categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
sequence |
|---|---|---|---|---|---|---|---|---|---|---|
cs.SI cs.LG physics.soc-ph stat.ML | null | 1210.4860 | null | null | http://arxiv.org/pdf/1210.4860v1 | 2012-10-16T17:38:22Z | 2012-10-16T17:38:22Z | Spectral Estimation of Conditional Random Graph Models for Large-Scale
Network Data | Generative models for graphs have been typically committed to strong prior
assumptions concerning the form of the modeled distributions. Moreover, the
vast majority of currently available models are either only suitable for
characterizing some particular network properties (such as degree distribution
or clustering coefficient), or they are aimed at estimating joint probability
distributions, which is often intractable in large-scale networks. In this
paper, we first propose a novel network statistic, based on the Laplacian
spectrum of graphs, which allows to dispense with any parametric assumption
concerning the modeled network properties. Second, we use the defined statistic
to develop the Fiedler random graph model, switching the focus from the
estimation of joint probability distributions to a more tractable conditional
estimation setting. After analyzing the dependence structure characterizing
Fiedler random graphs, we evaluate them experimentally in edge prediction over
several real-world networks, showing that they allow to reach a much higher
prediction accuracy than various alternative statistical models.
| [
"Antonino Freno, Mikaela Keller, Gemma C. Garriga, Marc Tommasi",
"['Antonino Freno' 'Mikaela Keller' 'Gemma C. Garriga' 'Marc Tommasi']"
] |
cs.LG stat.ML | null | 1210.4862 | null | null | http://arxiv.org/pdf/1210.4862v1 | 2012-10-16T17:38:45Z | 2012-10-16T17:38:45Z | Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits | We present and prove properties of a new offline policy evaluator for an
exploration learning setting which is superior to previous evaluators. In
particular, it simultaneously and correctly incorporates techniques from
importance weighting, doubly robust evaluation, and nonstationary policy
evaluation approaches. In addition, our approach allows generating longer
histories by careful control of a bias-variance tradeoff, and further decreases
variance by incorporating information about randomness of the target policy.
Empirical evidence from synthetic and realworld exploration learning problems
shows the new evaluator successfully unifies previous approaches and uses
information an order of magnitude more efficiently.
| [
"Miroslav Dudik, Dumitru Erhan, John Langford, Lihong Li",
"['Miroslav Dudik' 'Dumitru Erhan' 'John Langford' 'Lihong Li']"
] |
cs.LG stat.ML | null | 1210.4867 | null | null | http://arxiv.org/pdf/1210.4867v1 | 2012-10-16T17:39:37Z | 2012-10-16T17:39:37Z | Lifted Relational Variational Inference | Hybrid continuous-discrete models naturally represent many real-world
applications in robotics, finance, and environmental engineering. Inference
with large-scale models is challenging because relational structures
deteriorate rapidly during inference with observations. The main contribution
of this paper is an efficient relational variational inference algorithm that
factors largescale probability models into simpler variational models, composed
of mixtures of iid (Bernoulli) random variables. The algorithm takes
probability relational models of largescale hybrid systems and converts them to
a close-to-optimal variational models. Then, it efficiently calculates marginal
probabilities on the variational models by using a latent (or lifted) variable
elimination or a lifted stochastic sampling. This inference is unique because
it maintains the relational structure upon individual observations and during
inference steps.
| [
"['Jaesik Choi' 'Eyal Amir']",
"Jaesik Choi, Eyal Amir"
] |
cs.LG cs.IR stat.ML | null | 1210.4869 | null | null | http://arxiv.org/pdf/1210.4869v1 | 2012-10-16T17:40:52Z | 2012-10-16T17:40:52Z | Response Aware Model-Based Collaborative Filtering | Previous work on recommender systems mainly focus on fitting the ratings
provided by users. However, the response patterns, i.e., some items are rated
while others not, are generally ignored. We argue that failing to observe such
response patterns can lead to biased parameter estimation and sub-optimal model
performance. Although several pieces of work have tried to model users'
response patterns, they miss the effectiveness and interpretability of the
successful matrix factorization collaborative filtering approaches. To bridge
the gap, in this paper, we unify explicit response models and PMF to establish
the Response Aware Probabilistic Matrix Factorization (RAPMF) framework. We
show that RAPMF subsumes PMF as a special case. Empirically we demonstrate the
merits of RAPMF from various aspects.
| [
"Guang Ling, Haiqin Yang, Michael R. Lyu, Irwin King",
"['Guang Ling' 'Haiqin Yang' 'Michael R. Lyu' 'Irwin King']"
] |
cs.AI cs.LG | null | 1210.4870 | null | null | http://arxiv.org/pdf/1210.4870v1 | 2012-10-16T17:41:19Z | 2012-10-16T17:41:19Z | Crowdsourcing Control: Moving Beyond Multiple Choice | To ensure quality results from crowdsourced tasks, requesters often aggregate
worker responses and use one of a plethora of strategies to infer the correct
answer from the set of noisy responses. However, all current models assume
prior knowledge of all possible outcomes of the task. While not an unreasonable
assumption for tasks that can be posited as multiple-choice questions (e.g.
n-ary classification), we observe that many tasks do not naturally fit this
paradigm, but instead demand a free-response formulation where the outcome
space is of infinite size (e.g. audio transcription). We model such tasks with
a novel probabilistic graphical model, and design and implement LazySusan, a
decision-theoretic controller that dynamically requests responses as necessary
in order to infer answers to these tasks. We also design an EM algorithm to
jointly learn the parameters of our model while inferring the correct answers
to multiple tasks at a time. Live experiments on Amazon Mechanical Turk
demonstrate the superiority of LazySusan at solving SAT Math questions,
eliminating 83.2% of the error and achieving greater net utility compared to
the state-ofthe-art strategy, majority-voting. We also show in live experiments
that our EM algorithm outperforms majority-voting on a visualization task that
we design.
| [
"Christopher H. Lin, Mausam, Daniel Weld",
"['Christopher H. Lin' 'Mausam' 'Daniel Weld']"
] |
cs.LG cs.CL cs.IR stat.ML | null | 1210.4871 | null | null | http://arxiv.org/pdf/1210.4871v1 | 2012-10-16T17:41:30Z | 2012-10-16T17:41:30Z | Learning Mixtures of Submodular Shells with Application to Document
Summarization | We introduce a method to learn a mixture of submodular "shells" in a
large-margin setting. A submodular shell is an abstract submodular function
that can be instantiated with a ground set and a set of parameters to produce a
submodular function. A mixture of such shells can then also be so instantiated
to produce a more complex submodular function. What our algorithm learns are
the mixture weights over such shells. We provide a risk bound guarantee when
learning in a large-margin structured-prediction setting using a projected
subgradient method when only approximate submodular optimization is possible
(such as with submodular function maximization). We apply this method to the
problem of multi-document summarization and produce the best results reported
so far on the widely used NIST DUC-05 through DUC-07 document summarization
corpora.
| [
"Hui Lin, Jeff A. Bilmes",
"['Hui Lin' 'Jeff A. Bilmes']"
] |
cs.LG cs.CV stat.ML | null | 1210.4872 | null | null | http://arxiv.org/pdf/1210.4872v1 | 2012-10-16T17:41:42Z | 2012-10-16T17:41:42Z | Nested Dictionary Learning for Hierarchical Organization of Imagery and
Text | A tree-based dictionary learning model is developed for joint analysis of
imagery and associated text. The dictionary learning may be applied directly to
the imagery from patches, or to general feature vectors extracted from patches
or superpixels (using any existing method for image feature extraction). Each
image is associated with a path through the tree (from root to a leaf), and
each of the multiple patches in a given image is associated with one node in
that path. Nodes near the tree root are shared between multiple paths,
representing image characteristics that are common among different types of
images. Moving toward the leaves, nodes become specialized, representing
details in image classes. If available, words (text) are also jointly modeled,
with a path-dependent probability over words. The tree structure is inferred
via a nested Dirichlet process, and a retrospective stick-breaking sampler is
used to infer the tree depth and width.
| [
"Lingbo Li, XianXing Zhang, Mingyuan Zhou, Lawrence Carin",
"['Lingbo Li' 'XianXing Zhang' 'Mingyuan Zhou' 'Lawrence Carin']"
] |
cs.LG stat.ML | null | 1210.4876 | null | null | http://arxiv.org/pdf/1210.4876v1 | 2012-10-16T17:43:04Z | 2012-10-16T17:43:04Z | Active Imitation Learning via Reduction to I.I.D. Active Learning | In standard passive imitation learning, the goal is to learn a target policy
by passively observing full execution trajectories of it. Unfortunately,
generating such trajectories can require substantial expert effort and be
impractical in some cases. In this paper, we consider active imitation learning
with the goal of reducing this effort by querying the expert about the desired
action at individual states, which are selected based on answers to past
queries and the learner's interactions with an environment simulator. We
introduce a new approach based on reducing active imitation learning to i.i.d.
active learning, which can leverage progress in the i.i.d. setting. Our first
contribution, is to analyze reductions for both non-stationary and stationary
policies, showing that the label complexity (number of queries) of active
imitation learning can be substantially less than passive learning. Our second
contribution, is to introduce a practical algorithm inspired by the reductions,
which is shown to be highly effective in four test domains compared to a number
of alternatives.
| [
"Kshitij Judah, Alan Fern, Thomas G. Dietterich",
"['Kshitij Judah' 'Alan Fern' 'Thomas G. Dietterich']"
] |
cs.AI cs.GT cs.LG | null | 1210.4880 | null | null | http://arxiv.org/pdf/1210.4880v1 | 2012-10-16T17:43:47Z | 2012-10-16T17:43:47Z | Inferring Strategies from Limited Reconnaissance in Real-time Strategy
Games | In typical real-time strategy (RTS) games, enemy units are visible only when
they are within sight range of a friendly unit. Knowledge of an opponent's
disposition is limited to what can be observed through scouting. Information is
costly, since units dedicated to scouting are unavailable for other purposes,
and the enemy will resist scouting attempts. It is important to infer as much
as possible about the opponent's current and future strategy from the available
observations. We present a dynamic Bayes net model of strategies in the RTS
game Starcraft that combines a generative model of how strategies relate to
observable quantities with a principled framework for incorporating evidence
gained via scouting. We demonstrate the model's ability to infer unobserved
aspects of the game from realistic observations.
| [
"Jesse Hostetler, Ethan W. Dereszynski, Thomas G. Dietterich, Alan Fern",
"['Jesse Hostetler' 'Ethan W. Dereszynski' 'Thomas G. Dietterich'\n 'Alan Fern']"
] |
cs.LG stat.ML | null | 1210.4881 | null | null | http://arxiv.org/pdf/1210.4881v1 | 2012-10-16T17:43:59Z | 2012-10-16T17:43:59Z | Tightening Fractional Covering Upper Bounds on the Partition Function
for High-Order Region Graphs | In this paper we present a new approach for tightening upper bounds on the
partition function. Our upper bounds are based on fractional covering bounds on
the entropy function, and result in a concave program to compute these bounds
and a convex program to tighten them. To solve these programs effectively for
general region graphs we utilize the entropy barrier method, thus decomposing
the original programs by their dual programs and solve them with dual block
optimization scheme. The entropy barrier method provides an elegant framework
to generalize the message-passing scheme to high-order region graph, as well as
to solve the block dual steps in closed-form. This is a key for computational
relevancy for large problems with thousands of regions.
| [
"['Tamir Hazan' 'Jian Peng' 'Amnon Shashua']",
"Tamir Hazan, Jian Peng, Amnon Shashua"
] |
cs.LG cs.NA stat.ML | null | 1210.4883 | null | null | http://arxiv.org/pdf/1210.4883v1 | 2012-10-16T17:45:11Z | 2012-10-16T17:45:11Z | A Model-Based Approach to Rounding in Spectral Clustering | In spectral clustering, one defines a similarity matrix for a collection of
data points, transforms the matrix to get the Laplacian matrix, finds the
eigenvectors of the Laplacian matrix, and obtains a partition of the data using
the leading eigenvectors. The last step is sometimes referred to as rounding,
where one needs to decide how many leading eigenvectors to use, to determine
the number of clusters, and to partition the data points. In this paper, we
propose a novel method for rounding. The method differs from previous methods
in three ways. First, we relax the assumption that the number of clusters
equals the number of eigenvectors used. Second, when deciding the number of
leading eigenvectors to use, we not only rely on information contained in the
leading eigenvectors themselves, but also use subsequent eigenvectors. Third,
our method is model-based and solves all the three subproblems of rounding
using a class of graphical models called latent tree models. We evaluate our
method on both synthetic and real-world data. The results show that our method
works correctly in the ideal case where between-clusters similarity is 0, and
degrades gracefully as one moves away from the ideal case.
| [
"Leonard K. M. Poon, April H. Liu, Tengfei Liu, Nevin Lianwen Zhang",
"['Leonard K. M. Poon' 'April H. Liu' 'Tengfei Liu' 'Nevin Lianwen Zhang']"
] |
cs.LG stat.ML | null | 1210.4884 | null | null | http://arxiv.org/pdf/1210.4884v1 | 2012-10-16T17:45:30Z | 2012-10-16T17:45:30Z | A Spectral Algorithm for Latent Junction Trees | Latent variable models are an elegant framework for capturing rich
probabilistic dependencies in many applications. However, current approaches
typically parametrize these models using conditional probability tables, and
learning relies predominantly on local search heuristics such as Expectation
Maximization. Using tensor algebra, we propose an alternative parameterization
of latent variable models (where the model structures are junction trees) that
still allows for computation of marginals among observed variables. While this
novel representation leads to a moderate increase in the number of parameters
for junction trees of low treewidth, it lets us design a local-minimum-free
algorithm for learning this parameterization. The main computation of the
algorithm involves only tensor operations and SVDs which can be orders of
magnitude faster than EM algorithms for large datasets. To our knowledge, this
is the first provably consistent parameter learning technique for a large class
of low-treewidth latent graphical models beyond trees. We demonstrate the
advantages of our method on synthetic and real datasets.
| [
"Ankur P. Parikh, Le Song, Mariya Ishteva, Gabi Teodoru, Eric P. Xing",
"['Ankur P. Parikh' 'Le Song' 'Mariya Ishteva' 'Gabi Teodoru'\n 'Eric P. Xing']"
] |
cs.LG cs.AI stat.ML | null | 1210.4887 | null | null | http://arxiv.org/pdf/1210.4887v1 | 2012-10-16T17:46:07Z | 2012-10-16T17:46:07Z | Hilbert Space Embeddings of POMDPs | A nonparametric approach for policy learning for POMDPs is proposed. The
approach represents distributions over the states, observations, and actions as
embeddings in feature spaces, which are reproducing kernel Hilbert spaces.
