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Advances in Neural Information Processing Systems

Wenruo Bai, William S Noble, Jeff A Bilmes
We study the problem of maximizing deep submodular functions (DSFs) [13, 3] subject to a matroid constraint. DSFs are an expressive class of submodular functions that include, as strict subfamilies, the facility location, weighted coverage, and sums of concave composed with modular functions. We use a strategy similar to the continuous greedy approach [6], but we show that the multilinear extension of any DSF has a natural and computationally attainable concave relaxation that we can optimize using gradient ascent...
December 2018: Advances in Neural Information Processing Systems
Eli Sherman, Ilya Shpitser
The assumption that data samples are independent and identically distributed (iid) is standard in many areas of statistics and machine learning. Nevertheless, in some settings, such as social networks, infectious disease modeling, and reasoning with spatial and temporal data, this assumption is false. An extensive literature exists on making causal inferences under the iid assumption [17, 11, 26, 21], even when unobserved confounding bias may be present. But, as pointed out in [19], causal inference in non-iid contexts is challenging due to the presence of both unobserved confounding and data dependence...
December 2018: Advances in Neural Information Processing Systems
Arun Sai Suggala, Mladen Kolar, Pradeep Ravikumar
Non-parametric multivariate density estimation faces strong statistical and computational bottlenecks, and the more practical approaches impose near-parametric assumptions on the form of the density functions. In this paper, we leverage recent developments to propose a class of non-parametric models which have very attractive computational and statistical properties. Our approach relies on the simple function space assumption that the conditional distribution of each variable conditioned on the other variables has a non-parametric exponential family form...
December 2017: Advances in Neural Information Processing Systems
Ritambhara Singh, Jack Lanchantin, Arshdeep Sekhon, Yanjun Qi
The past decade has seen a revolution in genomic technologies that enabled a flood of genome-wide profiling of chromatin marks. Recent literature tried to understand gene regulation by predicting gene expression from large-scale chromatin measurements. Two fundamental challenges exist for such learning tasks: (1) genome-wide chromatin signals are spatially structured, high-dimensional and highly modular; and (2) the core aim is to understand what the relevant factors are and how they work together. Previous studies either failed to model complex dependencies among input signals or relied on separate feature analysis to explain the decisions...
December 2017: Advances in Neural Information Processing Systems
Zhaobin Kuang, Sinong Geng, David Page
We discover a screening rule for ℓ 1 -regularized Ising model estimation. The simple closed-form screening rule is a necessary and sufficient condition for exactly recovering the blockwise structure of a solution under any given regularization parameters. With enough sparsity, the screening rule can be combined with various optimization procedures to deliver solutions efficiently in practice. The screening rule is especially suitable for large-scale exploratory data analysis, where the number of variables in the dataset can be thousands while we are only interested in the relationship among a handful of variables within moderate-size clusters for interpretability...
December 2017: Advances in Neural Information Processing Systems
Xiangru Huang, Chandrajit Bajaj, Zhenxiao Liang, Qixing Huang
In this paper, we introduce a robust algorithm, TranSync , for the 1D translation synchronization problem, in which the aim is to recover the global coordinates of a set of nodes from noisy measurements of relative coordinates along an observation graph. The basic idea of TranSync is to apply truncated least squares, where the solution at each step is used to gradually prune out noisy measurements. We analyze TranSync under both deterministic and randomized noisy models, demonstrating its robustness and stability...
December 2017: Advances in Neural Information Processing Systems
Xiaoqian Wang, Hong Chen, Weidong Cai, Dinggang Shen, Heng Huang
Linear regression models have been successfully used to function estimation and model selection in high-dimensional data analysis. However, most existing methods are built on least squares with the mean square error (MSE) criterion, which are sensitive to outliers and their performance may be degraded for heavy-tailed noise. In this paper, we go beyond this criterion by investigating the regularized modal regression from a statistical learning viewpoint. A new regularized modal regression model is proposed for estimation and variable selection, which is robust to outliers, heavy-tailed noise, and skewed noise...
