Paper Group ANR 27
Generalizing the Kelly strategy. Ten Steps of EM Suffice for Mixtures of Two Gaussians. Shaping the Future through Innovations: From Medical Imaging to Precision Medicine. An evaluation of randomized machine learning methods for redundant data: Predicting short and medium-term suicide risk from administrative records and risk assessments. On Restri …
Generalizing the Kelly strategy
| Title | Generalizing the Kelly strategy |
| Authors | Arjun Viswanathan |
| Abstract | Prompted by a recent experiment by Victor Haghani and Richard Dewey, this note generalises the Kelly strategy (optimal for simple investment games with log utility) to a large class of practical utility functions and including the effect of extraneous wealth. A counterintuitive result is proved : for any continuous, concave, differentiable utility function, the optimal choice at every point depends only on the probability of reaching that point. The practical calculation of the optimal action at every stage is made possible through use of the binomial expansion, reducing the problem size from exponential to quadratic. Applications include (better) automatic investing and risk taking under uncertainty. |
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| Published | 2016-11-28 |
| URL | http://arxiv.org/abs/1611.09130v3 |
| http://arxiv.org/pdf/1611.09130v3.pdf | |
| PWC | https://paperswithcode.com/paper/generalizing-the-kelly-strategy |
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Ten Steps of EM Suffice for Mixtures of Two Gaussians
| Title | Ten Steps of EM Suffice for Mixtures of Two Gaussians |
| Authors | Constantinos Daskalakis, Christos Tzamos, Manolis Zampetakis |
| Abstract | The Expectation-Maximization (EM) algorithm is a widely used method for maximum likelihood estimation in models with latent variables. For estimating mixtures of Gaussians, its iteration can be viewed as a soft version of the k-means clustering algorithm. Despite its wide use and applications, there are essentially no known convergence guarantees for this method. We provide global convergence guarantees for mixtures of two Gaussians with known covariance matrices. We show that the population version of EM, where the algorithm is given access to infinitely many samples from the mixture, converges geometrically to the correct mean vectors, and provide simple, closed-form expressions for the convergence rate. As a simple illustration, we show that, in one dimension, ten steps of the EM algorithm initialized at infinity result in less than 1% error estimation of the means. In the finite sample regime, we show that, under a random initialization, $\tilde{O}(d/\epsilon^2)$ samples suffice to compute the unknown vectors to within $\epsilon$ in Mahalanobis distance, where $d$ is the dimension. In particular, the error rate of the EM based estimator is $\tilde{O}\left(\sqrt{d \over n}\right)$ where $n$ is the number of samples, which is optimal up to logarithmic factors. |
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| Published | 2016-09-01 |
| URL | http://arxiv.org/abs/1609.00368v5 |
| http://arxiv.org/pdf/1609.00368v5.pdf | |
| PWC | https://paperswithcode.com/paper/ten-steps-of-em-suffice-for-mixtures-of-two |
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Shaping the Future through Innovations: From Medical Imaging to Precision Medicine
| Title | Shaping the Future through Innovations: From Medical Imaging to Precision Medicine |
| Authors | Dorin Comaniciu, Klaus Engel, Bogdan Georgescu, Tommaso Mansi |
| Abstract | Medical images constitute a source of information essential for disease diagnosis, treatment and follow-up. In addition, due to its patient-specific nature, imaging information represents a critical component required for advancing precision medicine into clinical practice. This manuscript describes recently developed technologies for better handling of image information: photorealistic visualization of medical images with Cinematic Rendering, artificial agents for in-depth image understanding, support for minimally invasive procedures, and patient-specific computational models with enhanced predictive power. Throughout the manuscript we will analyze the capabilities of such technologies and extrapolate on their potential impact to advance the quality of medical care, while reducing its cost. |
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| Published | 2016-05-01 |
| URL | http://arxiv.org/abs/1605.02029v2 |
| http://arxiv.org/pdf/1605.02029v2.pdf | |
| PWC | https://paperswithcode.com/paper/shaping-the-future-through-innovations-from |
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An evaluation of randomized machine learning methods for redundant data: Predicting short and medium-term suicide risk from administrative records and risk assessments
| Title | An evaluation of randomized machine learning methods for redundant data: Predicting short and medium-term suicide risk from administrative records and risk assessments |