Distributions over states given the observations are obtained by applying the
kernel Bayes' rule to these distribution embeddings. Policies and value
functions are defined on the feature space over states, which leads to a
feature space expression for the Bellman equation. Value iteration may then be
used to estimate the optimal value function and associated policy. Experimental
results confirm that the correct policy is learned using the feature space
representation.
| [
"['Yu Nishiyama' 'Abdeslam Boularias' 'Arthur Gretton' 'Kenji Fukumizu']",
"Yu Nishiyama, Abdeslam Boularias, Arthur Gretton, Kenji Fukumizu"
] |
cs.LG cs.AI stat.ML | null | 1210.4888 | null | null | http://arxiv.org/pdf/1210.4888v1 | 2012-10-16T17:46:17Z | 2012-10-16T17:46:17Z | Local Structure Discovery in Bayesian Networks | Learning a Bayesian network structure from data is an NP-hard problem and
thus exact algorithms are feasible only for small data sets. Therefore, network
structures for larger networks are usually learned with various heuristics.
Another approach to scaling up the structure learning is local learning. In
local learning, the modeler has one or more target variables that are of
special interest; he wants to learn the structure near the target variables and
is not interested in the rest of the variables. In this paper, we present a
score-based local learning algorithm called SLL. We conjecture that our
algorithm is theoretically sound in the sense that it is optimal in the limit
of large sample size. Empirical results suggest that SLL is competitive when
compared to the constraint-based HITON algorithm. We also study the prospects
of constructing the network structure for the whole node set based on local
results by presenting two algorithms and comparing them to several heuristics.
| [
"Teppo Niinimaki, Pekka Parviainen",
"['Teppo Niinimaki' 'Pekka Parviainen']"
] |
cs.LG cs.AI stat.ML | null | 1210.4889 | null | null | http://arxiv.org/pdf/1210.4889v1 | 2012-10-16T17:46:26Z | 2012-10-16T17:46:26Z | Learning STRIPS Operators from Noisy and Incomplete Observations | Agents learning to act autonomously in real-world domains must acquire a
model of the dynamics of the domain in which they operate. Learning domain
dynamics can be challenging, especially where an agent only has partial access
to the world state, and/or noisy external sensors. Even in standard STRIPS
domains, existing approaches cannot learn from noisy, incomplete observations
typical of real-world domains. We propose a method which learns STRIPS action
models in such domains, by decomposing the problem into first learning a
transition function between states in the form of a set of classifiers, and
then deriving explicit STRIPS rules from the classifiers' parameters. We
evaluate our approach on simulated standard planning domains from the
International Planning Competition, and show that it learns useful domain
descriptions from noisy, incomplete observations.
| [
"['Kira Mourao' 'Luke S. Zettlemoyer' 'Ronald P. A. Petrick'\n 'Mark Steedman']",
"Kira Mourao, Luke S. Zettlemoyer, Ronald P. A. Petrick, Mark Steedman"
] |
cs.LG stat.ML | null | 1210.4892 | null | null | http://arxiv.org/pdf/1210.4892v1 | 2012-10-16T17:47:18Z | 2012-10-16T17:47:18Z | Unsupervised Joint Alignment and Clustering using Bayesian
Nonparametrics | Joint alignment of a collection of functions is the process of independently
transforming the functions so that they appear more similar to each other.
Typically, such unsupervised alignment algorithms fail when presented with
complex data sets arising from multiple modalities or make restrictive
assumptions about the form of the functions or transformations, limiting their
generality. We present a transformed Bayesian infinite mixture model that can
simultaneously align and cluster a data set. Our model and associated learning
scheme offer two key advantages: the optimal number of clusters is determined
in a data-driven fashion through the use of a Dirichlet process prior, and it
can accommodate any transformation function parameterized by a continuous
parameter vector. As a result, it is applicable to a wide range of data types,
and transformation functions. We present positive results on synthetic
two-dimensional data, on a set of one-dimensional curves, and on various image
data sets, showing large improvements over previous work. We discuss several
variations of the model and conclude with directions for future work.
| [
"['Marwan A. Mattar' 'Allen R. Hanson' 'Erik G. Learned-Miller']",
"Marwan A. Mattar, Allen R. Hanson, Erik G. Learned-Miller"
] |
cs.LG stat.ML | null | 1210.4893 | null | null | http://arxiv.org/pdf/1210.4893v1 | 2012-10-16T17:47:32Z | 2012-10-16T17:47:32Z | Sparse Q-learning with Mirror Descent | This paper explores a new framework for reinforcement learning based on
online convex optimization, in particular mirror descent and related
algorithms. Mirror descent can be viewed as an enhanced gradient method,
particularly suited to minimization of convex functions in highdimensional
spaces. Unlike traditional gradient methods, mirror descent undertakes gradient
updates of weights in both the dual space and primal space, which are linked
together using a Legendre transform. Mirror descent can be viewed as a proximal
algorithm where the distance generating function used is a Bregman divergence.
A new class of proximal-gradient based temporal-difference (TD) methods are
presented based on different Bregman divergences, which are more powerful than
regular TD learning. Examples of Bregman divergences that are studied include
p-norm functions, and Mahalanobis distance based on the covariance of sample
gradients. A new family of sparse mirror-descent reinforcement learning methods
are proposed, which are able to find sparse fixed points of an l1-regularized
Bellman equation at significantly less computational cost than previous methods
based on second-order matrix methods. An experimental study of mirror-descent
reinforcement learning is presented using discrete and continuous Markov
decision processes.
| [
"Sridhar Mahadevan, Bo Liu",
"['Sridhar Mahadevan' 'Bo Liu']"
] |
cs.LG cs.AI stat.ML | null | 1210.4896 | null | null | http://arxiv.org/pdf/1210.4896v1 | 2012-10-16T17:48:08Z | 2012-10-16T17:48:08Z | Closed-Form Learning of Markov Networks from Dependency Networks | Markov networks (MNs) are a powerful way to compactly represent a joint
probability distribution, but most MN structure learning methods are very slow,
due to the high cost of evaluating candidates structures. Dependency networks
(DNs) represent a probability distribution as a set of conditional probability
distributions. DNs are very fast to learn, but the conditional distributions
may be inconsistent with each other and few inference algorithms support DNs.
In this paper, we present a closed-form method for converting a DN into an MN,
allowing us to enjoy both the efficiency of DN learning and the convenience of
the MN representation. When the DN is consistent, this conversion is exact. For
inconsistent DNs, we present averaging methods that significantly improve the
approximation. In experiments on 12 standard datasets, our methods are orders
of magnitude faster than and often more accurate than combining conditional
distributions using weight learning.
| [
"['Daniel Lowd']",
"Daniel Lowd"
] |
cs.LG stat.ML | null | 1210.4898 | null | null | http://arxiv.org/pdf/1210.4898v1 | 2012-10-16T17:50:15Z | 2012-10-16T17:50:15Z | Value Function Approximation in Noisy Environments Using Locally
Smoothed Regularized Approximate Linear Programs | Recently, Petrik et al. demonstrated that L1Regularized Approximate Linear
Programming (RALP) could produce value functions and policies which compared
favorably to established linear value function approximation techniques like
LSPI. RALP's success primarily stems from the ability to solve the feature
selection and value function approximation steps simultaneously. RALP's
performance guarantees become looser if sampled next states are used. For very
noisy domains, RALP requires an accurate model rather than samples, which can
be unrealistic in some practical scenarios. In this paper, we demonstrate this
weakness, and then introduce Locally Smoothed L1-Regularized Approximate Linear
Programming (LS-RALP). We demonstrate that LS-RALP mitigates inaccuracies
stemming from noise even without an accurate model. We show that, given some
smoothness assumptions, as the number of samples increases, error from noise
approaches zero, and provide experimental examples of LS-RALP's success on
common reinforcement learning benchmark problems.
| [
"Gavin Taylor, Ron Parr",
"['Gavin Taylor' 'Ron Parr']"
] |
cs.LG stat.ML | null | 1210.4899 | null | null | http://arxiv.org/pdf/1210.4899v1 | 2012-10-16T17:50:25Z | 2012-10-16T17:50:25Z | Fast Exact Inference for Recursive Cardinality Models | Cardinality potentials are a generally useful class of high order potential
that affect probabilities based on how many of D binary variables are active.
Maximum a posteriori (MAP) inference for cardinality potential models is
well-understood, with efficient computations taking O(DlogD) time. Yet
efficient marginalization and sampling have not been addressed as thoroughly in
the machine learning community. We show that there exists a simple algorithm
for computing marginal probabilities and drawing exact joint samples that runs
in O(Dlog2 D) time, and we show how to frame the algorithm as efficient belief
propagation in a low order tree-structured model that includes additional
auxiliary variables. We then develop a new, more general class of models,
termed Recursive Cardinality models, which take advantage of this efficiency.
Finally, we show how to do efficient exact inference in models composed of a
tree structure and a cardinality potential. We explore the expressive power of
Recursive Cardinality models and empirically demonstrate their utility.
| [
"Daniel Tarlow, Kevin Swersky, Richard S. Zemel, Ryan Prescott Adams,\n Brendan J. Frey",
"['Daniel Tarlow' 'Kevin Swersky' 'Richard S. Zemel' 'Ryan Prescott Adams'\n 'Brendan J. Frey']"
] |
cs.DS cs.LG stat.ML | null | 1210.4902 | null | null | http://arxiv.org/pdf/1210.4902v1 | 2012-10-16T17:51:21Z | 2012-10-16T17:51:21Z | Efficiently Searching for Frustrated Cycles in MAP Inference | Dual decomposition provides a tractable framework for designing algorithms
for finding the most probable (MAP) configuration in graphical models. However,
for many real-world inference problems, the typical decomposition has a large
integrality gap, due to frustrated cycles. One way to tighten the relaxation is
to introduce additional constraints that explicitly enforce cycle consistency.
Earlier work showed that cluster-pursuit algorithms, which iteratively
introduce cycle and other higherorder consistency constraints, allows one to
exactly solve many hard inference problems. However, these algorithms
explicitly enumerate a candidate set of clusters, limiting them to triplets or
other short cycles. We solve the search problem for cycle constraints, giving a
nearly linear time algorithm for finding the most frustrated cycle of arbitrary
length. We show how to use this search algorithm together with the dual
decomposition framework and clusterpursuit. The new algorithm exactly solves
MAP inference problems arising from relational classification and stereo
vision.
| [
"['David Sontag' 'Do Kook Choe' 'Yitao Li']",
"David Sontag, Do Kook Choe, Yitao Li"
] |
stat.ML cs.LG | null | 1210.4905 | null | null | http://arxiv.org/pdf/1210.4905v1 | 2012-10-16T17:51:50Z | 2012-10-16T17:51:50Z | Latent Composite Likelihood Learning for the Structured Canonical
Correlation Model | Latent variable models are used to estimate variables of interest quantities
which are observable only up to some measurement error. In many studies, such
variables are known but not precisely quantifiable (such as "job satisfaction"
in social sciences and marketing, "analytical ability" in educational testing,
or "inflation" in economics). This leads to the development of measurement
instruments to record noisy indirect evidence for such unobserved variables
such as surveys, tests and price indexes. In such problems, there are
postulated latent variables and a given measurement model. At the same time,
other unantecipated latent variables can add further unmeasured confounding to
the observed variables. The problem is how to deal with unantecipated latents
variables. In this paper, we provide a method loosely inspired by canonical
correlation that makes use of background information concerning the "known"
latent variables. Given a partially specified structure, it provides a
structure learning approach to detect "unknown unknowns," the confounding
effect of potentially infinitely many other latent variables. This is done
without explicitly modeling such extra latent factors. Because of the special
structure of the problem, we are able to exploit a new variation of composite
likelihood fitting to efficiently learn this structure. Validation is provided
with experiments in synthetic data and the analysis of a large survey done with
a sample of over 100,000 staff members of the National Health Service of the
United Kingdom.
| [
"Ricardo Silva",
"['Ricardo Silva']"
] |
cs.LG stat.ML | null | 1210.4909 | null | null | http://arxiv.org/pdf/1210.4909v1 | 2012-10-16T17:53:17Z | 2012-10-16T17:53:17Z | Active Learning with Distributional Estimates | Active Learning (AL) is increasingly important in a broad range of
applications. Two main AL principles to obtain accurate classification with few
labeled data are refinement of the current decision boundary and exploration of
poorly sampled regions. In this paper we derive a novel AL scheme that balances
these two principles in a natural way. In contrast to many AL strategies, which
are based on an estimated class conditional probability ^p(y|x), a key
component of our approach is to view this quantity as a random variable, hence
explicitly considering the uncertainty in its estimated value. Our main
contribution is a novel mathematical framework for uncertainty-based AL, and a
corresponding AL scheme, where the uncertainty in ^p(y|x) is modeled by a
second-order distribution. On the practical side, we show how to approximate
such second-order distributions for kernel density classification. Finally, we
find that over a large number of UCI, USPS and Caltech4 datasets, our AL scheme
achieves significantly better learning curves than popular AL methods such as
uncertainty sampling and error reduction sampling, when all use the same kernel
density classifier.
| [
"Jens Roeder, Boaz Nadler, Kevin Kunzmann, Fred A. Hamprecht",
"['Jens Roeder' 'Boaz Nadler' 'Kevin Kunzmann' 'Fred A. Hamprecht']"
] |
cs.AI cs.LG stat.ML | null | 1210.4910 | null | null | http://arxiv.org/pdf/1210.4910v1 | 2012-10-16T17:53:29Z | 2012-10-16T17:53:29Z | New Advances and Theoretical Insights into EDML | EDML is a recently proposed algorithm for learning MAP parameters in Bayesian
networks. In this paper, we present a number of new advances and insights on
the EDML algorithm. First, we provide the multivalued extension of EDML,
originally proposed for Bayesian networks over binary variables. Next, we
identify a simplified characterization of EDML that further implies a simple
fixed-point algorithm for the convex optimization problem that underlies it.
This characterization further reveals a connection between EDML and EM: a fixed
point of EDML is a fixed point of EM, and vice versa. We thus identify also a
new characterization of EM fixed points, but in the semantics of EDML. Finally,
we propose a hybrid EDML/EM algorithm that takes advantage of the improved
empirical convergence behavior of EDML, while maintaining the monotonic
improvement property of EM.
| [
"Khaled S. Refaat, Arthur Choi, Adnan Darwiche",
"['Khaled S. Refaat' 'Arthur Choi' 'Adnan Darwiche']"
] |
cs.AI cs.LG stat.ML | null | 1210.4913 | null | null | http://arxiv.org/pdf/1210.4913v1 | 2012-10-16T17:55:57Z | 2012-10-16T17:55:57Z | An Improved Admissible Heuristic for Learning Optimal Bayesian Networks | Recently two search algorithms, A* and breadth-first branch and bound
(BFBnB), were developed based on a simple admissible heuristic for learning
Bayesian network structures that optimize a scoring function. The heuristic
represents a relaxation of the learning problem such that each variable chooses
optimal parents independently. As a result, the heuristic may contain many
directed cycles and result in a loose bound. This paper introduces an improved
admissible heuristic that tries to avoid directed cycles within small groups of
variables. A sparse representation is also introduced to store only the unique
optimal parent choices. Empirical results show that the new techniques
significantly improved the efficiency and scalability of A* and BFBnB on most
of datasets tested in this paper.
| [
"['Changhe Yuan' 'Brandon Malone']",
"Changhe Yuan, Brandon Malone"
] |
cs.LG cs.IR stat.ML | null | 1210.4914 | null | null | http://arxiv.org/pdf/1210.4914v1 | 2012-10-16T17:56:08Z | 2012-10-16T17:56:08Z | Latent Structured Ranking | Many latent (factorized) models have been proposed for recommendation tasks
like collaborative filtering and for ranking tasks like document or image
retrieval and annotation. Common to all those methods is that during inference
the items are scored independently by their similarity to the query in the
latent embedding space. The structure of the ranked list (i.e. considering the
set of items returned as a whole) is not taken into account. This can be a
problem because the set of top predictions can be either too diverse (contain
results that contradict each other) or are not diverse enough. In this paper we
introduce a method for learning latent structured rankings that improves over
existing methods by providing the right blend of predictions at the top of the
ranked list. Particular emphasis is put on making this method scalable.
