Accepted Papers ICML New York City. No Oops, You Wont Do It Again Mechanisms for Self correction in Crowdsourcing. Eigen is a C template library for linear algebra matrices, vectors, numerical solvers, and related algorithms. Rzvpxx.png' alt='Linear Program Polynomial Interpolation Matrix Movie' title='Linear Program Polynomial Interpolation Matrix Movie' />Nihar Shah UC Berkeley, Dengyong Zhou Microsoft Research. Paper Abstract. Crowdsourcing is a very popular means of obtaining the large amounts of labeled data that modern machine learning methods require. Although cheap and fast to obtain, crowdsourced labels suffer from significant amounts of error, thereby degrading the performance of downstream machine learning tasks. With the goal of improving the quality of the labeled data, we seek to mitigate the many errors that occur due to silly mistakes or inadvertent errors by crowdsourcing workers. We propose a two stage setting for crowdsourcing where the worker first answers the questions, and is then allowed to change her answers after looking at a noisy reference answer. Rejuvenation The Meters Rarest. We mathematically formulate this process and develop mechanisms to incentivize workers to act appropriately. Y1.png' alt='Linear Program Polynomial Interpolation Matrix Credit' title='Linear Program Polynomial Interpolation Matrix Credit' />This program fits a straight line to a given set of coordinates using the method of least squares linear regression. The coefficients of the line, coefficient of. Index ImageMagick Examples Preface and Index Converting Color to GrayScale Image Level Adjustments. Negating Images reversing black and white Level Adjustment. This chapter gives a detailed discussion of barycentric Lagrange and Hermite interpolation and extends this to rational interpolation with a specified denominator. We. Cryptology ePrint Archive Search Results 20171000 PDF No right to remain silent Isolating Malicious Mixes Hemi Leibowitz and Ania Piotrowska and George. Linear Program Polynomial Interpolation Matrix ReloadedOur mathematical guarantees show that our mechanism incentivizes the workers to answer honestly in both stages, and refrain from answering randomly in the first stage or simply copying in the second. Numerical experiments reveal a significant boost in performance that such self correction can provide when using crowdsourcing to train machine learning algorithms. Stochastically Transitive Models for Pairwise Comparisons Statistical and Computational Issues. Nihar Shah UC Berkeley, Sivaraman Balakrishnan CMU, Aditya Guntuboyina UC Berkeley, Martin Wainwright UC Berkeley. Paper Abstract. There are various parametric models for analyzing pairwise comparison data, including the Bradley Terry Luce BTL and Thurstone models, but their reliance on strong parametric assumptions is limiting. In this work, we study a flexible model for pairwise comparisons, under which the probabilities of outcomes are required only to satisfy a natural form of stochastic transitivity. This class includes parametric models including the BTL and Thurstone models as special cases, but is considerably more general. We provide various examples of models in this broader stochastically transitive class for which classical parametric models provide poor fits. Despite this greater flexibility, we show that the matrix of probabilities can be estimated at the same rate as in standard parametric models. On the other hand, unlike in the BTL and Thurstone models, computing the minimax optimal estimator in the stochastically transitive model is non trivial, and we explore various computationally tractable alternatives. We show that a simple singular value thresholding algorithm is statistically consistent but does not achieve the minimax rate. We then propose and study algorithms that achieve the minimax rate over interesting sub classes of the full stochastically transitive class. We complement our theoretical results with thorough numerical simulations. Uprooting and Rerooting Graphical Models. Adrian Weller University of Cambridge. Paper Abstract. We show how any binary pairwise model may be uprooted to a fully symmetric model, wherein original singleton potentials are transformed to potentials on edges to an added variable, and then rerooted to a new model on the original number of variables. The new model is essentially equivalent to the original model, with the same partition function and allowing recovery of the original marginals or a MAP conguration, yet may have very different computational properties that allow much more efficient inference. This meta approach deepens our understanding, may be applied to any existing algorithm to yield improved methods in practice, generalizes earlier theoretical results, and reveals a remarkable interpretation of the triplet consistent polytope. A Deep Learning Approach to Unsupervised Ensemble Learning. Uri Shaham Yale University, Xiuyuan Cheng, Omer Dror, Ariel Jaffe, Boaz Nadler, Joseph Chang, Yuval Kluger Paper Abstract. We show how deep learning methods can be applied in the context of crowdsourcing and unsupervised ensemble learning. First, we prove that the popular model of Dawid and Skene, which assumes that all classifiers areconditionally independent, is em equivalent to a Restricted Boltzmann Machine RBM with a single hidden node. Hence, under this model, the posterior probabilities of the true labels can be instead estimated via a trained RBM. Next, to address the more general case, where classifiers may strongly violate the conditional independence assumption,we propose to apply RBM based Deep Neural Net DNN. Experimental results on various simulated and real world datasets demonstrate that our proposed DNN approachoutperforms other state of the art methods, in particular when the data violates the conditional independence assumption. Revisiting Semi Supervised Learning with Graph Embeddings. Zhilin Yang Carnegie Mellon University, William Cohen CMU, Ruslan Salakhudinov U. Toronto. Paper Abstract. We present a semi supervised learning framework based on graph embeddings. Given a graph between instances, we train an embedding for each instance to jointly predict the class label and the neighborhood context in the graph. We develop both transductive and inductive variants of our method. In the transductive variant of our method, the class labels are determined by both the learned embeddings and input feature vectors, while in the inductive variant, the embeddings are defined as a parametric function of the feature vectors, so predictions can be made on instances not seen during training. On a large and diverse set of benchmark tasks, including text classification, distantly supervised entity extraction, and entity classification, we show improved performance over many of the existing models. Guided Cost Learning Deep Inverse Optimal Control via Policy Optimization. Chelsea Finn UC Berkeley, Sergey Levine, Pieter Abbeel Berkeley. Paper Abstract. Reinforcement learning can acquire complex behaviors from high level specifications. However, defining a cost function that can be optimized effectively and encodes the correct task is challenging in practice. We explore how inverse optimal control IOC can be used to learn behaviors from demonstrations, with applications to torque control of high dimensional robotic systems. Our method addresses two key challenges in inverse optimal control first, the need for informative features and effective regularization to impose structure on the cost, and second, the difficulty of learning the cost function under unknown dynamics for high dimensional continuous systems. To address the former challenge, we present an algorithm capable of learning arbitrary nonlinear cost functions, such as neural networks, without meticulous feature engineering. To address the latter challenge, we formulate an efficient sample based approximation for Max. Ent IOC. We evaluate our method on a series of simulated tasks and real world robotic manipulation problems, demonstrating substantial improvement over prior methods both in terms of task complexity and sample efficiency. Diversity Promoting Bayesian Learning of Latent Variable Models. Pengtao Xie Carnegie Mellon University, Jun Zhu Tsinghua, Eric Xing CMUPaper Abstract. In learning latent variable models LVMs, it is important to effectively capture infrequent patterns and shrink model size without sacrificing modeling power. Various studies have been done to diversify a LVM, which aim to learn a diverse set of latent components in LVMs. Most existing studies fall into a frequentist style regularization framework, where the components are learned via point estimation. In this paper, we investigate how to diversify LVMs in the paradigm of Bayesian learning, which has advantages complementary to point estimation, such as alleviating overfitting via model averaging and quantifying uncertainty. We propose two approaches that have complementary advantages.