pwc-archive/papers-with-abstracts huggingface dataset:
Analysis of Learning from Positive and Unlabeled Data
Authors: Marthinus C. Du Plessis, Gang Niu, Masashi Sugiyama
Date: 2014-12-01 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: General Classification, Outlier Detection

Learning a classifier from positive and unlabeled data is an important class of classification problems that are conceivable in many practical applications. In this paper, we first show that this problem can be solved by cost-sensitive learning between positive and unlabeled data. We then show that convex surrogate loss functions such as the hinge loss may lead to a wrong classification boundary due to an intrinsic bias, but the problem can be avoided by using non-convex loss functions such as the ramp loss. We next analyze the excess risk when the class prior is estimated from data, and show that the classification accuracy is not sensitive to class prior estimation if the unlabeled data is dominated by the positive data (this is naturally satisfied in inlier-based outlier detection because inliers are dominant in the unlabeled dataset). Finally, we provide generalization error bounds and show that, for an equal number of labeled and unlabeled samples, the generalization error of learning only from positive and unlabeled samples is no worse than $2\sqrt{2}$ times the fully supervised case. These theoretical findings are also validated through experiments.
Constrained convex minimization via model-based excessive gap
Authors: Quoc Tran-Dinh, Volkan Cevher
Date: 2014-12-01 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: model

We introduce a model-based excessive gap technique to analyze first-order primal- dual methods for constrained convex minimization. As a result, we construct first- order primal-dual methods with optimal convergence rates on the primal objec- tive residual and the primal feasibility gap of their iterates separately. Through a dual smoothing and prox-center selection strategy, our framework subsumes the augmented Lagrangian, alternating direction, and dual fast-gradient methods as special cases, where our rates apply.
Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data
Authors: Florian Stimberg, Andreas Ruttor, Manfred Opper
Date: 2014-12-01 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Bayesian Inference

We introduce a model where the rate of an inhomogeneous Poisson process is modified by a Chinese restaurant process. Applying a MCMC sampler to this model allows us to do posterior Bayesian inference about the number of states in Poisson-like data. Our sampler is shown to get accurate results for synthetic data and we apply it to V1 neuron spike data to find discrete firing rate states depending on the orientation of a stimulus.
Optimal decision-making with time-varying evidence reliability
Authors: Jan Drugowitsch, Ruben Moreno-Bote, Alexandre Pouget
Date: 2014-12-01 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Decision Making

Previous theoretical and experimental work on optimal decision-making was restricted to the artificial setting of a reliability of the momentary sensory evidence that remained constant within single trials. The work presented here describes the computation and characterization of optimal decision-making in the more realistic case of an evidence reliability that varies across time even within a trial. It shows that, in this case, the optimal behavior is determined by a bound in the decision maker's belief that depends only on the current, but not the past, reliability. We furthermore demonstrate that simpler heuristics fail to match the optimal performance for certain characteristics of the process that determines the time-course of this reliability, causing a drop in reward rate by more than 50%.
Learning Shuffle Ideals Under Restricted Distributions
Authors: Dongqu Chen
Date: 2014-12-01 00:00:00
Proceeding: NeurIPS 2014 12

The class of shuffle ideals is a fundamental sub-family of regular languages. The shuffle ideal generated by a string set $U$ is the collection of all strings containing some string $u \in U$ as a (not necessarily contiguous) subsequence. In spite of its apparent simplicity, the problem of learning a shuffle ideal from given data is known to be computationally intractable. In this paper, we study the PAC learnability of shuffle ideals and present positive results on this learning problem under element-wise independent and identical distributions and Markovian distributions in the statistical query model. A constrained generalization to learning shuffle ideals under product distributions is also provided. In the empirical direction, we propose a heuristic algorithm for learning shuffle ideals from given labeled strings under general unrestricted distributions. Experiments demonstrate the advantage for both efficiency and accuracy of our algorithm.
Discriminative Unsupervised Feature Learning with Convolutional Neural Networks
Authors: Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller, Thomas Brox
Date: 2014-12-01 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: General Classification, Image Classification, Object Recognition

Current methods for training convolutional neural networks depend on large amounts of labeled samples for supervised training. In this paper we present an approach for training a convolutional neural network using only unlabeled data. We train the network to discriminate between a set of surrogate classes. Each surrogate class is formed by applying a variety of transformations to a randomly sampled 'seed' image patch. We find that this simple feature learning algorithm is surprisingly successful when applied to visual object recognition. The feature representation learned by our algorithm achieves classification results matching or outperforming the current state-of-the-art for unsupervised learning on several popular datasets (STL-10, CIFAR-10, Caltech-101).
Distance-Based Network Recovery under Feature Correlation
Authors: David Adametz, Volker Roth
Date: 2014-12-01 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Feature Correlation

