pwc-archive/papers-with-abstracts huggingface dataset:
Efficient Rectangular Maximal-Volume Algorithm for Rating Elicitation in Collaborative Filtering
Authors: Alexander Fonarev, Alexander Mikhalev, Pavel Serdyukov, Gleb Gusev, Ivan Oseledets
Date: 2016-10-16 00:00:00
Tasks: Collaborative Filtering

Cold start problem in Collaborative Filtering can be solved by asking new
users to rate a small seed set of representative items or by asking
representative users to rate a new item. The question is how to build a seed
set that can give enough preference information for making good
recommendations. One of the most successful approaches, called Representative
Based Matrix Factorization, is based on Maxvol algorithm. Unfortunately, this
approach has one important limitation --- a seed set of a particular size
requires a rating matrix factorization of fixed rank that should coincide with
that size. This is not necessarily optimal in the general case. In the current
paper, we introduce a fast algorithm for an analytical generalization of this
approach that we call Rectangular Maxvol. It allows the rank of factorization
to be lower than the required size of the seed set. Moreover, the paper
includes the theoretical analysis of the method's error, the complexity
analysis of the existing methods and the comparison to the state-of-the-art
approaches.
Active Nearest-Neighbor Learning in Metric Spaces
Authors: Aryeh Kontorovich, Sivan Sabato, Ruth Urner
Date: 2016-05-22 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Active Learning, Model Selection

We propose a pool-based non-parametric active learning algorithm for general
metric spaces, called MArgin Regularized Metric Active Nearest Neighbor
(MARMANN), which outputs a nearest-neighbor classifier. We give prediction
error guarantees that depend on the noisy-margin properties of the input
sample, and are competitive with those obtained by previously proposed passive
learners. We prove that the label complexity of MARMANN is significantly lower
than that of any passive learner with similar error guarantees. MARMANN is
based on a generalized sample compression scheme, and a new label-efficient
active model-selection procedure.
Term-Class-Max-Support (TCMS): A Simple Text Document Categorization Approach Using Term-Class Relevance Measure
Authors: D. S. Guru, Mahamad Suhil
Date: 2016-10-16 00:00:00
Tasks: Benchmarking, Text Categorization

In this paper, a simple text categorization method using term-class relevance
measures is proposed. Initially, text documents are processed to extract
significant terms present in them. For every term extracted from a document, we
compute its importance in preserving the content of a class through a novel
term-weighting scheme known as Term_Class Relevance (TCR) measure proposed by
Guru and Suhil (2015) [1]. In this way, for every term, its relevance for all
the classes present in the corpus is computed and stored in the knowledgebase.
During testing, the terms present in the test document are extracted and the
term-class relevance of each term is obtained from the stored knowledgebase. To
achieve quick search of term weights, Btree indexing data structure has been
adapted. Finally, the class which receives maximum support in terms of
term-class relevance is decided to be the class of the given test document. The
proposed method works in logarithmic complexity in testing time and simple to
implement when compared to any other text categorization techniques available
in literature. The experiments conducted on various benchmarking datasets have
revealed that the performance of the proposed method is satisfactory and
encouraging.
Context-Aware Online Learning for Course Recommendation of MOOC Big Data
Authors: Yifan Hou, Pan Zhou, Ting Wang, Li Yu, Yuchong Hu, Dapeng Wu
Date: 2016-10-11 00:00:00
Tasks: Recommendation Systems