Empirical results on large scale image annotation and music recommendation
tasks show improvements over existing approaches.
| [
"Jason Weston, John Blitzer",
"['Jason Weston' 'John Blitzer']"
] |
cs.LG stat.ML | null | 1210.4917 | null | null | http://arxiv.org/pdf/1210.4917v1 | 2012-10-16T17:56:43Z | 2012-10-16T17:56:43Z | Fast Graph Construction Using Auction Algorithm | In practical machine learning systems, graph based data representation has
been widely used in various learning paradigms, ranging from unsupervised
clustering to supervised classification. Besides those applications with
natural graph or network structure data, such as social network analysis and
relational learning, many other applications often involve a critical step in
converting data vectors to an adjacency graph. In particular, a sparse subgraph
extracted from the original graph is often required due to both theoretic and
practical needs. Previous study clearly shows that the performance of different
learning algorithms, e.g., clustering and classification, benefits from such
sparse subgraphs with balanced node connectivity. However, the existing graph
construction methods are either computationally expensive or with
unsatisfactory performance. In this paper, we utilize a scalable method called
auction algorithm and its parallel extension to recover a sparse yet nearly
balanced subgraph with significantly reduced computational cost. Empirical
study and comparison with the state-ofart approaches clearly demonstrate the
superiority of the proposed method in both efficiency and accuracy.
| [
"Jun Wang, Yinglong Xia",
"['Jun Wang' 'Yinglong Xia']"
] |
cs.LG cs.AI stat.ML | null | 1210.4918 | null | null | http://arxiv.org/pdf/1210.4918v1 | 2012-10-16T17:56:54Z | 2012-10-16T17:56:54Z | Dynamic Teaching in Sequential Decision Making Environments | We describe theoretical bounds and a practical algorithm for teaching a model
by demonstration in a sequential decision making environment. Unlike previous
efforts that have optimized learners that watch a teacher demonstrate a static
policy, we focus on the teacher as a decision maker who can dynamically choose
different policies to teach different parts of the environment. We develop
several teaching frameworks based on previously defined supervised protocols,
such as Teaching Dimension, extending them to handle noise and sequences of
inputs encountered in an MDP.We provide theoretical bounds on the learnability
of several important model classes in this setting and suggest a practical
algorithm for dynamic teaching.
| [
"Thomas J. Walsh, Sergiu Goschin",
"['Thomas J. Walsh' 'Sergiu Goschin']"
] |
cs.LG cs.CE stat.ML | null | 1210.4919 | null | null | http://arxiv.org/pdf/1210.4919v1 | 2012-10-16T17:57:06Z | 2012-10-16T17:57:06Z | Latent Dirichlet Allocation Uncovers Spectral Characteristics of Drought
Stressed Plants | Understanding the adaptation process of plants to drought stress is essential
in improving management practices, breeding strategies as well as engineering
viable crops for a sustainable agriculture in the coming decades.
Hyper-spectral imaging provides a particularly promising approach to gain such
understanding since it allows to discover non-destructively spectral
characteristics of plants governed primarily by scattering and absorption
characteristics of the leaf internal structure and biochemical constituents.
Several drought stress indices have been derived using hyper-spectral imaging.
However, they are typically based on few hyper-spectral images only, rely on
interpretations of experts, and consider few wavelengths only. In this study,
we present the first data-driven approach to discovering spectral drought
stress indices, treating it as an unsupervised labeling problem at massive
scale. To make use of short range dependencies of spectral wavelengths, we
develop an online variational Bayes algorithm for latent Dirichlet allocation
with convolved Dirichlet regularizer. This approach scales to massive datasets
and, hence, provides a more objective complement to plant physiological
practices. The spectral topics found conform to plant physiological knowledge
and can be computed in a fraction of the time compared to existing LDA
approaches.
| [
"Mirwaes Wahabzada, Kristian Kersting, Christian Bauckhage, Christoph\n Roemer, Agim Ballvora, Francisco Pinto, Uwe Rascher, Jens Leon, Lutz Ploemer",
"['Mirwaes Wahabzada' 'Kristian Kersting' 'Christian Bauckhage'\n 'Christoph Roemer' 'Agim Ballvora' 'Francisco Pinto' 'Uwe Rascher'\n 'Jens Leon' 'Lutz Ploemer']"
] |
cs.LG cs.IR stat.ML | null | 1210.4920 | null | null | http://arxiv.org/pdf/1210.4920v1 | 2012-10-16T17:57:22Z | 2012-10-16T17:57:22Z | Factorized Multi-Modal Topic Model | Multi-modal data collections, such as corpora of paired images and text
snippets, require analysis methods beyond single-view component and topic
models. For continuous observations the current dominant approach is based on
extensions of canonical correlation analysis, factorizing the variation into
components shared by the different modalities and those private to each of
them. For count data, multiple variants of topic models attempting to tie the
modalities together have been presented. All of these, however, lack the
ability to learn components private to one modality, and consequently will try
to force dependencies even between minimally correlating modalities. In this
work we combine the two approaches by presenting a novel HDP-based topic model
that automatically learns both shared and private topics. The model is shown to
be especially useful for querying the contents of one domain given samples of
the other.
| [
"['Seppo Virtanen' 'Yangqing Jia' 'Arto Klami' 'Trevor Darrell']",
"Seppo Virtanen, Yangqing Jia, Arto Klami, Trevor Darrell"
] |
cs.LG cs.CV cs.NA | null | 1210.5034 | null | null | http://arxiv.org/pdf/1210.5034v2 | 2012-10-21T06:17:08Z | 2012-10-18T06:27:10Z | Optimal Computational Trade-Off of Inexact Proximal Methods | In this paper, we investigate the trade-off between convergence rate and
computational cost when minimizing a composite functional with
proximal-gradient methods, which are popular optimisation tools in machine
learning. We consider the case when the proximity operator is computed via an
iterative procedure, which provides an approximation of the exact proximity
operator. In that case, we obtain algorithms with two nested loops. We show
that the strategy that minimizes the computational cost to reach a solution
with a desired accuracy in finite time is to set the number of inner iterations
to a constant, which differs from the strategy indicated by a convergence rate
analysis. In the process, we also present a new procedure called SIP (that is
Speedy Inexact Proximal-gradient algorithm) that is both computationally
efficient and easy to implement. Our numerical experiments confirm the
theoretical findings and suggest that SIP can be a very competitive alternative
to the standard procedure.
| [
"Pierre Machart (LIF, LSIS), Sandrine Anthoine (LATP), Luca Baldassarre\n (EPFL)",
"['Pierre Machart' 'Sandrine Anthoine' 'Luca Baldassarre']"
] |
cs.DC cs.LG | null | 1210.5128 | null | null | http://arxiv.org/pdf/1210.5128v1 | 2012-10-18T14:02:12Z | 2012-10-18T14:02:12Z | A Novel Learning Algorithm for Bayesian Network and Its Efficient
Implementation on GPU | Computational inference of causal relationships underlying complex networks,
such as gene-regulatory pathways, is NP-complete due to its combinatorial
nature when permuting all possible interactions. Markov chain Monte Carlo
(MCMC) has been introduced to sample only part of the combinations while still
guaranteeing convergence and traversability, which therefore becomes widely
used. However, MCMC is not able to perform efficiently enough for networks that
have more than 15~20 nodes because of the computational complexity. In this
paper, we use general purpose processor (GPP) and general purpose graphics
processing unit (GPGPU) to implement and accelerate a novel Bayesian network
learning algorithm. With a hash-table-based memory-saving strategy and a novel
task assigning strategy, we achieve a 10-fold acceleration per iteration than
using a serial GPP. Specially, we use a greedy method to search for the best
graph from a given order. We incorporate a prior component in the current
scoring function, which further facilitates the searching. Overall, we are able
to apply this system to networks with more than 60 nodes, allowing inferences
and modeling of bigger and more complex networks than current methods.
| [
"['Yu Wang' 'Weikang Qian' 'Shuchang Zhang' 'Bo Yuan']",
"Yu Wang, Weikang Qian, Shuchang Zhang and Bo Yuan"
] |
cs.LG stat.ML | null | 1210.5135 | null | null | http://arxiv.org/pdf/1210.5135v1 | 2012-10-18T14:15:40Z | 2012-10-18T14:15:40Z | LSBN: A Large-Scale Bayesian Structure Learning Framework for Model
Averaging | The motivation for this paper is to apply Bayesian structure learning using
Model Averaging in large-scale networks. Currently, Bayesian model averaging
algorithm is applicable to networks with only tens of variables, restrained by
its super-exponential complexity. We present a novel framework, called
LSBN(Large-Scale Bayesian Network), making it possible to handle networks with
infinite size by following the principle of divide-and-conquer. The method of
LSBN comprises three steps. In general, LSBN first performs the partition by
using a second-order partition strategy, which achieves more robust results.
LSBN conducts sampling and structure learning within each overlapping community
after the community is isolated from other variables by Markov Blanket. Finally
LSBN employs an efficient algorithm, to merge structures of overlapping
communities into a whole. In comparison with other four state-of-art
large-scale network structure learning algorithms such as ARACNE, PC, Greedy
Search and MMHC, LSBN shows comparable results in five common benchmark
datasets, evaluated by precision, recall and f-score. What's more, LSBN makes
it possible to learn large-scale Bayesian structure by Model Averaging which
used to be intractable. In summary, LSBN provides an scalable and parallel
framework for the reconstruction of network structures. Besides, the complete
information of overlapping communities serves as the byproduct, which could be
used to mine meaningful clusters in biological networks, such as
protein-protein-interaction network or gene regulatory network, as well as in
social network.
| [
"['Yang Lu' 'Mengying Wang' 'Menglu Li' 'Qili Zhu' 'Bo Yuan']",
"Yang Lu, Mengying Wang, Menglu Li, Qili Zhu, Bo Yuan"
] |
stat.ML cs.LG | null | 1210.5196 | null | null | http://arxiv.org/pdf/1210.5196v1 | 2012-10-18T17:30:43Z | 2012-10-18T17:30:43Z | Matrix reconstruction with the local max norm | We introduce a new family of matrix norms, the "local max" norms,
generalizing existing methods such as the max norm, the trace norm (nuclear
norm), and the weighted or smoothed weighted trace norms, which have been
extensively used in the literature as regularizers for matrix reconstruction
problems. We show that this new family can be used to interpolate between the
(weighted or unweighted) trace norm and the more conservative max norm. We test
this interpolation on simulated data and on the large-scale Netflix and
MovieLens ratings data, and find improved accuracy relative to the existing
matrix norms. We also provide theoretical results showing learning guarantees
for some of the new norms.
| [
"['Rina Foygel' 'Nathan Srebro' 'Ruslan Salakhutdinov']",
"Rina Foygel, Nathan Srebro, Ruslan Salakhutdinov"
] |
cs.IT cs.LG math.IT math.NA | null | 1210.5323 | null | null | http://arxiv.org/pdf/1210.5323v3 | 2013-07-17T05:50:32Z | 2012-10-19T06:03:05Z | The performance of orthogonal multi-matching pursuit under RIP | The orthogonal multi-matching pursuit (OMMP) is a natural extension of
orthogonal matching pursuit (OMP). We denote the OMMP with the parameter $M$ as
OMMP(M) where $M\geq 1$ is an integer. The main difference between OMP and
OMMP(M) is that OMMP(M) selects $M$ atoms per iteration, while OMP only adds
one atom to the optimal atom set. In this paper, we study the performance of
orthogonal multi-matching pursuit (OMMP) under RIP. In particular, we show
that, when the measurement matrix A satisfies $(9s, 1/10)$-RIP, there exists an
absolutely constant $M_0\leq 8$ so that OMMP(M_0) can recover $s$-sparse signal
within $s$ iterations. We furthermore prove that, for slowly-decaying
$s$-sparse signal, OMMP(M) can recover s-sparse signal within $O(\frac{s}{M})$
iterations for a large class of $M$. In particular, for $M=s^a$ with $a\in
[0,1/2]$, OMMP(M) can recover slowly-decaying $s$-sparse signal within
$O(s^{1-a})$ iterations. The result implies that OMMP can reduce the
computational complexity heavily.
| [
"Zhiqiang Xu",
"['Zhiqiang Xu']"
] |
cond-mat.dis-nn cond-mat.stat-mech cs.LG stat.ML | null | 1210.5338 | null | null | http://arxiv.org/pdf/1210.5338v2 | 2013-02-01T17:32:44Z | 2012-10-19T08:08:55Z | Pairwise MRF Calibration by Perturbation of the Bethe Reference Point | We investigate different ways of generating approximate solutions to the
pairwise Markov random field (MRF) selection problem. We focus mainly on the
inverse Ising problem, but discuss also the somewhat related inverse Gaussian
problem because both types of MRF are suitable for inference tasks with the
belief propagation algorithm (BP) under certain conditions. Our approach
consists in to take a Bethe mean-field solution obtained with a maximum
spanning tree (MST) of pairwise mutual information, referred to as the
\emph{Bethe reference point}, for further perturbation procedures. We consider
three different ways following this idea: in the first one, we select and
calibrate iteratively the optimal links to be added starting from the Bethe
reference point; the second one is based on the observation that the natural
gradient can be computed analytically at the Bethe point; in the third one,
assuming no local field and using low temperature expansion we develop a dual
loop joint model based on a well chosen fundamental cycle basis. We indeed
identify a subclass of planar models, which we refer to as \emph{Bethe-dual
graph models}, having possibly many loops, but characterized by a singly
connected dual factor graph, for which the partition function and the linear
response can be computed exactly in respectively O(N) and $O(N^2)$ operations,
thanks to a dual weight propagation (DWP) message passing procedure that we set
up. When restricted to this subclass of models, the inverse Ising problem being
convex, becomes tractable at any temperature. Experimental tests on various
datasets with refined $L_0$ or $L_1$ regularization procedures indicate that
these approaches may be competitive and useful alternatives to existing ones.
| [
"Cyril Furtlehner, Yufei Han, Jean-Marc Lasgouttes and Victorin Martin",
"['Cyril Furtlehner' 'Yufei Han' 'Jean-Marc Lasgouttes' 'Victorin Martin']"
] |
cs.LG | 10.1109/TSP.2012.2226446 | 1210.5394 | null | null | http://arxiv.org/abs/1210.5394v1 | 2012-10-19T12:15:28Z | 2012-10-19T12:15:28Z | Bayesian Estimation for Continuous-Time Sparse Stochastic Processes | We consider continuous-time sparse stochastic processes from which we have
only a finite number of noisy/noiseless samples. Our goal is to estimate the
noiseless samples (denoising) and the signal in-between (interpolation
problem).
