mtybilly/Instruct2DS huggingface dataset:
Accelerated Mini-Batch Stochastic Dual Coordinate Ascent
Authors: Tong Zhang Shai Shalev-Shwartz
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: Stochastic dual coordinate ascent (SDCA) is an effective technique for solving regularized loss minimization problems in machine learning. This paper considers an extension of SDCA under the mini-batch setting that is often used in practice. Our main contribution is to introduce an accelerated mini-batch version of SDCA and prove a fast convergence rate for this method. We discuss an implementation of our method over a parallel computing system, and compare the results to both the vanilla stochastic dual coordinate ascent and to the accelerated deterministic gradient descent method of Nesterov [2007].
A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks
Authors: Qirong Ho, Eric P Xing Junming Yin
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: We propose a scalable approach for making inference about latent spaces of large networks. With a succinct representation of networks as a bag of triangular motifs, a parsimonious statistical model, and an efficient stochastic variational inference algorithm, we are able to analyze real networks with over a million vertices and hundreds of latent roles on a single machine in a matter of hours, a setting that is out of reach for many existing methods. When compared to the state-of-the-art probabilistic approaches, our method is several orders of magnitude faster, with competitive or improved accuracy for latent space recovery and link prediction.
Multi-Prediction Deep Boltzmann Machines
Authors: Mehdi Mirza, Aaron Courville, Yoshua Bengio Ian Goodfellow
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: We introduce the Multi-Prediction Deep Boltzmann Machine (MP-DBM). The MP-DBM can be seen as a single probabilistic model trained to maximize a variational approximation to the generalized pseudolikelihood, or as a family of recurrent nets that share parameters and approximately solve different inference problems. Prior methods of training DBMs either do not perform well on classification tasks or require an initial learning pass that trains the DBM greedily, one layer at a time. The MP-DBM does not require greedy layerwise pretraining, and outperforms the standard DBM at classification, classification with missing inputs, and mean field prediction tasks.
Learning and using language via recursive pragmatic reasoning about other agents
Authors: Noah Goodman, Michael Frank Nathaniel J Smith
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: Language users are remarkably good at making inferences about speakers' intentions in context, and children learning their native language also display substantial skill in acquiring the meanings of unknown words. These two cases are deeply related: Language users invent new terms in conversation, and language learners learn the literal meanings of words based on their pragmatic inferences about how those words are used.  While pragmatic inference and word learning have both been independently characterized in probabilistic terms, no current work unifies these two. We describe a model in which language learners assume that they jointly approximate a shared, external lexicon and reason recursively about the goals of others in using this lexicon. This model captures phenomena in word learning and pragmatic inference; it additionally leads to insights about the emergence of communicative systems in conversation and the mechanisms by which pragmatic inferences become incorporated into word meanings.
Reinforcement Learning in Robust Markov Decision Processes
Authors: Huan Xu, Shie Mannor Shiau Hong Lim
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: An important challenge in Markov decision processes is to ensure robustness with respect to unexpected or adversarial system behavior while taking advantage of well-behaving parts of the system. We consider a problem setting where some unknown parts of the state space can have arbitrary transitions while other parts are purely stochastic. We devise an algorithm that is adaptive to potentially adversarial behavior and show that it achieves similar regret bounds as the purely stochastic case.
Regularized Spectral Clustering under the Degree-Corrected Stochastic Blockmodel
Authors: Karl Rohe Tai Qin
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: Spectral clustering is a fast and popular algorithm for finding clusters in networks. Recently, Chaudhuri et al. and Amini et al. proposed variations on the algorithm that artificially inflate the node degrees for improved statistical performance.  The current paper extends the previous theoretical results to the more canonical spectral clustering algorithm in a way that removes any assumption on the minimum degree and  provides guidance on the choice of tuning parameter.  Moreover, our results show how the star shape" in the eigenvectors--which are consistently observed in empirical networks--can be explained by the Degree-Corrected Stochastic Blockmodel and the Extended Planted Partition model, two statistical model that allow for highly heterogeneous degrees.  Throughout, the paper characterizes and justifies several of the variations of the spectral clustering algorithm in terms of these models.  "
A Novel Two-Step Method for Cross Language Representation Learning
Authors: Yuhong Guo Min Xiao
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: Cross language text classiﬁcation is an important learning task in natural language processing. A critical challenge of cross language learning lies in that words of different languages are in disjoint feature spaces. In this paper, we propose a two-step representation learning method to bridge the feature spaces of different languages by exploiting a set of parallel bilingual documents. Speciﬁcally, we ﬁrst formulate a matrix completion problem to produce a complete parallel document-term matrix for all documents in two languages, and then induce a cross-lingual document representation by applying latent semantic indexing on the obtained matrix. We use a projected gradient descent algorithm to solve the formulated matrix completion problem with convergence guarantees. The proposed approach is evaluated by conducting a set of experiments with cross language sentiment classiﬁcation tasks on Amazon product reviews. The experimental results demonstrate that the proposed learning approach outperforms a number of comparison cross language representation learning methods, especially when the number of parallel bilingual documents is small.
