pwc-archive/papers-with-abstracts huggingface dataset:
Vision and Learning for Deliberative Monocular Cluttered Flight
Authors: Debadeepta Dey, Kumar Shaurya Shankar, Sam Zeng, Rupesh Mehta, M. Talha Agcayazi, Christopher Eriksen, Shreyansh Daftry, Martial Hebert, J. Andrew Bagnell
Date: 2014-11-24 00:00:00
Tasks: Depth Estimation, Depth Prediction, feature selection

Cameras provide a rich source of information while being passive, cheap and
lightweight for small and medium Unmanned Aerial Vehicles (UAVs). In this work
we present the first implementation of receding horizon control, which is
widely used in ground vehicles, with monocular vision as the only sensing mode
for autonomous UAV flight in dense clutter. We make it feasible on UAVs via a
number of contributions: novel coupling of perception and control via relevant
and diverse, multiple interpretations of the scene around the robot, leveraging
recent advances in machine learning to showcase anytime budgeted cost-sensitive
feature selection, and fast non-linear regression for monocular depth
prediction. We empirically demonstrate the efficacy of our novel pipeline via
real world experiments of more than 2 kms through dense trees with a quadrotor
built from off-the-shelf parts. Moreover our pipeline is designed to combine
information from other modalities like stereo and lidar as well if available.
On the Decreasing Power of Kernel and Distance based Nonparametric Hypothesis Tests in High Dimensions
Authors: Sashank J. Reddi, Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry Wasserman
Date: 2014-06-09 00:00:00
Tasks: Two-sample testing

This paper is about two related decision theoretic problems, nonparametric
two-sample testing and independence testing. There is a belief that two
recently proposed solutions, based on kernels and distances between pairs of
points, behave well in high-dimensional settings. We identify different sources
of misconception that give rise to the above belief. Specifically, we
differentiate the hardness of estimation of test statistics from the hardness
of testing whether these statistics are zero or not, and explicitly discuss a
notion of "fair" alternative hypotheses for these problems as dimension
increases. We then demonstrate that the power of these tests actually drops
polynomially with increasing dimension against fair alternatives. We end with
some theoretical insights and shed light on the \textit{median heuristic} for
kernel bandwidth selection. Our work advances the current understanding of the
power of modern nonparametric hypothesis tests in high dimensions.
On the High-dimensional Power of Linear-time Kernel Two-Sample Testing under Mean-difference Alternatives
Authors: Aaditya Ramdas, Sashank J. Reddi, Barnabas Poczos, Aarti Singh, Larry Wasserman
Date: 2014-11-23 00:00:00
Tasks: Two-sample testing

Nonparametric two sample testing deals with the question of consistently
deciding if two distributions are different, given samples from both, without
making any parametric assumptions about the form of the distributions. The
current literature is split into two kinds of tests - those which are
consistent without any assumptions about how the distributions may differ
(\textit{general} alternatives), and those which are designed to specifically
test easier alternatives, like a difference in means (\textit{mean-shift}
alternatives).
  The main contribution of this paper is to explicitly characterize the power
of a popular nonparametric two sample test, designed for general alternatives,
under a mean-shift alternative in the high-dimensional setting. Specifically,
we explicitly derive the power of the linear-time Maximum Mean Discrepancy
statistic using the Gaussian kernel, where the dimension and sample size can
both tend to infinity at any rate, and the two distributions differ in their
means. As a corollary, we find that if the signal-to-noise ratio is held
constant, then the test's power goes to one if the number of samples increases
faster than the dimension increases. This is the first explicit power
derivation for a general nonparametric test in the high-dimensional setting,
and also the first analysis of how tests designed for general alternatives
perform when faced with easier ones.
Optimal variable selection in multi-group sparse discriminant analysis
Authors: Irina Gaynanova, Mladen Kolar
Date: 2014-11-23 00:00:00
Tasks: Variable Selection