We present an inference method for Gaussian graphical models when only pairwise distances of n objects are observed. Formally, this is a problem of estimating an n x n covariance matrix from the Mahalanobis distances dMH(xi, xj), where object xi lives in a latent feature space. We solve the problem in fully Bayesian fashion by integrating over the Matrix-Normal likelihood and a Matrix-Gamma prior; the resulting Matrix-T posterior enables network recovery even under strongly correlated features. Hereby, we generalize TiWnet, which assumes Euclidean distances with strict feature independence. In spite of the greatly increased flexibility, our model neither loses statistical power nor entails more computational cost. We argue that the extension is highly relevant as it yields significantly better results in both synthetic and real-world experiments, which is successfully demonstrated for a network of biological pathways in cancer patients.
Bandit Convex Optimization: Towards Tight Bounds
Authors: Elad Hazan, Kfir Levy
Date: 2014-12-01 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Decision Making, Decision Making Under Uncertainty, Open-Ended Question Answering

Bandit Convex Optimization (BCO) is a fundamental framework for decision making under uncertainty, which generalizes many problems from the realm of online and statistical learning. While the special case of linear cost functions is well understood, a gap on the attainable regret for BCO with nonlinear losses remains an important open question. In this paper we take a step towards understanding the best attainable regret bounds for BCO: we give an efficient and near-optimal regret algorithm for BCO with strongly-convex and smooth loss functions. In contrast to previous works on BCO that use time invariant exploration schemes, our method employs an exploration scheme that shrinks with time.
Projective dictionary pair learning for pattern classification
Authors: Shuhang Gu, Lei Zhang, WangMeng Zuo, Xiangchu Feng
Date: 2014-12-01 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Classification, Dictionary Learning, General Classification

Discriminative dictionary learning (DL) has been widely studied in various pattern classification problems. Most of the existing DL methods aim to learn a synthesis dictionary to represent the input signal while enforcing the representation coefficients and/or representation residual to be discriminative. However, the $\ell_0$ or $\ell_1$-norm sparsity constraint on the representation coefficients adopted in many DL methods makes the training and testing phases time consuming. We propose a new discriminative DL framework, namely projective dictionary pair learning (DPL), which learns a synthesis dictionary and an analysis dictionary jointly to achieve the goal of signal representation and discrimination. Compared with conventional DL methods, the proposed DPL method can not only greatly reduce the time complexity in the training and testing phases, but also lead to very competitive accuracies in a variety of visual classification tasks.
Provable Submodular Minimization using Wolfe's Algorithm
Authors: Deeparnab Chakrabarty, Prateek Jain, Pravesh Kothari
Date: 2014-12-01 00:00:00
Proceeding: NeurIPS 2014 12

Owing to several applications in large scale learning and vision problems, fast submodular function minimization (SFM) has become a critical problem. Theoretically, unconstrained SFM can be performed in polynomial time (Iwata and Orlin 2009), however these algorithms are not practical. In 1976, Wolfe proposed an algorithm to find the minimum Euclidean norm point in a polytope, and in 1980, Fujishige showed how Wolfe's algorithm can be used for SFM. For general submodular functions, the Fujishige-Wolfe minimum norm algorithm seems to have the best empirical performance. Despite its good practical performance, theoretically very little is known about Wolfe's minimum norm algorithm -- to our knowledge the only result is an exponential time analysis due to Wolfe himself. In this paper we give a maiden convergence analysis of Wolfe's algorithm. We prove that in t iterations, Wolfe's algorithm returns a O(1/t)-approximate solution to the min-norm point. We also prove a robust version of Fujishige's theorem which shows that an O(1/n^2)-approximate solution to the min-norm point problem implies exact submodular minimization. As a corollary, we get the first pseudo-polynomial time guarantee for the Fujishige-Wolfe minimum norm algorithm for submodular function minimization. In particular, we show that the min-norm point algorithm solves SFM in O(n^7F^2)-time, where $F$ is an upper bound on the maximum change a single element can cause in the function value.
Exploiting easy data in online optimization
Authors: Amir Sani, Gergely Neu, Alessandro Lazaric
Date: 2014-12-01 00:00:00
Proceeding: NeurIPS 2014 12