Graphical Models for Inference with Missing Data
Authors: Judea Pearl, Jin Tian Karthika Mohan
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: We address the problem of deciding whether there exists a consistent estimator of a given relation Q, when data are missing not at random. We employ a formal representation called `Missingness Graphs' to explicitly portray the causal mechanisms responsible for missingness and to encode dependencies between these mechanisms and the variables being measured. Using this representation, we define the notion of \textit{recoverability} which ensures that, for a given missingness-graph $G$ and a given query $Q$ an algorithm exists such that in the limit of large samples, it produces an estimate of $Q$ \textit{as if} no data were missing. We further present conditions that the graph should satisfy in order for recoverability to hold and devise algorithms to detect the presence of these conditions.
Convex Tensor Decomposition via Structured Schatten Norm Regularization
Authors: Taiji Suzuki Ryota Tomioka
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: We propose a new class of structured Schatten norms for tensors that includes two recently  proposed norms (overlapped'' and "latent'') for convex-optimization-based  tensor decomposition. Based on the properties of the structured Schatten norms, we mathematically analyze the performance of "latent'' approach for tensor decomposition, which was empirically found to perform better than the "overlapped'' approach in some settings. We show theoretically that this is indeed the case. In particular, when the unknown true tensor is low-rank in a specific mode, this approach performs as well as knowing the mode with the smallest rank. Along the way, we show a novel duality result for structures Schatten norms, which is also interesting in the general context of structured sparsity. We confirm through  numerical simulations that our theory can precisely predict the scaling behaviour of the mean squared error.  "
Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression
Authors: Miguel Lazaro-Gredilla Michalis Titsias RC AUEB
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: We introduce a novel variational method that allows to approximately integrate out kernel hyperparameters, such as length-scales, in Gaussian process regression. This approach consists of a novel variant of the variational framework that has been recently developed for the Gaussian process latent variable model which additionally makes use of a standardised representation of the Gaussian process. We consider this technique for learning Mahalanobis distance metrics in a Gaussian process regression setting and provide experimental evaluations and comparisons with existing methods by considering  datasets with high-dimensional inputs.
Efficient Online Inference for Bayesian Nonparametric Relational Models
Authors: Prem Gopalan, David Blei, Erik Sudderth Dae Il Kim
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: Stochastic block models characterize observed network relationships via latent community memberships. In large social networks, we expect entities to participate in multiple communities, and the number of communities to grow with the network size. We introduce a new model for these phenomena, the hierarchical Dirichlet process relational model, which allows nodes to have mixed membership in an unbounded set of communities. To allow scalable learning, we derive an online stochastic variational inference algorithm. Focusing on assortative models of undirected networks, we also propose an efficient structured mean field variational bound, and online methods for automatically pruning unused communities. Compared to state-of-the-art online learning methods for parametric relational models, we show significantly improved perplexity and link prediction accuracy for sparse networks with tens of thousands of nodes. We also showcase an analysis of LittleSis, a large network of who-knows-who at the heights of business and government.
Convergence of Monte Carlo Tree Search in Simultaneous Move Games
Authors: Vojta Kovarik, Marc Lanctot, Branislav Bosansky Viliam Lisy
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: In this paper, we study Monte Carlo tree search (MCTS) in zero-sum extensive-form games with perfect information and simultaneous moves. We present a general template of MCTS algorithms for these games, which can be instantiated by various selection methods. We formally prove that if a selection method is $\epsilon$-Hannan consistent in a matrix game and satisfies additional requirements on exploration, then the MCTS algorithm eventually converges to an approximate Nash equilibrium (NE) of the extensive-form game. We empirically evaluate this claim using regret matching and Exp3 as the selection methods on randomly generated and worst case games. We confirm the formal result and show that additional MCTS variants also converge to approximate NE on the evaluated games.
