Paper Group ANR 646
Probabilistic Learning of Torque Controllers from Kinematic and Force Constraints. Crowdsourcing Multiple Choice Science Questions. A General Framework for Robust Interactive Learning. Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect. Provably Optimal Algorithms for Generalized Linear Contextual Bandits. Towards Neural Mach …
Probabilistic Learning of Torque Controllers from Kinematic and Force Constraints
| Title | Probabilistic Learning of Torque Controllers from Kinematic and Force Constraints |
| Authors | João Silvério, Yanlong Huang, Leonel Rozo, Sylvain Calinon, Darwin G. Caldwell |
| Abstract | When learning skills from demonstrations, one is often required to think in advance about the appropriate task representation (usually in either operational or configuration space). We here propose a probabilistic approach for simultaneously learning and synthesizing torque control commands which take into account task space, joint space and force constraints. We treat the problem by considering different torque controllers acting on the robot, whose relevance is learned probabilistically from demonstrations. This information is used to combine the controllers by exploiting the properties of Gaussian distributions, generating new torque commands that satisfy the important features of the task. We validate the approach in two experimental scenarios using 7-DoF torquecontrolled manipulators, with tasks that require the consideration of different controllers to be properly executed. |
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| Published | 2017-12-19 |
| URL | http://arxiv.org/abs/1712.07249v2 |
| http://arxiv.org/pdf/1712.07249v2.pdf | |
| PWC | https://paperswithcode.com/paper/probabilistic-learning-of-torque-controllers |
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Crowdsourcing Multiple Choice Science Questions
| Title | Crowdsourcing Multiple Choice Science Questions |
| Authors | Johannes Welbl, Nelson F. Liu, Matt Gardner |
| Abstract | We present a novel method for obtaining high-quality, domain-targeted multiple choice questions from crowd workers. Generating these questions can be difficult without trading away originality, relevance or diversity in the answer options. Our method addresses these problems by leveraging a large corpus of domain-specific text and a small set of existing questions. It produces model suggestions for document selection and answer distractor choice which aid the human question generation process. With this method we have assembled SciQ, a dataset of 13.7K multiple choice science exam questions (Dataset available at http://allenai.org/data.html). We demonstrate that the method produces in-domain questions by providing an analysis of this new dataset and by showing that humans cannot distinguish the crowdsourced questions from original questions. When using SciQ as additional training data to existing questions, we observe accuracy improvements on real science exams. |
| Tasks | Question Generation |
| Published | 2017-07-19 |
| URL | http://arxiv.org/abs/1707.06209v1 |
| http://arxiv.org/pdf/1707.06209v1.pdf | |
| PWC | https://paperswithcode.com/paper/crowdsourcing-multiple-choice-science |
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A General Framework for Robust Interactive Learning
| Title | A General Framework for Robust Interactive Learning |
| Authors | Ehsan Emamjomeh-Zadeh, David Kempe |
| Abstract | We propose a general framework for interactively learning models, such as (binary or non-binary) classifiers, orderings/rankings of items, or clusterings of data points. Our framework is based on a generalization of Angluin’s equivalence query model and Littlestone’s online learning model: in each iteration, the algorithm proposes a model, and the user either accepts it or reveals a specific mistake in the proposal. The feedback is correct only with probability $p > 1/2$ (and adversarially incorrect with probability $1 - p$), i.e., the algorithm must be able to learn in the presence of arbitrary noise. The algorithm’s goal is to learn the ground truth model using few iterations. Our general framework is based on a graph representation of the models and user feedback. To be able to learn efficiently, it is sufficient that there be a graph $G$ whose nodes are the models and (weighted) edges capture the user feedback, with the property that if $s, s^$ are the proposed and target models, respectively, then any (correct) user feedback $s'$ must lie on a shortest $s$-$s^$ path in $G$. Under this one assumption, there is a natural algorithm reminiscent of the Multiplicative Weights Update algorithm, which will efficiently learn $s^*$ even in the presence of noise in the user’s feedback. From this general result, we rederive with barely any extra effort classic results on learning of classifiers and a recent result on interactive clustering; in addition, we easily obtain new interactive learning algorithms for ordering/ranking. |
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| Published | 2017-10-16 |
| URL | http://arxiv.org/abs/1710.05422v1 |
| http://arxiv.org/pdf/1710.05422v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-general-framework-for-robust-interactive |
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Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect
| Title | Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect |
| Authors | Tong Wang, Cynthia Rudin |
