Paper Group ANR 195
Oracle-Efficient Online Learning and Auction Design. Multi-Modality Fusion based on Consensus-Voting and 3D Convolution for Isolated Gesture Recognition. Formulas for Counting the Sizes of Markov Equivalence Classes of Directed Acyclic Graphs. Learning to superoptimize programs. UAV-based Autonomous Image Acquisition with Multi-View Stereo Quality …
Oracle-Efficient Online Learning and Auction Design
| Title | Oracle-Efficient Online Learning and Auction Design |
| Authors | Miroslav Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan |
| Abstract | We consider the design of computationally efficient online learning algorithms in an adversarial setting in which the learner has access to an offline optimization oracle. We present an algorithm called Generalized Follow-the-Perturbed-Leader and provide conditions under which it is oracle-efficient while achieving vanishing regret. Our results make significant progress on an open problem raised by Hazan and Koren, who showed that oracle-efficient algorithms do not exist in general and asked whether one can identify properties under which oracle-efficient online learning may be possible. Our auction-design framework considers an auctioneer learning an optimal auction for a sequence of adversarially selected valuations with the goal of achieving revenue that is almost as good as the optimal auction in hindsight, among a class of auctions. We give oracle-efficient learning results for: (1) VCG auctions with bidder-specific reserves in single-parameter settings, (2) envy-free item pricing in multi-item auctions, and (3) s-level auctions of Morgenstern and Roughgarden for single-item settings. The last result leads to an approximation of the overall optimal Myerson auction when bidders’ valuations are drawn according to a fast-mixing Markov process, extending prior work that only gave such guarantees for the i.i.d. setting. Finally, we derive various extensions, including: (1) oracle-efficient algorithms for the contextual learning setting in which the learner has access to side information (such as bidder demographics), (2) learning with approximate oracles such as those based on Maximal-in-Range algorithms, and (3) no-regret bidding in simultaneous auctions, resolving an open problem of Daskalakis and Syrgkanis. |
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| Published | 2016-11-05 |
| URL | https://arxiv.org/abs/1611.01688v3 |
| https://arxiv.org/pdf/1611.01688v3.pdf | |
| PWC | https://paperswithcode.com/paper/oracle-efficient-online-learning-and-auction |
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Multi-Modality Fusion based on Consensus-Voting and 3D Convolution for Isolated Gesture Recognition
| Title | Multi-Modality Fusion based on Consensus-Voting and 3D Convolution for Isolated Gesture Recognition |
| Authors | Jiali Duan, Shuai Zhou, Jun Wan, Xiaoyuan Guo, Stan Z. Li |
| Abstract | Recently, the popularity of depth-sensors such as Kinect has made depth videos easily available while its advantages have not been fully exploited. This paper investigates, for gesture recognition, to explore the spatial and temporal information complementarily embedded in RGB and depth sequences. We propose a convolutional twostream consensus voting network (2SCVN) which explicitly models both the short-term and long-term structure of the RGB sequences. To alleviate distractions from background, a 3d depth-saliency ConvNet stream (3DDSN) is aggregated in parallel to identify subtle motion characteristics. These two components in an unified framework significantly improve the recognition accuracy. On the challenging Chalearn IsoGD benchmark, our proposed method outperforms the first place on the leader-board by a large margin (10.29%) while also achieving the best result on RGBD-HuDaAct dataset (96.74%). Both quantitative experiments and qualitative analysis shows the effectiveness of our proposed framework and codes will be released to facilitate future research. |
| Tasks | Gesture Recognition |
| Published | 2016-11-21 |
| URL | http://arxiv.org/abs/1611.06689v2 |
| http://arxiv.org/pdf/1611.06689v2.pdf | |
| PWC | https://paperswithcode.com/paper/multi-modality-fusion-based-on-consensus |
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Formulas for Counting the Sizes of Markov Equivalence Classes of Directed Acyclic Graphs
| Title | Formulas for Counting the Sizes of Markov Equivalence Classes of Directed Acyclic Graphs |
| Authors | Yangbo He, Bin Yu |