This article considers the problem of multi-group classification in the
setting where the number of variables $p$ is larger than the number of
observations $n$. Several methods have been proposed in the literature that
address this problem, however their variable selection performance is either
unknown or suboptimal to the results known in the two-group case. In this work
we provide sharp conditions for the consistent recovery of relevant variables
in the multi-group case using the discriminant analysis proposal of Gaynanova
et al., 2014. We achieve the rates of convergence that attain the optimal
scaling of the sample size $n$, number of variables $p$ and the sparsity level
$s$. These rates are significantly faster than the best known results in the
multi-group case. Moreover, they coincide with the optimal minimax rates for
the two-group case. We validate our theoretical results with numerical
analysis.
A Convex Formulation for Spectral Shrunk Clustering
Authors: Xiaojun Chang, Feiping Nie, Zhigang Ma, Yi Yang, Xiaofang Zhou
Date: 2014-11-23 00:00:00
Tasks: Clustering, Dimensionality Reduction

Spectral clustering is a fundamental technique in the field of data mining
and information processing. Most existing spectral clustering algorithms
integrate dimensionality reduction into the clustering process assisted by
manifold learning in the original space. However, the manifold in
reduced-dimensional subspace is likely to exhibit altered properties in
contrast with the original space. Thus, applying manifold information obtained
from the original space to the clustering process in a low-dimensional subspace
is prone to inferior performance. Aiming to address this issue, we propose a
novel convex algorithm that mines the manifold structure in the low-dimensional
subspace. In addition, our unified learning process makes the manifold learning
particularly tailored for the clustering. Compared with other related methods,
the proposed algorithm results in more structured clustering result. To
validate the efficacy of the proposed algorithm, we perform extensive
experiments on several benchmark datasets in comparison with some
state-of-the-art clustering approaches. The experimental results demonstrate
that the proposed algorithm has quite promising clustering performance.
Diversifying Sparsity Using Variational Determinantal Point Processes
Authors: Nematollah Kayhan Batmanghelich, Gerald Quon, Alex Kulesza, Manolis Kellis, Polina Golland, Luke Bornn
Date: 2014-11-23 00:00:00
Tasks: Diversity, feature selection, Point Processes

We propose a novel diverse feature selection method based on determinantal
point processes (DPPs). Our model enables one to flexibly define diversity
based on the covariance of features (similar to orthogonal matching pursuit) or
alternatively based on side information. We introduce our approach in the
context of Bayesian sparse regression, employing a DPP as a variational
approximation to the true spike and slab posterior distribution. We
subsequently show how this variational DPP approximation generalizes and
extends mean-field approximation, and can be learned efficiently by exploiting
the fast sampling properties of DPPs. Our motivating application comes from
bioinformatics, where we aim to identify a diverse set of genes whose
expression profiles predict a tumor type where the diversity is defined with
respect to a gene-gene interaction network. We also explore an application in
spatial statistics. In both cases, we demonstrate that the proposed method
yields significantly more diverse feature sets than classic sparse methods,
without compromising accuracy.
Revenue Optimization in Posted-Price Auctions with Strategic Buyers
Authors: Mehryar Mohri, Andres Muñoz Medina
Date: 2014-11-23 00:00:00

We study revenue optimization learning algorithms for posted-price auctions
with strategic buyers. We analyze a very broad family of monotone regret
minimization algorithms for this problem, which includes the previously best
known algorithm, and show that no algorithm in that family admits a strategic
regret more favorable than $\Omega(\sqrt{T})$. We then introduce a new
algorithm that achieves a strategic regret differing from the lower bound only
by a factor in $O(\log T)$, an exponential improvement upon the previous best
algorithm. Our new algorithm admits a natural analysis and simpler proofs, and
the ideas behind its design are general. We also report the results of
empirical evaluations comparing our algorithm with the previous state of the
art and show a consistent exponential improvement in several different
scenarios.
Discrete Bayesian Networks: The Exact Posterior Marginal Distributions
Authors: Do Le Paul Minh
Date: 2014-11-23 00:00:00

