pwc-archive/papers-with-abstracts huggingface dataset:
AdaNet: Adaptive Structural Learning of Artificial Neural Networks
Authors: Corinna Cortes, Xavi Gonzalvo, Vitaly Kuznetsov, Mehryar Mohri, Scott Yang
Date: 2016-07-05 00:00:00
Proceeding: ICML 2017 8
Tasks: Binary Classification, General Classification

We present new algorithms for adaptively learning artificial neural networks.
Our algorithms (AdaNet) adaptively learn both the structure of the network and
its weights. They are based on a solid theoretical analysis, including
data-dependent generalization guarantees that we prove and discuss in detail.
We report the results of large-scale experiments with one of our algorithms on
several binary classification tasks extracted from the CIFAR-10 dataset. The
results demonstrate that our algorithm can automatically learn network
structures with very competitive performance accuracies when compared with
those achieved for neural networks found by standard approaches.
The Shape of Data and Probability Measures
Authors: Diego Hernán Díaz Martínez, Facundo Mémoli, Washington Mio
Date: 2015-09-15 00:00:00
Tasks: Clustering

We introduce the notion of multiscale covariance tensor fields (CTF)
associated with Euclidean random variables as a gateway to the shape of their
distributions. Multiscale CTFs quantify variation of the data about every point
in the data landscape at all spatial scales, unlike the usual covariance tensor
that only quantifies global variation about the mean. Empirical forms of
localized covariance previously have been used in data analysis and
visualization, but we develop a framework for the systematic treatment of
theoretical questions and computational models based on localized covariance.
We prove strong stability theorems with respect to the Wasserstein distance
between probability measures, obtain consistency results, as well as estimates
for the rate of convergence of empirical CTFs. These results ensure that CTFs
are robust to sampling, noise and outliers. We provide numerous illustrations
of how CTFs let us extract shape from data and also apply CTFs to manifold
clustering, the problem of categorizing data points according to their noisy
membership in a collection of possibly intersecting, smooth submanifolds of
Euclidean space. We prove that the proposed manifold clustering method is
stable and carry out several experiments to validate the method.
Recurrent Batch Normalization
Authors: Tim Cooijmans, Nicolas Ballas, César Laurent, Çağlar Gülçehre, Aaron Courville
Date: 2016-03-30 00:00:00
Tasks: General Classification, Language Modeling, Language Modelling, Question Answering, Reading Comprehension, Sequential Image Classification

We propose a reparameterization of LSTM that brings the benefits of batch
normalization to recurrent neural networks. Whereas previous works only apply
batch normalization to the input-to-hidden transformation of RNNs, we
demonstrate that it is both possible and beneficial to batch-normalize the
hidden-to-hidden transition, thereby reducing internal covariate shift between
time steps. We evaluate our proposal on various sequential problems such as
sequence classification, language modeling and question answering. Our
empirical results show that our batch-normalized LSTM consistently leads to
faster convergence and improved generalization.
Can Boltzmann Machines Discover Cluster Updates ?
Authors: Lei Wang
Date: 2017-02-28 00:00:00
Tasks: Recommendation Systems

Boltzmann machines are physics informed generative models with wide
applications in machine learning. They can learn the probability distribution
from an input dataset and generate new samples accordingly. Applying them back
to physics, the Boltzmann machines are ideal recommender systems to accelerate
Monte Carlo simulation of physical systems due to their flexibility and
effectiveness. More intriguingly, we show that the generative sampling of the
Boltzmann Machines can even discover unknown cluster Monte Carlo algorithms.
The creative power comes from the latent representation of the Boltzmann
machines, which learn to mediate complex interactions and identify clusters of
the physical system. We demonstrate these findings with concrete examples of
the classical Ising model with and without four spin plaquette interactions.
Our results endorse a fresh research paradigm where intelligent machines are
designed to create or inspire human discovery of innovative algorithms.
BLC: Private Matrix Factorization Recommenders via Automatic Group Learning
Authors: Alessandro Checco, Giuseppe Bianchi, Doug Leith
Date: 2015-09-18 00:00:00