| Abstract | We introduce a novel generative model for interpretable subgroup analysis for causal inference applications, Causal Rule Sets (CRS). A CRS model uses a small set of short rules to capture a subgroup where the average treatment effect is elevated compared to the entire population. We present a Bayesian framework for learning a causal rule set. The Bayesian framework consists of a prior that favors simpler models and a Bayesian logistic regression that characterizes the relation between outcomes, attributes and subgroup membership. We find maximum a posteriori models using discrete Monte Carlo steps in the joint solution space of rules sets and parameters. We provide theoretically grounded heuristics and bounding strategies to improve search efficiency. Experiments show that the search algorithm can efficiently recover a true underlying subgroup and CRS shows consistently competitive performance compared to other state-of-the-art baseline methods. |
| Tasks | Causal Inference |
| Published | 2017-10-16 |
| URL | http://arxiv.org/abs/1710.05426v2 |
| http://arxiv.org/pdf/1710.05426v2.pdf | |
| PWC | https://paperswithcode.com/paper/causal-rule-sets-for-identifying-subgroups |
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Provably Optimal Algorithms for Generalized Linear Contextual Bandits
| Title | Provably Optimal Algorithms for Generalized Linear Contextual Bandits |
| Authors | Lihong Li, Yu Lu, Dengyong Zhou |
| Abstract | Contextual bandits are widely used in Internet services from news recommendation to advertising, and to Web search. Generalized linear models (logistical regression in particular) have demonstrated stronger performance than linear models in many applications where rewards are binary. However, most theoretical analyses on contextual bandits so far are on linear bandits. In this work, we propose an upper confidence bound based algorithm for generalized linear contextual bandits, which achieves an $\tilde{O}(\sqrt{dT})$ regret over $T$ rounds with $d$ dimensional feature vectors. This regret matches the minimax lower bound, up to logarithmic terms, and improves on the best previous result by a $\sqrt{d}$ factor, assuming the number of arms is fixed. A key component in our analysis is to establish a new, sharp finite-sample confidence bound for maximum-likelihood estimates in generalized linear models, which may be of independent interest. We also analyze a simpler upper confidence bound algorithm, which is useful in practice, and prove it to have optimal regret for certain cases. |
| Tasks | Multi-Armed Bandits |
| Published | 2017-02-28 |
| URL | http://arxiv.org/abs/1703.00048v2 |
| http://arxiv.org/pdf/1703.00048v2.pdf | |
| PWC | https://paperswithcode.com/paper/provably-optimal-algorithms-for-generalized |
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Towards Neural Machine Translation with Latent Tree Attention
| Title | Towards Neural Machine Translation with Latent Tree Attention |
| Authors | James Bradbury, Richard Socher |
| Abstract | Building models that take advantage of the hierarchical structure of language without a priori annotation is a longstanding goal in natural language processing. We introduce such a model for the task of machine translation, pairing a recurrent neural network grammar encoder with a novel attentional RNNG decoder and applying policy gradient reinforcement learning to induce unsupervised tree structures on both the source and target. When trained on character-level datasets with no explicit segmentation or parse annotation, the model learns a plausible segmentation and shallow parse, obtaining performance close to an attentional baseline. |
| Tasks | Machine Translation |
| Published | 2017-09-06 |
| URL | http://arxiv.org/abs/1709.01915v1 |
| http://arxiv.org/pdf/1709.01915v1.pdf | |
| PWC | https://paperswithcode.com/paper/towards-neural-machine-translation-with |
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Unique Information via Dependency Constraints
| Title | Unique Information via Dependency Constraints |
| Authors | Ryan G. James, Jeffrey Emenheiser, James P. Crutchfield |
| Abstract | The partial information decomposition (PID) is perhaps the leading proposal for resolving information shared between a set of sources and a target into redundant, synergistic, and unique constituents. Unfortunately, the PID framework has been hindered by a lack of a generally agreed-upon, multivariate method of quantifying the constituents. Here, we take a step toward rectifying this by developing a decomposition based on a new method that quantifies unique information. We first develop a broadly applicable method—the dependency decomposition—that delineates how statistical dependencies influence the structure of a joint distribution. The dependency decomposition then allows us to define a measure of the information about a target that can be uniquely attributed to a particular source as the least amount which the source-target statistical dependency can influence the information shared between the sources and the target. The result is the first measure that satisfies the core axioms of the PID framework while not satisfying the Blackwell relation, which depends on a particular interpretation of how the variables are related. This makes a key step forward to a practical PID. |
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| Published | 2017-09-19 |