In a Bayesian network, we wish to evaluate the marginal probability of a
query variable, which may be conditioned on the observed values of some
evidence variables. Here we first present our "border algorithm," which
converts a BN into a directed chain. For the polytrees, we then present in
details, with some modifications and within the border algorithm framework, the
"revised polytree algorithm" by Peot & Shachter (1991). Finally, we present our
"parentless polytree method," which, coupled with the border algorithm,
converts any Bayesian network into a polytree, rendering the complexity of our
inferences independent of the size of network, and linear with the number of
its evidence and query variables. All quantities in this paper have
probabilistic interpretations.
Target Fishing: A Single-Label or Multi-Label Problem?
Authors: Avid M. Afzal, Hamse Y. Mussa, Richard E. Turner, Andreas Bender, Robert C. Glen
Date: 2014-11-23 00:00:00
Tasks: General Classification, Multi-class Classification

According to Cobanoglu et al and Murphy, it is now widely acknowledged that
the single target paradigm (one protein or target, one disease, one drug) that
has been the dominant premise in drug development in the recent past is
untenable. More often than not, a drug-like compound (ligand) can be
promiscuous - that is, it can interact with more than one target protein. In
recent years, in in silico target prediction methods the promiscuity issue has
been approached computationally in different ways. In this study we confine
attention to the so-called ligand-based target prediction machine learning
approaches, commonly referred to as target-fishing. With a few exceptions, the
target-fishing approaches that are currently ubiquitous in cheminformatics
literature can be essentially viewed as single-label multi-classification
schemes; these approaches inherently bank on the single target paradigm
assumption that a ligand can home in on one specific target. In order to
address the ligand promiscuity issue, one might be able to cast target-fishing
as a multi-label multi-class classification problem. For illustrative and
comparison purposes, single-label and multi-label Naive Bayes classification
models (denoted here by SMM and MMM, respectively) for target-fishing were
implemented. The models were constructed and tested on 65,587 compounds and 308
targets retrieved from the ChEMBL17 database. SMM and MMM performed
differently: for 16,344 test compounds, the MMM model returned recall and
precision values of 0.8058 and 0.6622, respectively; the corresponding recall
and precision values yielded by the SMM model were 0.7805 and 0.7596,
respectively. However, at a significance level of 0.05 and one degree of
freedom McNemar test performed on the target prediction results returned by SMM
and MMM for the 16,344 test ligands gave a chi-squared value of 15.656, in
favour of the MMM approach.
Logics of Temporal-Epistemic Actions
Authors: Bryan Renne, Joshua Sack, Audrey Yap
Date: 2014-11-23 00:00:00

We present Dynamic Epistemic Temporal Logic, a framework for reasoning about
operations on multi-agent Kripke models that contain a designated temporal
relation. These operations are natural extensions of the well-known "action
models" from Dynamic Epistemic Logic. Our "temporal action models" may be used
to define a number of informational actions that can modify the "objective"
temporal structure of a model along with the agents' basic and higher-order
knowledge and beliefs about this structure, including their beliefs about the
time. In essence, this approach provides one way to extend the domain of action
model-style operations from atemporal Kripke models to temporal Kripke models
in a manner that allows actions to control the flow of time. We present a
number of examples to illustrate the subtleties involved in interpreting the
effects of our extended action models on temporal Kripke models. We also study
preservation of important epistemic-temporal properties of temporal Kripke
models under temporal action model-induced operations, provide complete
axiomatizations for two theories of temporal action models, and connect our
approach with previous work on time in Dynamic Epistemic Logic.
Detection of Non-Stationary Photometric Perturbations on Projection Screens
Authors: Miguel Castañeda-Garay, Oscar Belmonte-Fernández, Hebert Pérez-Rosés, Antonio Diaz-Tula
Date: 2014-11-23 00:00:00

Interfaces based on projection screens have become increasingly more popular
in recent years, mainly due to the large screen size and resolution that they
provide, as well as their stereo-vision capabilities. This work shows a local
method for real-time detection of non-stationary photometric perturbations in
projected images by means of computer vision techniques. The method is based on
the computation of differences between the images in the projector's frame
buffer and the corresponding images on the projection screen observed by the
camera. It is robust under spatial variations in the intensity of light emitted
by the projector on the projection surface and also robust under stationary
photometric perturbations caused by external factors. Moreover, we describe the
experiments carried out to show the reliability of the method.
A Boosting Framework on Grounds of Online Learning
Authors: Tofigh Naghibi, Beat Pfister
Date: 2014-09-25 00:00:00
Proceeding: NeurIPS 2014 12

