EDML is a recent algorithm for learning the parameters of a Bayesian Network (BN). EDML works by 'removing' some edges from a Bayesian or Markov Network in order to produce a simplified network in which exact inference is feasible. EDML removes edges in a manner tailored to parameter learning, by recognizing repeated structures in the so-called "meta" network.  While originally proposed for learning BN parameters, in this paper the authors show that EDML can be interpreted more generally  as a coordinate ascent algorithm. After recasting EDML in this light, they then show how it can be applied to learning in Markov Networks (MNs). Finally, they provide some preliminary experimental results on learning the parameters of small grids. In doing so, they compare EDML to an off-the-shelf conjugate gradient method.  

**Assessment: **
There does not appear to be much novelty here. Parameter learning in BNs and MNs inherently involves optimization of a real valued function and coordinate ascent approaches (e.g. iterative proportional fitting) have been studied previously. While casting EDML in this manner is interesting, the benefit of doing so is not made clear - why is this perspective useful? If this perspective provided insight on how to optimize a particular loss function (e.g. 0/1 loss) then it would be very helpful. Finally, in the case of incomplete data, the authors claim that EDML is both different and more efficient than EM. I don't immediately see why this is true - variational forms of EM exist and this appears to be a specific form. 

This paper presents a multi-task Bayesian optimization approach to hyper-parameter setting in machine learning models. In particular, it leverages previous work on multi-task GP learning with decomposable covariance functions and Bayesian optimization of expensive cost functions. Previous work has shown that decomposable covariance functions can be useful in multi-task regression problems (e.g. \cite{conf/nips/BonillaCW07}) and that Bayesian optimization based on response-surfaces can also be useful for hyper-parameter tuning of machine learning algorithms \cite{conf/nips/SnoekLA12} \cite{conf/icml/BergstraYC13}. 

The paper combines the decomposable covariance assumption \cite{conf/nips/BonillaCW07} and Bayesian optimization based on expected improvement \cite{journals/jgo/Jones01} and entropy search \cite{conf/icml/BergstraYC13} to show empirically that it is possible to : 
1. Transfer optimization knowledge across related problems, addressing e.g. the cold-start problem 
2. Optimize an aggregate of different objective functions with applications to speeding-up cross validation 
3. Use information from a smaller problem to help optimize a bigger problem faster 

Positive experimental results are shown on synthetic data (Branin-Hoo function), optimizing logistic regression hyper-parameters and optimizing hyper-parameters of online LDA on real data. 

Motivated by recent attempts to learn very large networks this work proposes an approach for reducing the number of free parameters in neural-network type architectures. The method is based on the intuition that there is typically strong redundancy in the learned parameters (for instance, the first layer filters of of NNs applied to images are smooth): The authors suggest to learn only a subset of the parameter values and to then predicted the remaining ones through some form of interpolation. The proposed approach is evaluated for several architectures (MLP, convolutional NN, reconstruction-ICA) and different vision datasets (MNIST, CIFAR, STL-10). The results suggest that in general it is sufficient to learn fewer than 50% of the parameters without any loss in performance (significantly fewer parameters seem sufficient for MNIST). 

The method is relatively simple: The authors assume a low-rank decomposition of the weight matrix and then further fix one of the two matrices using prior knowledge about the data (e.g., in the vision case, exploiting the fact that nearby pixels - and weights - tend to be correlated). This can be interpreted as predicting the "unobserved" parameters from the subset of learned filter weights via kernel ridge regression, where the kernel captures prior knowledge about the topology / "smoothness" of the weights. For the situation when such prior knowledge is not available the authors describe a way to learn a suitable kernel from data. 

The idea of reducing the number of parameters in NN-like architectures through connectivity constraints in itself is of course not novel, and the authors provide a pretty good discussion of related work in section 5. Their method is very closely related to the idea of factorizing weight matrices as is, for instance, commonly done for 3-way RBMs (e.g. ref [22] in the paper), but also occasionally for standard RBMs (e.g. [R1], missing in the paper). The present papers differs from these in that the authors propose to exploit prior knowledge to constrain one of the matrices. As also discussed by the authors, the approach can further be interpreted as a particular type of pooling -- a strategy commonly employed in convolutional neural networks. Another view of the proposed approach is that the filters are represented as a linear combination of basis functions (in the paper, the particular form of the basis functions is determined by the choice of kernel). Such representations have been explored in various forms and to various ends in the computer vision and signal processing literature (see e.g. [R2,R3,R4,R5]). [R4,R5], for instance, represent filters in terms of a linear combination of basis functions that reduce the computational complexity of the filtering process). 

This paper proposes an approach to one-pass SVD based on a blocked variant of the power method, which variance is reduced within each block of streaming data, and compares to exact batch SVD. 
Figure 1d is offered as an example where the proposed Algo 1 can scale to data for which the authors claim to be so large that "traditional batch methods" could not be run and reported. Yet there are many existing well-known SVD methods which are routinely used for even larger data sets than the largest here (sparse 8.2M vectors for 120k dimensions). These include the EMPCA (Roweiss 1998) and fast randomized SVD (Haiko et at 2011), both of which the author's cite. Why were these methods (both very simple to implement efficiently even in Matlab, etc.) not reported for this data? Especially necessary to compare against is the randomized SVD, since it too can be done in one-pass (see Haiko et al); although that cited paper discusses the tradeoffs in doing multiple passes -- something this paper does not even discuss. The authors say it took "a few hours" for Algo 1 to extract the top 7 components. Methods like the randomized SVD family of Haiko et al scale linearly in those parameters (n=8.2M and d=120k and k=7 and the number of non-zeros of the sparse data) and typically run in less than 1 hour for even larger data sets. So, demonstrating both the speed and accuracy of the proposed Algo 1 compared to the randomized algorithms seems necessary at this point, to establish the practical significance of this proposed approach.