By relying on tools from the theory of splines, we derive the joint a priori
distribution of the samples and show how this probability density function can
be factorized. The factorization enables us to tractably implement the maximum
a posteriori and minimum mean-square error (MMSE) criteria as two statistical
approaches for estimating the unknowns. We compare the derived statistical
methods with well-known techniques for the recovery of sparse signals, such as
the $\ell_1$ norm and Log ($\ell_1$-$\ell_0$ relaxation) regularization
methods. The simulation results show that, under certain conditions, the
performance of the regularization techniques can be very close to that of the
MMSE estimator.
| [
"Arash Amini, Ulugbek S. Kamilov, Emrah Bostan and Michael Unser",
"['Arash Amini' 'Ulugbek S. Kamilov' 'Emrah Bostan' 'Michael Unser']"
] |
stat.ML cs.LG cs.NE | null | 1210.5474 | null | null | http://arxiv.org/pdf/1210.5474v1 | 2012-10-19T17:16:48Z | 2012-10-19T17:16:48Z | Disentangling Factors of Variation via Generative Entangling | Here we propose a novel model family with the objective of learning to
disentangle the factors of variation in data. Our approach is based on the
spike-and-slab restricted Boltzmann machine which we generalize to include
higher-order interactions among multiple latent variables. Seen from a
generative perspective, the multiplicative interactions emulates the entangling
of factors of variation. Inference in the model can be seen as disentangling
these generative factors. Unlike previous attempts at disentangling latent
factors, the proposed model is trained using no supervised information
regarding the latent factors. We apply our model to the task of facial
expression classification.
| [
"Guillaume Desjardins and Aaron Courville and Yoshua Bengio",
"['Guillaume Desjardins' 'Aaron Courville' 'Yoshua Bengio']"
] |
cs.LG | null | 1210.5544 | null | null | http://arxiv.org/pdf/1210.5544v1 | 2012-10-19T21:31:50Z | 2012-10-19T21:31:50Z | Online Learning in Decentralized Multiuser Resource Sharing Problems | In this paper, we consider the general scenario of resource sharing in a
decentralized system when the resource rewards/qualities are time-varying and
unknown to the users, and using the same resource by multiple users leads to
reduced quality due to resource sharing. Firstly, we consider a
user-independent reward model with no communication between the users, where a
user gets feedback about the congestion level in the resource it uses.
Secondly, we consider user-specific rewards and allow costly communication
between the users. The users have a cooperative goal of achieving the highest
system utility. There are multiple obstacles in achieving this goal such as the
decentralized nature of the system, unknown resource qualities, communication,
computation and switching costs. We propose distributed learning algorithms
with logarithmic regret with respect to the optimal allocation. Our logarithmic
regret result holds under both i.i.d. and Markovian reward models, as well as
under communication, computation and switching costs.
| [
"['Cem Tekin' 'Mingyan Liu']",
"Cem Tekin, Mingyan Liu"
] |
stat.ML cs.LG | 10.1002/sam.11184 | 1210.5631 | null | null | http://arxiv.org/abs/1210.5631v2 | 2013-01-04T22:52:39Z | 2012-10-20T14:39:39Z | Content-boosted Matrix Factorization Techniques for Recommender Systems | Many businesses are using recommender systems for marketing outreach.
Recommendation algorithms can be either based on content or driven by
collaborative filtering. We study different ways to incorporate content
information directly into the matrix factorization approach of collaborative
filtering. These content-boosted matrix factorization algorithms not only
improve recommendation accuracy, but also provide useful insights about the
contents, as well as make recommendations more easily interpretable.
| [
"['Jennifer Nguyen' 'Mu Zhu']",
"Jennifer Nguyen, Mu Zhu"
] |
cs.CV cs.AI cs.LG | null | 1210.5644 | null | null | http://arxiv.org/pdf/1210.5644v1 | 2012-10-20T17:41:23Z | 2012-10-20T17:41:23Z | Efficient Inference in Fully Connected CRFs with Gaussian Edge
Potentials | Most state-of-the-art techniques for multi-class image segmentation and
labeling use conditional random fields defined over pixels or image regions.
While region-level models often feature dense pairwise connectivity,
pixel-level models are considerably larger and have only permitted sparse graph
structures. In this paper, we consider fully connected CRF models defined on
the complete set of pixels in an image. The resulting graphs have billions of
edges, making traditional inference algorithms impractical. Our main
contribution is a highly efficient approximate inference algorithm for fully
connected CRF models in which the pairwise edge potentials are defined by a
linear combination of Gaussian kernels. Our experiments demonstrate that dense
connectivity at the pixel level substantially improves segmentation and
labeling accuracy.
| [
"['Philipp Krähenbühl' 'Vladlen Koltun']",
"Philipp Kr\\\"ahenb\\\"uhl and Vladlen Koltun"
] |
math.ST cs.LG stat.TH | null | 1210.5830 | null | null | http://arxiv.org/pdf/1210.5830v3 | 2015-10-11T11:10:53Z | 2012-10-22T08:22:57Z | Choice of V for V-Fold Cross-Validation in Least-Squares Density
Estimation | This paper studies V-fold cross-validation for model selection in
least-squares density estimation. The goal is to provide theoretical grounds
for choosing V in order to minimize the least-squares loss of the selected
estimator. We first prove a non-asymptotic oracle inequality for V-fold
cross-validation and its bias-corrected version (V-fold penalization). In
particular, this result implies that V-fold penalization is asymptotically
optimal in the nonparametric case. Then, we compute the variance of V-fold
cross-validation and related criteria, as well as the variance of key
quantities for model selection performance. We show that these variances depend
on V like 1+4/(V-1), at least in some particular cases, suggesting that the
performance increases much from V=2 to V=5 or 10, and then is almost constant.
Overall, this can explain the common advice to take V=5---at least in our
setting and when the computational power is limited---, as supported by some
simulation experiments. An oracle inequality and exact formulas for the
variance are also proved for Monte-Carlo cross-validation, also known as
repeated cross-validation, where the parameter V is replaced by the number B of
random splits of the data.
| [
"Sylvain Arlot (SIERRA, DI-ENS), Matthieu Lerasle (JAD)",
"['Sylvain Arlot' 'Matthieu Lerasle']"
] |
cs.LG stat.ML | null | 1210.5840 | null | null | http://arxiv.org/pdf/1210.5840v1 | 2012-10-22T08:55:13Z | 2012-10-22T08:55:13Z | Supervised Learning with Similarity Functions | We address the problem of general supervised learning when data can only be
accessed through an (indefinite) similarity function between data points.
Existing work on learning with indefinite kernels has concentrated solely on
binary/multi-class classification problems. We propose a model that is generic
enough to handle any supervised learning task and also subsumes the model
previously proposed for classification. We give a "goodness" criterion for
similarity functions w.r.t. a given supervised learning task and then adapt a
well-known landmarking technique to provide efficient algorithms for supervised
learning using "good" similarity functions. We demonstrate the effectiveness of
our model on three important super-vised learning problems: a) real-valued
regression, b) ordinal regression and c) ranking where we show that our method
guarantees bounded generalization error. Furthermore, for the case of
real-valued regression, we give a natural goodness definition that, when used
in conjunction with a recent result in sparse vector recovery, guarantees a
sparse predictor with bounded generalization error. Finally, we report results
of our learning algorithms on regression and ordinal regression tasks using
non-PSD similarity functions and demonstrate the effectiveness of our
algorithms, especially that of the sparse landmark selection algorithm that
achieves significantly higher accuracies than the baseline methods while
offering reduced computational costs.
| [
"['Purushottam Kar' 'Prateek Jain']",
"Purushottam Kar and Prateek Jain"
] |
stat.ML cs.LG | null | 1210.5873 | null | null | http://arxiv.org/pdf/1210.5873v1 | 2012-10-22T11:17:31Z | 2012-10-22T11:17:31Z | Initialization of Self-Organizing Maps: Principal Components Versus
Random Initialization. A Case Study | The performance of the Self-Organizing Map (SOM) algorithm is dependent on
the initial weights of the map. The different initialization methods can
broadly be classified into random and data analysis based initialization
approach. In this paper, the performance of random initialization (RI) approach
is compared to that of principal component initialization (PCI) in which the
initial map weights are chosen from the space of the principal component.
Performance is evaluated by the fraction of variance unexplained (FVU).
Datasets were classified into quasi-linear and non-linear and it was observed
that RI performed better for non-linear datasets; however the performance of
PCI approach remains inconclusive for quasi-linear datasets.
| [
"A. A. Akinduko and E. M. Mirkes",
"['A. A. Akinduko' 'E. M. Mirkes']"
] |
cs.LG stat.ML | null | 1210.6001 | null | null | http://arxiv.org/pdf/1210.6001v3 | 2013-06-07T09:45:45Z | 2012-10-22T19:02:21Z | Reducing statistical time-series problems to binary classification | We show how binary classification methods developed to work on i.i.d. data
can be used for solving statistical problems that are seemingly unrelated to
classification and concern highly-dependent time series. Specifically, the
problems of time-series clustering, homogeneity testing and the three-sample
problem are addressed. The algorithms that we construct for solving these
problems are based on a new metric between time-series distributions, which can
be evaluated using binary classification methods. Universal consistency of the
proposed algorithms is proven under most general assumptions. The theoretical
results are illustrated with experiments on synthetic and real-world data.
| [
"['Daniil Ryabko' 'Jérémie Mary']",
"Daniil Ryabko, J\\'er\\'emie Mary"
] |
cs.DS cs.IR cs.LG | null | 1210.6287 | null | null | http://arxiv.org/pdf/1210.6287v2 | 2012-10-26T19:14:20Z | 2012-10-23T16:51:31Z | Fast Exact Max-Kernel Search | The wide applicability of kernels makes the problem of max-kernel search
ubiquitous and more general than the usual similarity search in metric spaces.
We focus on solving this problem efficiently. We begin by characterizing the
inherent hardness of the max-kernel search problem with a novel notion of
directional concentration. Following that, we present a method to use an $O(n
\log n)$ algorithm to index any set of objects (points in $\Real^\dims$ or
abstract objects) directly in the Hilbert space without any explicit feature
representations of the objects in this space. We present the first provably
$O(\log n)$ algorithm for exact max-kernel search using this index. Empirical
results for a variety of data sets as well as abstract objects demonstrate up
to 4 orders of magnitude speedup in some cases. Extensions for approximate
max-kernel search are also presented.
| [
"['Ryan R. Curtin' 'Parikshit Ram' 'Alexander G. Gray']",
"Ryan R. Curtin, Parikshit Ram, Alexander G. Gray"
] |
cs.LG | null | 1210.6292 | null | null | http://arxiv.org/pdf/1210.6292v2 | 2014-02-06T11:29:49Z | 2012-10-23T17:12:01Z | A density-sensitive hierarchical clustering method | We define a hierarchical clustering method: $\alpha$-unchaining single
linkage or $SL(\alpha)$. The input of this algorithm is a finite metric space
and a certain parameter $\alpha$. This method is sensitive to the density of
the distribution and offers some solution to the so called chaining effect. We
also define a modified version, $SL^*(\alpha)$, to treat the chaining through
points or small blocks. We study the theoretical properties of these methods
and offer some theoretical background for the treatment of chaining effects.
| [
"\\'Alvaro Mart\\'inez-P\\'erez",
"['Álvaro Martínez-Pérez']"
] |
cs.MS cs.CV cs.LG | null | 1210.6293 | null | null | http://arxiv.org/pdf/1210.6293v1 | 2012-10-23T17:15:03Z | 2012-10-23T17:15:03Z | MLPACK: A Scalable C++ Machine Learning Library | MLPACK is a state-of-the-art, scalable, multi-platform C++ machine learning
library released in late 2011 offering both a simple, consistent API accessible
to novice users and high performance and flexibility to expert users by
leveraging modern features of C++. MLPACK provides cutting-edge algorithms
whose benchmarks exhibit far better performance than other leading machine
learning libraries. MLPACK version 1.0.3, licensed under the LGPL, is available
at http://www.mlpack.org.
| [
"Ryan R. Curtin, James R. Cline, N.P. Slagle, William B. March,\n Parikshit Ram, Nishant A. Mehta, Alexander G. Gray",
"['Ryan R. Curtin' 'James R. Cline' 'N. P. Slagle' 'William B. March'\n 'Parikshit Ram' 'Nishant A. Mehta' 'Alexander G. Gray']"
] |
stat.ML cs.LG cs.SI physics.soc-ph q-fin.ST | 10.1371/journal.pone.0064846 | 1210.6321 | null | null | http://arxiv.org/abs/1210.6321v4 | 2013-03-23T14:34:36Z | 2012-10-23T18:31:46Z | High quality topic extraction from business news explains abnormal
financial market volatility | Understanding the mutual relationships between information flows and social
activity in society today is one of the cornerstones of the social sciences. In
financial economics, the key issue in this regard is understanding and
quantifying how news of all possible types (geopolitical, environmental,
social, financial, economic, etc.) affect trading and the pricing of firms in
organized stock markets. In this article, we seek to address this issue by
performing an analysis of more than 24 million news records provided by
Thompson Reuters and of their relationship with trading activity for 206 major
stocks in the S&P US stock index. We show that the whole landscape of news that
affect stock price movements can be automatically summarized via simple
regularized regressions between trading activity and news information pieces
decomposed, with the help of simple topic modeling techniques, into their
"thematic" features. Using these methods, we are able to estimate and quantify
the impacts of news on trading. We introduce network-based visualization
techniques to represent the whole landscape of news information associated with
a basket of stocks. The examination of the words that are representative of the
topic distributions confirms that our method is able to extract the significant
pieces of information influencing the stock market. Our results show that one
of the most puzzling stylized fact in financial economies, namely that at
certain times trading volumes appear to be "abnormally large," can be partially
explained by the flow of news. In this sense, our results prove that there is
no "excess trading," when restricting to times when news are genuinely novel
and provide relevant financial information.
| [
"Ryohei Hisano, Didier Sornette, Takayuki Mizuno, Takaaki Ohnishi,\n Tsutomu Watanabe",
"['Ryohei Hisano' 'Didier Sornette' 'Takayuki Mizuno' 'Takaaki Ohnishi'\n 'Tsutomu Watanabe']"
] |
cs.SI cs.CY cs.LG | null | 1210.6497 | null | null | http://arxiv.org/pdf/1210.6497v1 | 2012-10-24T11:51:21Z | 2012-10-24T11:51:21Z | Topic-Level Opinion Influence Model(TOIM): An Investigation Using
Tencent Micro-Blogging | Mining user opinion from Micro-Blogging has been extensively studied on the
most popular social networking sites such as Twitter and Facebook in the U.S.,
but few studies have been done on Micro-Blogging websites in other countries
(e.g. China). In this paper, we analyze the social opinion influence on
Tencent, one of the largest Micro-Blogging websites in China, endeavoring to
unveil the behavior patterns of Chinese Micro-Blogging users. This paper
proposes a Topic-Level Opinion Influence Model (TOIM) that simultaneously
incorporates topic factor and social direct influence in a unified
probabilistic framework. Based on TOIM, two topic level opinion influence
propagation and aggregation algorithms are developed to consider the indirect
influence: CP (Conservative Propagation) and NCP (None Conservative
Propagation). Users' historical social interaction records are leveraged by
TOIM to construct their progressive opinions and neighbors' opinion influence
through a statistical learning process, which can be further utilized to
predict users' future opinions on some specific topics. To evaluate and test
this proposed model, an experiment was designed and a sub-dataset from Tencent
Micro-Blogging was used. The experimental results show that TOIM outperforms
baseline methods on predicting users' opinion. The applications of CP and NCP
have no significant differences and could significantly improve recall and
F1-measure of TOIM.
| [
"Daifeng Li, Ying Ding, Xin Shuai, Golden Guo-zheng Sun, Jie Tang,\n Zhipeng Luo, Jingwei Zhang and Guo Zhang",
"['Daifeng Li' 'Ying Ding' 'Xin Shuai' 'Golden Guo-zheng Sun' 'Jie Tang'\n 'Zhipeng Luo' 'Jingwei Zhang' 'Guo Zhang']"
] |
cs.NE cs.LG stat.ML | 10.1007/s13218-012-0207-2 | 1210.6511 | null | null | http://arxiv.org/abs/1210.6511v1 | 2012-10-24T12:37:53Z | 2012-10-24T12:37:53Z | Neural Networks for Complex Data | Artificial neural networks are simple and efficient machine learning tools.