The Massive Open Online Course (MOOC) has expanded significantly in recent
years. With the widespread of MOOC, the opportunity to study the fascinating
courses for free has attracted numerous people of diverse educational
backgrounds all over the world. In the big data era, a key research topic for
MOOC is how to mine the needed courses in the massive course databases in cloud
for each individual student accurately and rapidly as the number of courses is
increasing fleetly. In this respect, the key challenge is how to realize
personalized course recommendation as well as to reduce the computing and
storage costs for the tremendous course data. In this paper, we propose a big
data-supported, context-aware online learning-based course recommender system
that could handle the dynamic and infinitely massive datasets, which recommends
courses by using personalized context information and historical statistics.
The context-awareness takes the personal preferences into consideration, making
the recommendation suitable for people with different backgrounds. Besides, the
algorithm achieves the sublinear regret performance, which means it can
gradually recommend the mostly preferred and matched courses to students. In
addition, our storage module is expanded to the distributed-connected storage
nodes, where the devised algorithm can handle massive course storage problems
from heterogeneous sources of course datasets. Comparing to existing
algorithms, our proposed algorithms achieve the linear time complexity and
space complexity. Experiment results verify the superiority of our algorithms
when comparing with existing ones in the MOOC big data setting.
PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions
Authors: Michael Figurnov, Aijan Ibraimova, Dmitry Vetrov, Pushmeet Kohli
Date: 2015-04-30 00:00:00
Proceeding: NeurIPS 2016 12

We propose a novel approach to reduce the computational cost of evaluation of
convolutional neural networks, a factor that has hindered their deployment in
low-power devices such as mobile phones. Inspired by the loop perforation
technique from source code optimization, we speed up the bottleneck
convolutional layers by skipping their evaluation in some of the spatial
positions. We propose and analyze several strategies of choosing these
positions. We demonstrate that perforation can accelerate modern convolutional
networks such as AlexNet and VGG-16 by a factor of 2x - 4x. Additionally, we
show that perforation is complementary to the recently proposed acceleration
method of Zhang et al.
Beyond Spatial Auto-Regressive Models: Predicting Housing Prices with Satellite Imagery
Authors: Archith J. Bency, Swati Rallapalli, Raghu K. Ganti, Mudhakar Srivatsa, B. S. Manjunath
Date: 2016-10-16 00:00:00

When modeling geo-spatial data, it is critical to capture spatial
correlations for achieving high accuracy. Spatial Auto-Regression (SAR) is a
common tool used to model such data, where the spatial contiguity matrix (W)
encodes the spatial correlations. However, the efficacy of SAR is limited by
two factors. First, it depends on the choice of contiguity matrix, which is
typically not learnt from data, but instead, is assumed to be known apriori.
Second, it assumes that the observations can be explained by linear models. In
this paper, we propose a Convolutional Neural Network (CNN) framework to model
geo-spatial data (specifi- cally housing prices), to learn the spatial
correlations automatically. We show that neighborhood information embedded in
satellite imagery can be leveraged to achieve the desired spatial smoothing. An
additional upside of our framework is the relaxation of linear assumption on
the data. Specific challenges we tackle while implementing our framework
include, (i) how much of the neighborhood is relevant while estimating housing
prices? (ii) what is the right approach to capture multiple resolutions of
satellite imagery? and (iii) what other data-sources can help improve the
estimation of spatial correlations? We demonstrate a marked improvement of 57%
on top of the SAR baseline through the use of features from deep neural
networks for the cities of London, Birmingham and Liverpool.
Dynamic Stacked Generalization for Node Classification on Networks
Authors: Zhen Han, Alyson Wilson
Date: 2016-10-16 00:00:00
Tasks: Classification, General Classification, Node Classification

We propose a novel stacked generalization (stacking) method as a dynamic
ensemble technique using a pool of heterogeneous classifiers for node label
classification on networks. The proposed method assigns component models a set
of functional coefficients, which can vary smoothly with certain topological
features of a node. Compared to the traditional stacking model, the proposed
method can dynamically adjust the weights of individual models as we move
across the graph and provide a more versatile and significantly more accurate
stacking model for label prediction on a network. We demonstrate the benefits
of the proposed model using both a simulation study and real data analysis.
Communication-efficient Distributed Sparse Linear Discriminant Analysis
Authors: Lu Tian, Quanquan Gu
Date: 2016-10-15 00:00:00
Tasks: Model Selection