Learning to Pass Expectation Propagation Messages
Authors: Daniel Tarlow, John Winn Nicolas Heess
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: Expectation Propagation (EP) is a popular approximate posterior inference algorithm that often  provides a fast and accurate alternative to sampling-based methods. However, while the EP framework in theory allows for complex non-Gaussian factors, there is still a significant practical barrier to using them within EP, because doing so requires the implementation of message update operators, which can be difficult and require hand-crafted approximations. In this work, we study the question of whether it is possible to automatically derive fast and accurate EP updates by learning a discriminative model e.g., a neural network or random forest) to map EP message inputs to EP message outputs.  We address the practical concerns that arise in the process, and we provide empirical analysis on several challenging and diverse factors, indicating that there is a space of factors where this approach appears promising.
Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits
Authors: Brooks Paige, Ari Pakman, Liam Paninski Ben Shababo
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: We develop an inference and optimal design procedure for recovering synaptic weights in neural microcircuits. We base our procedure on data from an experiment in which populations of putative presynaptic neurons can be stimulated while a subthreshold recording is made from a single postsynaptic neuron. We present a realistic statistical model which accounts for the main sources of variability in this experiment and allows for large amounts of information about the biological system to be incorporated if available. We then present a simpler model to facilitate online experimental design which entails the use of efficient Bayesian inference. The optimized approach results in equal quality posterior estimates of the synaptic weights in roughly half the number of experimental trials under experimentally realistic conditions, tested on synthetic data generated from the full model.
Action is in the Eye of the Beholder: Eye-gaze Driven Model for Spatio-Temporal Action Localization
Authors: Michalis Raptis, Leonid Sigal, Greg Mori Nataliya Shapovalova
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: We propose a new weakly-supervised structured learning approach for recognition and spatio-temporal localization of actions in video. As part of the proposed approach we develop a generalization of the Max-Path search algorithm, which allows us to efficiently search over a structured space of multiple spatio-temporal paths, while also allowing to incorporate context information into the model. Instead of using spatial annotations, in the form of bounding boxes, to guide the latent model during training, we utilize human gaze data in the form of a weak supervisory signal. This is achieved by incorporating gaze, along with the classification, into the structured loss within the latent SVM learning framework. Experiments on a challenging benchmark dataset, UCF-Sports, show that our model is more accurate, in terms of classification, and achieves state-of-the-art results in localization. In addition, we show how our model can produce top-down saliency maps conditioned on the classification label and localized latent paths.
Integrated Non-Factorized Variational Inference
Authors: Xuejun Liao, Lawrence Carin Shaobo Han
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: We present a non-factorized variational method for full posterior inference in Bayesian hierarchical models,  with the goal of capturing the posterior variable dependencies via efficient and possibly parallel computation. Our approach unifies the integrated nested Laplace approximation (INLA) under the variational framework. The proposed method is applicable in more challenging scenarios than typically assumed by INLA,  such as Bayesian Lasso,  which is characterized by the non-differentiability of the $\ell_{1}$ norm arising from independent Laplace priors. We derive an upper bound for the Kullback-Leibler divergence,  which yields a fast closed-form solution via decoupled optimization. Our method is a reliable analytic alternative to Markov chain Monte Carlo (MCMC), and it results in a tighter evidence lower bound than that of mean-field variational Bayes (VB) method.
A Gang of Bandits
Authors: Claudio Gentile, Giovanni Zappella Nicolò Cesa-Bianchi
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: Multi-armed bandit problems are receiving a great deal of attention because they adequately formalize the exploration-exploitation trade-offs arising in several industrially relevant applications, such as online advertisement and, more generally, recommendation systems.  In many cases, however, these applications have a strong social component, whose integration in the bandit algorithm could lead to a dramatic performance increase. For instance, we may want to serve content to a group of users by taking advantage of an underlying network of social relationships among them. In this paper, we introduce novel algorithmic approaches to the solution of such networked bandit problems. More specifically, we design and analyze a global strategy which allocates a bandit algorithm to each network node (user) and allows it to “share” signals (contexts and payoffs) with the neghboring nodes. We then derive two more scalable variants of this strategy based on different ways of clustering the graph nodes. We experimentally compare the algorithm and its variants to state-of-the-art methods for contextual bandits that do not use the relational information. Our experiments, carried out on synthetic and real-world datasets, show a marked increase in prediction performance obtained by exploiting the network structure.