| Authors | Thuong Nguyen, Truyen Tran, Shivapratap Gopakumar, Dinh Phung, Svetha Venkatesh |
| Abstract | Accurate prediction of suicide risk in mental health patients remains an open problem. Existing methods including clinician judgments have acceptable sensitivity, but yield many false positives. Exploiting administrative data has a great potential, but the data has high dimensionality and redundancies in the recording processes. We investigate the efficacy of three most effective randomized machine learning techniques random forests, gradient boosting machines, and deep neural nets with dropout in predicting suicide risk. Using a cohort of mental health patients from a regional Australian hospital, we compare the predictive performance with popular traditional approaches clinician judgments based on a checklist, sparse logistic regression and decision trees. The randomized methods demonstrated robustness against data redundancies and superior predictive performance on AUC and F-measure. |
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| Published | 2016-05-03 |
| URL | http://arxiv.org/abs/1605.01116v1 |
| http://arxiv.org/pdf/1605.01116v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-evaluation-of-randomized-machine-learning |
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On Restricted Nonnegative Matrix Factorization
| Title | On Restricted Nonnegative Matrix Factorization |
| Authors | Dmitry Chistikov, Stefan Kiefer, Ines Marušić, Mahsa Shirmohammadi, James Worrell |
| Abstract | Nonnegative matrix factorization (NMF) is the problem of decomposing a given nonnegative $n \times m$ matrix $M$ into a product of a nonnegative $n \times d$ matrix $W$ and a nonnegative $d \times m$ matrix $H$. Restricted NMF requires in addition that the column spaces of $M$ and $W$ coincide. Finding the minimal inner dimension $d$ is known to be NP-hard, both for NMF and restricted NMF. We show that restricted NMF is closely related to a question about the nature of minimal probabilistic automata, posed by Paz in his seminal 1971 textbook. We use this connection to answer Paz’s question negatively, thus falsifying a positive answer claimed in 1974. Furthermore, we investigate whether a rational matrix $M$ always has a restricted NMF of minimal inner dimension whose factors $W$ and $H$ are also rational. We show that this holds for matrices $M$ of rank at most $3$ and we exhibit a rank-$4$ matrix for which $W$ and $H$ require irrational entries. |
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| Published | 2016-05-23 |
| URL | http://arxiv.org/abs/1605.07061v1 |
| http://arxiv.org/pdf/1605.07061v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-restricted-nonnegative-matrix |
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Cross-Lingual Morphological Tagging for Low-Resource Languages
| Title | Cross-Lingual Morphological Tagging for Low-Resource Languages |
| Authors | Jan Buys, Jan A. Botha |
| Abstract | Morphologically rich languages often lack the annotated linguistic resources required to develop accurate natural language processing tools. We propose models suitable for training morphological taggers with rich tagsets for low-resource languages without using direct supervision. Our approach extends existing approaches of projecting part-of-speech tags across languages, using bitext to infer constraints on the possible tags for a given word type or token. We propose a tagging model using Wsabie, a discriminative embedding-based model with rank-based learning. In our evaluation on 11 languages, on average this model performs on par with a baseline weakly-supervised HMM, while being more scalable. Multilingual experiments show that the method performs best when projecting between related language pairs. Despite the inherently lossy projection, we show that the morphological tags predicted by our models improve the downstream performance of a parser by +0.6 LAS on average. |
| Tasks | Morphological Tagging |
| Published | 2016-06-14 |
| URL | http://arxiv.org/abs/1606.04279v1 |
| http://arxiv.org/pdf/1606.04279v1.pdf | |
| PWC | https://paperswithcode.com/paper/cross-lingual-morphological-tagging-for-low |
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Community Detection with Node Attributes and its Generalization
| Title | Community Detection with Node Attributes and its Generalization |
| Authors | Yuan Li |