Defined originally in the traditional setting of simple vector data, neural
network models have evolved to address more and more difficulties of complex
real world problems, ranging from time evolving data to sophisticated data
structures such as graphs and functions. This paper summarizes advances on
those themes from the last decade, with a focus on results obtained by members
of the SAMM team of Universit\'e Paris 1
| [
"Marie Cottrell (SAMM), Madalina Olteanu (SAMM), Fabrice Rossi (SAMM),\n Joseph Rynkiewicz (SAMM), Nathalie Villa-Vialaneix (SAMM)",
"['Marie Cottrell' 'Madalina Olteanu' 'Fabrice Rossi' 'Joseph Rynkiewicz'\n 'Nathalie Villa-Vialaneix']"
] |
cs.LG cs.CV stat.ML | null | 1210.6707 | null | null | http://arxiv.org/pdf/1210.6707v1 | 2012-10-24T23:57:35Z | 2012-10-24T23:57:35Z | Clustering hidden Markov models with variational HEM | The hidden Markov model (HMM) is a widely-used generative model that copes
with sequential data, assuming that each observation is conditioned on the
state of a hidden Markov chain. In this paper, we derive a novel algorithm to
cluster HMMs based on the hierarchical EM (HEM) algorithm. The proposed
algorithm i) clusters a given collection of HMMs into groups of HMMs that are
similar, in terms of the distributions they represent, and ii) characterizes
each group by a "cluster center", i.e., a novel HMM that is representative for
the group, in a manner that is consistent with the underlying generative model
of the HMM. To cope with intractable inference in the E-step, the HEM algorithm
is formulated as a variational optimization problem, and efficiently solved for
the HMM case by leveraging an appropriate variational approximation. The
benefits of the proposed algorithm, which we call variational HEM (VHEM), are
demonstrated on several tasks involving time-series data, such as hierarchical
clustering of motion capture sequences, and automatic annotation and retrieval
of music and of online hand-writing data, showing improvements over current
methods. In particular, our variational HEM algorithm effectively leverages
large amounts of data when learning annotation models by using an efficient
hierarchical estimation procedure, which reduces learning times and memory
requirements, while improving model robustness through better regularization.
| [
"['Emanuele Coviello' 'Antoni B. Chan' 'Gert R. G. Lanckriet']",
"Emanuele Coviello and Antoni B. Chan and Gert R.G. Lanckriet"
] |
stat.ML cs.LG | 10.1109/TPAMI.2014.2318728 | 1210.6738 | null | null | http://arxiv.org/abs/1210.6738v4 | 2014-05-02T16:36:57Z | 2012-10-25T04:25:00Z | Nested Hierarchical Dirichlet Processes | We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical
topic modeling. The nHDP is a generalization of the nested Chinese restaurant
process (nCRP) that allows each word to follow its own path to a topic node
according to a document-specific distribution on a shared tree. This alleviates
the rigid, single-path formulation of the nCRP, allowing a document to more
easily express thematic borrowings as a random effect. We derive a stochastic
variational inference algorithm for the model, in addition to a greedy subtree
selection method for each document, which allows for efficient inference using
massive collections of text documents. We demonstrate our algorithm on 1.8
million documents from The New York Times and 3.3 million documents from
Wikipedia.
| [
"John Paisley, Chong Wang, David M. Blei and Michael I. Jordan",
"['John Paisley' 'Chong Wang' 'David M. Blei' 'Michael I. Jordan']"
] |
cs.LG cs.SD | null | 1210.6766 | null | null | http://arxiv.org/pdf/1210.6766v1 | 2012-10-25T09:22:59Z | 2012-10-25T09:22:59Z | Structured Sparsity Models for Multiparty Speech Recovery from
Reverberant Recordings | We tackle the multi-party speech recovery problem through modeling the
acoustic of the reverberant chambers. Our approach exploits structured sparsity
models to perform room modeling and speech recovery. We propose a scheme for
characterizing the room acoustic from the unknown competing speech sources
relying on localization of the early images of the speakers by sparse
approximation of the spatial spectra of the virtual sources in a free-space
model. The images are then clustered exploiting the low-rank structure of the
spectro-temporal components belonging to each source. This enables us to
identify the early support of the room impulse response function and its unique
map to the room geometry. To further tackle the ambiguity of the reflection
ratios, we propose a novel formulation of the reverberation model and estimate
the absorption coefficients through a convex optimization exploiting joint
sparsity model formulated upon spatio-spectral sparsity of concurrent speech
representation. The acoustic parameters are then incorporated for separating
individual speech signals through either structured sparse recovery or inverse
filtering the acoustic channels. The experiments conducted on real data
recordings demonstrate the effectiveness of the proposed approach for
multi-party speech recovery and recognition.
| [
"Afsaneh Asaei, Mohammad Golbabaee, Herv\\'e Bourlard, Volkan Cevher",
"['Afsaneh Asaei' 'Mohammad Golbabaee' 'Hervé Bourlard' 'Volkan Cevher']"
] |
cs.CE cs.LG | null | 1210.6891 | null | null | http://arxiv.org/pdf/1210.6891v1 | 2012-10-24T05:56:45Z | 2012-10-24T05:56:45Z | Predicting Near-Future Churners and Win-Backs in the Telecommunications
Industry | In this work, we presented the strategies and techniques that we have
developed for predicting the near-future churners and win-backs for a telecom
company. On a large-scale and real-world database containing customer profiles
and some transaction data from a telecom company, we first analyzed the data
schema, developed feature computation strategies and then extracted a large set
of relevant features that can be associated with the customer churning and
returning behaviors. Our features include both the original driver factors as
well as some derived features. We evaluated our features on the imbalance
corrected dataset, i.e. under-sampled dataset and compare a large number of
existing machine learning tools, especially decision tree-based classifiers,
for predicting the churners and win-backs. In general, we find RandomForest and
SimpleCart learning algorithms generally perform well and tend to provide us
with highly competitive prediction performance. Among the top-15 driver factors
that signal the churn behavior, we find that the service utilization, e.g. last
two months' download and upload volume, last three months' average upload and
download, and the payment related factors are the most indicative features for
predicting if churn will happen soon. Such features can collectively tell
discrepancies between the service plans, payments and the dynamically changing
utilization needs of the customers. Our proposed features and their
computational strategy exhibit reasonable precision performance to predict
churn behavior in near future.
| [
"Clifton Phua, Hong Cao, Jo\\~ao B\\'artolo Gomes, Minh Nhut Nguyen",
"['Clifton Phua' 'Hong Cao' 'João Bártolo Gomes' 'Minh Nhut Nguyen']"
] |
q-bio.MN cs.CE cs.LG q-bio.GN stat.ML | null | 1210.6912 | null | null | http://arxiv.org/pdf/1210.6912v1 | 2012-10-25T17:13:57Z | 2012-10-25T17:13:57Z | Enhancing the functional content of protein interaction networks | Protein interaction networks are a promising type of data for studying
complex biological systems. However, despite the rich information embedded in
these networks, they face important data quality challenges of noise and
incompleteness that adversely affect the results obtained from their analysis.
Here, we explore the use of the concept of common neighborhood similarity
(CNS), which is a form of local structure in networks, to address these issues.
Although several CNS measures have been proposed in the literature, an
understanding of their relative efficacies for the analysis of interaction
networks has been lacking. We follow the framework of graph transformation to
convert the given interaction network into a transformed network corresponding
to a variety of CNS measures evaluated. The effectiveness of each measure is
then estimated by comparing the quality of protein function predictions
obtained from its corresponding transformed network with those from the
original network. Using a large set of S. cerevisiae interactions, and a set of
136 GO terms, we find that several of the transformed networks produce more
accurate predictions than those obtained from the original network. In
particular, the $HC.cont$ measure proposed here performs particularly well for
this task. Further investigation reveals that the two major factors
contributing to this improvement are the abilities of CNS measures, especially
$HC.cont$, to prune out noisy edges and introduce new links between
functionally related proteins.
| [
"['Gaurav Pandey' 'Sahil Manocha' 'Gowtham Atluri' 'Vipin Kumar']",
"Gaurav Pandey and Sahil Manocha and Gowtham Atluri and Vipin Kumar"
] |
cs.SI cs.CY cs.LG | null | 1210.7047 | null | null | http://arxiv.org/pdf/1210.7047v1 | 2012-10-26T03:04:34Z | 2012-10-26T03:04:34Z | User-level Weibo Recommendation incorporating Social Influence based on
Semi-Supervised Algorithm | Tencent Weibo, as one of the most popular micro-blogging services in China,
has attracted millions of users, producing 30-60 millions of weibo (similar as
tweet in Twitter) daily. With the overload problem of user generate content,
Tencent users find it is more and more hard to browse and find valuable
information at the first time. In this paper, we propose a Factor Graph based
weibo recommendation algorithm TSI-WR (Topic-Level Social Influence based Weibo
Recommendation), which could help Tencent users to find most suitable
information. The main innovation is that we consider both direct and indirect
social influence from topic level based on social balance theory. The main
advantages of adopting this strategy are that it could first build a more
accurate description of latent relationship between two users with weak
connections, which could help to solve the data sparsity problem; second
provide a more accurate recommendation for a certain user from a wider range.
Other meaningful contextual information is also combined into our model, which
include: Users profile, Users influence, Content of weibos, Topic information
of weibos and etc. We also design a semi-supervised algorithm to further reduce
the influence of data sparisty. The experiments show that all the selected
variables are important and the proposed model outperforms several baseline
methods.
| [
"Daifeng Li, Zhipeng Luo, Golden Guo-zheng Sun, Jie Tang, Jingwei Zhang",
"['Daifeng Li' 'Zhipeng Luo' 'Golden Guo-zheng Sun' 'Jie Tang'\n 'Jingwei Zhang']"
] |
stat.ML cs.LG math.OC | null | 1210.7054 | null | null | http://arxiv.org/pdf/1210.7054v1 | 2012-10-26T05:35:26Z | 2012-10-26T05:35:26Z | Large-Scale Sparse Principal Component Analysis with Application to Text
Data | Sparse PCA provides a linear combination of small number of features that
maximizes variance across data. Although Sparse PCA has apparent advantages
compared to PCA, such as better interpretability, it is generally thought to be
computationally much more expensive. In this paper, we demonstrate the
surprising fact that sparse PCA can be easier than PCA in practice, and that it
can be reliably applied to very large data sets. This comes from a rigorous
feature elimination pre-processing result, coupled with the favorable fact that
features in real-life data typically have exponentially decreasing variances,
which allows for many features to be eliminated. We introduce a fast block
coordinate ascent algorithm with much better computational complexity than the
existing first-order ones. We provide experimental results obtained on text
corpora involving millions of documents and hundreds of thousands of features.
These results illustrate how Sparse PCA can help organize a large corpus of
text data in a user-interpretable way, providing an attractive alternative
approach to topic models.
| [
"Youwei Zhang, Laurent El Ghaoui",
"['Youwei Zhang' 'Laurent El Ghaoui']"
] |
cs.LG cs.IR stat.ML | null | 1210.7056 | null | null | http://arxiv.org/pdf/1210.7056v1 | 2012-10-26T05:36:57Z | 2012-10-26T05:36:57Z | Selective Transfer Learning for Cross Domain Recommendation | Collaborative filtering (CF) aims to predict users' ratings on items
according to historical user-item preference data. In many real-world
applications, preference data are usually sparse, which would make models
overfit and fail to give accurate predictions. Recently, several research works
show that by transferring knowledge from some manually selected source domains,
the data sparseness problem could be mitigated. However for most cases, parts
of source domain data are not consistent with the observations in the target
domain, which may misguide the target domain model building. In this paper, we
propose a novel criterion based on empirical prediction error and its variance
to better capture the consistency across domains in CF settings. Consequently,
we embed this criterion into a boosting framework to perform selective
knowledge transfer. Comparing to several state-of-the-art methods, we show that
our proposed selective transfer learning framework can significantly improve
the accuracy of rating prediction tasks on several real-world recommendation
tasks.
| [
"Zhongqi Lu and Erheng Zhong and Lili Zhao and Wei Xiang and Weike Pan\n and Qiang Yang",
"['Zhongqi Lu' 'Erheng Zhong' 'Lili Zhao' 'Wei Xiang' 'Weike Pan'\n 'Qiang Yang']"
] |
cs.CV cs.LG math.OC stat.ML | null | 1210.7070 | null | null | http://arxiv.org/pdf/1210.7070v3 | 2012-11-02T10:11:10Z | 2012-10-26T09:08:55Z | A Multiscale Framework for Challenging Discrete Optimization | Current state-of-the-art discrete optimization methods struggle behind when
it comes to challenging contrast-enhancing discrete energies (i.e., favoring
different labels for neighboring variables). This work suggests a multiscale
approach for these challenging problems. Deriving an algebraic representation
allows us to coarsen any pair-wise energy using any interpolation in a
principled algebraic manner. Furthermore, we propose an energy-aware
interpolation operator that efficiently exposes the multiscale landscape of the
energy yielding an effective coarse-to-fine optimization scheme. Results on
challenging contrast-enhancing energies show significant improvement over
state-of-the-art methods.
| [
"Shai Bagon and Meirav Galun",
"['Shai Bagon' 'Meirav Galun']"
] |
cs.CV cs.LG math.OC stat.ML | null | 1210.7362 | null | null | http://arxiv.org/pdf/1210.7362v2 | 2012-11-07T21:09:53Z | 2012-10-27T19:12:49Z | Discrete Energy Minimization, beyond Submodularity: Applications and
Approximations | In this thesis I explore challenging discrete energy minimization problems
that arise mainly in the context of computer vision tasks. This work motivates
the use of such "hard-to-optimize" non-submodular functionals, and proposes
methods and algorithms to cope with the NP-hardness of their optimization.