This paper identifies and resolves a basic gap in the design of streaming PCA algorithms. It is shown that a block stochastic streaming version of the power method recovers the dominant rank-k PCA subspace with optimal memory requirements and sample complexity not too worse than batch PCA (which maintains the covariance matrix explicitly), assuming that streaming data is drawn from a natural probabilistic generative model. The paper is excellently written and provides intuitions for the analysis, starting with exact rank 1 and exact rank k case to the general rank k approximation problem. Some empirical analysis is also provided illustrating the approach for PCA on large document-term matrices.

The authors introduce a functional called the "harmonic loss" and show that (a) it characterizes smoothness in the sense that functions with small harmonic loss change little across large cuts (to be precise, the cut has to be a level set separator) (b) several algorithms for learning on graphs implicitly try to find functions that minimize the harmonic loss, subject to some constraints. 

The "harmonic loss" they define is essentially the (signed) divergence $\nabla f$ of the function across the cut, so it's not surprising that it should be closely related to smoothness. In classical vector calculus one would take the inner product of this divergence with itself and use the identity 

< $\nabla f, \nabla f $> = < $f, \nabla^2 f $> 

to argue that functions with small variation, i.e., small $| \nabla f |^2$ almost everywhere can be found by solving the Laplace equation. On graphs, modulo some tweaking with edge weights, essentially the same holds, leading to minimizing the quadratic form $ f^\top L f$, which is at the heart of all spectral methods. So in this sense, I am not surprised. 

Alternatively, one can minimize the integral of $| \nabla f |$, which is the total variation, and leads to a different type of regularization ($l1$ rather than $l2$ is one way to put it). The "harmonic loss" introduced in this paper is essentially this total variation, except there is no absolute value sign. Among all this fairly standard stuff, the interesting thing about the paper is that for the purpose of analyzing algorithms one can get away with only considering this divergence across cuts that separate level sets of $f$, and in that case all the gradients point in the same direction so one can drop the absolute value sign. This is nice because the "harmonic loss" becomes linear and a bunch of things about it are very easy to prove. At least this is my interpretation of what the paper is about. 

The paper discusses a number of extensions to the Skip-gram model previously proposed by Mikolov et al (citation [7] in the paper): which learns linear word embeddings that are particularly useful for analogical reasoning type tasks. The extensions proposed (namely, negative sampling and sub-sampling of high frequency words) enable extremely fast training of the model on large scale datasets. This also results in significantly improved performance as compared to previously proposed techniques based on neural networks. The authors also provide a method for training phrase level embeddings by slightly tweaking the original training algorithm. 

This paper proposes 3 improvements for the skip-gram model which allows for learning embeddings for words. The first improvement is subsampling frequent word, the second is the use of a simplified version of noise constrastive estimation (NCE) and finally they propose a method to learn idiomatic phrase embeddings. In all three cases the improvements are somewhat ad-hoc. In practice, both the subsampling and negative samples help to improve generalization substantially on an analogical reasoning task. The paper reviews related work and furthers the interesting topic of additive compositionality in embeddings. 

The article does not propose any explanation as to why the negative sampling produces better results than NCE which it is suppose to loosely approximate. In fact it doesn't explain why besides the obvious generalization gain the negative sampling scheme should be preferred to NCE since they achieve similar speeds. 

This paper discusses a new approach to binary matrix factorization 
that is motivated by recent developments in non-negative matrix 
factorization. The goal of 
the paper is to present an algorithm for finding a factorization of a 
matrix in the form $D = T A$ where the entries of $T$ are in 
$\{0,1\}$. Such a model has wide applicability and is of interest to 
the ML community. The algorithm has provable recovery guarantees in 
the case of noiseless observations. A modified algorithm is applied 
to the noisy setting; however, the authors do not establish recovery 
guarantees. 

The paper presents an algorithm for low-rank matrix factorization with constraints on one of the factors should be binary. The paper has several novel contributions for this problem. The algorithm guarantees the exact solution with the time complexity of $O(mr2^r+mnr)$, where previous approach (E. Meeds et al., NIPS 2007) uses MCMC algorithm so that it cannot guarantee a global convergence. Under additional assumptions on the binary factor matrix $T$, the uniqueness of $T$ is proved which means that each data point has a unique representation with the columns of $T$. Using Littlewood-Offord lemma, the paper computes a theoretical speed-up factor for their heuristic of the candidate binary vector set reduction step. 

This paper proposes to learning expectation propagation (EP) message update operators from data that would enable fast and efficient approximate inference in situations where computing these operators is otherwise intractable. 

This paper attacks the problem of computing the intractable low dimensional statistics in EP message passing by training a neural network. Training data is obtained using importance sampling and assuming that we know the forward model. The paper appears technically correct, honest about shortcomings, provides an original approach to a known challenge within EP and nicely illustrates the developed method in a number of well-chosen examples.

The authors propose a method for learning a mapping from input messages to the output message in the context of expectation propagation. The method can be thought of as a sort of "compilation" step, where there is a one-time cost of closely approximating the true output messages using important sampling, after which a neural network is trained to reproduce the output messages in the context of future inference queries. 

The authors present a robust low rank kernel embedding related to higher order tensors and 
latent variable models. In general the work is interesting and promising. It provides synergies between machine learning, kernel methods, tensors and latent variable models. 