| Abstract | The sizes of Markov equivalence classes of directed acyclic graphs play important roles in measuring the uncertainty and complexity in causal learning. A Markov equivalence class can be represented by an essential graph and its undirected subgraphs determine the size of the class. In this paper, we develop a method to derive the formulas for counting the sizes of Markov equivalence classes. We first introduce a new concept of core graph. The size of a Markov equivalence class of interest is a polynomial of the number of vertices given its core graph. Then, we discuss the recursive and explicit formula of the polynomial, and provide an algorithm to derive the size formula via symbolic computation for any given core graph. The proposed size formula derivation sheds light on the relationships between the size of a Markov equivalence class and its representation graph, and makes size counting efficient, even when the essential graphs contain non-sparse undirected subgraphs. |
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| Published | 2016-10-23 |
| URL | http://arxiv.org/abs/1610.07921v1 |
| http://arxiv.org/pdf/1610.07921v1.pdf | |
| PWC | https://paperswithcode.com/paper/formulas-for-counting-the-sizes-of-markov |
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Learning to superoptimize programs
| Title | Learning to superoptimize programs |
| Authors | Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, Pushmeet Kohli |
| Abstract | Code super-optimization is the task of transforming any given program to a more efficient version while preserving its input-output behaviour. In some sense, it is similar to the paraphrase problem from natural language processing where the intention is to change the syntax of an utterance without changing its semantics. Code-optimization has been the subject of years of research that has resulted in the development of rule-based transformation strategies that are used by compilers. More recently, however, a class of stochastic search based methods have been shown to outperform these strategies. This approach involves repeated sampling of modifications to the program from a proposal distribution, which are accepted or rejected based on whether they preserve correctness, and the improvement they achieve. These methods, however, neither learn from past behaviour nor do they try to leverage the semantics of the program under consideration. Motivated by this observation, we present a novel learning based approach for code super-optimization. Intuitively, our method works by learning the proposal distribution using unbiased estimators of the gradient of the expected improvement. Experiments on benchmarks comprising of automatically generated as well as existing (“Hacker’s Delight”) programs show that the proposed method is able to significantly outperform state of the art approaches for code super-optimization. |
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| Published | 2016-11-06 |
| URL | http://arxiv.org/abs/1611.01787v3 |
| http://arxiv.org/pdf/1611.01787v3.pdf | |
| PWC | https://paperswithcode.com/paper/learning-to-superoptimize-programs |
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UAV-based Autonomous Image Acquisition with Multi-View Stereo Quality Assurance by Confidence Prediction
| Title | UAV-based Autonomous Image Acquisition with Multi-View Stereo Quality Assurance by Confidence Prediction |
| Authors | Christian Mostegel, Markus Rumpler, Friedrich Fraundorfer, Horst Bischof |
| Abstract | In this paper we present an autonomous system for acquiring close-range high-resolution images that maximize the quality of a later-on 3D reconstruction with respect to coverage, ground resolution and 3D uncertainty. In contrast to previous work, our system uses the already acquired images to predict the confidence in the output of a dense multi-view stereo approach without executing it. This confidence encodes the likelihood of a successful reconstruction with respect to the observed scene and potential camera constellations. Our prediction module runs in real-time and can be trained without any externally recorded ground truth. We use the confidence prediction for on-site quality assurance and for planning further views that are tailored for a specific multi-view stereo approach with respect to the given scene. We demonstrate the capabilities of our approach with an autonomous Unmanned Aerial Vehicle (UAV) in a challenging outdoor scenario. |
| Tasks | 3D Reconstruction |
| Published | 2016-05-06 |
| URL | http://arxiv.org/abs/1605.01923v1 |
| http://arxiv.org/pdf/1605.01923v1.pdf | |
| PWC | https://paperswithcode.com/paper/uav-based-autonomous-image-acquisition-with |
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How many faces can be recognized? Performance extrapolation for multi-class classification