Consequently, this thesis revolves around two axes: applications and
approximations. The applications axis motivates the use of such
"hard-to-optimize" energies by introducing new tasks. As the energies become
less constrained and structured one gains more expressive power for the
objective function achieving more accurate models. Results show how
challenging, hard-to-optimize, energies are more adequate for certain computer
vision applications. To overcome the resulting challenging optimization tasks
the second axis of this thesis proposes approximation algorithms to cope with
the NP-hardness of the optimization. Experiments show that these new methods
yield good results for representative challenging problems.
| [
"Shai Bagon",
"['Shai Bagon']"
] |
cs.CV cs.LG stat.ML | null | 1210.7461 | null | null | http://arxiv.org/pdf/1210.7461v1 | 2012-10-28T13:55:07Z | 2012-10-28T13:55:07Z | Recognizing Static Signs from the Brazilian Sign Language: Comparing
Large-Margin Decision Directed Acyclic Graphs, Voting Support Vector Machines
and Artificial Neural Networks | In this paper, we explore and detail our experiments in a
high-dimensionality, multi-class image classification problem often found in
the automatic recognition of Sign Languages. Here, our efforts are directed
towards comparing the characteristics, advantages and drawbacks of creating and
training Support Vector Machines disposed in a Directed Acyclic Graph and
Artificial Neural Networks to classify signs from the Brazilian Sign Language
(LIBRAS). We explore how the different heuristics, hyperparameters and
multi-class decision schemes affect the performance, efficiency and ease of use
for each classifier. We provide hyperparameter surface maps capturing accuracy
and efficiency, comparisons between DDAGs and 1-vs-1 SVMs, and effects of
heuristics when training ANNs with Resilient Backpropagation. We report
statistically significant results using Cohen's Kappa statistic for contingency
tables.
| [
"C\\'esar Roberto de Souza, Ednaldo Brigante Pizzolato, Mauro dos Santos\n Anjo",
"['César Roberto de Souza' 'Ednaldo Brigante Pizzolato'\n 'Mauro dos Santos Anjo']"
] |
cs.LG math.NA stat.ML | null | 1210.7559 | null | null | http://arxiv.org/pdf/1210.7559v4 | 2014-11-13T22:43:15Z | 2012-10-29T04:38:41Z | Tensor decompositions for learning latent variable models | This work considers a computationally and statistically efficient parameter
estimation method for a wide class of latent variable models---including
Gaussian mixture models, hidden Markov models, and latent Dirichlet
allocation---which exploits a certain tensor structure in their low-order
observable moments (typically, of second- and third-order). Specifically,
parameter estimation is reduced to the problem of extracting a certain
(orthogonal) decomposition of a symmetric tensor derived from the moments; this
decomposition can be viewed as a natural generalization of the singular value
decomposition for matrices. Although tensor decompositions are generally
intractable to compute, the decomposition of these specially structured tensors
can be efficiently obtained by a variety of approaches, including power
iterations and maximization approaches (similar to the case of matrices). A
detailed analysis of a robust tensor power method is provided, establishing an
analogue of Wedin's perturbation theorem for the singular vectors of matrices.
This implies a robust and computationally tractable estimation approach for
several popular latent variable models.
| [
"['Anima Anandkumar' 'Rong Ge' 'Daniel Hsu' 'Sham M. Kakade'\n 'Matus Telgarsky']",
"Anima Anandkumar and Rong Ge and Daniel Hsu and Sham M. Kakade and\n Matus Telgarsky"
] |
cs.LG | null | 1210.7657 | null | null | http://arxiv.org/pdf/1210.7657v1 | 2012-10-29T13:30:27Z | 2012-10-29T13:30:27Z | Text Classification with Compression Algorithms | This work concerns a comparison of SVM kernel methods in text categorization
tasks. In particular I define a kernel function that estimates the similarity
between two objects computing by their compressed lengths. In fact, compression
algorithms can detect arbitrarily long dependencies within the text strings.
Data text vectorization looses information in feature extractions and is highly
sensitive by textual language. Furthermore, these methods are language
independent and require no text preprocessing. Moreover, the accuracy computed
on the datasets (Web-KB, 20ng and Reuters-21578), in some case, is greater than
Gaussian, linear and polynomial kernels. The method limits are represented by
computational time complexity of the Gram matrix and by very poor performance
on non-textual datasets.
| [
"['Antonio Giuliano Zippo']",
"Antonio Giuliano Zippo"
] |
cs.LG cs.AI | null | 1210.8291 | null | null | http://arxiv.org/pdf/1210.8291v1 | 2012-10-31T10:42:32Z | 2012-10-31T10:42:32Z | Learning in the Model Space for Fault Diagnosis | The emergence of large scaled sensor networks facilitates the collection of
large amounts of real-time data to monitor and control complex engineering
systems. However, in many cases the collected data may be incomplete or
inconsistent, while the underlying environment may be time-varying or
un-formulated. In this paper, we have developed an innovative cognitive fault
diagnosis framework that tackles the above challenges. This framework
investigates fault diagnosis in the model space instead of in the signal space.
Learning in the model space is implemented by fitting a series of models using
a series of signal segments selected with a rolling window. By investigating
the learning techniques in the fitted model space, faulty models can be
discriminated from healthy models using one-class learning algorithm. The
framework enables us to construct fault library when unknown faults occur,
which can be regarded as cognitive fault isolation. This paper also
theoretically investigates how to measure the pairwise distance between two
models in the model space and incorporates the model distance into the learning
algorithm in the model space. The results on three benchmark applications and
one simulated model for the Barcelona water distribution network have confirmed
the effectiveness of the proposed framework.
| [
"Huanhuan Chen, Peter Tino, Xin Yao, and Ali Rodan",
"['Huanhuan Chen' 'Peter Tino' 'Xin Yao' 'Ali Rodan']"
] |
stat.ML cs.AI cs.LG | null | 1210.8353 | null | null | http://arxiv.org/pdf/1210.8353v1 | 2012-10-31T14:55:50Z | 2012-10-31T14:55:50Z | Temporal Autoencoding Restricted Boltzmann Machine | Much work has been done refining and characterizing the receptive fields
learned by deep learning algorithms. A lot of this work has focused on the
development of Gabor-like filters learned when enforcing sparsity constraints
on a natural image dataset. Little work however has investigated how these
filters might expand to the temporal domain, namely through training on natural
movies. Here we investigate exactly this problem in established temporal deep
learning algorithms as well as a new learning paradigm suggested here, the
Temporal Autoencoding Restricted Boltzmann Machine (TARBM).
| [
"['Chris Häusler' 'Alex Susemihl']",
"Chris H\\\"ausler, Alex Susemihl"
] |
cs.AI cs.LG | null | 1210.8385 | null | null | http://arxiv.org/pdf/1210.8385v1 | 2012-10-31T16:41:37Z | 2012-10-31T16:41:37Z | First Experiments with PowerPlay | Like a scientist or a playing child, PowerPlay not only learns new skills to
solve given problems, but also invents new interesting problems by itself. By
design, it continually comes up with the fastest to find, initially novel, but
eventually solvable tasks. It also continually simplifies or compresses or
speeds up solutions to previous tasks. Here we describe first experiments with
PowerPlay. A self-delimiting recurrent neural network SLIM RNN is used as a
general computational problem solving architecture. Its connection weights can
encode arbitrary, self-delimiting, halting or non-halting programs affecting
both environment (through effectors) and internal states encoding abstractions
of event sequences. Our PowerPlay-driven SLIM RNN learns to become an
increasingly general solver of self-invented problems, continually adding new
problem solving procedures to its growing skill repertoire. Extending a recent
conference paper, we identify interesting, emerging, developmental stages of
our open-ended system. We also show how it automatically self-modularizes,
frequently re-using code for previously invented skills, always trying to
invent novel tasks that can be quickly validated because they do not require
too many weight changes affecting too many previous tasks.
| [
"Rupesh Kumar Srivastava, Bas R. Steunebrink and J\\\"urgen Schmidhuber",
"['Rupesh Kumar Srivastava' 'Bas R. Steunebrink' 'Jürgen Schmidhuber']"
] |
cs.LG stat.ML | null | 1211.0025 | null | null | http://arxiv.org/pdf/1211.0025v2 | 2014-06-21T13:47:44Z | 2012-10-31T20:38:04Z | Venn-Abers predictors | This paper continues study, both theoretical and empirical, of the method of
Venn prediction, concentrating on binary prediction problems. Venn predictors
produce probability-type predictions for the labels of test objects which are
guaranteed to be well calibrated under the standard assumption that the
observations are generated independently from the same distribution. We give a
simple formalization and proof of this property. We also introduce Venn-Abers
predictors, a new class of Venn predictors based on the idea of isotonic
regression, and report promising empirical results both for Venn-Abers
predictors and for their more computationally efficient simplified version.
| [
"Vladimir Vovk and Ivan Petej",
"['Vladimir Vovk' 'Ivan Petej']"
] |
cs.SI cs.LG stat.ML | null | 1211.0028 | null | null | http://arxiv.org/pdf/1211.0028v1 | 2012-10-31T20:56:16Z | 2012-10-31T20:56:16Z | Understanding the Interaction between Interests, Conversations and
Friendships in Facebook | In this paper, we explore salient questions about user interests,
conversations and friendships in the Facebook social network, using a novel
latent space model that integrates several data types. A key challenge of
studying Facebook's data is the wide range of data modalities such as text,
network links, and categorical labels. Our latent space model seamlessly
combines all three data modalities over millions of users, allowing us to study
the interplay between user friendships, interests, and higher-order
network-wide social trends on Facebook. The recovered insights not only answer
our initial questions, but also reveal surprising facts about user interests in
the context of Facebook's ecosystem. We also confirm that our results are
significant with respect to evidential information from the study subjects.
| [
"['Qirong Ho' 'Rong Yan' 'Rajat Raina' 'Eric P. Xing']",
"Qirong Ho, Rong Yan, Rajat Raina, Eric P. Xing"
] |
cs.DM cs.LG cs.SI | 10.1109/MSP.2012.2235192 | 1211.0053 | null | null | http://arxiv.org/abs/1211.0053v2 | 2013-03-10T15:04:40Z | 2012-10-31T23:18:43Z | The Emerging Field of Signal Processing on Graphs: Extending
High-Dimensional Data Analysis to Networks and Other Irregular Domains | In applications such as social, energy, transportation, sensor, and neuronal
networks, high-dimensional data naturally reside on the vertices of weighted
graphs. The emerging field of signal processing on graphs merges algebraic and
spectral graph theoretic concepts with computational harmonic analysis to
process such signals on graphs. In this tutorial overview, we outline the main
challenges of the area, discuss different ways to define graph spectral
domains, which are the analogues to the classical frequency domain, and
highlight the importance of incorporating the irregular structures of graph
data domains when processing signals on graphs. We then review methods to
generalize fundamental operations such as filtering, translation, modulation,
dilation, and downsampling to the graph setting, and survey the localized,
multiscale transforms that have been proposed to efficiently extract
information from high-dimensional data on graphs. We conclude with a brief
discussion of open issues and possible extensions.
| [
"David I Shuman, Sunil K. Narang, Pascal Frossard, Antonio Ortega, and\n Pierre Vandergheynst",
"['David I Shuman' 'Sunil K. Narang' 'Pascal Frossard' 'Antonio Ortega'\n 'Pierre Vandergheynst']"
] |
math.OC cs.LG math.NA stat.CO stat.ML | null | 1211.0056 | null | null | http://arxiv.org/pdf/1211.0056v2 | 2012-11-02T04:04:35Z | 2012-10-31T23:47:04Z | Iterative Hard Thresholding Methods for $l_0$ Regularized Convex Cone
Programming | In this paper we consider $l_0$ regularized convex cone programming problems.
In particular, we first propose an iterative hard thresholding (IHT) method and
its variant for solving $l_0$ regularized box constrained convex programming.
We show that the sequence generated by these methods converges to a local
minimizer. Also, we establish the iteration complexity of the IHT method for
finding an $\epsilon$-local-optimal solution. We then propose a method for
solving $l_0$ regularized convex cone programming by applying the IHT method to
its quadratic penalty relaxation and establish its iteration complexity for
finding an $\epsilon$-approximate local minimizer. Finally, we propose a
variant of this method in which the associated penalty parameter is dynamically
updated, and show that every accumulation point is a local minimizer of the
problem.
| [
"Zhaosong Lu",
"['Zhaosong Lu']"
] |
cs.LG | null | 1211.0210 | null | null | http://arxiv.org/pdf/1211.0210v1 | 2012-11-01T15:52:11Z | 2012-11-01T15:52:11Z | Extension of TSVM to Multi-Class and Hierarchical Text Classification
Problems With General Losses | Transductive SVM (TSVM) is a well known semi-supervised large margin learning
method for binary text classification. In this paper we extend this method to
multi-class and hierarchical classification problems. We point out that the
determination of labels of unlabeled examples with fixed classifier weights is
a linear programming problem. We devise an efficient technique for solving it.
The method is applicable to general loss functions. We demonstrate the value of
the new method using large margin loss on a number of multi-class and
hierarchical classification datasets. For maxent loss we show empirically that
our method is better than expectation regularization/constraint and posterior
regularization methods, and competitive with the version of entropy
regularization method which uses label constraints.
| [
"['Sathiya Keerthi Selvaraj' 'Sundararajan Sellamanickam' 'Shirish Shevade']",
"Sathiya Keerthi Selvaraj, Sundararajan Sellamanickam, Shirish Shevade"
] |
stat.ML cs.LG math.PR | null | 1211.0358 | null | null | http://arxiv.org/pdf/1211.0358v2 | 2013-03-23T01:23:34Z | 2012-11-02T03:13:08Z | Deep Gaussian Processes | In this paper we introduce deep Gaussian process (GP) models. Deep GPs are a
deep belief network based on Gaussian process mappings. The data is modeled as
the output of a multivariate GP. The inputs to that Gaussian process are then
governed by another GP. A single layer model is equivalent to a standard GP or
the GP latent variable model (GP-LVM). We perform inference in the model by
approximate variational marginalization. This results in a strict lower bound
on the marginal likelihood of the model which we use for model selection
(number of layers and nodes per layer). Deep belief networks are typically
applied to relatively large data sets using stochastic gradient descent for
optimization. Our fully Bayesian treatment allows for the application of deep
models even when data is scarce. Model selection by our variational bound shows
that a five layer hierarchy is justified even when modelling a digit data set
containing only 150 examples.
| [
"['Andreas C. Damianou' 'Neil D. Lawrence']",
"Andreas C. Damianou, Neil D. Lawrence"
] |
cs.LG cond-mat.dis-nn stat.ML | null | 1211.0439 | null | null | http://arxiv.org/pdf/1211.0439v1 | 2012-11-02T12:46:24Z | 2012-11-02T12:46:24Z | Learning curves for multi-task Gaussian process regression | We study the average case performance of multi-task Gaussian process (GP)
regression as captured in the learning curve, i.e. the average Bayes error for
a chosen task versus the total number of examples $n$ for all tasks. For GP
covariances that are the product of an input-dependent covariance function and
a free-form inter-task covariance matrix, we show that accurate approximations
for the learning curve can be obtained for an arbitrary number of tasks $T$. We
use these to study the asymptotic learning behaviour for large $n$.