The RKHS embedding of a joint probability distribution between two variables involves the notion of covariance operators. For joint distributions over multiple variables, a tensor operator is needed. The paper defines these objects together with appropriate inner product, norms and reshaping operations on them. The paper then notes that in the presence of latent variables where the conditional dependence structure is a tree, these operators are low-rank when reshaped along the edges connecting latent variables. A low-rank decomposition of the embedding is then proposed that can be implemented on Gram matrices. Empirical results on density estimation tasks are impressive. 

The paper presents a framework to learn to classify images that can come either from known or unknown classes. This is done by first mapping both images and classes into a joint embedding space. Furthermore, the probability of an image being of an unknown class is estimated using a mixture of Gaussians. Experiments on CIFAR-10 show how performance vary depending on the threshold use to determine if an image is of a known class or not.

The model first tries to detect whether an image contains an object from a so-far unseen category. If not, the model relies on a regular, state-of-the art supervised classifier to assign the image to known classes. Otherwise, it attempts to identify what this object is, based on a comparison between the image and each unseen class, in a learned joint image/class representation space. The method relies on pre-trained word representations, extracted from unlabelled text, to represent the classes. Experiments evaluate the compromise between classification accuracy on the seen classes and the unseen classes, as a threshold for identifying an unseen class is varied. 

**Object detection** is the task of drawing one bounding box around each instance of the type of object one wants to detect. Typically, image classification is done before object detection. With neural networks, the usual procedure for object detection is to train a classification network, replace the last layer with a regression layer which essentially predicts pixel-wise if the object is there or not. An bounding box inference algorithm is added at last to make a consistent prediction.

This paper presents a fast ICA algorithm that works best under Gaussian noise. This is demonstrated with components simulated from different univariate distributions and variable Gaussian noise. 

The writing is clear. The paper is incremental in the sense that it builds on ideas from (Belkin et. al, 2013) but focuses on speeding up and improving their cumulant-based approach. 
This is achieved via 
1. a Hessian expansion of the cumulant-tensor-based quasi-orthogonalization. 
2. gradient-based iterations that preserve quasi-orthogonalization of the latent factors (noised case) as well as whitening in the noiseless case. 

This paper proposes a cumulant based independent component analysis (ICA) algorithm for source separation in the presence of additive Gaussian noise. The algorithm is somewhat incremental building upon Refs [2] and [3], but appear technically correct with experimental results confirming the claims made. The algorithms used for benchmarking assume no additive noise but is like InfoMax often quite robust to addition of noise. 

The paper presents a method for learning layers of representation and for completing missing queries both in input and labels in single procedure unlike some other methods like deep boltzmann machines (DBM). It is a recurrent net following the same operations as DBM with the goal of predicting a subset of inputs from its complement. Parts of paper are badly written, especially model explanation and multi-inference section, nevertheless the paper should be published and I hope the authors will rewrite them. 

Deep Boltzmann Machines (DBNs) are usually initialized by greedily training a stack of RBMs, and then fine-tuning the overall model using persistent contrastive divergence (PCD). To perform classification, one typically provides the mean-field features to a separate classifier (e.g. a MLP) which is trained discriminatively. Therefore the overall process is somewhat ad-hoc, consisting of L + 2 models (where L is the number of hidden layers) each with its own objective. This paper presents a holistic training procedure for DBNs which has a single training stage (where both input and output variables are predicted) producing models which can classify directly as well as efficiently performing other tasks such as imputing missing inputs. The main technical contribution is the mechanism by which training is performed; a way of training DBNs which uses the mean field equations for the DBN to induce recurrent nets that are trained to solve different inference tasks (essentially predicting different subsets of observed variables). 

The paper uses a subsampling-based method to speed up ratio matching training of 
RBMs on high-dimensional sparse binary data. The proposed approach is a simple 
adaptation of the method proposed by Dauphin et al. (2011) for denoising 
autoencoders. 

This paper develops an algorithm that can successfully train RBMs on very high dimensional but sparse input data, such as often arises in NLP problems. The algorithm adapts a previous method developed for denoising autoencoders for use with RBMs. The authors present extensive experimental results verifying that their method learns a good generative model; provides unbiased gradient estimates; attains a two order of magnitude speed up on large sparse problems relative to the standard implementation; and yields state of the art performance on a number of NLP tasks. They also document the curious result that using a biased version of their estimator in fact leads to better performance on the classification tasks they tested. 

The paper addresses how to estimate and maximize influence in large networks, where influence of node (or set of nodes) A is the expected number of nodes that will eventually adopt a certain idea following the initial adoption by A. The authors develop an algorithm for estimating influence within a given time frame, then use it as the basis of a greedy algorithm to find a given number of nodes to (approximately) maximize influence within the given time frame. They present theoretical bounds and an experimental evaluation of the algorithm. 

The authors build on an extensive list of existing work, which is appropriately cited. The most relevant is the work by Gomez-Rodriguez & Scholkopf (2012) \cite{conf/icml/Gomez-RodriguezS12}, which provides an exact analytical solution to the identical formulation of the influence estimation problem. The main innovation in the present paper is a fast randomized algorithm for estimating influence, which is based on the algorithm for estimating neighborhood size by Cohen (1997) \cite{journals/jcss/Cohen97}. This approximation allows more flexibility in modeling the flows through the edges, is substantially faster than the analytical solution, and scales well with network size. Overall, this is a solid paper on an important topic of practical relevance. 