December 2017: Advances in Neural Information Processing Systems
Tri Dao, Christopher De Sa, Christopher Ré
Kernel methods have recently attracted resurgent interest, showing performance competitive with deep neural networks in tasks such as speech recognition. The random Fourier features map is a technique commonly used to scale up kernel machines, but employing the randomized feature map means that O ( ε -2 ) samples are required to achieve an approximation error of at most ε . We investigate some alternative schemes for constructing feature maps that are deterministic, rather than random, by approximating the kernel in the frequency domain using Gaussian quadrature...
December 2017: Advances in Neural Information Processing Systems
Paroma Varma, Bryan He, Payal Bajaj, Imon Banerjee, Nishith Khandwala, Daniel L Rubin, Christopher Ré
Obtaining enough labeled data to robustly train complex discriminative models is a major bottleneck in the machine learning pipeline. A popular solution is combining multiple sources of weak supervision using generative models. The structure of these models affects training label quality, but is difficult to learn without any ground truth labels. We instead rely on these weak supervision sources having some structure by virtue of being encoded programmatically. We present Coral, a paradigm that infers generative model structure by statically analyzing the code for these heuristics, thus reducing the data required to learn structure significantly...
December 2017: Advances in Neural Information Processing Systems
Alexander J Ratner, Henry R Ehrenberg, Zeshan Hussain, Jared Dunnmon, Christopher Ré
Data augmentation is a ubiquitous technique for increasing the size of labeled training sets by leveraging task-specific data transformations that preserve class labels. While it is often easy for domain experts to specify individual transformations, constructing and tuning the more sophisticated compositions typically needed to achieve state-of-the-art results is a time-consuming manual task in practice. We propose a method for automating this process by learning a generative sequence model over user-specified transformation functions using a generative adversarial approach...
December 2017: Advances in Neural Information Processing Systems
Kristjan Greenewald, Ambuj Tewari, Predrag Klasnja, Susan Murphy
Contextual bandits have become popular as they offer a middle ground between very simple approaches based on multi-armed bandits and very complex approaches using the full power of reinforcement learning. They have demonstrated success in web applications and have a rich body of associated theoretical guarantees. Linear models are well understood theoretically and preferred by practitioners because they are not only easily interpretable but also simple to implement and debug. Furthermore, if the linear model is true, we get very strong performance guarantees...
December 2017: Advances in Neural Information Processing Systems
Alexander Ratner, Christopher De Sa, Sen Wu, Daniel Selsam, Christopher Ré
Large labeled training sets are the critical building blocks of supervised learning methods and are key enablers of deep learning techniques. For some applications, creating labeled training sets is the most time-consuming and expensive part of applying machine learning. We therefore propose a paradigm for the programmatic creation of training sets called data programming in which users express weak supervision strategies or domain heuristics as labeling functions , which are programs that label subsets of the data, but that are noisy and may conflict...
December 2016: Advances in Neural Information Processing Systems
Ian E H Yen, Xiangru Huang, Kai Zhong, Ruohan Zhang, Pradeep Ravikumar, Inderjit S Dhillon
Many applications of machine learning involve structured outputs with large domains, where learning of a structured predictor is prohibitive due to repetitive calls to an expensive inference oracle. In this work, we show that by decomposing training of a Structural Support Vector Machine (SVM) into a series of multiclass SVM problems connected through messages, one can replace an expensive structured oracle with Factorwise Maximization Oracles (FMOs) that allow efficient implementation of complexity sublinear to the factor domain...
December 2016: Advances in Neural Information Processing Systems
Avinava Dubey, Sashank J Reddi, Barnabás Póczos, Alexander J Smola, Eric P Xing, Sinead A Williamson
Stochastic gradient-based Monte Carlo methods such as stochastic gradient Langevin dynamics are useful tools for posterior inference on large scale datasets in many machine learning applications. These methods scale to large datasets by using noisy gradients calculated using a mini-batch or subset of the dataset. However, the high variance inherent in these noisy gradients degrades performance and leads to slower mixing. In this paper, we present techniques for reducing variance in stochastic gradient Langevin dynamics, yielding novel stochastic Monte Carlo methods that improve performance by reducing the variance in the stochastic gradient...