Surprisingly, multi-task learning can be asymptotically essentially useless, in
the sense that examples from other tasks help only when the degree of
inter-task correlation, $\rho$, is near its maximal value $\rho=1$. This effect
is most extreme for learning of smooth target functions as described by e.g.
squared exponential kernels. We also demonstrate that when learning many tasks,
the learning curves separate into an initial phase, where the Bayes error on
each task is reduced down to a plateau value by "collective learning" even
though most tasks have not seen examples, and a final decay that occurs once
the number of examples is proportional to the number of tasks.
| [
"['Simon R. F. Ashton' 'Peter Sollich']",
"Simon R. F. Ashton and Peter Sollich"
] |
cs.NI cs.LG | null | 1211.0447 | null | null | http://arxiv.org/pdf/1211.0447v1 | 2012-11-02T13:21:48Z | 2012-11-02T13:21:48Z | Ordinal Rating of Network Performance and Inference by Matrix Completion | This paper addresses the large-scale acquisition of end-to-end network
performance. We made two distinct contributions: ordinal rating of network
performance and inference by matrix completion. The former reduces measurement
costs and unifies various metrics which eases their processing in applications.
The latter enables scalable and accurate inference with no requirement of
structural information of the network nor geometric constraints. By combining
both, the acquisition problem bears strong similarities to recommender systems.
This paper investigates the applicability of various matrix factorization
models used in recommender systems. We found that the simple regularized matrix
factorization is not only practical but also produces accurate results that are
beneficial for peer selection.
| [
"Wei Du and Yongjun Liao and and Pierre Geurts and Guy Leduc",
"['Wei Du' 'Yongjun Liao' 'and Pierre Geurts' 'Guy Leduc']"
] |
cs.IT cs.LG math.IT stat.ML | null | 1211.0587 | null | null | http://arxiv.org/pdf/1211.0587v2 | 2012-11-21T12:52:44Z | 2012-11-03T00:41:46Z | Partition Tree Weighting | This paper introduces the Partition Tree Weighting technique, an efficient
meta-algorithm for piecewise stationary sources. The technique works by
performing Bayesian model averaging over a large class of possible partitions
of the data into locally stationary segments. It uses a prior, closely related
to the Context Tree Weighting technique of Willems, that is well suited to data
compression applications. Our technique can be applied to any coding
distribution at an additional time and space cost only logarithmic in the
sequence length. We provide a competitive analysis of the redundancy of our
method, and explore its application in a variety of settings. The order of the
redundancy and the complexity of our algorithm matches those of the best
competitors available in the literature, and the new algorithm exhibits a
superior complexity-performance trade-off in our experiments.
| [
"Joel Veness, Martha White, Michael Bowling, Andr\\'as Gy\\\"orgy",
"['Joel Veness' 'Martha White' 'Michael Bowling' 'András György']"
] |
cs.LG cs.DS | null | 1211.0616 | null | null | http://arxiv.org/pdf/1211.0616v4 | 2014-05-10T11:15:05Z | 2012-11-03T15:14:40Z | The complexity of learning halfspaces using generalized linear methods | Many popular learning algorithms (E.g. Regression, Fourier-Transform based
algorithms, Kernel SVM and Kernel ridge regression) operate by reducing the
problem to a convex optimization problem over a vector space of functions.
These methods offer the currently best approach to several central problems
such as learning half spaces and learning DNF's. In addition they are widely
used in numerous application domains. Despite their importance, there are still
very few proof techniques to show limits on the power of these algorithms.
We study the performance of this approach in the problem of (agnostically and
improperly) learning halfspaces with margin $\gamma$. Let $\mathcal{D}$ be a
distribution over labeled examples. The $\gamma$-margin error of a hyperplane
$h$ is the probability of an example to fall on the wrong side of $h$ or at a
distance $\le\gamma$ from it. The $\gamma$-margin error of the best $h$ is
denoted $\mathrm{Err}_\gamma(\mathcal{D})$. An $\alpha(\gamma)$-approximation
algorithm receives $\gamma,\epsilon$ as input and, using i.i.d. samples of
$\mathcal{D}$, outputs a classifier with error rate $\le
\alpha(\gamma)\mathrm{Err}_\gamma(\mathcal{D}) + \epsilon$. Such an algorithm
is efficient if it uses $\mathrm{poly}(\frac{1}{\gamma},\frac{1}{\epsilon})$
samples and runs in time polynomial in the sample size.
The best approximation ratio achievable by an efficient algorithm is
$O\left(\frac{1/\gamma}{\sqrt{\log(1/\gamma)}}\right)$ and is achieved using an
algorithm from the above class. Our main result shows that the approximation
ratio of every efficient algorithm from this family must be $\ge
\Omega\left(\frac{1/\gamma}{\mathrm{poly}\left(\log\left(1/\gamma\right)\right)}\right)$,
essentially matching the best known upper bound.
| [
"Amit Daniely and Nati Linial and Shai Shalev-Shwartz",
"['Amit Daniely' 'Nati Linial' 'Shai Shalev-Shwartz']"
] |
cs.LG math.OC stat.ML | null | 1211.0632 | null | null | http://arxiv.org/pdf/1211.0632v2 | 2013-01-22T17:07:37Z | 2012-11-03T19:05:56Z | Stochastic ADMM for Nonsmooth Optimization | We present a stochastic setting for optimization problems with nonsmooth
convex separable objective functions over linear equality constraints. To solve
such problems, we propose a stochastic Alternating Direction Method of
Multipliers (ADMM) algorithm. Our algorithm applies to a more general class of
nonsmooth convex functions that does not necessarily have a closed-form
solution by minimizing the augmented function directly. We also demonstrate the
rates of convergence for our algorithm under various structural assumptions of
the stochastic functions: $O(1/\sqrt{t})$ for convex functions and $O(\log
t/t)$ for strongly convex functions. Compared to previous literature, we
establish the convergence rate of ADMM algorithm, for the first time, in terms
of both the objective value and the feasibility violation.
| [
"['Hua Ouyang' 'Niao He' 'Alexander Gray']",
"Hua Ouyang, Niao He, Alexander Gray"
] |
math.ST cs.LG stat.ML stat.TH | 10.1214/12-AOS979 | 1211.0801 | null | null | http://arxiv.org/abs/1211.0801v1 | 2012-11-05T09:36:40Z | 2012-11-05T09:36:40Z | Discussion: Latent variable graphical model selection via convex
optimization | Discussion of "Latent variable graphical model selection via convex
optimization" by Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky
[arXiv:1008.1290].
| [
"['Ming Yuan']",
"Ming Yuan"
] |
null | null | 1211.0806 | null | null | http://arxiv.org/abs/1211.0806v1 | 2012-11-05T09:51:07Z | 2012-11-05T09:51:07Z | Discussion: Latent variable graphical model selection via convex
optimization | Discussion of "Latent variable graphical model selection via convex optimization" by Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky [arXiv:1008.1290]. | [
"['Steffen Lauritzen' 'Nicolai Meinshausen']"
] |
null | null | 1211.0808 | null | null | http://arxiv.org/abs/1211.0808v1 | 2012-11-05T09:59:33Z | 2012-11-05T09:59:33Z | Discussion: Latent variable graphical model selection via convex
optimization | Discussion of "Latent variable graphical model selection via convex optimization" by Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky [arXiv:1008.1290]. | [
"['Martin J. Wainwright']"
] |
null | null | 1211.0817 | null | null | http://arxiv.org/abs/1211.0817v1 | 2012-11-05T10:32:57Z | 2012-11-05T10:32:57Z | Discussion: Latent variable graphical model selection via convex
optimization | Discussion of "Latent variable graphical model selection via convex optimization" by Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky [arXiv:1008.1290]. | [
"['Emmanuel J. Candés' 'Mahdi Soltanolkotabi']"
] |
math.ST cs.LG stat.ML stat.TH | 10.1214/12-AOS1020 | 1211.0835 | null | null | http://arxiv.org/abs/1211.0835v1 | 2012-11-05T11:33:03Z | 2012-11-05T11:33:03Z | Rejoinder: Latent variable graphical model selection via convex
optimization | Rejoinder to "Latent variable graphical model selection via convex
optimization" by Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky
[arXiv:1008.1290].
| [
"['Venkat Chandrasekaran' 'Pablo A. Parrilo' 'Alan S. Willsky']",
"Venkat Chandrasekaran, Pablo A. Parrilo, Alan S. Willsky"
] |
stat.ML cs.LG | null | 1211.0879 | null | null | http://arxiv.org/pdf/1211.0879v1 | 2012-11-05T14:48:15Z | 2012-11-05T14:48:15Z | Comparing K-Nearest Neighbors and Potential Energy Method in
classification problem. A case study using KNN applet by E.M. Mirkes and real
life benchmark data sets | K-nearest neighbors (KNN) method is used in many supervised learning
classification problems. Potential Energy (PE) method is also developed for
classification problems based on its physical metaphor. The energy potential
used in the experiments are Yukawa potential and Gaussian Potential. In this
paper, I use both applet and MATLAB program with real life benchmark data to
analyze the performances of KNN and PE method in classification problems. The
results show that in general, KNN and PE methods have similar performance. In
particular, PE with Yukawa potential has worse performance than KNN when the
density of the data is higher in the distribution of the database. When the
Gaussian potential is applied, the results from PE and KNN have similar
behavior. The indicators used are correlation coefficients and information
gain.
| [
"['Yanshan Shi']",
"Yanshan Shi"
] |
stat.ML cs.LG | null | 1211.0889 | null | null | http://arxiv.org/pdf/1211.0889v3 | 2013-05-04T06:22:23Z | 2012-11-02T07:42:54Z | APPLE: Approximate Path for Penalized Likelihood Estimators | In high-dimensional data analysis, penalized likelihood estimators are shown
to provide superior results in both variable selection and parameter
estimation. A new algorithm, APPLE, is proposed for calculating the Approximate
Path for Penalized Likelihood Estimators. Both the convex penalty (such as
LASSO) and the nonconvex penalty (such as SCAD and MCP) cases are considered.
The APPLE efficiently computes the solution path for the penalized likelihood
estimator using a hybrid of the modified predictor-corrector method and the
coordinate-descent algorithm. APPLE is compared with several well-known
packages via simulation and analysis of two gene expression data sets.
| [
"['Yi Yu' 'Yang Feng']",
"Yi Yu and Yang Feng"
] |
cs.AI cs.LG cs.PF stat.ML | null | 1211.0906 | null | null | http://arxiv.org/pdf/1211.0906v2 | 2013-10-26T09:00:50Z | 2012-11-05T16:15:16Z | Algorithm Runtime Prediction: Methods & Evaluation | Perhaps surprisingly, it is possible to predict how long an algorithm will
take to run on a previously unseen input, using machine learning techniques to
build a model of the algorithm's runtime as a function of problem-specific
instance features. Such models have important applications to algorithm
analysis, portfolio-based algorithm selection, and the automatic configuration
of parameterized algorithms. Over the past decade, a wide variety of techniques
have been studied for building such models. Here, we describe extensions and
improvements of existing models, new families of models, and -- perhaps most
importantly -- a much more thorough treatment of algorithm parameters as model
inputs. We also comprehensively describe new and existing features for
predicting algorithm runtime for propositional satisfiability (SAT), travelling
salesperson (TSP) and mixed integer programming (MIP) problems. We evaluate
these innovations through the largest empirical analysis of its kind, comparing
to a wide range of runtime modelling techniques from the literature. Our
experiments consider 11 algorithms and 35 instance distributions; they also
span a very wide range of SAT, MIP, and TSP instances, with the least
structured having been generated uniformly at random and the most structured
having emerged from real industrial applications. Overall, we demonstrate that
our new models yield substantially better runtime predictions than previous
approaches in terms of their generalization to new problem instances, to new
algorithms from a parameterized space, and to both simultaneously.
| [
"Frank Hutter, Lin Xu, Holger H. Hoos, Kevin Leyton-Brown",
"['Frank Hutter' 'Lin Xu' 'Holger H. Hoos' 'Kevin Leyton-Brown']"
] |
cs.LG cs.AI | null | 1211.0996 | null | null | http://arxiv.org/pdf/1211.0996v2 | 2013-04-17T21:04:47Z | 2012-11-05T20:42:16Z | Learning using Local Membership Queries | We introduce a new model of membership query (MQ) learning, where the
learning algorithm is restricted to query points that are \emph{close} to
random examples drawn from the underlying distribution. The learning model is
intermediate between the PAC model (Valiant, 1984) and the PAC+MQ model (where
the queries are allowed to be arbitrary points).
Membership query algorithms are not popular among machine learning
practitioners. Apart from the obvious difficulty of adaptively querying
labelers, it has also been observed that querying \emph{unnatural} points leads
to increased noise from human labelers (Lang and Baum, 1992). This motivates
our study of learning algorithms that make queries that are close to examples
generated from the data distribution.
We restrict our attention to functions defined on the $n$-dimensional Boolean
hypercube and say that a membership query is local if its Hamming distance from
some example in the (random) training data is at most $O(\log(n))$. We show the
following results in this model:
(i) The class of sparse polynomials (with coefficients in R) over $\{0,1\}^n$
is polynomial time learnable under a large class of \emph{locally smooth}
distributions using $O(\log(n))$-local queries. This class also includes the
class of $O(\log(n))$-depth decision trees.
(ii) The class of polynomial-sized decision trees is polynomial time
learnable under product distributions using $O(\log(n))$-local queries.
(iii) The class of polynomial size DNF formulas is learnable under the
uniform distribution using $O(\log(n))$-local queries in time
$n^{O(\log(\log(n)))}$.
(iv) In addition we prove a number of results relating the proposed model to
the traditional PAC model and the PAC+MQ model.
| [
"Pranjal Awasthi, Vitaly Feldman, Varun Kanade",
"['Pranjal Awasthi' 'Vitaly Feldman' 'Varun Kanade']"
] |
cs.CC cs.DS cs.IT cs.LG math.IT | null | 1211.1041 | null | null | http://arxiv.org/pdf/1211.1041v3 | 2013-12-03T21:51:26Z | 2012-11-05T21:39:22Z | Algorithms and Hardness for Robust Subspace Recovery | We consider a fundamental problem in unsupervised learning called
\emph{subspace recovery}: given a collection of $m$ points in $\mathbb{R}^n$,
if many but not necessarily all of these points are contained in a
$d$-dimensional subspace $T$ can we find it? The points contained in $T$ are
called {\em inliers} and the remaining points are {\em outliers}. This problem
has received considerable attention in computer science and in statistics. Yet
efficient algorithms from computer science are not robust to {\em adversarial}
outliers, and the estimators from robust statistics are hard to compute in high
dimensions.