Multiclass Total Variation Clustering
Authors: Thomas Laurent, David Uminsky, James von Brecht Xavier Bresson
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: Ideas from the image processing literature have recently motivated a new set of clustering algorithms that rely on the concept of total variation. While these algorithms perform well for bi-partitioning tasks, their recursive extensions yield unimpressive results for multiclass clustering tasks. This paper presents a general framework for multiclass total variation clustering that does not rely on recursion. The results greatly outperform previous total variation algorithms and compare well with state-of-the-art NMF approaches.
Simultaneous Rectification and Alignment via Robust Recovery of Low-rank Tensors
Authors: Di Wang, Zhengyuan Zhou, Yi Ma Xiaoqin Zhang
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: In this work, we propose a general method for recovering low-rank three-order tensors, in which the data can be deformed by some unknown transformation and corrupted by arbitrary sparse errors. Since the unfolding matrices of a tensor are interdependent, we introduce auxiliary variables and relax the hard equality constraints by the augmented Lagrange multiplier method. To improve the computational efficiency, we introduce a proximal gradient step to the alternating direction minimization method. We have provided proof for the convergence of the linearized version of the problem which is the inner loop of the overall algorithm. Both simulations and experiments show that our methods are more efficient and effective than previous work. The proposed method can be easily applied to simultaneously  rectify and align multiple images or videos frames. In this context, the state-of-the-art algorithms RASL'' and "TILT'' can be viewed as two special cases of our work, and yet each only performs part of the function of our method."
BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables
Authors: Matyas A Sustik, Inderjit S Dhillon, Pradeep K Ravikumar, Russell Poldrack Cho-Jui Hsieh
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: The l1-regularized Gaussian maximum likelihood estimator (MLE) has been shown to have strong statistical guarantees in recovering a sparse inverse covariance matrix even under high-dimensional settings. However, it requires solving a difficult non-smooth log-determinant program with number of parameters scaling quadratically with the number of Gaussian variables. State-of-the-art methods thus do not scale to problems with more than 20,000 variables. In this paper, we develop an algorithm BigQUIC, which can solve 1 million dimensional l1-regularized Gaussian MLE problems (which would thus have 1000 billion parameters) using a single machine, with bounded memory. In order to do so, we carefully exploit the underlying structure of the problem. Our innovations include a novel block-coordinate descent method with the blocks chosen via a clustering scheme to minimize repeated computations; and allowing for inexact computation of specific components. In spite of these modifications,  we are able to theoretically analyze our procedure and show that BigQUIC can achieve super-linear or even quadratic convergence rates.
Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching
Authors: Pablo Sprechmann, Joshua Vogelstein, Pablo Muse, Guillermo Sapiro Marcelo Fiori
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: Graph matching is a challenging problem with very important applications in a wide range of fields, from image and video analysis to biological and biomedical problems. We propose a robust graph matching algorithm inspired in sparsity-related techniques. We cast the problem, resembling group or collaborative sparsity formulations, as a non-smooth convex optimization problem that can be efficiently solved using augmented Lagrangian techniques. The method can deal with weighted or unweighted graphs, as well as multimodal data, where different graphs represent different types of data. The proposed approach is also naturally integrated with collaborative graph inference techniques, solving general network inference problems where the observed variables, possibly coming from different modalities, are not in correspondence. The algorithm is tested and compared with state-of-the-art graph matching techniques in both synthetic and real graphs. We also present results on multimodal graphs and applications to collaborative inference of brain connectivity from alignment-free functional magnetic resonance imaging (fMRI) data.
Optimal integration of visual speed across different spatiotemporal frequency channels
Authors: Alan Stocker Matjaz Jogan
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: How does the human visual system compute the speed of a coherent motion stimulus that contains motion energy in different spatiotemporal frequency bands? Here we propose that perceived speed is the result of optimal integration of speed information from independent spatiotemporal frequency tuned channels. We formalize this hypothesis with a Bayesian observer model that treats the channel activity as independent cues, which are optimally combined with a prior expectation for slow speeds. We test the model against behavioral data from a 2AFC speed discrimination task with which we measured subjects' perceived speed of drifting sinusoidal gratings with different contrasts and spatial frequencies, and of various combinations of these single gratings. We find that perceived speed of the combined stimuli is independent of the relative phase of the underlying grating components, and that the perceptual biases and discrimination thresholds are always smaller for the combined stimuli, supporting the cue combination hypothesis. The proposed Bayesian model fits the data well, accounting for perceptual biases and thresholds of both simple and combined stimuli. Fits are improved if we assume that the channel responses are subject to divisive normalization, which is in line with physiological evidence.  Our results provide an important step toward a more complete model of visual motion perception that can predict perceived speeds for stimuli of arbitrary spatial structure.