By exploiting the duality between boosting and online learning, we present a
boosting framework which proves to be extremely powerful thanks to employing
the vast knowledge available in the online learning area. Using this framework,
we develop various algorithms to address multiple practically and theoretically
interesting questions including sparse boosting, smooth-distribution boosting,
agnostic learning and some generalization to double-projection online learning
algorithms, as a by-product.
Balanced k-Means and Min-Cut Clustering
Authors: Xiaojun Chang, Feiping Nie, Zhigang Ma, Yi Yang
Date: 2014-11-23 00:00:00
Tasks: Clustering

Clustering is an effective technique in data mining to generate groups that
are the matter of interest. Among various clustering approaches, the family of
k-means algorithms and min-cut algorithms gain most popularity due to their
simplicity and efficacy. The classical k-means algorithm partitions a number of
data points into several subsets by iteratively updating the clustering centers
and the associated data points. By contrast, a weighted undirected graph is
constructed in min-cut algorithms which partition the vertices of the graph
into two sets. However, existing clustering algorithms tend to cluster minority
of data points into a subset, which shall be avoided when the target dataset is
balanced. To achieve more accurate clustering for balanced dataset, we propose
to leverage exclusive lasso on k-means and min-cut to regulate the balance
degree of the clustering results. By optimizing our objective functions that
build atop the exclusive lasso, we can make the clustering result as much
balanced as possible. Extensive experiments on several large-scale datasets
validate the advantage of the proposed algorithms compared to the
state-of-the-art clustering algorithms.
A Convex Sparse PCA for Feature Analysis
Authors: Xiaojun Chang, Feiping Nie, Yi Yang, Heng Huang
Date: 2014-11-23 00:00:00
Tasks: Dimensionality Reduction, feature selection, regression

Principal component analysis (PCA) has been widely applied to dimensionality
reduction and data pre-processing for different applications in engineering,
biology and social science. Classical PCA and its variants seek for linear
projections of the original variables to obtain a low dimensional feature
representation with maximal variance. One limitation is that it is very
difficult to interpret the results of PCA. In addition, the classical PCA is
vulnerable to certain noisy data. In this paper, we propose a convex sparse
principal component analysis (CSPCA) algorithm and apply it to feature
analysis. First we show that PCA can be formulated as a low-rank regression
optimization problem. Based on the discussion, the l 2 , 1 -norm minimization
is incorporated into the objective function to make the regression coefficients
sparse, thereby robust to the outliers. In addition, based on the sparse model
used in CSPCA, an optimal weight is assigned to each of the original feature,
which in turn provides the output with good interpretability. With the output
of our CSPCA, we can effectively analyze the importance of each feature under
the PCA criteria. The objective function is convex, and we propose an iterative
algorithm to optimize it. We apply the CSPCA algorithm to feature selection and
conduct extensive experiments on six different benchmark datasets. Experimental
results demonstrate that the proposed algorithm outperforms state-of-the-art
unsupervised feature selection algorithms.
Low-Rank and Sparse Matrix Decomposition with a-priori knowledge for Dynamic 3D MRI reconstruction
Authors: Dornoosh Zonoobi, Shahrooz Faghih Roohi, Ashraf A. Kassim
Date: 2014-11-23 00:00:00
Tasks: Compressive Sensing, MRI Reconstruction

It has been recently shown that incorporating priori knowledge significantly
improves the performance of basic compressive sensing based approaches. We have
managed to successfully exploit this idea for recovering a matrix as a
summation of a Low-rank and a Sparse component from compressive measurements.
When applied to the problem of construction of 4D Cardiac MR image sequences in
real-time from highly under-sampled $k-$space data, our proposed method
achieves superior reconstruction quality compared to the other state-of-the-art
methods.
Efficient Minimax Signal Detection on Graphs
Authors: Jing Qian, Venkatesh Saligrama
Date: 2014-11-23 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Community Detection