| Abstract | Community detection algorithms are fundamental tools to understand organizational principles in social networks. With the increasing power of social media platforms, when detecting communities there are two possi- ble sources of information one can use: the structure of social network and node attributes. However structure of social networks and node attributes are often interpreted separately in the research of community detection. When these two sources are interpreted simultaneously, one common as- sumption shared by previous studies is that nodes attributes are correlated with communities. In this paper, we present a model that is capable of combining topology information and nodes attributes information with- out assuming correlation. This new model can recover communities with higher accuracy even when node attributes and communities are uncorre- lated. We derive the detectability threshold for this model and use Belief Propagation (BP) to make inference. This algorithm is optimal in the sense that it can recover community all the way down to the threshold. This new model is also with the potential to handle edge content and dynamic settings. |
| Tasks | Community Detection |
| Published | 2016-04-12 |
| URL | http://arxiv.org/abs/1604.03601v1 |
| http://arxiv.org/pdf/1604.03601v1.pdf | |
| PWC | https://paperswithcode.com/paper/community-detection-with-node-attributes-and |
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Interactive Prior Elicitation of Feature Similarities for Small Sample Size Prediction
| Title | Interactive Prior Elicitation of Feature Similarities for Small Sample Size Prediction |
| Authors | Homayun Afrabandpey, Tomi Peltola, Samuel Kaski |
| Abstract | Regression under the “small $n$, large $p$” conditions, of small sample size $n$ and large number of features $p$ in the learning data set, is a recurring setting in which learning from data is difficult. With prior knowledge about relationships of the features, $p$ can effectively be reduced, but explicating such prior knowledge is difficult for experts. In this paper we introduce a new method for eliciting expert prior knowledge about the similarity of the roles of features in the prediction task. The key idea is to use an interactive multidimensional-scaling (MDS) type scatterplot display of the features to elicit the similarity relationships, and then use the elicited relationships in the prior distribution of prediction parameters. Specifically, for learning to predict a target variable with Bayesian linear regression, the feature relationships are used to construct a Gaussian prior with a full covariance matrix for the regression coefficients. Evaluation of our method in experiments with simulated and real users on text data confirm that prior elicitation of feature similarities improves prediction accuracy. Furthermore, elicitation with an interactive scatterplot display outperforms straightforward elicitation where the users choose feature pairs from a feature list. |
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| Published | 2016-12-08 |
| URL | http://arxiv.org/abs/1612.02802v2 |
| http://arxiv.org/pdf/1612.02802v2.pdf | |
| PWC | https://paperswithcode.com/paper/interactive-prior-elicitation-of-feature |
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cvpaper.challenge in 2015 - A review of CVPR2015 and DeepSurvey
| Title | cvpaper.challenge in 2015 - A review of CVPR2015 and DeepSurvey |
| Authors | Hirokatsu Kataoka, Yudai Miyashita, Tomoaki Yamabe, Soma Shirakabe, Shin’ichi Sato, Hironori Hoshino, Ryo Kato, Kaori Abe, Takaaki Imanari, Naomichi Kobayashi, Shinichiro Morita, Akio Nakamura |
| Abstract | The “cvpaper.challenge” is a group composed of members from AIST, Tokyo Denki Univ. (TDU), and Univ. of Tsukuba that aims to systematically summarize papers on computer vision, pattern recognition, and related fields. For this particular review, we focused on reading the ALL 602 conference papers presented at the CVPR2015, the premier annual computer vision event held in June 2015, in order to grasp the trends in the field. Further, we are proposing “DeepSurvey” as a mechanism embodying the entire process from the reading through all the papers, the generation of ideas, and to the writing of paper. |
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| Published | 2016-05-26 |
| URL | http://arxiv.org/abs/1605.08247v1 |
| http://arxiv.org/pdf/1605.08247v1.pdf | |
| PWC | https://paperswithcode.com/paper/cvpaperchallenge-in-2015-a-review-of-cvpr2015 |
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Extracting Temporal and Causal Relations between Events
| Title | Extracting Temporal and Causal Relations between Events |
| Authors | Paramita Mirza |
| Abstract | Structured information resulting from temporal information processing is crucial for a variety of natural language processing tasks, for instance to generate timeline summarization of events from news documents, or to answer temporal/causal-related questions about some events. In this thesis we present a framework for an integrated temporal and causal relation extraction system. We first develop a robust extraction component for each type of relations, i.e. temporal order and causality. We then combine the two extraction components into an integrated relation extraction system, CATENA—CAusal and Temporal relation Extraction from NAtural language texts—, by utilizing the presumption about event precedence in causality, that causing events must happened BEFORE resulting events. Several resources and techniques to improve our relation extraction systems are also discussed, including word embeddings and training data expansion. Finally, we report our adaptation efforts of temporal information processing for languages other than English, namely Italian and Indonesian. |