The authors introduce two new submodular optimization problems and 
investigate approximation algorithms for them. The problems are 
natural generalizations of many previous problems: there is a covering 
problem ($min\\{ f(X) : g(X) \ge c\\}$) and a packing or knapsack problem 
($max\\{ g(X) : f(X) \le b\\}$), where both f and g are submodular. These 
generalize well-known previously studied versions of the problems 
usually assume that f is modular. They show that there is an intimate 
relationship between the two problems: any polynomial-time 
bi-criterion algorithm for one problem implies one for the other 
problem (with similar approximation factors) using a simple reduction. 
They then present a general iterative framework for solving the two 
problems by replacing either f or g by tight upper or lower bounds 
(often modular) at each iteration. These tend to reduce the problem 
at each iteration to a simpler subproblem for which there are existing 
algorithms with approximation guarantees. In many cases, they are able 
to translate these into approximation guarantees for the more general 
problem. Their approximation bounds are curvature-dependent and 
highlight the importance of this quantity on the difficulty of the 
problem. The authors also present a hardness result that matches their 
best approximation guarantees up to log factors, show that a number of 
existing approximation algorithms (e.g. greedy ones) for the simpler 
problem variants can be recovered from their framework by using 
specific modular bounds, and show experimentally that the simpler 
algorithm variants may perform as well as the ones with better 
approximation guarantees in practice. 

The paper studies the problem of memory storage with discrete (digital) synapses. Previous work established that memory capacity can be increased by adding a cascade of (latent) states but the optimal state transition dynamics was unknown and the actual dynamics was usually hand-picked using some heuristic rules. In this paper the authors aim to derive the optimal transition dynamics for synaptic cascades. They first derive an upper bound on achievable memory capacity and show that simple models with linear chain structures can approach (achieve) this bound. 

The paper applies the theory if ergodic Markov chains in continuous time to the analysis of the memory properties of online learning in synapses with intrinsic states extending earlier work of Abbott, Fusi and their co-workers. 

Finding the objective functions that regions of the nervous system are optimized for is a central question in neuroscience, providing a central computational principle behind neural representation in a given region. One common objective is to maximize the Shannon Information the neural response encodes about the input (infomax). This is supported by some experimental. Another is to minimize the decoding error when the neural population is decoded for a particular variable or variable. This has also been found to have some experimental evidence. These two different objectives are similar in some circumstances, giving similar predictions, in other cases they differ more. 

Studies finding model optimal distributions of neural population tuning that minimizes decoding error (L2-min) have mostly considered 1-dimensional stimuli. In this paper the authors extend substantially on this, by developing analytical methods for finding the optimal distributions of neural tuning for higher dimensional stimuli. Their methods apply under certain limited conditions , such as when there is an equal number of neurons as stimulus dimensions (diffeomorphic). The authors compare their results to the infomax solution (in most detail for the 2D case), and find fairly similar results in some respects, but with two key differences. That the L2-min basis functions are more orthogonal than the infomax, and that the L2-min has discrete solutions rather than the continuum found for infomax. A consequence of these differences is that L2-min representations encode more correlated signals. 

The paper investigates how correlation among synaptic weights, not correlation among neural activity, influences the retrieval performance of auto-associative memory. Authors studied two types of well-known learning rules, additive learning rule (e.g., Hebbian learning) and palimpsest learning rule (e.g., cascade learning), and showed that synaptic correlations are induced in most of the cases. They also investigated optimal retrieval dynamics and showed that there exists a local version of dynamics that can be implemented in neural networks (except for an XOR cascade model). 

In a GP regression model, the process outputs can be integrated over analytically, but this is not so for (a) inputs and (b) kernel hyperparameters. Titsias etal 2010 showed a very clever way to do (a) with a particular variational technique (the goal was to do density estimation). In this paper, (b) is tackled, which requires some nontrivial extensions of Titsias etal. In particular, they show how to decouple the GP prior from the kernel hyperparameters. This is a simple trick, but very effective for what they want to do. They also treat the large number of kernel hyperparameters with an additional level of ARD and show how the ARD hyperparameters can be solved for analytically, which is nice. 

This paper studies a linear latent factor model, where one observes "examples" consisting of high-dimensional vectors $x_1, x_2, ..\in R^d$, and one wants to predict "labels" consisting of scalars $y_1, y_2, ... \in R$. Crucially, one is working in the "one-shot learning" regime, where the number of training examples n is small (say, $n=2$ or $n=10$), while the dimension d is large (say, $d \rightarrow \infty$). This paper considers a well-known method, principal component regression (PCR), and proves some somewhat surprising theoretical results: PCR is inconsistent, but a modified PCR estimator is weakly consistent; the modified estimator is obtained by "expanding" the PCR estimator, which is different from the usual "shrinkage" methods for high-dimensional data. 

This paper aims to provide an analysis for principle component 
regression in the setting where the feature vectors $x$. The authors 
let $x = v + e$ where $e$ is some corruption of the nominal feature 
vector $v$; and $v = a u$ where $a \sim N(0,\eta^2 \gamma^2 d)$ while 
the observations $y = \theta/(\gamma \sqrt{d}) \langle v,u \rangle + \xi$. This 
formulation is slightly different than the standard one because our 
design vectors are noisy, which can pose challenges in identifying the 
linear relationship between $x$ and $y$. Thus, using the top principle 
components of $x$ is a standard method used in order to help 
regularize the estimation. The paper is relevant to the ML 
community. The key message of using a bias-corrected estimate of $y$ 
is interesting, but not necessarily new. Handling bias in regularized 
methods is a common problem (cf. Regularization and variable selection 
via the Elastic Net, Zou and Hastie, 2005). The authors present 
theoretical analysis to justify their results. I find the paper 
interesting; however I am not sure if the number of new results and 
level of insights warrants acceptance. 