| Title | How many faces can be recognized? Performance extrapolation for multi-class classification |
| Authors | Charles Y. Zheng, Rakesh Achanta, Yuval Benjamini |
| Abstract | The difficulty of multi-class classification generally increases with the number of classes. Using data from a subset of the classes, can we predict how well a classifier will scale with an increased number of classes? Under the assumption that the classes are sampled exchangeably, and under the assumption that the classifier is generative (e.g. QDA or Naive Bayes), we show that the expected accuracy when the classifier is trained on $k$ classes is the $k-1$st moment of a \emph{conditional accuracy distribution}, which can be estimated from data. This provides the theoretical foundation for performance extrapolation based on pseudolikelihood, unbiased estimation, and high-dimensional asymptotics. We investigate the robustness of our methods to non-generative classifiers in simulations and one optical character recognition example. |
| Tasks | Optical Character Recognition |
| Published | 2016-06-16 |
| URL | http://arxiv.org/abs/1606.05228v1 |
| http://arxiv.org/pdf/1606.05228v1.pdf | |
| PWC | https://paperswithcode.com/paper/how-many-faces-can-be-recognized-performance |
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A DEMATEL-Based Completion Method for Incomplete Pairwise Comparison Matrix in AHP
| Title | A DEMATEL-Based Completion Method for Incomplete Pairwise Comparison Matrix in AHP |
| Authors | Xinyi Zhou, Yong Hu, Yong Deng, Felix T. S. Chan, Alessio Ishizak |
| Abstract | Pairwise comparison matrix as a crucial component of AHP, presents the prefer- ence relations among alternatives. However, in many cases, the pairwise comparison matrix is difficult to complete, which obstructs the subsequent operations of the clas- sical AHP. In this paper, based on DEMATEL which has ability to derive the total relation matrix from direct relation matrix, a new completion method for incomplete pairwise comparison matrix is proposed. The proposed method provides a new per- spective to estimate the missing values with explicit physical meaning. Besides, the proposed method has low computational cost. This promising method has a wide application in multi-criteria decision-making. |
| Tasks | Decision Making |
| Published | 2016-07-23 |
| URL | http://arxiv.org/abs/1607.08116v1 |
| http://arxiv.org/pdf/1607.08116v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-dematel-based-completion-method-for |
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Gaussian Process Domain Experts for Model Adaptation in Facial Behavior Analysis
| Title | Gaussian Process Domain Experts for Model Adaptation in Facial Behavior Analysis |
| Authors | Stefanos Eleftheriadis, Ognjen Rudovic, Marc P. Deisenroth, Maja Pantic |
| Abstract | We present a novel approach for supervised domain adaptation that is based upon the probabilistic framework of Gaussian processes (GPs). Specifically, we introduce domain-specific GPs as local experts for facial expression classification from face images. The adaptation of the classifier is facilitated in probabilistic fashion by conditioning the target expert on multiple source experts. Furthermore, in contrast to existing adaptation approaches, we also learn a target expert from available target data solely. Then, a single and confident classifier is obtained by combining the predictions from multiple experts based on their confidence. Learning of the model is efficient and requires no retraining/reweighting of the source classifiers. We evaluate the proposed approach on two publicly available datasets for multi-class (MultiPIE) and multi-label (DISFA) facial expression classification. To this end, we perform adaptation of two contextual factors: ‘where’ (view) and ‘who’ (subject). We show in our experiments that the proposed approach consistently outperforms both source and target classifiers, while using as few as 30 target examples. It also outperforms the state-of-the-art approaches for supervised domain adaptation. |
| Tasks | Domain Adaptation, Gaussian Processes |
| Published | 2016-04-11 |
| URL | http://arxiv.org/abs/1604.02917v2 |
| http://arxiv.org/pdf/1604.02917v2.pdf | |
| PWC | https://paperswithcode.com/paper/gaussian-process-domain-experts-for-model |
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Exact Simulation of Noncircular or Improper Complex-Valued Stationary Gaussian Processes using Circulant Embedding
| Title | Exact Simulation of Noncircular or Improper Complex-Valued Stationary Gaussian Processes using Circulant Embedding |