We propose a privacy-enhanced matrix factorization recommender that exploits
the fact that users can often be grouped together by interest. This allows a
form of "hiding in the crowd" privacy. We introduce a novel matrix
factorization approach suited to making recommendations in a shared group (or
nym) setting and the BLC algorithm for carrying out this matrix factorization
in a privacy-enhanced manner. We demonstrate that the increased privacy does
not come at the cost of reduced recommendation accuracy.
Transition-based versus State-based Reward Functions for MDPs with Value-at-Risk
Authors: Shuai Ma, Jia Yuan Yu
Date: 2016-12-07 00:00:00
Tasks: Reinforcement Learning

In reinforcement learning, the reward function on current state and action is
widely used. When the objective is about the expectation of the (discounted)
total reward only, it works perfectly. However, if the objective involves the
total reward distribution, the result will be wrong. This paper studies
Value-at-Risk (VaR) problems in short- and long-horizon Markov decision
processes (MDPs) with two reward functions, which share the same expectations.
Firstly we show that with VaR objective, when the real reward function is
transition-based (with respect to action and both current and next states), the
simplified (state-based, with respect to action and current state only) reward
function will change the VaR. Secondly, for long-horizon MDPs, we estimate the
VaR function with the aid of spectral theory and the central limit theorem.
Thirdly, since the estimation method is for a Markov reward process with the
reward function on current state only, we present a transformation algorithm
for the Markov reward process with the reward function on current and next
states, in order to estimate the VaR function with an intact total reward
distribution.
Density estimation using Real NVP
Authors: Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio
Date: 2016-05-27 00:00:00
Tasks: BIG-bench Machine Learning, Density Estimation, Image Generation

Unsupervised learning of probabilistic models is a central yet challenging
problem in machine learning. Specifically, designing models with tractable
learning, sampling, inference and evaluation is crucial in solving this task.
We extend the space of such models using real-valued non-volume preserving
(real NVP) transformations, a set of powerful invertible and learnable
transformations, resulting in an unsupervised learning algorithm with exact
log-likelihood computation, exact sampling, exact inference of latent
variables, and an interpretable latent space. We demonstrate its ability to
model natural images on four datasets through sampling, log-likelihood
evaluation and latent variable manipulations.
eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry Keys
Authors: Joshua Saxe, Konstantin Berlin
Date: 2017-02-27 00:00:00
Tasks: BIG-bench Machine Learning, Intrusion Detection

For years security machine learning research has promised to obviate the need
for signature based detection by automatically learning to detect indicators of
attack. Unfortunately, this vision hasn't come to fruition: in fact, developing
and maintaining today's security machine learning systems can require
engineering resources that are comparable to that of signature-based detection
systems, due in part to the need to develop and continuously tune the
"features" these machine learning systems look at as attacks evolve. Deep
learning, a subfield of machine learning, promises to change this by operating
on raw input signals and automating the process of feature design and
extraction. In this paper we propose the eXpose neural network, which uses a
deep learning approach we have developed to take generic, raw short character
strings as input (a common case for security inputs, which include artifacts
like potentially malicious URLs, file paths, named pipes, named mutexes, and
registry keys), and learns to simultaneously extract features and classify
using character-level embeddings and convolutional neural network. In addition
to completely automating the feature design and extraction process, eXpose
outperforms manual feature extraction based baselines on all of the intrusion
detection problems we tested it on, yielding a 5%-10% detection rate gain at
0.1% false positive rate compared to these baselines.
Optimal Experiment Design for Causal Discovery from Fixed Number of Experiments
Authors: AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash
Date: 2017-02-27 00:00:00
Tasks: Causal Discovery