We propose a communication-efficient distributed estimation method for sparse
linear discriminant analysis (LDA) in the high dimensional regime. Our method
distributes the data of size $N$ into $m$ machines, and estimates a local
sparse LDA estimator on each machine using the data subset of size $N/m$. After
the distributed estimation, our method aggregates the debiased local estimators
from $m$ machines, and sparsifies the aggregated estimator. We show that the
aggregated estimator attains the same statistical rate as the centralized
estimation method, as long as the number of machines $m$ is chosen
appropriately. Moreover, we prove that our method can attain the model
selection consistency under a milder condition than the centralized method.
Experiments on both synthetic and real datasets corroborate our theory.
Sample Efficient Optimization for Learning Controllers for Bipedal Locomotion
Authors: Rika Antonova, Akshara Rai, Christopher G. Atkeson
Date: 2016-10-15 00:00:00
Tasks: Bayesian Optimization

Learning policies for bipedal locomotion can be difficult, as experiments are
expensive and simulation does not usually transfer well to hardware. To counter
this, we need al- gorithms that are sample efficient and inherently safe.
Bayesian Optimization is a powerful sample-efficient tool for optimizing
non-convex black-box functions. However, its performance can degrade in higher
dimensions. We develop a distance metric for bipedal locomotion that enhances
the sample-efficiency of Bayesian Optimization and use it to train a 16
dimensional neuromuscular model for planar walking. This distance metric
reflects some basic gait features of healthy walking and helps us quickly
eliminate a majority of unstable controllers. With our approach we can learn
policies for walking in less than 100 trials for a range of challenging
settings. In simulation, we show results on two different costs and on various
terrains including rough ground and ramps, sloping upwards and downwards. We
also perturb our models with unknown inertial disturbances analogous with
differences between simulation and hardware. These results are promising, as
they indicate that this method can potentially be used to learn control
policies on hardware.
Recovering the Missing Link: Predicting Class-Attribute Associations for Unsupervised Zero-Shot Learning
Authors: Ziad Al-Halah, Makarand Tapaswi, Rainer Stiefelhagen
Date: 2016-10-15 00:00:00
Proceeding: CVPR 2016 6
Tasks: Attribute, zero-shot-classification, Zero-Shot Learning

Collecting training images for all visual categories is not only expensive
but also impractical. Zero-shot learning (ZSL), especially using attributes,
offers a pragmatic solution to this problem. However, at test time most
attribute-based methods require a full description of attribute associations
for each unseen class. Providing these associations is time consuming and often
requires domain specific knowledge. In this work, we aim to carry out
attribute-based zero-shot classification in an unsupervised manner. We propose
an approach to learn relations that couples class embeddings with their
corresponding attributes. Given only the name of an unseen class, the learned
relationship model is used to automatically predict the class-attribute
associations. Furthermore, our model facilitates transferring attributes across
data sets without additional effort. Integrating knowledge from multiple
sources results in a significant additional improvement in performance. We
evaluate on two public data sets: Animals with Attributes and aPascal/aYahoo.
Our approach outperforms state-of-the-art methods in both predicting
class-attribute associations and unsupervised ZSL by a large margin.
Similarity Learning for Time Series Classification
Authors: Maria-Irina Nicolae, Éric Gaussier, Amaury Habrard, Marc Sebban
Date: 2016-10-15 00:00:00
Tasks: Classification, Dynamic Time Warping, General Classification, Time Series, Time Series Analysis, Time Series Classification

Multivariate time series naturally exist in many fields, like energy,
bioinformatics, signal processing, and finance. Most of these applications need
to be able to compare these structured data. In this context, dynamic time
warping (DTW) is probably the most common comparison measure. However, not much
research effort has been put into improving it by learning. In this paper, we
propose a novel method for learning similarities based on DTW, in order to
improve time series classification. Making use of the uniform stability
framework, we provide the first theoretical guarantees in the form of a
generalization bound for linear classification. The experimental study shows
that the proposed approach is efficient, while yielding sparse classifiers.
An Adaptive Test of Independence with Analytic Kernel Embeddings
Authors: Wittawat Jitkrittum, Zoltan Szabo, Arthur Gretton
Date: 2016-10-15 00:00:00
Proceeding: ICML 2017 8