| Tasks | Relation Extraction, Timeline Summarization, Word Embeddings |
| Published | 2016-04-27 |
| URL | http://arxiv.org/abs/1604.08120v1 |
| http://arxiv.org/pdf/1604.08120v1.pdf | |
| PWC | https://paperswithcode.com/paper/extracting-temporal-and-causal-relations |
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Triplet Similarity Embedding for Face Verification
| Title | Triplet Similarity Embedding for Face Verification |
| Authors | Swami Sankaranarayanan, Azadeh Alavi, Rama Chellappa |
| Abstract | In this work, we present an unconstrained face verification algorithm and evaluate it on the recently released IJB-A dataset that aims to push the boundaries of face verification methods. The proposed algorithm couples a deep CNN-based approach with a low-dimensional discriminative embedding learnt using triplet similarity constraints in a large margin fashion. Aside from yielding performance improvement, this embedding provides significant advantages in terms of memory and post-processing operations like hashing and visualization. Experiments on the IJB-A dataset show that the proposed algorithm outperforms state of the art methods in verification and identification metrics, while requiring less training time. |
| Tasks | Face Verification |
| Published | 2016-02-10 |
| URL | http://arxiv.org/abs/1602.03418v2 |
| http://arxiv.org/pdf/1602.03418v2.pdf | |
| PWC | https://paperswithcode.com/paper/triplet-similarity-embedding-for-face |
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Hard-Clustering with Gaussian Mixture Models
| Title | Hard-Clustering with Gaussian Mixture Models |
| Authors | Johannes Blömer, Sascha Brauer, Kathrin Bujna |
| Abstract | Training the parameters of statistical models to describe a given data set is a central task in the field of data mining and machine learning. A very popular and powerful way of parameter estimation is the method of maximum likelihood estimation (MLE). Among the most widely used families of statistical models are mixture models, especially, mixtures of Gaussian distributions. A popular hard-clustering variant of the MLE problem is the so-called complete-data maximum likelihood estimation (CMLE) method. The standard approach to solve the CMLE problem is the Classification-Expectation-Maximization (CEM) algorithm. Unfortunately, it is only guaranteed that the algorithm converges to some (possibly arbitrarily poor) stationary point of the objective function. In this paper, we present two algorithms for a restricted version of the CMLE problem. That is, our algorithms approximate reasonable solutions to the CMLE problem which satisfy certain natural properties. Moreover, they compute solutions whose cost (i.e. complete-data log-likelihood values) are at most a factor $(1+\epsilon)$ worse than the cost of the solutions that we search for. Note the CMLE problem in its most general, i.e. unrestricted, form is not well defined and allows for trivial optimal solutions that can be thought of as degenerated solutions. |
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| Published | 2016-03-21 |
| URL | http://arxiv.org/abs/1603.06478v1 |
| http://arxiv.org/pdf/1603.06478v1.pdf | |
| PWC | https://paperswithcode.com/paper/hard-clustering-with-gaussian-mixture-models |
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Unscented Bayesian Optimization for Safe Robot Grasping
| Title | Unscented Bayesian Optimization for Safe Robot Grasping |
| Authors | José Nogueira, Ruben Martinez-Cantin, Alexandre Bernardino, Lorenzo Jamone |
| Abstract | We address the robot grasp optimization problem of unknown objects considering uncertainty in the input space. Grasping unknown objects can be achieved by using a trial and error exploration strategy. Bayesian optimization is a sample efficient optimization algorithm that is especially suitable for this setups as it actively reduces the number of trials for learning about the function to optimize. In fact, this active object exploration is the same strategy that infants do to learn optimal grasps. One problem that arises while learning grasping policies is that some configurations of grasp parameters may be very sensitive to error in the relative pose between the object and robot end-effector. We call these configurations unsafe because small errors during grasp execution may turn good grasps into bad grasps. Therefore, to reduce the risk of grasp failure, grasps should be planned in safe