We study the problem of causal structure learning over a set of random
variables when the experimenter is allowed to perform at most $M$ experiments
in a non-adaptive manner. We consider the optimal learning strategy in terms of
minimizing the portions of the structure that remains unknown given the limited
number of experiments in both Bayesian and minimax setting. We characterize the
theoretical optimal solution and propose an algorithm, which designs the
experiments efficiently in terms of time complexity. We show that for bounded
degree graphs, in the minimax case and in the Bayesian case with uniform
priors, our proposed algorithm is a $\rho$-approximation algorithm, where
$\rho$ is independent of the order of the underlying graph. Simulations on both
synthetic and real data show that the performance of our algorithm is very
close to the optimal solution.
Nearly Maximally Predictive Features and Their Dimensions
Authors: Sarah E. Marzen, James P. Crutchfield
Date: 2017-02-27 00:00:00

Scientific explanation often requires inferring maximally predictive features
from a given data set. Unfortunately, the collection of minimal maximally
predictive features for most stochastic processes is uncountably infinite. In
such cases, one compromises and instead seeks nearly maximally predictive
features. Here, we derive upper-bounds on the rates at which the number and the
coding cost of nearly maximally predictive features scales with desired
predictive power. The rates are determined by the fractal dimensions of a
process' mixed-state distribution. These results, in turn, show how widely-used
finite-order Markov models can fail as predictors and that mixed-state
predictive features offer a substantial improvement.
Multimodal Clustering for Community Detection
Authors: Dmitry I. Ignatov, Alexander Semenov, Daria Komissarova, Dmitry V. Gnatyshak
Date: 2017-02-27 00:00:00
Tasks: Attribute, Clustering, Community Detection

Multimodal clustering is an unsupervised technique for mining interesting
patterns in $n$-adic binary relations or $n$-mode networks. Among different
types of such generalized patterns one can find biclusters and formal concepts
(maximal bicliques) for 2-mode case, triclusters and triconcepts for 3-mode
case, closed $n$-sets for $n$-mode case, etc. Object-attribute biclustering
(OA-biclustering) for mining large binary datatables (formal contexts or 2-mode
networks) arose by the end of the last decade due to intractability of
computation problems related to formal concepts; this type of patterns was
proposed as a meaningful and scalable approximation of formal concepts. In this
paper, our aim is to present recent advance in OA-biclustering and its
extensions to mining multi-mode communities in SNA setting. We also discuss
connection between clustering coefficients known in SNA community for 1-mode
and 2-mode networks and OA-bicluster density, the main quality measure of an
OA-bicluster. Our experiments with 2-, 3-, and 4-mode large real-world networks
show that this type of patterns is suitable for community detection in
multi-mode cases within reasonable time even though the number of corresponding
$n$-cliques is still unknown due to computation difficulties. An interpretation
of OA-biclusters for 1-mode networks is provided as well.
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic
Authors: Shixiang Gu, Timothy Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine
Date: 2016-11-07 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, MuJoCo, Policy Gradient Methods, Q-Learning, Reinforcement Learning, Reinforcement Learning (RL)

Model-free deep reinforcement learning (RL) methods have been successful in a
wide variety of simulated domains. However, a major obstacle facing deep RL in
the real world is their high sample complexity. Batch policy gradient methods
offer stable learning, but at the cost of high variance, which often requires
large batches. TD-style methods, such as off-policy actor-critic and
Q-learning, are more sample-efficient but biased, and often require costly
hyperparameter sweeps to stabilize. In this work, we aim to develop methods
that combine the stability of policy gradients with the efficiency of
off-policy RL. We present Q-Prop, a policy gradient method that uses a Taylor
expansion of the off-policy critic as a control variate. Q-Prop is both sample
efficient and stable, and effectively combines the benefits of on-policy and
off-policy methods. We analyze the connection between Q-Prop and existing
model-free algorithms, and use control variate theory to derive two variants of
Q-Prop with conservative and aggressive adaptation. We show that conservative
Q-Prop provides substantial gains in sample efficiency over trust region policy
optimization (TRPO) with generalized advantage estimation (GAE), and improves
stability over deep deterministic policy gradient (DDPG), the state-of-the-art
on-policy and off-policy methods, on OpenAI Gym's MuJoCo continuous control
environments.
Active Learning Using Uncertainty Information
Authors: Yazhou Yang, Marco Loog
Date: 2017-02-27 00:00:00
Tasks: Active Learning