| URL | http://arxiv.org/abs/1709.06653v3 |
| http://arxiv.org/pdf/1709.06653v3.pdf | |
| PWC | https://paperswithcode.com/paper/unique-information-via-dependency-constraints |
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Adaptive Consensus ADMM for Distributed Optimization
| Title | Adaptive Consensus ADMM for Distributed Optimization |
| Authors | Zheng Xu, Gavin Taylor, Hao Li, Mario Figueiredo, Xiaoming Yuan, Tom Goldstein |
| Abstract | The alternating direction method of multipliers (ADMM) is commonly used for distributed model fitting problems, but its performance and reliability depend strongly on user-defined penalty parameters. We study distributed ADMM methods that boost performance by using different fine-tuned algorithm parameters on each worker node. We present a O(1/k) convergence rate for adaptive ADMM methods with node-specific parameters, and propose adaptive consensus ADMM (ACADMM), which automatically tunes parameters without user oversight. |
| Tasks | Distributed Optimization |
| Published | 2017-06-09 |
| URL | http://arxiv.org/abs/1706.02869v2 |
| http://arxiv.org/pdf/1706.02869v2.pdf | |
| PWC | https://paperswithcode.com/paper/adaptive-consensus-admm-for-distributed |
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Convex Parameterizations and Fidelity Bounds for Nonlinear Identification and Reduced-Order Modelling
| Title | Convex Parameterizations and Fidelity Bounds for Nonlinear Identification and Reduced-Order Modelling |
| Authors | Mark M. Tobenkin, Ian R. Manchester, Alexandre Megretski |
| Abstract | Model instability and poor prediction of long-term behavior are common problems when modeling dynamical systems using nonlinear “black-box” techniques. Direct optimization of the long-term predictions, often called simulation error minimization, leads to optimization problems that are generally non-convex in the model parameters and suffer from multiple local minima. In this work we present methods which address these problems through convex optimization, based on Lagrangian relaxation, dissipation inequalities, contraction theory, and semidefinite programming. We demonstrate the proposed methods with a model order reduction task for electronic circuit design and the identification of a pneumatic actuator from experiment. |
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| Published | 2017-01-23 |
| URL | http://arxiv.org/abs/1701.06652v1 |
| http://arxiv.org/pdf/1701.06652v1.pdf | |
| PWC | https://paperswithcode.com/paper/convex-parameterizations-and-fidelity-bounds |
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Genetic Algorithms for Mentor-Assisted Evaluation Function Optimization
| Title | Genetic Algorithms for Mentor-Assisted Evaluation Function Optimization |
| Authors | Eli David, Moshe Koppel, Nathan S. Netanyahu |
| Abstract | In this paper we demonstrate how genetic algorithms can be used to reverse engineer an evaluation function’s parameters for computer chess. Our results show that using an appropriate mentor, we can evolve a program that is on par with top tournament-playing chess programs, outperforming a two-time World Computer Chess Champion. This performance gain is achieved by evolving a program with a smaller number of parameters in its evaluation function to mimic the behavior of a superior mentor which uses a more extensive evaluation function. In principle, our mentor-assisted approach could be used in a wide range of problems for which appropriate mentors are available. |
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| Published | 2017-11-18 |
| URL | http://arxiv.org/abs/1711.06839v1 |
| http://arxiv.org/pdf/1711.06839v1.pdf | |
| PWC | https://paperswithcode.com/paper/genetic-algorithms-for-mentor-assisted |
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Addressing Item-Cold Start Problem in Recommendation Systems using Model Based Approach and Deep Learning
| Title | Addressing Item-Cold Start Problem in Recommendation Systems using Model Based Approach and Deep Learning |
| Authors | Ivica Obadić, Gjorgji Madjarov, Ivica Dimitrovski, Dejan Gjorgjevikj |
| Abstract | Traditional recommendation systems rely on past usage data in order to generate new recommendations. Those approaches fail to generate sensible recommendations for new users and items into the system due to missing information about their past interactions. In this paper, we propose a solution for successfully addressing item-cold start problem which uses model-based approach and recent advances in deep learning. In particular, we use latent factor model for recommendation, and predict the latent factors from item’s descriptions using convolutional neural network when they cannot be obtained from usage data. Latent factors obtained by applying matrix factorization to the available usage data are used as ground truth to train the convolutional neural network. To create latent factor representations for the new items, the convolutional neural network uses their textual description. The results from the experiments reveal that the proposed approach significantly outperforms several baseline estimators. |
| Tasks | Recommendation Systems |
| Published | 2017-06-18 |
| URL | http://arxiv.org/abs/1706.05730v1 |
| http://arxiv.org/pdf/1706.05730v1.pdf | |
| PWC | https://paperswithcode.com/paper/addressing-item-cold-start-problem-in |
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Learned Watershed: End-to-End Learning of Seeded Segmentation
