v2.pdf
PWC	https://paperswithcode.com/paper/metaselector-meta-learning-for-recommendation
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Solving Billion-Scale Knapsack Problems


Title	Solving Billion-Scale Knapsack Problems
Authors	Xingwen Zhang, Feng Qi, Zhigang Hua, Shuang Yang
Abstract	Knapsack problems (KPs) are common in industry, but solving KPs is known to be NP-hard and has been tractable only at a relatively small scale. This paper examines KPs in a slightly generalized form and shows that they can be solved nearly optimally at scale via distributed algorithms. The proposed approach can be implemented fairly easily with off-the-shelf distributed computing frameworks (e.g. MPI, Hadoop, Spark). As an example, our implementation leads to one of the most efficient KP solvers known to date – capable to solve KPs at an unprecedented scale (e.g., KPs with 1 billion decision variables and 1 billion constraints can be solved within 1 hour). The system has been deployed to production and called on a daily basis, yielding significant business impacts at Ant Financial.
Tasks
Published	2020-02-02
URL	https://arxiv.org/abs/2002.00352v1
PDF	https://arxiv.org/pdf/2002.00352v1.pdf
PWC	https://paperswithcode.com/paper/solving-billion-scale-knapsack-problems
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Peri-Diagnostic Decision Support Through Cost-Efficient Feature Acquisition at Test-Time


Title	Peri-Diagnostic Decision Support Through Cost-Efficient Feature Acquisition at Test-Time
Authors	Gerome Vivar, Kamilia Mullakaeva, Andreas Zwergal, Nassir Navab, Seyed-Ahmad Ahmadi
Abstract	Computer-aided diagnosis (CADx) algorithms in medicine provide patient-specific decision support for physicians. These algorithms are usually applied after full acquisition of high-dimensional multimodal examination data, and often assume feature-completeness. This, however, is rarely the case due to examination costs, invasiveness, or a lack of indication. A sub-problem in CADx, which to our knowledge has received very little attention among the CADx community so far, is to guide the physician during the entire peri-diagnostic workflow, including the acquisition stage. We model the following question, asked from a physician’s perspective: ‘‘Given the evidence collected so far, which examination should I perform next, in order to achieve the most accurate and efficient diagnostic prediction?''. In this work, we propose a novel approach which is enticingly simple: use dropout at the input layer, and integrated gradients of the trained network at test-time to attribute feature importance dynamically. We validate and explain the effectiveness of our proposed approach using two public medical and two synthetic datasets. Results show that our proposed approach is more cost- and feature-efficient than prior approaches and achieves a higher overall accuracy. This directly translates to less unnecessary examinations for patients, and a quicker, less costly and more accurate decision support for the physician.
Tasks	Feature Importance
Published	2020-03-31
URL	https://arxiv.org/abs/2003.14127v1
PDF	https://arxiv.org/pdf/2003.14127v1.pdf
PWC	https://paperswithcode.com/paper/peri-diagnostic-decision-support-through-cost
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From unbiased MDI Feature Importance to Explainable AI for Trees


Title	From unbiased MDI Feature Importance to Explainable AI for Trees
Authors	Markus Loecher
Abstract	We attempt to give a unifying view of the various recent attempts to (i) improve the interpretability of tree-based models and (ii) debias the the default variable-importance measure in random Forests, Gini importance. In particular, we demonstrate a common thread among the out-of-bag based bias correction methods and their connection to local explanation for trees. In addition, we point out a bias caused by the inclusion of inbag data in the newly developed explainable AI for trees algorithms.
Tasks	Feature Importance
Published	2020-03-26
URL	https://arxiv.org/abs/2003.12043v1
PDF	https://arxiv.org/pdf/2003.12043v1.pdf
PWC	https://paperswithcode.com/paper/from-unbiased-mdi-feature-importance-to
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Policy-Aware Model Learning for Policy Gradient Methods


Title	Policy-Aware Model Learning for Policy Gradient Methods
Authors	Romina Abachi, Mohammad Ghavamzadeh, Amir-massoud Farahmand
Abstract	This paper considers the problem of learning a model in model-based reinforcement learning (MBRL). We examine how the planning module of an MBRL algorithm uses the model, and propose that the model learning module should incorporate the way the planner is going to use the model. This is in contrast to conventional model learning approaches, such as those based on maximum likelihood estimate, that learn a predictive model of the environment without explicitly considering the interaction of the model and the planner. We focus on policy gradient type of planning algorithms and derive new loss functions for model learning that incorporate how the planner uses the model. We call this approach Policy-Aware Model Learning (PAML). We theoretically analyze a generic model-based policy gradient algorithm and provide a convergence guarantee for the optimized policy. We also empirically evaluate PAML on some benchmark problems, showing promising results.
Tasks	Policy Gradient Methods
Published	2020-02-28
URL	https://arxiv.org/abs/2003.00030v1
PDF	https://arxiv.org/pdf/2003.00030v1.pdf
PWC	https://paperswithcode.com/paper/policy-aware-model-learning-for-policy
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TRACER: A Framework for Facilitating Accurate and Interpretable Analytics for High Stakes Applications