Translating Embeddings for Modeling Multi-relational Data
Authors: Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko Antoine Bordes
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: We consider the problem of embedding entities and relationships of multi-relational data in low-dimensional vector spaces. Our objective is to propose a canonical model which is easy to train, contains a reduced number of parameters and can scale up to very large databases. Hence, we propose, TransE, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities. Despite its simplicity, this assumption proves to be powerful since extensive experiments show that TransE significantly outperforms state-of-the-art methods in link prediction on two knowledge bases. Besides, it can be successfully trained on a large scale data set with 1M entities, 25k relationships and more than 17M training samples.
Synthesizing Robust Plans under Incomplete Domain Models
Authors: Subbarao Kambhampati, Minh Do Tuan A Nguyen
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: Most current planners assume complete domain models and focus on generating correct plans. Unfortunately, domain modeling is a laborious and error-prone task, thus real world agents have to plan with incomplete domain models. While domain experts cannot guarantee completeness, often they are able to circumscribe the incompleteness of the model by providing annotations as to which parts of the domain model may be incomplete. In such cases, the goal should be to synthesize plans that are robust with respect to any known incompleteness of the domain. In this paper, we first introduce annotations expressing the knowledge of the domain incompleteness and formalize the notion of plan robustness with respect to an incomplete domain model. We then show an approach to compiling the problem of finding robust plans to the conformant probabilistic planning problem, and present experimental results with Probabilistic-FF planner.
Learning Gaussian Graphical Models with Observed or Latent FVSs
Authors: Alan Willsky Ying Liu
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: Gaussian Graphical Models (GGMs) or Gauss Markov random fields are widely used in many applications, and the trade-off between the modeling capacity and the efficiency of learning and inference has been an important research problem. In this paper, we study the family of GGMs with small feedback vertex sets (FVSs), where an FVS is a set of nodes whose removal breaks all the cycles. Exact inference such as computing the marginal distributions and the partition function has complexity $O(k^{2}n)$  using message-passing algorithms, where k  is the size of the FVS, and n  is the total number of nodes. We propose efficient structure learning algorithms for two cases: 1) All nodes are observed, which is useful in modeling social or flight networks where the FVS nodes often correspond to a small number of high-degree nodes, or hubs, while the rest of the networks is modeled by a tree. Regardless of the maximum degree, without knowing the full graph structure, we can exactly compute the maximum likelihood estimate in $O(kn^2+n^2\log n)$  if the FVS is known or in polynomial time if the FVS is unknown but has bounded size. 2) The FVS nodes are latent variables, where structure learning is equivalent to decomposing a inverse covariance matrix (exactly or approximately) into the sum of a tree-structured matrix and a low-rank matrix. By incorporating efficient inference into the learning steps, we can obtain a learning algorithm using alternating low-rank correction with complexity $O(kn^{2}+n^{2}\log n)$  per iteration. We also perform experiments using both synthetic data as well as real data of flight delays to demonstrate the modeling capacity with FVSs of various sizes. We show that empirically the family of GGMs of size $O(\log n)$ strikes a good balance between the modeling capacity and the efficiency.
Extracting regions of interest from biological images with convolutional sparse block coding
Authors: Adam M Packer, Noah Pettit, Henry Dalgleish, Michael Hausser, Maneesh Sahani Marius Pachitariu
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: Biological tissue is often composed of cells with similar morphologies replicated throughout large volumes and many biological applications rely on the accurate identification of these cells and their locations from image data. Here we develop a generative model that captures the regularities present in images composed of repeating elements of a few different types. Formally, the model can be described as convolutional sparse block coding. For inference we use a variant of convolutional matching pursuit adapted to block-based representations. We extend the K-SVD learning algorithm to subspaces by retaining several principal vectors from the SVD decomposition instead of just one. Good models with little cross-talk between subspaces can be obtained by learning the blocks incrementally. We perform extensive experiments on simulated images and the inference algorithm consistently recovers a large proportion of the cells with a small number of false positives. We fit the convolutional model to noisy GCaMP6 two-photon images of spiking neurons and to Nissl-stained slices of cortical tissue and show that it recovers cell body locations without supervision. The flexibility of the block-based representation is reflected in the variability of the recovered cell shapes.