| Authors | Adam M. Sykulski, Donald B. Percival |
| Abstract | This paper provides an algorithm for simulating improper (or noncircular) complex-valued stationary Gaussian processes. The technique utilizes recently developed methods for multivariate Gaussian processes from the circulant embedding literature. The method can be performed in $\mathcal{O}(n\log_2 n)$ operations, where $n$ is the length of the desired sequence. The method is exact, except when eigenvalues of prescribed circulant matrices are negative. We evaluate the performance of the algorithm empirically, and provide a practical example where the method is guaranteed to be exact for all $n$, with an improper fractional Gaussian noise process. |
| Tasks | Gaussian Processes |
| Published | 2016-05-17 |
| URL | http://arxiv.org/abs/1605.05278v2 |
| http://arxiv.org/pdf/1605.05278v2.pdf | |
| PWC | https://paperswithcode.com/paper/exact-simulation-of-noncircular-or-improper |
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The new hybrid COAW method for solving multi-objective problems
| Title | The new hybrid COAW method for solving multi-objective problems |
| Authors | Zeinab Borhanifar, Elham Shadkam |
| Abstract | In this article using Cuckoo Optimization Algorithm and simple additive weighting method the hybrid COAW algorithm is presented to solve multi-objective problems. Cuckoo algorithm is an efficient and structured method for solving nonlinear continuous problems. The created Pareto frontiers of the COAW proposed algorithm are exact and have good dispersion. This method has a high speed in finding the Pareto frontiers and identifies the beginning and end points of Pareto frontiers properly. In order to validation the proposed algorithm, several experimental problems were analyzed. The results of which indicate the proper effectiveness of COAW algorithm for solving multi-objective problems. |
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| Published | 2016-01-06 |
| URL | http://arxiv.org/abs/1611.00577v1 |
| http://arxiv.org/pdf/1611.00577v1.pdf | |
| PWC | https://paperswithcode.com/paper/the-new-hybrid-coaw-method-for-solving-multi |
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ModelWizard: Toward Interactive Model Construction
| Title | ModelWizard: Toward Interactive Model Construction |
| Authors | Dylan Hutchison |
| Abstract | Data scientists engage in model construction to discover machine learning models that well explain a dataset, in terms of predictiveness, understandability and generalization across domains. Questions such as “what if we model common cause Z” and “what if Y’s dependence on X reverses” inspire many candidate models to consider and compare, yet current tools emphasize constructing a final model all at once. To more naturally reflect exploration when debating numerous models, we propose an interactive model construction framework grounded in composable operations. Primitive operations capture core steps refining data and model that, when verified, form an inductive basis to prove model validity. Derived, composite operations enable advanced model families, both generic and specialized, abstracted away from low-level details. We prototype our envisioned framework in ModelWizard, a domain-specific language embedded in F# to construct Tabular models. We enumerate language design and demonstrate its use through several applications, emphasizing how language may facilitate creation of complex models. To future engineers designing data science languages and tools, we offer ModelWizard’s design as a new model construction paradigm, speeding discovery of our universe’s structure. |
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| Published | 2016-04-15 |
| URL | http://arxiv.org/abs/1604.04639v1 |
| http://arxiv.org/pdf/1604.04639v1.pdf | |
| PWC | https://paperswithcode.com/paper/modelwizard-toward-interactive-model |
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Probabilistic Models for Computerized Adaptive Testing: Experiments
| Title | Probabilistic Models for Computerized Adaptive Testing: Experiments |
| Authors | Martin Plajner, Jiří Vomlel |
| Abstract | This paper follows previous research we have already performed in the area of Bayesian networks models for CAT. We present models using Item Response Theory (IRT - standard CAT method), Bayesian networks, and neural networks. We conducted simulated CAT tests on empirical data. Results of these tests are presented for each model separately and compared. |
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| Published | 2016-01-28 |
| URL | http://arxiv.org/abs/1601.07929v2 |