December 2016: Advances in Neural Information Processing Systems
Hao Henry Zhou, Sathya N Ravi, Vamsi K Ithapu, Sterling C Johnson, Grace Wahba, Vikas Singh
Consider samples from two different data sources [Formula: see text] and [Formula: see text]. We only observe their transformed versions [Formula: see text] and [Formula: see text], for some known function class h (·) and g (·). Our goal is to perform a statistical test checking if P source = P target while removing the distortions induced by the transformations. This problem is closely related to domain adaptation, and in our case, is motivated by the need to combine clinical and imaging based biomarkers from multiple sites and/or batches - a fairly common impediment in conducting analyses with much larger sample sizes...
2016: Advances in Neural Information Processing Systems
Jin Lu, Guannan Liang, Jiangwen Sun, Jinbo Bi
Matrix completion methods can benefit from side information besides the partially observed matrix. The use of side features that describe the row and column entities of a matrix has been shown to reduce the sample complexity for completing the matrix. We propose a novel sparse formulation that explicitly models the interaction between the row and column side features to approximate the matrix entries. Unlike early methods, this model does not require the low rank condition on the model parameter matrix. We prove that when the side features span the latent feature space of the matrix to be recovered, the number of observed entries needed for an exact recovery is O (log N ) where N is the size of the matrix...
2016: Advances in Neural Information Processing Systems
Lin Chen, Amin Karbasi, Forrest W Crawford
Most real-world networks are too large to be measured or studied directly and there is substantial interest in estimating global network properties from smaller sub-samples. One of the most important global properties is the number of vertices/nodes in the network. Estimating the number of vertices in a large network is a major challenge in computer science, epidemiology, demography, and intelligence analysis. In this paper we consider a population random graph G = (V, E) from the stochastic block model (SBM) with K communities/blocks...
2016: Advances in Neural Information Processing Systems
Lane T McIntosh, Niru Maheswaranathan, Aran Nayebi, Surya Ganguli, Stephen A Baccus
A central challenge in sensory neuroscience is to understand neural computations and circuit mechanisms that underlie the encoding of ethologically relevant, natural stimuli. In multilayered neural circuits, nonlinear processes such as synaptic transmission and spiking dynamics present a significant obstacle to the creation of accurate computational models of responses to natural stimuli. Here we demonstrate that deep convolutional neural networks (CNNs) capture retinal responses to natural scenes nearly to within the variability of a cell's response, and are markedly more accurate than linear-nonlinear (LN) models and Generalized Linear Models (GLMs)...
2016: Advances in Neural Information Processing Systems
Bryan He, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré
Gibbs sampling is a Markov Chain Monte Carlo sampling technique that iteratively samples variables from their conditional distributions. There are two common scan orders for the variables: random scan and systematic scan. Due to the benefits of locality in hardware, systematic scan is commonly used, even though most statistical guarantees are only for random scan. While it has been conjectured that the mixing times of random scan and systematic scan do not differ by more than a logarithmic factor, we show by counterexample that this is not the case, and we prove that that the mixing times do not differ by more than a polynomial factor under mild conditions...
2016: Advances in Neural Information Processing Systems
Vidyashankar Sivakumar, Arindam Banerjee, Pradeep Ravikumar
We consider the problem of high-dimensional structured estimation with norm-regularized estimators, such as Lasso, when the design matrix and noise are drawn from sub-exponential distributions. Existing results only consider sub-Gaussian designs and noise, and both the sample complexity and non-asymptotic estimation error have been shown to depend on the Gaussian width of suitable sets. In contrast, for the sub-exponential setting, we show that the sample complexity and the estimation error will depend on the exponential width of the corresponding sets, and the analysis holds for any norm...
December 2015: Advances in Neural Information Processing Systems
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