areas. We propose a new algorithm, Unscented Bayesian optimization that is able to perform sample efficient optimization while taking into consideration input noise to find safe optima. The contribution of Unscented Bayesian optimization is twofold as if provides a new decision process that drives exploration to safe regions and a new selection procedure that chooses the optimal in terms of its safety without extra analysis or computational cost. Both contributions are rooted on the strong theory behind the unscented transformation, a popular nonlinear approximation method. We show its advantages with respect to the classical Bayesian optimization both in synthetic problems and in realistic robot grasp simulations. The results highlights that our method achieves optimal and robust grasping policies after few trials while the selected grasps remain in safe regions. |
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| Published | 2016-03-07 |
| URL | http://arxiv.org/abs/1603.02038v1 |
| http://arxiv.org/pdf/1603.02038v1.pdf | |
| PWC | https://paperswithcode.com/paper/unscented-bayesian-optimization-for-safe |
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Guarantees in Wasserstein Distance for the Langevin Monte Carlo Algorithm
| Title | Guarantees in Wasserstein Distance for the Langevin Monte Carlo Algorithm |
| Authors | Thomas Bonis |
| Abstract | We study the problem of sampling from a distribution $\target$ using the Langevin Monte Carlo algorithm and provide rate of convergences for this algorithm in terms of Wasserstein distance of order $2$. Our result holds as long as the continuous diffusion process associated with the algorithm converges exponentially fast to the target distribution along with some technical assumptions. While such an exponential convergence holds for example in the log-concave measure case, it also holds for the more general case of asymptoticaly log-concave measures. Our results thus extends the known rates of convergence in total variation and Wasserstein distances which have only been obtained in the log-concave case. Moreover, using a sharper approximation bound of the continuous process, we obtain better asymptotic rates than traditional results. We also look into variations of the Langevin Monte Carlo algorithm using other discretization schemes. In a first time, we look into the use of the Ozaki’s discretization but are unable to obtain any significative improvement in terms of convergence rates compared to the Euler’s scheme. We then provide a (sub-optimal) way to study more general schemes, however our approach only holds for the log-concave case. |
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| Published | 2016-02-08 |
| URL | http://arxiv.org/abs/1602.02616v2 |
| http://arxiv.org/pdf/1602.02616v2.pdf | |
| PWC | https://paperswithcode.com/paper/guarantees-in-wasserstein-distance-for-the |
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Constructing Effective Personalized Policies Using Counterfactual Inference from Biased Data Sets with Many Features
| Title | Constructing Effective Personalized Policies Using Counterfactual Inference from Biased Data Sets with Many Features |
| Authors | Onur Atan, William R. Zame, Qiaojun Feng, Mihaela van der Schaar |
| Abstract | This paper proposes a novel approach for constructing effective personalized policies when the observed data lacks counter-factual information, is biased and possesses many features. The approach is applicable in a wide variety of settings from healthcare to advertising to education to finance. These settings have in common that the decision maker can observe, for each previous instance, an array of features of the instance, the action taken in that instance, and the reward realized – but not the rewards of actions that were not taken: the counterfactual information. Learning in such settings is made even more difficult because the observed data is typically biased by the existing policy (that generated the data) and because the array of features that might affect the reward in a particular instance – and hence should be taken into account in deciding on an action in each particular instance – is often vast. The approach presented here estimates propensity scores for the observed data, infers counterfactuals, identifies a (relatively small) number of features that are (most) relevant for each possible action and instance, and prescribes a policy to be followed. Comparison of the proposed algorithm against the state-of-art algorithm on actual datasets demonstrates that the proposed algorithm achieves a significant improvement in performance. |
| Tasks | Counterfactual Inference |
| Published | 2016-12-23 |
| URL | http://arxiv.org/abs/1612.08082v3 |
| http://arxiv.org/pdf/1612.08082v3.pdf | |
| PWC | https://paperswithcode.com/paper/constructing-effective-personalized-policies |
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