Many active learning methods belong to the retraining-based approaches, which
select one unlabeled instance, add it to the training set with its possible
labels, retrain the classification model, and evaluate the criteria that we
base our selection on. However, since the true label of the selected instance
is unknown, these methods resort to calculating the average-case or worse-case
performance with respect to the unknown label. In this paper, we propose a
different method to solve this problem. In particular, our method aims to make
use of the uncertainty information to enhance the performance of
retraining-based models. We apply our method to two state-of-the-art algorithms
and carry out extensive experiments on a wide variety of real-world datasets.
The results clearly demonstrate the effectiveness of the proposed method and
indicate it can reduce human labeling efforts in many real-life applications.
Image Analysis Using a Dual-Tree $M$-Band Wavelet Transform
Authors: Caroline Chaux, Laurent Duval, Jean-Christophe Pesquet
Date: 2017-02-27 00:00:00
Tasks: Denoising, Tree Decomposition

We propose a 2D generalization to the $M$-band case of the dual-tree
decomposition structure (initially proposed by N. Kingsbury and further
investigated by I. Selesnick) based on a Hilbert pair of wavelets. We
particularly address (\textit{i}) the construction of the dual basis and
(\textit{ii}) the resulting directional analysis. We also revisit the necessary
pre-processing stage in the $M$-band case. While several reconstructions are
possible because of the redundancy of the representation, we propose a new
optimal signal reconstruction technique, which minimizes potential estimation
errors. The effectiveness of the proposed $M$-band decomposition is
demonstrated via denoising comparisons on several image types (natural,
texture, seismics), with various $M$-band wavelets and thresholding strategies.
Significant improvements in terms of both overall noise reduction and direction
preservation are observed.
Semi-parametric Network Structure Discovery Models
Authors: Amir Dezfouli, Edwin V. Bonilla, Richard Nock
Date: 2017-02-27 00:00:00
Tasks: Uncertainty Quantification, Variational Inference

We propose a network structure discovery model for continuous observations
that generalizes linear causal models by incorporating a Gaussian process (GP)
prior on a network-independent component, and random sparsity and weight
matrices as the network-dependent parameters. This approach provides flexible
modeling of network-independent trends in the observations as well as
uncertainty quantification around the discovered network structure. We
establish a connection between our model and multi-task GPs and develop an
efficient stochastic variational inference algorithm for it. Furthermore, we
formally show that our approach is numerically stable and in fact numerically
easy to carry out almost everywhere on the support of the random variables
involved. Finally, we evaluate our model on three applications, showing that it
outperforms previous approaches. We provide a qualitative and quantitative
analysis of the structures discovered for domains such as the study of the full
genome regulation of the yeast Saccharomyces cerevisiae.
Bootstrap Model Aggregation for Distributed Statistical Learning
Authors: Jun Han, Qiang Liu
Date: 2016-07-04 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: model, Privacy Preserving

In distributed, or privacy-preserving learning, we are often given a set of
probabilistic models estimated from different local repositories, and asked to
combine them into a single model that gives efficient statistical estimation. A
simple method is to linearly average the parameters of the local models, which,
however, tends to be degenerate or not applicable on non-convex models, or
models with different parameter dimensions. One more practical strategy is to
generate bootstrap samples from the local models, and then learn a joint model
based on the combined bootstrap set. Unfortunately, the bootstrap procedure
introduces additional noise and can significantly deteriorate the performance.
In this work, we propose two variance reduction methods to correct the
bootstrap noise, including a weighted M-estimator that is both statistically
efficient and practically powerful. Both theoretical and empirical analysis is
provided to demonstrate our methods.
Deep Class Aware Denoising
Authors: Tal Remez, Or Litany, Raja Giryes, Alex M. Bronstein
Date: 2017-01-06 00:00:00
Tasks: Denoising, Image Denoising, Image Enhancement