The authors propose novel approaches for summarizing the posterior of partitions in infinite mixture models. Often in applications, the posterior of the partition is quite diffuse; thus, the default MAP estimate is unsatisfactory. The proposed approach is based on the cumulative block sizes, which counts the number of clusters of size $\ge k$, for $k=1, …,n$. They also examine the projected cumulative block sizes, when the partition is projected onto a subset of $\\{1,...,n\\}$. These quantities are summarized by the cumulative occurrence distribution, the per element information of a set, the entropy, the projected entropy, and the subset occurrence. Finally, they propose using an agglomerative clustering algorithm where the projection entropy is used to measure distances between sets. In illustrations, the posterior of the partition is summarized by the dendrogram produced from the entropy agglomerative algorithm, along with existing summaries such as the posterior histogram of the number of clusters and the pairwise occurrences. 

The paper addresses the problem of finding a policy with a high expected return and a bounded variance. The paper considers both the discounted and the average reward cases. The authors propose formulate this problem as a constrained optimization problem, where the gradient of the Lagrangian dual function is estimated form samples. This gradient is composed of the gradient of the expected return and the gradient of the expected squared return. Both gradients need to be estimated in every state. The authors use a linear function approximation to generalize the gradient estimates to states that were not encountered in the samples. The authors use stochastic perturbation to evaluate the gradients in particular states by sampling two trajectories, one with policy parameters theta and another with policy parameters theta+beta, where beta is a perturbation random variable. The policy parameters are updated in an actor-critic scheme. The authors prove that the proposed optimization method converges to a local optimum. Numerical experiments on a traffic lights control problem show that the proposed technique finds a policy with a slightly higher risque than the optimal solution, but with a significantly lower variance. 

This paper presents a generative model for natural image patches which takes into account occlusions and the translation invariance of features. The model consists of a set of masks and a set of features which can be translated throughout the patch. Given a set of translations for the masks and features the patch is then generated by sampling (conditionally) independent Gaussian noise. An inference framework for the parameters is proposed and is demonstrated on synthetic data with convincing results. Additionally, experiments are run on natural image patches and the method learns a set of masks and features for natural images. When combined together the resulting receptive fields look mostly like Gabors, but some of them have a globular structures. 

This paper revisits the idea of decision DAGs for classification. Unlike a decision tree, a decision DAG is able to merge nodes at each layer, preventing the tree from growing exponentially with depth. This represents an alternative to decision-trees utilizing pruning methods as a means of controlling model size and preventing overfitting. The paper casts learning with this model as an empirical risk minimization problem, where the idea is to learn both the DAG structure along with the split parameters of each node. Two algorithms are presented to learn the structure and parameters in a greedy layer-wise manner using an information-gain based objective. Compared to several baseline approaches using ensembles of fixed-size decision trees, ensembles of decision DAGs seem to provide improved generalization performance for a given model size (as measured by the total number of nodes in the ensemble). 

A method of estimating a density (up to constants) from an unweighted, directed k nearest neighbor graph is described. It is assumed (more or less) that the density is continuously differentiable, supported on a compact and connected subset of $R^d$ with non-empty interior and a smooth boundary, and is upper- and lower-bounded on its support. 

The paper introduces a new approach of how classical policy search can be combined and improved with trajectory optimization methods serving as exploration strategy. An optimization criteria with the goal of finding optimal policy parameters is decomposed with a variational approach. The variational distribution is approximated as Gaussian distribution which allows a solution with the iterative LQR algorithm. The overall algorithm uses expectation maximization to iterate between minimizing the KL divergence of the variational decomposition and maximizing the lower bound with respect to the policy parameters. 

This paper addresses one simple but potentially very important point: That Dirichlet process mixture models can be inconsistent in the number of mixture components that they infer. This is important because DPs are nowadays widely used in various types of statistical modeling, for example when building clustering type algorithms. This can have real-world implications, for example when clustering breast cancer data with the aim of identifying distinct disease subtypes. Such subtypes are used in clinical practice to inform treatment, so identifying the correct number of clusters (and hence subtypes) has a very important real-world impact. 

The paper focuses on proofs concerning two specific cases where the DP turns out to be inconsistent. Both consider the case of the "standard normal DPM", where the likelihood is a univariate normal distribution with unit variance, the mean of which is subject to a normal prior with unit variance. The first proof shows that, if the data are drawn i.i.d. from a zero-mean, unit-variance normal (hence matching the assumed DPM model), $P(T=1 | \text{data})$ does not converge to 1. The second proof takes this further, demonstrating that in fact$ P(T=1 | \text{data}) -> 0 $

The authors propose a new deep architecture, which combines the hierarchy of deep learning with time-series modeling known from HMMs or recurrent neural networks. The proposed training algorithm builds the network layer-by-layer using supervised (pre-)training a next-letter prediction objective. The experiments demonstrate that after training very large networks for about 10 days, the network performance on a Wikipedia dataset published by Hinton et al. improves over previous work. The authors then proceed to analyze and discuss details of how the network approaches its task. For example, long-term dependencies are modeled in higher layers, correspondence between opening and closing parenthesis are modeled as a “pseudo-stable attractor-like state”. 

The authors propose to accelerate the stochastic gradient optimization algorithm by reducing the variance of the noisy gradient estimate by using the 'control variate' trick (a standard variance reduction technique for Monte Carlo simulations, explained in [3] for example). The control variate is a vector which hopefully has high correlation with the noisy gradient but for which the expectation is easier to compute. Standard convergence rates for stochastic gradient optimization depend on the variance of the gradient estimates, and thus a variance reduction technique should yield an acceleration of convergence. The authors give examples of control variates by using Taylor approximations of the gradient estimate for the optimization problem arising in regularized logistic regression as well as for MAP estimation for the latent Dirichlet Allocation (LDA) model. They compare constant step-size SGD with and without variance reduction for logistic regression on the covtype dataset, claiming that the variance reduction allows to use bigger step-sizes without having the problem of high variance and thus yields faster empirical convergence. For LDA, they compare the adaptive step-size version of the stochastic optimization method of [10] with and without variance reduction, showing a faster convergence on the held-out test log-likelihood on three large corpora. 