Title	TRACER: A Framework for Facilitating Accurate and Interpretable Analytics for High Stakes Applications
Authors	Kaiping Zheng, Shaofeng Cai, Horng Ruey Chua, Wei Wang, Kee Yuan Ngiam, Beng Chin Ooi
Abstract	In high stakes applications such as healthcare and finance analytics, the interpretability of predictive models is required and necessary for domain practitioners to trust the predictions. Traditional machine learning models, e.g., logistic regression (LR), are easy to interpret in nature. However, many of these models aggregate time-series data without considering the temporal correlations and variations. Therefore, their performance cannot match up to recurrent neural network (RNN) based models, which are nonetheless difficult to interpret. In this paper, we propose a general framework TRACER to facilitate accurate and interpretable predictions, with a novel model TITV devised for healthcare analytics and other high stakes applications such as financial investment and risk management. Different from LR and other existing RNN-based models, TITV is designed to capture both the time-invariant and the time-variant feature importance using a feature-wise transformation subnetwork and a self-attention subnetwork, for the feature influence shared over the entire time series and the time-related importance respectively. Healthcare analytics is adopted as a driving use case, and we note that the proposed TRACER is also applicable to other domains, e.g., fintech. We evaluate the accuracy of TRACER extensively in two real-world hospital datasets, and our doctors/clinicians further validate the interpretability of TRACER in both the patient level and the feature level. Besides, TRACER is also validated in a high stakes financial application and a critical temperature forecasting application. The experimental results confirm that TRACER facilitates both accurate and interpretable analytics for high stakes applications.
Tasks	Feature Importance, Time Series
Published	2020-03-24
URL	https://arxiv.org/abs/2003.12012v1
PDF	https://arxiv.org/pdf/2003.12012v1.pdf
PWC	https://paperswithcode.com/paper/tracer-a-framework-for-facilitating-accurate
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Framework

Information Foraging for Enhancing Implicit Feedback in Content-based Image Recommendation


Title	Information Foraging for Enhancing Implicit Feedback in Content-based Image Recommendation
Authors	Amit Kumar Jaiswal, Haiming Liu, Ingo Frommholz
Abstract	User implicit feedback plays an important role in recommender systems. However, finding implicit features is a tedious task. This paper aims to identify users’ preferences through implicit behavioural signals for image recommendation based on the Information Scent Model of Information Foraging Theory. In the first part, we hypothesise that the users’ perception is improved with visual cues in the images as behavioural signals that provide users’ information scent during information seeking. We designed a content-based image recommendation system to explore which image attributes (i.e., visual cues or bookmarks) help users find their desired image. We found that users prefer recommendations predicated by visual cues and therefore consider the visual cues as good information scent for their information seeking. In the second part, we investigated if visual cues in the images together with the images itself can be better perceived by the users than each of them on its own. We evaluated the information scent artifacts in image recommendation on the Pinterest image collection and the WikiArt dataset. We find our proposed image recommendation system supports the implicit signals through Information Foraging explanation of the information scent model.
Tasks	Recommendation Systems
Published	2020-01-19
URL	https://arxiv.org/abs/2001.06765v1
PDF	https://arxiv.org/pdf/2001.06765v1.pdf
PWC	https://paperswithcode.com/paper/information-foraging-for-enhancing-implicit
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EEV Dataset: Predicting Expressions Evoked by Diverse Videos


Title	EEV Dataset: Predicting Expressions Evoked by Diverse Videos
Authors	Jennifer J. Sun, Ting Liu, Alan S. Cowen, Florian Schroff, Hartwig Adam, Gautam Prasad
Abstract	When we watch videos, the visual and auditory information we experience can evoke a range of affective responses. The ability to automatically predict evoked affect from videos can help recommendation systems and social machines better interact with their users. Here, we introduce the Evoked Expressions in Videos (EEV) dataset, a large-scale dataset for studying viewer responses to videos based on their facial expressions. The dataset consists of a total of 4.8 million annotations of viewer facial reactions to 18,541 videos. We use a publicly available video corpus to obtain a diverse set of video content. The training split is fully machine-annotated, while the validation and test splits have both human and machine annotations. We verify the performance of our machine annotations with human raters to have an average precision of 73.3%. We establish baseline performance on the EEV dataset using an existing multimodal recurrent model. Our results show that affective information can be learned from EEV, but with a MAP of 20.32%, there is potential for improvement. This gap motivates the need for new approaches for understanding affective content. Our transfer learning experiments show an improvement in performance on the LIRIS-ACCEDE video dataset when pre-trained on EEV. We hope that the size and diversity of the EEV dataset will encourage further explorations in video understanding and affective computing.
Tasks	Recommendation Systems, Transfer Learning, Video Understanding
Published	2020-01-15
URL	https://arxiv.org/abs/2001.05488v1
PDF	https://arxiv.org/pdf/2001.05488v1.pdf
PWC	https://paperswithcode.com/paper/eev-dataset-predicting-expressions-evoked-by
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