| http://arxiv.org/pdf/1601.07929v2.pdf | |
| PWC | https://paperswithcode.com/paper/probabilistic-models-for-computerized-1 |
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Scene Flow Estimation: A Survey
| Title | Scene Flow Estimation: A Survey |
| Authors | Zike Yan, Xuezhi Xiang |
| Abstract | This paper is the first to review the scene flow estimation field, which analyzes and compares methods, technical challenges, evaluation methodologies and performance of scene flow estimation. Existing algorithms are categorized in terms of scene representation, data source, and calculation scheme, and the pros and cons in each category are compared briefly. The datasets and evaluation protocols are enumerated, and the performance of the most representative methods is presented. A future vision is illustrated with few questions arisen for discussion. This survey presents a general introduction and analysis of scene flow estimation. |
| Tasks | Scene Flow Estimation |
| Published | 2016-12-08 |
| URL | http://arxiv.org/abs/1612.02590v3 |
| http://arxiv.org/pdf/1612.02590v3.pdf | |
| PWC | https://paperswithcode.com/paper/scene-flow-estimation-a-survey |
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The AMU-UEDIN Submission to the WMT16 News Translation Task: Attention-based NMT Models as Feature Functions in Phrase-based SMT
| Title | The AMU-UEDIN Submission to the WMT16 News Translation Task: Attention-based NMT Models as Feature Functions in Phrase-based SMT |
| Authors | Marcin Junczys-Dowmunt, Tomasz Dwojak, Rico Sennrich |
| Abstract | This paper describes the AMU-UEDIN submissions to the WMT 2016 shared task on news translation. We explore methods of decode-time integration of attention-based neural translation models with phrase-based statistical machine translation. Efficient batch-algorithms for GPU-querying are proposed and implemented. For English-Russian, our system stays behind the state-of-the-art pure neural models in terms of BLEU. Among restricted systems, manual evaluation places it in the first cluster tied with the pure neural model. For the Russian-English task, our submission achieves the top BLEU result, outperforming the best pure neural system by 1.1 BLEU points and our own phrase-based baseline by 1.6 BLEU. After manual evaluation, this system is the best restricted system in its own cluster. In follow-up experiments we improve results by additional 0.8 BLEU. |
| Tasks | Machine Translation |
| Published | 2016-05-16 |
| URL | http://arxiv.org/abs/1605.04809v3 |
| http://arxiv.org/pdf/1605.04809v3.pdf | |
| PWC | https://paperswithcode.com/paper/the-amu-uedin-submission-to-the-wmt16-news |
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Hybrid Repeat/Multi-point Sampling for Highly Volatile Objective Functions
| Title | Hybrid Repeat/Multi-point Sampling for Highly Volatile Objective Functions |
| Authors | Brett Israelsen, Nisar Ahmed |
| Abstract | A key drawback of the current generation of artificial decision-makers is that they do not adapt well to changes in unexpected situations. This paper addresses the situation in which an AI for aerial dog fighting, with tunable parameters that govern its behavior, will optimize behavior with respect to an objective function that must be evaluated and learned through simulations. Once this objective function has been modeled, the agent can then choose its desired behavior in different situations. Bayesian optimization with a Gaussian Process surrogate is used as the method for investigating the objective function. One key benefit is that during optimization the Gaussian Process learns a global estimate of the true objective function, with predicted outcomes and a statistical measure of confidence in areas that haven’t been investigated yet. However, standard Bayesian optimization does not perform consistently or provide an accurate Gaussian Process surrogate function for highly volatile objective functions. We treat these problems by introducing a novel sampling technique called Hybrid Repeat/Multi-point Sampling. This technique gives the AI ability to learn optimum behaviors in a highly uncertain environment. More importantly, it not only improves the reliability of the optimization, but also creates a better model of the entire objective surface. With this improved model the agent is equipped to better adapt behaviors. |
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| Published | 2016-12-13 |
| URL | http://arxiv.org/abs/1612.03981v1 |
| http://arxiv.org/pdf/1612.03981v1.pdf | |
| PWC | https://paperswithcode.com/paper/hybrid-repeatmulti-point-sampling-for-highly |
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