This paper presents a model inspired by the SAGE (Sparse Additive GEnerative) model of Eisenstein et al. The authors use a different approach for modeling the "background" component of the model. SAGE uses the same background model for all; the authors allow different backgrounds for different topics/classification labels/etc., but try to keep the background matrix low rank. To make inference faster when using this low rank constraint, they use a bound on the likelihood function that avoids the log-sum-exp calculations from SAGE. Experimental results are positive for a few different tasks.

Sparse additive models represent sets of distributions over large vocabularies as log-linear combinations of a dense, shared background vector and a sparse, distribution-specific vector. The paper presents a modification that allows distributions to have distinct background vectors, but requires that the matrix of background vectors be low-rank. This method leads to better predictive performance in a labeled classification task and in a mixed-membership LDA-like setting. 

Previous work on SAGE introduced a new model for text. It built a lexical distribution by adding deviation components to a fixed background. The model presented in this paper SAM-LRB, builds on SAGE and claims to improve it by two additions. First, providing a unique background for each class/topic. Second, providing an approximation of log-likelihood so as to provide a faster learning and inference algorithm in comparison to SAGE. 

The paper proposes a new image representation for recognition based on a stacking of two layers of Fisher vector encoders, with the first layer capturing semi-local information and the second performing sum-pooling aggregation over the entire picture. The approach is inspired by the recent success of deep convolutional networks (CNN). The key-difference is that the architecture proposed in this paper is predominantly hand-designed with relatively few parameters learned compared to CNNs. This is both the strength and the weakness of the approach as it leads to much faster training but also slighter lower accuracy compared to fully learned deep networks. 

This paper uses Fisher Vectors as inner building blocks in a recognition architecture. The basic Fisher vector module had previously demonstrated superior performance in recognition application. Here, it is augmented with discriminative linear projection for dimensionality reduction, and multiscale local pooling, to make it suitable for stacking. Inputs of all layers are jointly used for classification.

This paper considers a class of structural equation models for times series data.  The models allow nonlinear instantaneous effects and lagged effects. On the other hand, Granger-causality based methods do not allow instantaneous effects and a linear non-Gaussian method TS-LiNGAM (Hyvarinen et al., ICML2008, JMLR2010) assumes linear effects. 

This paper introduces a model and procedure for learning instantaneous and lagged causal relationships among variables in a time series when each causal relationship is either identifiable in the sense of the additive noise model (Hoyer et al. 2009) or exhibits a time structure. The learning procedure finds a causal order by iteratively fitting VAR or GAM models where each variable is a function of all other variables and making the variable with the least dependence the lowest variable in the order. Excess parents are then pruned to produce the summary causal graph (where x->y indicates either an instantaneous or lagged cause up to the order of the VAR or GAM model that is fit). Experiments show that the method outperforms competing methods and returns no results in cases where the model can be identified (rather than wrong results). 

This paper provides one of the most natural examples of a learning problem for which the problem becomes computationally tractable when given a sufficient amount of data, but is computationally intractable (though still information theoretically tractable) when given a smaller quantity of data. This computational intractability is based on a complexity-theoretic assumption about the hardness of distinguishing satisfiable 3SAT formulas from random ones at a given clause density (more specifically, the 3MAJ variant of the conjecture). 

The specific problem considered by the authors is learning halfspaces over 3-sparse vectors. The authors complement their negative results with nearly matching positive results (if one believes a significantly stronger complexity theoretic conjecture-- that hardness persists even for random formulae whose density is $n^\mu$ over the satistfiability threshold). Sadly, the algorithmic results are described in the Appendix, and are not discussed. It seems like they are essentially modifications of Hazan et al.'s 2012, though it would be greatly appreciated if the authors included a high-level discussion of the algorithm. Even if no formal proofs of correctness will fit in the body, a description of the algorithm would be helpful. 

This paper examines the problem of approximate graph matching (isomorphism). Given graphs G, H with p nodes, represented by respective adjacency matrices A, B, Find a permutation matrix P that best "matches" AP and PB. 

This paper poses the multimodal graph matching problem as a convex optimization problem, and solves it using augmented Langrangian techniques (viz., ADMM). This is an important problem with application in several fields. Experimental results on synthetic and multiple real world datasets demonstrate effectiveness of the proposed approach.

This paper proposes an image-based model for visual clutter perception ("a crowded, disorderly state"). For a given image, the model begins by applying an existing superpixel clustering then computing the intensity, colour and orientation histograms of pixels within each superpixel. Boundaries between adjacent superpixels are then retained or merged to create "proto-objects". The novel merging algorithm acts on the Earth Movers Distance (EMD), a measure of the similarity between two histograms. The distribution of histogram distances in each image for each image feature is modeled as a mixture of two Weibull distributions. The crossover point between the two distributions (or a fixed cumulative percentile if a single distribution is preferred by model selection) is used as the threshold point for merging: an edge is labelled ``similar'', and the superpixels merged, if the pair of superpixels exceed the threshold point for all three features. The clutter value for each image is the ratio of the final number of proto-objects to the initial number of superpixels (i.e. 0 = no proto-objects, not cluttered; 1 = all superpixels are proto-objects). 
The model is validated by comparing to human clutter rankings of a subset of an existing image database. Human observers rank images from least to most cluttered, then the median ranking for each image is used as the ground truth for clutter perception. The new model correlates more highly with human rankings of clutter than a number of previous clutter perception and image segmentation models (including human object segmentation from a previous study). 