A new computationally efficient dependence measure, and an adaptive
statistical test of independence, are proposed. The dependence measure is the
difference between analytic embeddings of the joint distribution and the
product of the marginals, evaluated at a finite set of locations (features).
These features are chosen so as to maximize a lower bound on the test power,
resulting in a test that is data-efficient, and that runs in linear time (with
respect to the sample size n). The optimized features can be interpreted as
evidence to reject the null hypothesis, indicating regions in the joint domain
where the joint distribution and the product of the marginals differ most.
Consistency of the independence test is established, for an appropriate choice
of features. In real-world benchmarks, independence tests using the optimized
features perform comparably to the state-of-the-art quadratic-time HSIC test,
and outperform competing O(n) and O(n log n) tests.
CNNdroid: GPU-Accelerated Execution of Trained Deep Convolutional Neural Networks on Android
Authors: Seyyed Salar Latifi Oskouei, Hossein Golestani, Matin Hashemi, Soheil Ghiasi
Date: 2015-11-23 00:00:00
Tasks: BIG-bench Machine Learning, GPU

Many mobile applications running on smartphones and wearable devices would
potentially benefit from the accuracy and scalability of deep CNN-based machine
learning algorithms. However, performance and energy consumption limitations
make the execution of such computationally intensive algorithms on mobile
devices prohibitive. We present a GPU-accelerated library, dubbed CNNdroid, for
execution of trained deep CNNs on Android-based mobile devices. Empirical
evaluations show that CNNdroid achieves up to 60X speedup and 130X energy
saving on current mobile devices. The CNNdroid open source library is available
for download at https://github.com/ENCP/CNNdroid
Unsupervised clustering under the Union of Polyhedral Cones (UOPC) model
Authors: Wenqi Wang, Vaneet Aggarwal, Shuchin Aeron
Date: 2016-10-15 00:00:00
Tasks: Clustering

In this paper, we consider clustering data that is assumed to come from one
of finitely many pointed convex polyhedral cones. This model is referred to as
the Union of Polyhedral Cones (UOPC) model. Similar to the Union of Subspaces
(UOS) model where each data from each subspace is generated from a (unknown)
basis, in the UOPC model each data from each cone is assumed to be generated
from a finite number of (unknown) \emph{extreme rays}.To cluster data under
this model, we consider several algorithms - (a) Sparse Subspace Clustering by
Non-negative constraints Lasso (NCL), (b) Least squares approximation (LSA),
and (c) K-nearest neighbor (KNN) algorithm to arrive at affinity between data
points. Spectral Clustering (SC) is then applied on the resulting affinity
matrix to cluster data into different polyhedral cones. We show that on an
average KNN outperforms both NCL and LSA and for this algorithm we provide the
deterministic conditions for correct clustering. For an affinity measure
between the cones it is shown that as long as the cones are not very coherent
and as long as the density of data within each cone exceeds a threshold, KNN
leads to accurate clustering. Finally, simulation results on real datasets
(MNIST and YaleFace datasets) depict that the proposed algorithm works well on
real data indicating the utility of the UOPC model and the proposed algorithm.
Incremental One-Class Models for Data Classification
Authors: Takoua Kefi, Riadh Ksantini, M. Becha Kaaniche, Adel Bouhoula
Date: 2016-10-15 00:00:00
Tasks: Classification, class-incremental learning, Class Incremental Learning, General Classification, Incremental Learning

In this paper we outline a PhD research plan. This research contributes to
the field of one-class incremental learning and classification in case of
non-stationary environments. The goal of this PhD is to define a new
classification framework able to deal with very small learning dataset at the
beginning of the process and with abilities to adjust itself according to the
variability of the incoming data which create large scale datasets. As a
preliminary work, incremental Covariance-guided One-Class Support Vector
Machine is proposed to deal with sequentially obtained data. It is inspired
from COSVM which put more emphasis on the low variance directions while keeping
the basic formulation of incremental One-Class Support Vector Machine
untouched. The incremental procedure is introduced by controlling the possible
changes of support vectors after the addition of new data points, thanks to the
Karush-Kuhn-Tucker conditions, that have to be maintained on all previously
acquired data. Comparative experimental results with contemporary incremental
and non-incremental one-class classifiers on numerous artificial and real data
sets show that our method results in significantly better classification
performance.
Online Nonnegative Matrix Factorization with Outliers
Authors: Renbo Zhao, Vincent Y. F. Tan
Date: 2016-04-10 00:00:00
Tasks: Computational Efficiency, Denoising, Image Denoising, Shadow Removal