Training and Analysing Deep Recurrent Neural Networks
Authors: Benjamin Schrauwen Michiel Hermans
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: Time series often have a temporal hierarchy, with information that is spread out over multiple time scales. Common recurrent neural networks, however, do not explicitly accommodate such a hierarchy, and most research on them has been focusing on training algorithms rather than on their basic architecture. In this pa- per we study the effect of a hierarchy of recurrent neural networks on processing time series. Here, each layer is a recurrent network which receives the hidden state of the previous layer as input. This architecture allows us to perform hi- erarchical processing on difficult temporal tasks, and more naturally capture the structure of time series. We show that they reach state-of-the-art performance for recurrent networks in character-level language modelling when trained with sim- ple stochastic gradient descent. We also offer an analysis of the different emergent time scales.
Low-Rank Matrix and Tensor Completion via Adaptive Sampling
Authors: Aarti Singh Akshay Krishnamurthy
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: We study low rank matrix and tensor completion and propose novel algorithms that employ adaptive sampling schemes to obtain strong performance guarantees for these problems. Our algorithms exploit adaptivity to identify entries that are highly informative for identifying the column space of the matrix (tensor) and consequently, our results hold even when the row space is highly coherent, in contrast with previous analysis of matrix completion. In the absence of noise, we show that one can exactly recover a $n \times n$ matrix of rank $r$ using $O(r^2 n \log(r))$ observations, which is better than the best known bound under random sampling. We also show that one can recover an order $T$ tensor using $O(r^{2(T-1)}T^2 n \log(r))$. For noisy recovery, we show that one can consistently estimate a low rank matrix corrupted with noise using $O(nr \textrm{polylog}(n))$ observations. We complement our study with simulations that verify our theoretical guarantees and demonstrate the scalability of our algorithms.
Fast Determinantal Point Process Sampling with Application to Clustering
Authors: Byungkon Kang
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: Determinantal Point Process (DPP) has gained much popularity for modeling sets of diverse items. The gist of DPP is that the probability of choosing a particular set of items is proportional to the determinant of a positive definite matrix that defines the similarity of those items. However, computing the determinant requires time cubic in the number of items, and is hence impractical for large sets. In this paper, we address this problem by constructing a rapidly mixing Markov chain, from which we can acquire a sample from the given DPP in sub-cubic time.  In addition, we show that this framework can be extended to sampling from cardinality-constrained DPPs. As an application, we show how our sampling algorithm can be used to provide a fast heuristic for determining the number of clusters, resulting in better clustering.
Matrix factorization with binary components
Authors: Matthias Hein, Pavlo Lutsik Martin Slawski
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: Motivated by an application in computational biology, we consider constrained low-rank matrix factorization problems with $\{0,1\}$-constraints on one of the factors. In addition to the the non-convexity shared with more general matrix factorization schemes, our problem is further complicated by a combinatorial constraint set of size $2^{m \cdot r}$, where $m$ is the dimension of the data points and $r$ the rank of the factorization. Despite apparent intractability, we provide $-$in the line of recent work on non-negative matrix factorization by Arora et al.~(2012)$-$ an algorithm that provably recovers the underlying factorization in the exact case with operations of the order $O(m r 2^r + mnr)$ in the worst case. To obtain that result, we invoke theory centered around a fundamental result in combinatorics, the Littlewood-Offord lemma.
Reshaping Visual Datasets for Domain Adaptation
Authors: Kristen Grauman, Fei Sha Boqing Gong
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: In visual recognition problems, the common data distribution mismatches between training and testing make domain adaptation essential. However, image data is difficult to manually divide into the discrete domains required by adaptation algorithms, and the standard practice of equating datasets with domains is a weak proxy for all the real conditions that alter the statistics in complex ways (lighting, pose, background, resolution, etc.) We propose an approach to automatically discover latent domains in image or video datasets. Our formulation imposes two key properties on domains: maximum distinctiveness and maximum learnability. By maximum distinctiveness, we require the underlying distributions of the identified domains to be different from each other; by maximum learnability, we ensure that a strong discriminative model can be learned from the domain. We devise a nonparametric representation and efficient optimization procedure for distinctiveness, which, when coupled with our learnability constraint, can successfully discover domains among both training and test data. We extensively evaluate our approach on object recognition and human activity recognition tasks.