This paper derives a new empirical PAC Bayesian bound by combining an existing (non-empirical) PAC Bayesian Berstein bound (i.e., involving the true variance of the loss values) with a PAC Bayesian analysis of the concentration of the empirical variance around its true value. This new bound has the advantage of being tighter when the empirical variance is small compared to the empirical loss. Experiments on real and empirical data with simple models compare the new bound with the usual empirical PAC Bayesian bound confirming the advantage. 

This paper proposes a new method for Dec-POMDP planning that is built out of several components. The first is a new way of solving cooperative Bayesian games using an integer linear program. The second is the transformation of the Dec-POMDP to a belief POMDP in which a "centralized mediator" must select at each timestep the best action for each agent-belief pair. The third is to automate the discovery of optimal belief compression by dividing each timestep into two parts, the first corresponding to the original Dec-POMDP and the second giving each agent a chance to select how its beliefs in that timestep are mapped to a bounded set and thus compressed. The fourth assembles these components together into a point-based value iteration method that solves the resulting belief POMDP using a varient of PERSEUS in which the CBG solver is used to compute maximizations. 

Three contributions are made: 

* An approach to convert DEC-POMDPs to bounded belief DEC-POMDPs 
* An approach to convert bounded belief DEC-POMDPs to POMDPs with exponentially many actions 
* An integer linear program to optimize one-step look-ahead policies in POMDPs with exponentially many actions 

The paper deals with an interesting theoretical question concerning the proximity operator. It investigates when the proximity of the sum of two convex functions decomposes into the composition of the corresponding proximity operators. The problem is interesting since in the applications there is a growing interest in building complex regularizers by adding several simple terms. 
They pursues a quite complete study. After proving a simple sufficient condition (Theorem 1), they gives the main result of the paper (Theorem 4): it is a complete characterization of the property (for a function) of being radial versus the property of being "well-coupled" with positively homogeneous functions (where well-coupled means that the prox of the sum of the couple decomposes into the composition of the two individual prox map). They also consider the case of polyhedral gauge functions, deriving a sufficient condition which is expressed by means of a cone invariance property. Examples are provided which show several prox-decomposition results, recovering known facts (in a simpler way) but also proving new ones. 

The value of the paper is mainly on the theoretical side. It sheds light on the mechanism of composing proximity operators and unifies several particular results that were spread in the literature. The article is well written and technically sound. The only fault I see is that perhaps some times is not completely rigorous as I explain in the following. 

The authors build their work on top of the generalized conditional gradient (GCG) method for sparse optimization. In particular, GCG methods require computation of the polar operator for the sparse regularization function (an example is the dual norm if the regularization function is an atomic norm). In this work, the authors identify a class of regularization functions, which are based on an underlying subset cost function. The key idea is a to 'lift' the regularizer into a higher dimensional space together with some constraints in the higher-dimensional space, where it has the property of 'marginalized modularity' allowing it to be reformulated as a linear program. Finally, the approach is generalized to general proximal objectives. The results demonstrate that the method is able to achieve better objective values in much less CPU time when compared with another polar operator method and accelerated proximal gradient (APG) on group Lasso and path coding problems. 

This paper is related with the problem of demand estimation in multi-heterogeneous agents, specifically, to classify agents and estimate preferences of each agent type using agents’ ranking data of different alternatives. The problem is important since it has great practical value in studying underlying preference distributions of multiple agents. To tackle the problem, the authors introduce generalized random utility models (GRUM), provide RJMCMC algorithms for parameter estimation in GRUM and theoretically establish conditions for identifiability for the model. Experimental results on both synthetic and real dataset show the model’s effectiveness. 

This paper proposes a new subspace clustering algorithm called Low Rank Sparse Subspace Clustering (LRSSC) and aims to study the conditions under which it is guaranteed to produce a correct clustering. The correctness is defined in terms of two properties. The self-expressiveness property (SEP) captures whether a data point is expressed as a linear combination of other points in the same subspace. The graph connectivity property (GCP) captures whether the points in one subspace form a connected component of the graph formed by all the data points. The LRSSC algorithm builds on two existing subspace clustering algorithms, SSC and LRR, which have complementary properties. The solution of LRR is guaranteed to satisfy the SEP under the strong assumption of independent subspaces and the GCP under weak assumptions (shown in this paper). On the other hand, the solution of SSC is guaranteed to satisfy the SEP under milder conditions, even with noisy data or data corrupted with outliers, but the solution of SSC need not satisfy the GCP. This paper combines the objective functions of both methods with the hope of obtaining a method that satisfies both SEP and GEP for some range of values of the relative weight between the two objective functions. Theorem 1 derives conditions under which LRSSC satisfies SEP in the deterministic case. These conditions are natural generalizations of existing conditions for SSC. But they are actually weaker than existing conditions. Theorem 2 derives conditions under which LRSSC satisfies SEP in the random case (data drawn at random from randomly drawn subspaces). Overall, it is shown that when the weight of the SSC term is large enough and the ratio of the data dimension to the subspace dimension grows with the log of the number of points, then LRSSC is guaranteed to satisfy SEP with high probability. I say high, because it does not tend to 1. Finally, Proposition 1 and Lemma 4 show that LRR satisfies GCP (presumably almost surely). Experiments support that for a range of the SSC weight, LRSCC works. Additional experiments on model selection show the usefulness of the analysis. 

The authors explore different optimization strategies for 1-D continuous functions and their relationship to how people optimize the functions. They used a wide variety of continuous functions (with one exception): polynomial, exponential, trigonometric, and the Dirac function. They also explore how people interpolate and extrapolate noisy samples from a latent function (which has a long tradition in psychology under the name of function learning) and how people select an additional sample to observe under the task of interpolating or extrapolating. Over all, they found that Gaussian processes do a better job at describing human performance than any of the approx. 20 other tested optimization methods. 