We consider the problem of online optimization, where a learner chooses a decision from a given decision set and suffers some loss associated with the decision and the state of the environment. The learner's objective is to minimize its cumulative regret against the best fixed decision in hindsight. Over the past few decades numerous variants have been considered, with many algorithms designed to achieve sub-linear regret in the worst case. However, this level of robustness comes at a cost. Proposed algorithms are often over-conservative, failing to adapt to the actual complexity of the loss sequence which is often far from the worst case. In this paper we introduce a general algorithm that, provided with a safe learning algorithm and an opportunistic benchmark, can effectively combine good worst-case guarantees with much improved performance on easy data. We derive general theoretical bounds on the regret of the proposed algorithm and discuss its implementation in a wide range of applications, notably in the problem of learning with shifting experts (a recent COLT open problem). Finally, we provide numerical simulations in the setting of prediction with expert advice with comparisons to the state of the art.
Sparse Multi-Task Reinforcement Learning
Authors: Daniele Calandriello, Alessandro Lazaric, Marcello Restelli
Date: 2014-12-01 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In multi-task reinforcement learning (MTRL), the objective is to simultaneously learn multiple tasks and exploit their similarity to improve the performance w.r.t.\ single-task learning. In this paper we investigate the case when all the tasks can be accurately represented in a linear approximation space using the same small subset of the original (large) set of features. This is equivalent to assuming that the weight vectors of the task value functions are \textit{jointly sparse}, i.e., the set of their non-zero components is small and it is shared across tasks. Building on existing results in multi-task regression, we develop two multi-task extensions of the fitted $Q$-iteration algorithm. While the first algorithm assumes that the tasks are jointly sparse in the given representation, the second one learns a transformation of the features in the attempt of finding a more sparse representation. For both algorithms we provide a sample complexity analysis and numerical simulations.
Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology
Authors: Remi Lemonnier, Kevin Scaman, Nicolas Vayatis
Date: 2014-12-01 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Epidemiology

In this paper, we derive theoretical bounds for the long-term influence of a node in an Independent Cascade Model (ICM). We relate these bounds to the spectral radius of a particular matrix and show that the behavior is sub-critical when this spectral radius is lower than 1. More specifically, we point out that, in general networks, the sub-critical regime behaves in O(sqrt(n)) where n is the size of the network, and that this upper bound is met for star-shaped networks. We apply our results to epidemiology and percolation on arbitrary networks, and derive a bound for the critical value beyond which a giant connected component arises. Finally, we show empirically the tightness of our bounds for a large family of networks.
Self-Adaptable Templates for Feature Coding
Authors: Xavier Boix, Gemma Roig, Salomon Diether, Luc V. Gool
Date: 2014-12-01 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: image-classification, Image Classification, Object Recognition, Quantization

Hierarchical feed-forward networks have been successfully applied in object recognition. At each level of the hierarchy, features are extracted and encoded, followed by a pooling step. Within this processing pipeline, the common trend is to learn the feature coding templates, often referred as codebook entries, filters, or over-complete basis. Recently, an approach that apparently does not use templates has been shown to obtain very promising results. This is the second-order pooling (O2P). In this paper, we analyze O2P as a coding-pooling scheme. We find that at testing phase, O2P automatically adapts the feature coding templates to the input features, rather than using templates learned during the training phase. From this finding, we are able to bring common concepts of coding-pooling schemes to O2P, such as feature quantization. This allows for significant accuracy improvements of O2P in standard benchmarks of image classification, namely Caltech101 and VOC07.
Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks
Authors: Mario Marchand, Hongyu Su, Emilie Morvant, Juho Rousu, John S. Shawe-Taylor
Date: 2014-12-01 00:00:00
Proceeding: NeurIPS 2014 12

We show that the usual score function for conditional Markov networks can be written as the expectation over the scores of their spanning trees. We also show that a small random sample of these output trees can attain a significant fraction of the margin obtained by the complete graph and we provide conditions under which we can perform tractable inference. The experimental results confirm that practical learning is scalable to realistic datasets using this approach.
Stochastic Network Design in Bidirected Trees
Authors: Xiaojian Wu, Daniel R. Sheldon, Shlomo Zilberstein
Date: 2014-12-01 00:00:00
Proceeding: NeurIPS 2014 12

We investigate the problem of stochastic network design in bidirected trees. In this problem, an underlying phenomenon (e.g., a behavior, rumor, or disease) starts at multiple sources in a tree and spreads in both directions along its edges. Actions can be taken to increase the probability of propagation on edges, and the goal is to maximize the total amount of spread away from all sources. Our main result is a rounded dynamic programming approach that leads to a fully polynomial-time approximation scheme (FPTAS), that is, an algorithm that can find (1−ε)-optimal solutions for any problem instance in time polynomial in the input size and 1/ε. Our algorithm outperforms competing approaches on a motivating problem from computational sustainability to remove barriers in river networks to restore the health of aquatic ecosystems.
Learning convolution filters for inverse covariance estimation of neural network connectivity
Authors: George Mohler
Date: 2014-12-01 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: CPU, Time Series, Time Series Analysis