We propose a unified and systematic framework for performing online
nonnegative matrix factorization in the presence of outliers. Our framework is
particularly suited to large-scale data. We propose two solvers based on
projected gradient descent and the alternating direction method of multipliers.
We prove that the sequence of objective values converges almost surely by
appealing to the quasi-martingale convergence theorem. We also show the
sequence of learned dictionaries converges to the set of stationary points of
the expected loss function almost surely. In addition, we extend our basic
problem formulation to various settings with different constraints and
regularizers. We also adapt the solvers and analyses to each setting. We
perform extensive experiments on both synthetic and real datasets. These
experiments demonstrate the computational efficiency and efficacy of our
algorithms on tasks such as (parts-based) basis learning, image denoising,
shadow removal and foreground-background separation.
Filter based Taxonomy Modification for Improving Hierarchical Classification
Authors: Azad Naik, Huzefa Rangwala
Date: 2016-03-02 00:00:00
Tasks: Classification, General Classification

Hierarchical Classification (HC) is a supervised learning problem where
unlabeled instances are classified into a taxonomy of classes. Several methods
that utilize the hierarchical structure have been developed to improve the HC
performance. However, in most cases apriori defined hierarchical structure by
domain experts is inconsistent; as a consequence performance improvement is not
noticeable in comparison to flat classification methods. We propose a scalable
data-driven filter based rewiring approach to modify an expert-defined
hierarchy. Experimental comparisons of top-down HC with our modified hierarchy,
on a wide range of datasets shows classification performance improvement over
the baseline hierarchy (i:e:, defined by expert), clustered hierarchy and
flattening based hierarchy modification approaches. In comparison to existing
rewiring approaches, our developed method (rewHier) is computationally
efficient, enabling it to scale to datasets with large numbers of classes,
instances and features. We also show that our modified hierarchy leads to
improved classification performance for classes with few training samples in
comparison to flat and state-of-the-art HC approaches.
A Closed Form Solution to Multi-View Low-Rank Regression
Authors: Shuai Zheng, Xiao Cai, Chris Ding, Feiping Nie, Heng Huang
Date: 2016-10-14 00:00:00
Tasks: Form, regression

Real life data often includes information from different channels. For
example, in computer vision, we can describe an image using different image
features, such as pixel intensity, color, HOG, GIST feature, SIFT features,
etc.. These different aspects of the same objects are often called multi-view
(or multi-modal) data. Low-rank regression model has been proved to be an
effective learning mechanism by exploring the low-rank structure of real life
data. But previous low-rank regression model only works on single view data. In
this paper, we propose a multi-view low-rank regression model by imposing
low-rank constraints on multi-view regression model. Most importantly, we
provide a closed-form solution to the multi-view low-rank regression model.
Extensive experiments on 4 multi-view datasets show that the multi-view
low-rank regression model outperforms single-view regression model and reveals
that multi-view low-rank structure is very helpful.
Asymptotic Analysis of Objectives based on Fisher Information in Active Learning
Authors: Jamshid Sourati, Murat Akcakaya, Todd K. Leen, Deniz Erdogmus, Jennifer G. Dy
Date: 2016-05-27 00:00:00
Tasks: Active Learning

Obtaining labels can be costly and time-consuming. Active learning allows a
learning algorithm to intelligently query samples to be labeled for efficient
learning. Fisher information ratio (FIR) has been used as an objective for
selecting queries in active learning. However, little is known about the theory
behind the use of FIR for active learning. There is a gap between the
underlying theory and the motivation of its usage in practice. In this paper,
we attempt to fill this gap and provide a rigorous framework for analyzing
existing FIR-based active learning methods. In particular, we show that FIR can
be asymptotically viewed as an upper bound of the expected variance of the
log-likelihood ratio. Additionally, our analysis suggests a unifying framework
that not only enables us to make theoretical comparisons among the existing
querying methods based on FIR, but also allows us to give insight into the
development of new active learning approaches based on this objective.