Perfect Associative Learning with Spike-Timing-Dependent Plasticity
Authors: Maren Westkott, Klaus Pawelzik Christian Albers
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: Recent extensions of the Perceptron, as e.g. the Tempotron, suggest that this theoretical concept is highly relevant also for understanding networks of spiking neurons in the brain. It is not known, however, how the computational power of the Perceptron and of its variants might be accomplished by the plasticity mechanisms of real synapses. Here we prove that spike-timing-dependent plasticity having an anti-Hebbian form for excitatory synapses as well as a spike-timing-dependent plasticity of Hebbian shape for inhibitory synapses are sufficient for realizing the original Perceptron Learning Rule if the respective plasticity mechanisms act in concert with the hyperpolarisation of the post-synaptic neurons. We also show that with these simple yet biologically realistic dynamics Tempotrons are efficiently learned. The proposed mechanism might underly the acquisition of mappings of spatio-temporal activity patterns in one area of the brain onto other spatio-temporal spike patterns in another region and of long term memories in cortex. Our results underline that learning processes in realistic networks of spiking neurons depend crucially on the interactions of synaptic plasticity mechanisms with the dynamics of participating neurons.
Tracking Time-varying Graphical Structure
Authors: David Danks Erich Kummerfeld
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: Structure learning algorithms for graphical models have focused almost exclusively on stable environments in which the underlying generative process does not change; that is, they assume that the generating model is globally stationary. In real-world environments, however, such changes often occur without warning or signal. Real-world data often come from generating models that are only locally stationary. In this paper, we present LoSST, a novel, heuristic structure learning algorithm that tracks changes in graphical model structure or parameters in a dynamic, real-time manner. We show by simulation that the algorithm performs comparably to batch-mode learning when the generating graphical structure is globally stationary, and significantly better when it is only locally stationary.
Phase Retrieval using Alternating Minimization
Authors: Prateek Jain, Sujay Sanghavi Praneeth Netrapalli
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: Phase retrieval problems involve solving linear equations, but with missing sign (or phase, for complex numbers) information. Over the last two decades, a popular generic empirical approach to the many variants of this problem has been one of alternating minimization; i.e. alternating between estimating the missing phase information, and the candidate solution. In this paper, we show that a simple alternating minimization algorithm geometrically converges to the solution of one such problem -- finding a vector $x$ from $y,A$, where $y = |A'x|$ and $|z|$ denotes a vector of element-wise magnitudes of $z$ -- under the assumption that $A$ is Gaussian.  Empirically, our algorithm performs similar to recently proposed convex techniques for this variant (which are based on lifting" to a convex matrix problem) in sample complexity and robustness to noise. However, our algorithm is much more efficient and can scale to large problems. Analytically, we show geometric convergence to the solution, and sample complexity that is off by log factors from obvious lower bounds. We also establish close to optimal scaling for the case when the unknown vector is sparse. Our work represents the only known proof of alternating minimization for any variant of phase retrieval problems in the non-convex setting."
Unsupervised Structure Learning of Stochastic And-Or Grammars
Authors: Maria Pavlovskaia, Song-Chun Zhu Kewei Tu
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: Stochastic And-Or grammars compactly represent both compositionality and reconfigurability and have been used to model different types of data such as images and events. We present a unified formalization of stochastic And-Or grammars that is agnostic to the type of the data being modeled, and propose an unsupervised approach to learning the structures as well as the parameters of such grammars. Starting from a trivial initial grammar, our approach iteratively induces compositions and reconfigurations in a unified manner and optimizes the posterior probability of the grammar. In our empirical evaluation, we applied our approach to learning event grammars and image grammars and achieved comparable or better performance than previous approaches.