The increasing demand for high image quality in mobile devices brings forth
the need for better computational enhancement techniques, and image denoising
in particular. At the same time, the images captured by these devices can be
categorized into a small set of semantic classes. However simple, this
observation has not been exploited in image denoising until now. In this paper,
we demonstrate how the reconstruction quality improves when a denoiser is aware
of the type of content in the image. To this end, we first propose a new fully
convolutional deep neural network architecture which is simple yet powerful as
it achieves state-of-the-art performance even without being class-aware. We
further show that a significant boost in performance of up to $0.4$ dB PSNR can
be achieved by making our network class-aware, namely, by fine-tuning it for
images belonging to a specific semantic class. Relying on the hugely successful
existing image classifiers, this research advocates for using a class-aware
approach in all image enhancement tasks.
A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Examples
Authors: Beilun Wang, Ji Gao, Yanjun Qi
Date: 2016-12-01 00:00:00
Tasks: Representation Learning

Most machine learning classifiers, including deep neural networks, are
vulnerable to adversarial examples. Such inputs are typically generated by
adding small but purposeful modifications that lead to incorrect outputs while
imperceptible to human eyes. The goal of this paper is not to introduce a
single method, but to make theoretical steps towards fully understanding
adversarial examples. By using concepts from topology, our theoretical analysis
brings forth the key reasons why an adversarial example can fool a classifier
($f_1$) and adds its oracle ($f_2$, like human eyes) in such analysis. By
investigating the topological relationship between two (pseudo)metric spaces
corresponding to predictor $f_1$ and oracle $f_2$, we develop necessary and
sufficient conditions that can determine if $f_1$ is always robust
(strong-robust) against adversarial examples according to $f_2$. Interestingly
our theorems indicate that just one unnecessary feature can make $f_1$ not
strong-robust, and the right feature representation learning is the key to
getting a classifier that is both accurate and strong-robust.
Depth Separation for Neural Networks
Authors: Amit Daniely
Date: 2017-02-27 00:00:00

Let $f:\mathbb{S}^{d-1}\times \mathbb{S}^{d-1}\to\mathbb{S}$ be a function of
the form $f(\mathbf{x},\mathbf{x}') = g(\langle\mathbf{x},\mathbf{x}'\rangle)$
for $g:[-1,1]\to \mathbb{R}$. We give a simple proof that shows that poly-size
depth two neural networks with (exponentially) bounded weights cannot
approximate $f$ whenever $g$ cannot be approximated by a low degree polynomial.
Moreover, for many $g$'s, such as $g(x)=\sin(\pi d^3x)$, the number of neurons
must be $2^{\Omega\left(d\log(d)\right)}$. Furthermore, the result holds
w.r.t.\ the uniform distribution on $\mathbb{S}^{d-1}\times \mathbb{S}^{d-1}$.
As many functions of the above form can be well approximated by poly-size depth
three networks with poly-bounded weights, this establishes a separation between
depth two and depth three networks w.r.t.\ the uniform distribution on
$\mathbb{S}^{d-1}\times \mathbb{S}^{d-1}$.
Memory-Efficient Global Refinement of Decision-Tree Ensembles and its Application to Face Alignment
Authors: Nenad Markuš, Ivan Gogić, Igor S. Pandžić, Jörgen Ahlberg
Date: 2017-02-27 00:00:00
Tasks: Face Alignment, Quantization, regression

Ren et al. recently introduced a method for aggregating multiple decision
trees into a strong predictor by interpreting a path taken by a sample down
each tree as a binary vector and performing linear regression on top of these
vectors stacked together. They provided experimental evidence that the method
offers advantages over the usual approaches for combining decision trees
(random forests and boosting). The method truly shines when the regression
target is a large vector with correlated dimensions, such as a 2D face shape
represented with the positions of several facial landmarks. However, we argue
that their basic method is not applicable in many practical scenarios due to
large memory requirements. This paper shows how this issue can be solved
through the use of quantization and architectural changes of the predictor that
maps decision tree-derived encodings to the desired output.