Several problems such as network intrusion, community detection, and disease
outbreak can be described by observations attributed to nodes or edges of a
graph. In these applications presence of intrusion, community or disease
outbreak is characterized by novel observations on some unknown connected
subgraph. These problems can be formulated in terms of optimization of suitable
objectives on connected subgraphs, a problem which is generally computationally
difficult. We overcome the combinatorics of connectivity by embedding connected
subgraphs into linear matrix inequalities (LMI). Computationally efficient
tests are then realized by optimizing convex objective functions subject to
these LMI constraints. We prove, by means of a novel Euclidean embedding
argument, that our tests are minimax optimal for exponential family of
distributions on 1-D and 2-D lattices. We show that internal conductance of the
connected subgraph family plays a fundamental role in characterizing
detectability.
Kickback cuts Backprop's red-tape: Biologically plausible credit assignment in neural networks
Authors: David Balduzzi, Hastagiri Vanchinathan, Joachim Buhmann
Date: 2014-11-23 00:00:00
Tasks: regression

Error backpropagation is an extremely effective algorithm for assigning
credit in artificial neural networks. However, weight updates under Backprop
depend on lengthy recursive computations and require separate output and error
messages -- features not shared by biological neurons, that are perhaps
unnecessary. In this paper, we revisit Backprop and the credit assignment
problem. We first decompose Backprop into a collection of interacting learning
algorithms; provide regret bounds on the performance of these sub-algorithms;
and factorize Backprop's error signals. Using these results, we derive a new
credit assignment algorithm for nonparametric regression, Kickback, that is
significantly simpler than Backprop. Finally, we provide a sufficient condition
for Kickback to follow error gradients, and show that Kickback matches
Backprop's performance on real-world regression benchmarks.
Optimizing the CVaR via Sampling
Authors: Aviv Tamar, Yonatan Glassner, Shie Mannor
Date: 2014-04-15 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Conditional Value at Risk (CVaR) is a prominent risk measure that is being
used extensively in various domains. We develop a new formula for the gradient
of the CVaR in the form of a conditional expectation. Based on this formula, we
propose a novel sampling-based estimator for the CVaR gradient, in the spirit
of the likelihood-ratio method. We analyze the bias of the estimator, and prove
the convergence of a corresponding stochastic gradient descent algorithm to a
local CVaR optimum. Our method allows to consider CVaR optimization in new
domains. As an example, we consider a reinforcement learning application, and
learn a risk-sensitive controller for the game of Tetris.
Incremental Learning of Event Definitions with Inductive Logic Programming
Authors: Nikos Katzouris, Alexander Artikis, George Paliouras
Date: 2014-02-24 00:00:00
Tasks: Activity Recognition, Incremental Learning, Inductive Learning, Inductive logic programming

Event recognition systems rely on properly engineered knowledge bases of
event definitions to infer occurrences of events in time. The manual
development of such knowledge is a tedious and error-prone task, thus
event-based applications may benefit from automated knowledge construction
techniques, such as Inductive Logic Programming (ILP), which combines machine
learning with the declarative and formal semantics of First-Order Logic.
However, learning temporal logical formalisms, which are typically utilized by
logic-based Event Recognition systems is a challenging task, which most ILP
systems cannot fully undertake. In addition, event-based data is usually
massive and collected at different times and under various circumstances.
Ideally, systems that learn from temporal data should be able to operate in an
incremental mode, that is, revise prior constructed knowledge in the face of
new evidence. Most ILP systems are batch learners, in the sense that in order
to account for new evidence they have no alternative but to forget past
knowledge and learn from scratch. Given the increased inherent complexity of
ILP and the volumes of real-life temporal data, this results to algorithms that
scale poorly. In this work we present an incremental method for learning and
revising event-based knowledge, in the form of Event Calculus programs. The
proposed algorithm relies on abductive-inductive learning and comprises a
scalable clause refinement methodology, based on a compressive summarization of
clause coverage in a stream of examples. We present an empirical evaluation of
our approach on real and synthetic data from activity recognition and city
transport applications.