Learning Multi-level Sparse Representations
Authors: Fred A. Hamprecht Ferran Diego Andilla
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: Bilinear approximation of a matrix is a powerful paradigm of unsupervised learning. In some applications, however, there is a natural hierarchy of concepts that ought to be reflected in the unsupervised analysis. For example, in the neurosciences image sequence considered here, there are the semantic concepts of pixel $\rightarrow$ neuron $\rightarrow$ assembly that should find their counterpart in the unsupervised analysis. Driven by this concrete problem, we propose a decomposition of the matrix of observations into a product of more than two sparse matrices, with the rank decreasing from lower to higher levels. In contrast to prior work, we allow for both hierarchical and heterarchical relations of lower-level to higher-level concepts. In addition, we learn the nature of these relations rather than imposing them. Finally, we describe an optimization scheme that allows to optimize the decomposition over all levels jointly, rather than in a greedy level-by-level fashion.   The proposed bilevel SHMF (sparse heterarchical matrix factorization) is the first formalism that allows to simultaneously interpret a calcium imaging sequence in terms of the constituent neurons, their membership in assemblies, and the time courses of both neurons and assemblies.  Experiments show that the proposed model fully recovers the structure from difficult synthetic data designed to imitate the experimental data. More importantly, bilevel SHMF yields plausible interpretations of real-world Calcium imaging data.
Estimation, Optimization, and Parallelism when Data is Sparse
Authors: Michael I Jordan, Brendan McMahan John Duchi
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: We study stochastic optimization problems when the \emph{data} is sparse,  which is in a sense dual to the current understanding of high-dimensional statistical learning and optimization. We highlight both the difficulties---in terms of increased sample complexity that sparse data necessitates---and the potential benefits, in terms of allowing parallelism and asynchrony in the design of algorithms. Concretely, we derive matching upper and lower bounds on the minimax rate for optimization and learning with sparse data, and we exhibit algorithms achieving these rates. Our algorithms are adaptive: they achieve the best possible rate for the data observed. We also show how leveraging sparsity leads to (still minimax optimal) parallel and asynchronous algorithms, providing experimental evidence complementing our theoretical results on medium to large-scale learning tasks.
Predictive PAC Learning and Process Decompositions
Authors: Aryeh Kontorovich Cosma Shalizi
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: We informally call a stochastic process learnable if it admits a generalization error approaching zero in probability for any concept class with finite VC-dimension (IID processes are the simplest example).  A mixture of learnable processes need not be learnable itself, and certainly its generalization error need not decay at the same rate. In this paper, we argue that it is natural in predictive PAC to condition not on the past observations but on the mixture component of the sample path. This definition not only matches what a realistic learner might demand, but also allows us to sidestep several otherwise grave problems in learning from dependent data. In particular, we give a novel PAC generalization bound for mixtures of learnable processes with a generalization error that is not worse than that of each mixture component.  We also provide a characterization of mixtures of absolutely regular ($\beta$-mixing) processes, of independent interest.
Scalable Inference for Logistic-Normal Topic Models
Authors: Jun Zhu, Zi Wang, Xun Zheng, Bo Zhang Jianfei Chen
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: Logistic-normal topic models can effectively discover correlation structures among latent topics. However, their inference remains a challenge because of the non-conjugacy between the logistic-normal prior and multinomial topic mixing proportions. Existing algorithms either make restricting mean-field assumptions or are not scalable to large-scale applications. This paper presents a partially collapsed Gibbs sampling algorithm that approaches the provably correct distribution by exploring the ideas of data augmentation. To improve time efficiency, we further present a parallel implementation that can deal with large-scale applications and learn the correlation structures of thousands of topics from millions of documents. Extensive empirical results demonstrate the promise.
A multi-agent control framework for co-adaptation in brain-computer interfaces
Authors: Roy Fox, Tony Jebara, Liam Paninski Josh S Merel
Published in: Advances in Neural Information Processing Systems 26  (NIPS 2013) (Main Conference Track), 2013

Abstract: In a closed-loop brain-computer interface (BCI), adaptive decoders are used to learn parameters suited to decoding the user's neural response. Feedback to the user provides information which permits the neural tuning to also adapt. We present an approach to model this process of co-adaptation between the encoding model of the neural signal and the decoding algorithm as a multi-agent formulation of the linear quadratic Gaussian (LQG) control problem. In simulation we characterize how decoding performance improves as the neural encoding and adaptive decoder optimize, qualitatively resembling experimentally demonstrated closed-loop improvement. We then propose a novel, modified decoder update rule which is aware of the fact that the encoder is also changing and show it can improve simulated co-adaptation dynamics. Our modeling approach offers promise for gaining insights into co-adaptation as well as improving user learning of BCI control in practical settings.