| Title | Learned Watershed: End-to-End Learning of Seeded Segmentation |
| Authors | Steffen Wolf, Lukas Schott, Ullrich Köthe, Fred Hamprecht |
| Abstract | Learned boundary maps are known to outperform hand- crafted ones as a basis for the watershed algorithm. We show, for the first time, how to train watershed computation jointly with boundary map prediction. The estimator for the merging priorities is cast as a neural network that is con- volutional (over space) and recurrent (over iterations). The latter allows learning of complex shape priors. The method gives the best known seeded segmentation results on the CREMI segmentation challenge. |
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| Published | 2017-04-07 |
| URL | http://arxiv.org/abs/1704.02249v2 |
| http://arxiv.org/pdf/1704.02249v2.pdf | |
| PWC | https://paperswithcode.com/paper/learned-watershed-end-to-end-learning-of |
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Deletion-Robust Submodular Maximization at Scale
| Title | Deletion-Robust Submodular Maximization at Scale |
| Authors | Ehsan Kazemi, Morteza Zadimoghaddam, Amin Karbasi |
| Abstract | Can we efficiently extract useful information from a large user-generated dataset while protecting the privacy of the users and/or ensuring fairness in representation. We cast this problem as an instance of a deletion-robust submodular maximization where part of the data may be deleted due to privacy concerns or fairness criteria. We propose the first memory-efficient centralized, streaming, and distributed methods with constant-factor approximation guarantees against any number of adversarial deletions. We extensively evaluate the performance of our algorithms against prior state-of-the-art on real-world applications, including (i) Uber-pick up locations with location privacy constraints; (ii) feature selection with fairness constraints for income prediction and crime rate prediction; and (iii) robust to deletion summarization of census data, consisting of 2,458,285 feature vectors. |
| Tasks | Feature Selection |
| Published | 2017-11-20 |
| URL | http://arxiv.org/abs/1711.07112v2 |
| http://arxiv.org/pdf/1711.07112v2.pdf | |
| PWC | https://paperswithcode.com/paper/deletion-robust-submodular-maximization-at |
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Diminishing Batch Normalization
| Title | Diminishing Batch Normalization |
| Authors | Yintai Ma, Diego Klabjan |
| Abstract | In this paper, we propose a generalization of the Batch Normalization (BN) algorithm, diminishing batch normalization (DBN), where we update the BN parameters in a diminishing moving average way. BN is very effective in accelerating the convergence of a neural network training phase that it has become a common practice. Our proposed DBN algorithm remains the overall structure of the original BN algorithm while introduces a weighted averaging update to some trainable parameters. We provide an analysis of the convergence of the DBN algorithm that converges to a stationary point with respect to trainable parameters. Our analysis can be easily generalized for original BN algorithm by setting some parameters to constant. To the best knowledge of authors, this analysis is the first of its kind for convergence with Batch Normalization introduced. We analyze a two-layer model with arbitrary activation function. The primary challenge of the analysis is the fact that some parameters are updated by gradient while others are not. The convergence analysis applies to any activation function that satisfies our common assumptions. In the numerical experiments, we test the proposed algorithm on complex modern CNN models with stochastic gradients and ReLU activation. We observe that DBN outperforms the original BN algorithm on MNIST, NI and CIFAR-10 datasets with reasonable complex FNN and CNN models. |
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| Published | 2017-05-22 |
| URL | http://arxiv.org/abs/1705.08011v2 |
| http://arxiv.org/pdf/1705.08011v2.pdf | |
| PWC | https://paperswithcode.com/paper/diminishing-batch-normalization |
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Random Binary Trees for Approximate Nearest Neighbour Search in Binary Space
| Title | Random Binary Trees for Approximate Nearest Neighbour Search in Binary Space |
| Authors | Michal Komorowski, Tomasz Trzcinski |
| Abstract | Approximate nearest neighbour (ANN) search is one of the most important problems in computer science fields such as data mining or computer vision. In this paper, we focus on ANN for high-dimensional binary vectors and we propose a simple yet powerful search method that uses Random Binary Search Trees (RBST). We apply our method to a dataset of 1.25M binary local feature descriptors obtained from a real-life image-based localisation system provided by Google as a part of Project Tango. An extensive evaluation of our method against the state-of-the-art variations of Locality Sensitive Hashing (LSH), namely Uniform LSH and Multi-probe LSH, shows the superiority of our method in terms of retrieval precision with performance boost of over 20% |
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| Published | 2017-08-09 |
| URL | http://arxiv.org/abs/1708.02976v2 |
| http://arxiv.org/pdf/1708.02976v2.pdf | |
| PWC | https://paperswithcode.com/paper/random-binary-trees-for-approximate-nearest |
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