This paper describes how to attack the zero-, one-, or few-shot recognition problem, where we have a fair amount of training data for some classes, but none or very few for some other classes. It does this using three different techniques, all combined in a single framework: using semantically-meaningful mid-layer knowledge (attributes), building a graph on new classes to exploit the manifold structure, and finally by using an attribute-based representation for building the graph structure (rather than low-level features), which improves performance. The method is evaluated on 3 different datasets (Animals with Attributes, ImageNet, and MPII Cooking composites), and shows improved performance on all compared to the state-of-the-art (slightly).

The authors considered robust optimization for polynomial optimization problems where the uncertainty set is a set of possible distributions of the parameter. In specific, this set is a ball around a density function estimated from data samples. The authors showed that this distributionally robust optimization formulation can be reduced to a polynomial optimization problem, hence computationally the robust counterpart is of the same hardness as the nominal (non-robust) problem, and can be solved using a tower of SDP known in literature. The authors also provide finite-sample guarantees for estimating the uncertainty set from data. Finally, they applied their methods to a water network problem. 

This work proposes an extension to the maximum margin clustering (MMC) method that introduces latent variables. The motivation for adding latent variables is that they can model additional data semantics, resulting in better final clusters. The authors introduce a latent MMC (LMMC) objective, state how to optimize it, and then apply it to the task of video clustering. For this task, the latent variables are tag words, and the affinity of a video for a tag is given by a pre-trained binary tag detector. Experiments show that LMMC consistently, and sometimes substantially, beats several reasonable baselines. 

The authors show by approximate analysis of two identical continuous attractor networks (Zhang 1996), reciprocally coupled by Gaussian weights, that such a network can approximately implement the Bayesian posterior solution for queue integration. 

This paper describes a creative alternative for topic modeling:  mixed-membership on a "counting grid." The advantage of this approach  seems to be that you can move smoothly across the grid, achieving a 
high effective number of topics while the spatial smoothing prevents  overfitting. The disadvantage seems to be that there are more  parameters (grid dimension and size, and window size). A variational  inference procedure that is somewhat to LDA is possible, although no speed/complexity comparisons are provided. The spatial nature of the  approach has potential advantages for visualization as well.

The authors propose a non-linear measure of dependence between two random variables. This turns out to be the canonical correlation between random, nonlinear projections of the variables after a copula transformation which renders the marginals of the r.vs invariant to linear transformations. 

The paper introduces a new method called RDC to measure the statistical dependence between random variables. It combines a copula transform to a variant of kernel CCA using random projections, resulting in a $O(n log n)$ complexity. Results on synthetic and real benchmark data show promising results for feature selection. 

The RDC is a non-linear dependency estimator that satisfies Renyi's criteria and exploits the very recent FastFood speedup trick (ICML13) \cite{journals/corr/LeSS14}. This is a straightforward recipe: 1) copularize the data, effectively preserving the dependency structure while ignoring the marginals, 2) sample k non-linear features of each datum (inspired from Bochner's theorem) and 3) solve the regular CCA eigenvalue problem on the resulting paired datasets. Ultimately, RDC feels like a copularised variation of kCCA (misleading as this may sound). Its efficiency is illustrated successfully on a set of classical non-linear bivariate dependency scenarios and 12 real datasets via a forward feature selection procedure. 

This computer vision paper uses an unsupervised, neural net based semantic embedding of a Wikipaedia text corpus trained using skip-gram coding to enhance the performance of the Krizhevsky et al deep network \cite{krizhevsky2012imagenet} that won the 2012 ImageNet large scale visual recognition challenge, particularly for zero-shot learning problems (i.e. previously unseen classes with some similarity to previously seen ones). The two networks are trained separately, then the output layer of \cite{krizhevsky2012imagenet} is replaced with a linear mapping to the semantic text representation and re-trained on ImageNet 1k using a dot product loss reminiscent of a structured output SVM one. The text representation is not currently re-trained. The model is tested on ImageNet 1k and 21k. With the semantic embedding output it does not quite manage to reproduce the ImageNet 1k flat-class hit rates of the original softmax-output model, but it does better than the original on hierarchical-class hit rates and on previously unseen classes from ImageNet 21k. For unseen classes, the improvements are modest in absolute terms (albeit somewhat larger in relative ones). 

It consists of the following steps: 
1. Learn an embedding of a large number of words in a Euclidean space. 
2. Learn a deep architecture which takes images as input and predicts one of 1,000 object categories. 
The 1,000 categories are a subset of the 'large number of words' of step (1). 
3. Remove the last layer of the visual model -- leaving what is referred to as the 'core' visual model. 
Replace it by the word embeddings and add a layer to map the core visual model output to the word embeddings. 

This paper continues a recent line of theoretical work that seeks to explain what autoencoders learn about the data-generating distribution. Of practical importance from this work have been ways to sample from autoencoders. Specifically, this paper picks up where \cite{journals/jmlr/AlainB14} left off. That paper was able to show that autoencoders (under a number of conditions) estimate the score (derivative of the log-density) of the data-generating distribution in a way that was proportional to the difference between reconstruction and input. However, it was these conditions that limited this work: it only considered Gaussian corruption, it only applied to continuous inputs, it was proven for only squared error, and was valid only in the limit of small corruption. The current paper connects the autoencoder training procedure to the implicit estimation of the data-generating distribution for arbitrary corruption, arbitrary reconstruction loss, and can handle both discrete and continuous variables for non-infinitesimal corruption noise. Moreover, the paper presents a new training algorithm called "walkback" which estimates the same distribution as the "vanilla" denoising algorithm, but, as experimental evidence suggests, may do so in a more efficient way.