Are there algorithms for subspace recovery that are both robust to outliers
and efficient? We give an algorithm that finds $T$ when it contains more than a
$\frac{d}{n}$ fraction of the points. Hence, for say $d = n/2$ this estimator
is both easy to compute and well-behaved when there are a constant fraction of
outliers. We prove that it is Small Set Expansion hard to find $T$ when the
fraction of errors is any larger, thus giving evidence that our estimator is an
{\em optimal} compromise between efficiency and robustness.
As it turns out, this basic problem has a surprising number of connections to
other areas including small set expansion, matroid theory and functional
analysis that we make use of here.
| [
"['Moritz Hardt' 'Ankur Moitra']",
"Moritz Hardt and Ankur Moitra"
] |
cs.LG stat.ML | null | 1211.1043 | null | null | http://arxiv.org/pdf/1211.1043v1 | 2012-11-05T21:40:38Z | 2012-11-05T21:40:38Z | Soft (Gaussian CDE) regression models and loss functions | Regression, unlike classification, has lacked a comprehensive and effective
approach to deal with cost-sensitive problems by the reuse (and not a
re-training) of general regression models. In this paper, a wide variety of
cost-sensitive problems in regression (such as bids, asymmetric losses and
rejection rules) can be solved effectively by a lightweight but powerful
approach, consisting of: (1) the conversion of any traditional one-parameter
crisp regression model into a two-parameter soft regression model, seen as a
normal conditional density estimator, by the use of newly-introduced enrichment
methods; and (2) the reframing of an enriched soft regression model to new
contexts by an instance-dependent optimisation of the expected loss derived
from the conditional normal distribution.
| [
"['Jose Hernandez-Orallo']",
"Jose Hernandez-Orallo"
] |
cs.LG math.ST stat.ML stat.TH | null | 1211.1082 | null | null | http://arxiv.org/pdf/1211.1082v3 | 2013-04-26T17:50:21Z | 2012-11-06T00:21:32Z | Active and passive learning of linear separators under log-concave
distributions | We provide new results concerning label efficient, polynomial time, passive
and active learning of linear separators. We prove that active learning
provides an exponential improvement over PAC (passive) learning of homogeneous
linear separators under nearly log-concave distributions. Building on this, we
provide a computationally efficient PAC algorithm with optimal (up to a
constant factor) sample complexity for such problems. This resolves an open
question concerning the sample complexity of efficient PAC algorithms under the
uniform distribution in the unit ball. Moreover, it provides the first bound
for a polynomial-time PAC algorithm that is tight for an interesting infinite
class of hypothesis functions under a general and natural class of
data-distributions, providing significant progress towards a longstanding open
question.
We also provide new bounds for active and passive learning in the case that
the data might not be linearly separable, both in the agnostic case and and
under the Tsybakov low-noise condition. To derive our results, we provide new
structural results for (nearly) log-concave distributions, which might be of
independent interest as well.
| [
"['Maria Florina Balcan' 'Philip M. Long']",
"Maria Florina Balcan and Philip M. Long"
] |
cs.CV cs.LG | null | 1211.1127 | null | null | http://arxiv.org/pdf/1211.1127v1 | 2012-11-06T07:26:49Z | 2012-11-06T07:26:49Z | Visual Transfer Learning: Informal Introduction and Literature Overview | Transfer learning techniques are important to handle small training sets and
to allow for quick generalization even from only a few examples. The following
paper is the introduction as well as the literature overview part of my thesis
related to the topic of transfer learning for visual recognition problems.
| [
"['Erik Rodner']",
"Erik Rodner"
] |
cs.LG cs.CV q-bio.NC | null | 1211.1255 | null | null | http://arxiv.org/pdf/1211.1255v1 | 2012-11-06T15:15:48Z | 2012-11-06T15:15:48Z | Handwritten digit recognition by bio-inspired hierarchical networks | The human brain processes information showing learning and prediction
abilities but the underlying neuronal mechanisms still remain unknown.
Recently, many studies prove that neuronal networks are able of both
generalizations and associations of sensory inputs. In this paper, following a
set of neurophysiological evidences, we propose a learning framework with a
strong biological plausibility that mimics prominent functions of cortical
circuitries. We developed the Inductive Conceptual Network (ICN), that is a
hierarchical bio-inspired network, able to learn invariant patterns by
Variable-order Markov Models implemented in its nodes. The outputs of the
top-most node of ICN hierarchy, representing the highest input generalization,
allow for automatic classification of inputs. We found that the ICN clusterized
MNIST images with an error of 5.73% and USPS images with an error of 12.56%.
| [
"Antonio G. Zippo, Giuliana Gelsomino, Sara Nencini, Gabriele E. M.\n Biella",
"['Antonio G. Zippo' 'Giuliana Gelsomino' 'Sara Nencini'\n 'Gabriele E. M. Biella']"
] |
stat.ML cond-mat.dis-nn cond-mat.stat-mech cs.LG | null | 1211.1328 | null | null | http://arxiv.org/pdf/1211.1328v2 | 2013-09-30T10:36:51Z | 2012-11-06T17:58:39Z | Random walk kernels and learning curves for Gaussian process regression
on random graphs | We consider learning on graphs, guided by kernels that encode similarity
between vertices. Our focus is on random walk kernels, the analogues of squared
exponential kernels in Euclidean spaces. We show that on large, locally
treelike, graphs these have some counter-intuitive properties, specifically in
the limit of large kernel lengthscales. We consider using these kernels as
covariance matrices of e.g.\ Gaussian processes (GPs). In this situation one
typically scales the prior globally to normalise the average of the prior
variance across vertices. We demonstrate that, in contrast to the Euclidean
case, this generically leads to significant variation in the prior variance
across vertices, which is undesirable from the probabilistic modelling point of
view. We suggest the random walk kernel should be normalised locally, so that
each vertex has the same prior variance, and analyse the consequences of this
by studying learning curves for Gaussian process regression. Numerical
calculations as well as novel theoretical predictions for the learning curves
using belief propagation make it clear that one obtains distinctly different
probabilistic models depending on the choice of normalisation. Our method for
predicting the learning curves using belief propagation is significantly more
accurate than previous approximations and should become exact in the limit of
large random graphs.
| [
"Matthew Urry and Peter Sollich",
"['Matthew Urry' 'Peter Sollich']"
] |
cs.LG | 10.1016/j.ins.2014.08.058 | 1211.1513 | null | null | http://arxiv.org/abs/1211.1513v2 | 2013-03-27T09:00:24Z | 2012-11-07T10:57:38Z | K-Plane Regression | In this paper, we present a novel algorithm for piecewise linear regression
which can learn continuous as well as discontinuous piecewise linear functions.
The main idea is to repeatedly partition the data and learn a liner model in in
each partition. While a simple algorithm incorporating this idea does not work
well, an interesting modification results in a good algorithm. The proposed
algorithm is similar in spirit to $k$-means clustering algorithm. We show that
our algorithm can also be viewed as an EM algorithm for maximum likelihood
estimation of parameters under a reasonable probability model. We empirically
demonstrate the effectiveness of our approach by comparing its performance with
the state of art regression learning algorithms on some real world datasets.
| [
"['Naresh Manwani' 'P. S. Sastry']",
"Naresh Manwani, P. S. Sastry"
] |
cs.CE cs.LG | null | 1211.1526 | null | null | http://arxiv.org/pdf/1211.1526v2 | 2012-11-08T13:54:13Z | 2012-11-07T12:47:57Z | Explosion prediction of oil gas using SVM and Logistic Regression | The prevention of dangerous chemical accidents is a primary problem of
industrial manufacturing. In the accidents of dangerous chemicals, the oil gas
explosion plays an important role. The essential task of the explosion
prevention is to estimate the better explosion limit of a given oil gas. In
this paper, Support Vector Machines (SVM) and Logistic Regression (LR) are used
to predict the explosion of oil gas. LR can get the explicit probability
formula of explosion, and the explosive range of the concentrations of oil gas
according to the concentration of oxygen. Meanwhile, SVM gives higher accuracy
of prediction. Furthermore, considering the practical requirements, the effects
of penalty parameter on the distribution of two types of errors are discussed.
| [
"['Xiaofei Wang' 'Mingming Zhang' 'Liyong Shen' 'Suixiang Gao']",
"Xiaofei Wang, Mingming Zhang, Liyong Shen, Suixiang Gao"
] |
cs.CV cs.LG | null | 1211.1544 | null | null | http://arxiv.org/pdf/1211.1544v3 | 2012-11-09T10:36:22Z | 2012-11-07T13:35:52Z | Image denoising with multi-layer perceptrons, part 1: comparison with
existing algorithms and with bounds | Image denoising can be described as the problem of mapping from a noisy image
to a noise-free image. The best currently available denoising methods
approximate this mapping with cleverly engineered algorithms. In this work we
attempt to learn this mapping directly with plain multi layer perceptrons (MLP)
applied to image patches. We will show that by training on large image
databases we are able to outperform the current state-of-the-art image
denoising methods. In addition, our method achieves results that are superior
to one type of theoretical bound and goes a large way toward closing the gap
with a second type of theoretical bound. Our approach is easily adapted to less
extensively studied types of noise, such as mixed Poisson-Gaussian noise, JPEG
artifacts, salt-and-pepper noise and noise resembling stripes, for which we
achieve excellent results as well. We will show that combining a block-matching
procedure with MLPs can further improve the results on certain images. In a
second paper, we detail the training trade-offs and the inner mechanisms of our
MLPs.
| [
"['Harold Christopher Burger' 'Christian J. Schuler' 'Stefan Harmeling']",
"Harold Christopher Burger, Christian J. Schuler, Stefan Harmeling"
] |
cs.LG cs.NA math.OC | null | 1211.1550 | null | null | http://arxiv.org/pdf/1211.1550v2 | 2012-11-12T17:50:39Z | 2012-11-07T13:49:26Z | A Riemannian geometry for low-rank matrix completion | We propose a new Riemannian geometry for fixed-rank matrices that is
specifically tailored to the low-rank matrix completion problem. Exploiting the
degree of freedom of a quotient space, we tune the metric on our search space
to the particular least square cost function. At one level, it illustrates in a
novel way how to exploit the versatile framework of optimization on quotient
manifold. At another level, our algorithm can be considered as an improved
version of LMaFit, the state-of-the-art Gauss-Seidel algorithm. We develop
necessary tools needed to perform both first-order and second-order
optimization. In particular, we propose gradient descent schemes (steepest
descent and conjugate gradient) and trust-region algorithms. We also show that,
thanks to the simplicity of the cost function, it is numerically cheap to
perform an exact linesearch given a search direction, which makes our
algorithms competitive with the state-of-the-art on standard low-rank matrix
completion instances.
| [
"['B. Mishra' 'K. Adithya Apuroop' 'R. Sepulchre']",
"B. Mishra, K. Adithya Apuroop and R. Sepulchre"
] |
cs.CV cs.LG | null | 1211.1552 | null | null | http://arxiv.org/pdf/1211.1552v1 | 2012-11-07T13:50:19Z | 2012-11-07T13:50:19Z | Image denoising with multi-layer perceptrons, part 2: training
trade-offs and analysis of their mechanisms | Image denoising can be described as the problem of mapping from a noisy image
to a noise-free image. In another paper, we show that multi-layer perceptrons
can achieve outstanding image denoising performance for various types of noise
(additive white Gaussian noise, mixed Poisson-Gaussian noise, JPEG artifacts,
salt-and-pepper noise and noise resembling stripes). In this work we discuss in
detail which trade-offs have to be considered during the training procedure. We
will show how to achieve good results and which pitfalls to avoid. By analysing
the activation patterns of the hidden units we are able to make observations
regarding the functioning principle of multi-layer perceptrons trained for
image denoising.
| [
"['Harold Christopher Burger' 'Christian J. Schuler' 'Stefan Harmeling']",
"Harold Christopher Burger, Christian J. Schuler, Stefan Harmeling"
] |
cs.RO cs.CV cs.LG cs.SY | null | 1211.1690 | null | null | http://arxiv.org/pdf/1211.1690v1 | 2012-11-07T21:20:23Z | 2012-11-07T21:20:23Z | Learning Monocular Reactive UAV Control in Cluttered Natural
Environments | Autonomous navigation for large Unmanned Aerial Vehicles (UAVs) is fairly
straight-forward, as expensive sensors and monitoring devices can be employed.
In contrast, obstacle avoidance remains a challenging task for Micro Aerial
Vehicles (MAVs) which operate at low altitude in cluttered environments. Unlike
large vehicles, MAVs can only carry very light sensors, such as cameras, making
autonomous navigation through obstacles much more challenging. In this paper,
we describe a system that navigates a small quadrotor helicopter autonomously
at low altitude through natural forest environments. Using only a single cheap
camera to perceive the environment, we are able to maintain a constant velocity
of up to 1.5m/s. Given a small set of human pilot demonstrations, we use recent
state-of-the-art imitation learning techniques to train a controller that can
avoid trees by adapting the MAVs heading. We demonstrate the performance of our
system in a more controlled environment indoors, and in real natural forest
environments outdoors.
| [
"Stephane Ross, Narek Melik-Barkhudarov, Kumar Shaurya Shankar, Andreas\n Wendel, Debadeepta Dey, J. Andrew Bagnell, Martial Hebert",
"['Stephane Ross' 'Narek Melik-Barkhudarov' 'Kumar Shaurya Shankar'\n 'Andreas Wendel' 'Debadeepta Dey' 'J. Andrew Bagnell' 'Martial Hebert']"
] |
cs.LG cs.DS stat.ML | null | 1211.1716 | null | null | http://arxiv.org/pdf/1211.1716v2 | 2013-06-09T04:43:53Z | 2012-11-07T22:50:51Z | Blind Signal Separation in the Presence of Gaussian Noise | A prototypical blind signal separation problem is the so-called cocktail
party problem, with n people talking simultaneously and n different microphones
within a room. The goal is to recover each speech signal from the microphone
inputs. Mathematically this can be modeled by assuming that we are given
samples from an n-dimensional random variable X=AS, where S is a vector whose
coordinates are independent random variables corresponding to each speaker. The
objective is to recover the matrix A^{-1} given random samples from X. A range
of techniques collectively known as Independent Component Analysis (ICA) have
been proposed to address this problem in the signal processing and machine
learning literature. Many of these techniques are based on using the kurtosis
or other cumulants to recover the components.
In this paper we propose a new algorithm for solving the blind signal
separation problem in the presence of additive Gaussian noise, when we are
given samples from X=AS+\eta, where \eta is drawn from an unknown, not
necessarily spherical n-dimensional Gaussian distribution. Our approach is
based on a method for decorrelating a sample with additive Gaussian noise under
the assumption that the underlying distribution is a linear transformation of a
distribution with independent components. Our decorrelation routine is based on
the properties of cumulant tensors and can be combined with any standard
cumulant-based method for ICA to get an algorithm that is provably robust in
the presence of Gaussian noise. We derive polynomial bounds for the sample
complexity and error propagation of our method.
| [
"Mikhail Belkin, Luis Rademacher, James Voss",
"['Mikhail Belkin' 'Luis Rademacher' 'James Voss']"
] |
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