We consider the problem of inferring direct neural network connections from Calcium imaging time series. Inverse covariance estimation has proven to be a fast and accurate method for learning macro- and micro-scale network connectivity in the brain and in a recent Kaggle Connectomics competition inverse covariance was the main component of several top ten solutions, including our own and the winning team's algorithm. However, the accuracy of inverse covariance estimation is highly sensitive to signal preprocessing of the Calcium fluorescence time series. Furthermore, brute force optimization methods such as grid search and coordinate ascent over signal processing parameters is a time intensive process, where learning may take several days and parameters that optimize one network may not generalize to networks with different size and parameters. In this paper we show how inverse covariance estimation can be dramatically improved using a simple convolution filter prior to applying sample covariance. Furthermore, these signal processing parameters can be learned quickly using a supervised optimization algorithm. In particular, we maximize a binomial log-likelihood loss function with respect to a convolution filter of the time series and the inverse covariance regularization parameter. Our proposed algorithm is relatively fast on networks the size of those in the competition (1000 neurons), producing AUC scores with similar accuracy to the winning solution in training time under 2 hours on a cpu. Prediction on new networks of the same size is carried out in less than 15 minutes, the time it takes to read in the data and write out the solution.
SerialRank: Spectral Ranking using Seriation
Authors: Fajwel Fogel, Alexandre d'Aspremont, Milan Vojnovic
Date: 2014-12-01 00:00:00
Proceeding: NeurIPS 2014 12

We describe a seriation algorithm for ranking a set of n items given pairwise comparisons between these items. Intuitively, the algorithm assigns similar rankings to items that compare similarly with all others. It does so by constructing a similarity matrix from pairwise comparisons, using seriation methods to reorder this matrix and construct a ranking. We first show that this spectral seriation algorithm recovers the true ranking when all pairwise comparisons are observed and consistent with a total order. We then show that ranking reconstruction is still exact even when some pairwise comparisons are corrupted or missing, and that seriation based spectral ranking is more robust to noise than other scoring methods. An additional benefit of the seriation formulation is that it allows us to solve semi-supervised ranking problems. Experiments on both synthetic and real datasets demonstrate that seriation based spectral ranking achieves competitive and in some cases superior performance compared to classical ranking methods.
Clamping Variables and Approximate Inference
Authors: Adrian Weller, Tony Jebara
Date: 2014-12-01 00:00:00
Proceeding: NeurIPS 2014 12

It was recently proved using graph covers (Ruozzi, 2012) that the Bethe partition function is upper bounded by the true partition function for a binary pairwise model that is attractive. Here we provide a new, arguably simpler proof from first principles. We make use of the idea of clamping a variable to a particular value. For an attractive model, we show that summing over the Bethe partition functions for each sub-model obtained after clamping any variable can only raise (and hence improve) the approximation. In fact, we derive a stronger result that may have other useful implications. Repeatedly clamping until we obtain a model with no cycles, where the Bethe approximation is exact, yields the result. We also provide a related lower bound on a broad class of approximate partition functions of general pairwise multi-label models that depends only on the topology. We demonstrate that clamping a few wisely chosen variables can be of practical value by dramatically reducing approximation error.
A Block-Coordinate Descent Approach for Large-scale Sparse Inverse Covariance Estimation
Authors: Eran Treister, Javier S. Turek
Date: 2014-12-01 00:00:00
Proceeding: NeurIPS 2014 12

The sparse inverse covariance estimation problem arises in many statistical applications in machine learning and signal processing. In this problem, the inverse of a covariance matrix of a multivariate normal distribution is estimated, assuming that it is sparse. An $\ell_1$ regularized log-determinant optimization problem is typically solved to approximate such matrices. Because of memory limitations, most existing algorithms are unable to handle large scale instances of this problem. In this paper we present a new block-coordinate descent approach for solving the problem for large-scale data sets. Our method treats the sought matrix block-by-block using quadratic approximations, and we show that this approach has advantages over existing methods in several aspects. Numerical experiments on both synthetic and real gene expression data demonstrate that our approach outperforms the existing state of the art methods, especially for large-scale problems.
Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials
Authors: Shenlong Wang, Alex Schwing, Raquel Urtasun
Date: 2014-12-01 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: 3D Reconstruction, Denoising, Image Denoising

In this paper, we prove that every multivariate polynomial with even degree can be decomposed into a sum of convex and concave polynomials. Motivated by this property, we exploit the concave-convex procedure to perform inference on continuous Markov random fields with polynomial potentials. In particular, we show that the concave-convex decomposition of polynomials can be expressed as a sum-of-squares optimization, which can be efficiently solved via semidefinite programming. We demonstrate the effectiveness of our approach in the context of 3D reconstruction, shape from shading and image denoising, and show that our approach significantly outperforms existing approaches in terms of efficiency as well as the quality of the retrieved solution.