pwc-archive/papers-with-abstracts huggingface dataset:
Differentially Private Empirical Risk Minimization Revisited: Faster and More General
Authors: Di Wang, Minwei Ye, Jinhui Xu
Date: 2018-02-14 00:00:00
Proceeding: NeurIPS 2017 12

In this paper we study the differentially private Empirical Risk Minimization
(ERM) problem in different settings. For smooth (strongly) convex loss function
with or without (non)-smooth regularization, we give algorithms that achieve
either optimal or near optimal utility bounds with less gradient complexity
compared with previous work. For ERM with smooth convex loss function in
high-dimensional ($p\gg n$) setting, we give an algorithm which achieves the
upper bound with less gradient complexity than previous ones. At last, we
generalize the expected excess empirical risk from convex loss functions to
non-convex ones satisfying the Polyak-Lojasiewicz condition and give a tighter
upper bound on the utility than the one in \cite{ijcai2017-548}.
Generating Plans that Predict Themselves
Authors: Jaime F. Fisac, Chang Liu, Jessica B. Hamrick, S. Shankar Sastry, J. Karl Hedrick, Thomas L. Griffiths, Anca D. Dragan
Date: 2018-02-14 00:00:00

Collaboration requires coordination, and we coordinate by anticipating our
teammates' future actions and adapting to their plan. In some cases, our
teammates' actions early on can give us a clear idea of what the remainder of
their plan is, i.e. what action sequence we should expect. In others, they
might leave us less confident, or even lead us to the wrong conclusion. Our
goal is for robot actions to fall in the first category: we want to enable
robots to select their actions in such a way that human collaborators can
easily use them to correctly anticipate what will follow. While previous work
has focused on finding initial plans that convey a set goal, here we focus on
finding two portions of a plan such that the initial portion conveys the final
one. We introduce $t$-\ACty{}: a measure that quantifies the accuracy and
confidence with which human observers can predict the remaining robot plan from
the overall task goal and the observed initial $t$ actions in the plan. We
contribute a method for generating $t$-predictable plans: we search for a full
plan that accomplishes the task, but in which the first $t$ actions make it as
easy as possible to infer the remaining ones. The result is often different
from the most efficient plan, in which the initial actions might leave a lot of
ambiguity as to how the task will be completed. Through an online experiment
and an in-person user study with physical robots, we find that our approach
outperforms a traditional efficiency-based planner in objective and subjective
collaboration metrics.
Necessary and Sufficient Null Space Condition for Nuclear Norm Minimization in Low-Rank Matrix Recovery
Authors: Jirong Yi, Weiyu Xu
Date: 2018-02-14 00:00:00
Tasks: Collaborative Filtering

Low-rank matrix recovery has found many applications in science and
engineering such as machine learning, signal processing, collaborative
filtering, system identification, and Euclidean embedding. But the low-rank
matrix recovery problem is an NP hard problem and thus challenging. A commonly
used heuristic approach is the nuclear norm minimization. In [12,14,15], the
authors established the necessary and sufficient null space conditions for
nuclear norm minimization to recover every possible low-rank matrix with rank
at most r (the strong null space condition). In addition, in [12], Oymak et al.
established a null space condition for successful recovery of a given low-rank
matrix (the weak null space condition) using nuclear norm minimization, and
derived the phase transition for the nuclear norm minimization. In this paper,
we show that the weak null space condition in [12] is only a sufficient
condition for successful matrix recovery using nuclear norm minimization, and
is not a necessary condition as claimed in [12]. In this paper, we further give
a weak null space condition for low-rank matrix recovery, which is both
necessary and sufficient for the success of nuclear norm minimization. At the
core of our derivation are an inequality for characterizing the nuclear norms
of block matrices, and the conditions for equality to hold in that inequality.
Imagination-Augmented Agents for Deep Reinforcement Learning
Authors: Théophane Weber, Sébastien Racanière, David P. Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adria Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter Battaglia, Demis Hassabis, David Silver, Daan Wierstra
Date: 2017-07-19 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Deep Reinforcement Learning, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce Imagination-Augmented Agents (I2As), a novel architecture for
deep reinforcement learning combining model-free and model-based aspects. In
contrast to most existing model-based reinforcement learning and planning
methods, which prescribe how a model should be used to arrive at a policy, I2As
learn to interpret predictions from a learned environment model to construct
implicit plans in arbitrary ways, by using the predictions as additional
context in deep policy networks. I2As show improved data efficiency,
performance, and robustness to model misspecification compared to several
baselines.
Who Killed Albert Einstein? From Open Data to Murder Mystery Games
Authors: Gabriella A. B. Barros, Michael Cerny Green, Antonios Liapis, Julian Togelius
Date: 2018-02-14 00:00:00
Tasks: Articles

This paper presents a framework for generating adventure games from open
data. Focusing on the murder mystery type of adventure games, the generator is
able to transform open data from Wikipedia articles, OpenStreetMap and images
from Wikimedia Commons into WikiMysteries. Every WikiMystery game revolves
around the murder of a person with a Wikipedia article and populates the game
with suspects who must be arrested by the player if guilty of the murder or
absolved if innocent. Starting from only one person as the victim, an extensive
generative pipeline finds suspects, their alibis, and paths connecting them
from open data, transforms open data into cities, buildings, non-player
characters, locks and keys and dialog options. The paper describes in detail
each generative step, provides a specific playthrough of one WikiMystery where
Albert Einstein is murdered, and evaluates the outcomes of games generated for
the 100 most influential people of the 20th century.
Directly Estimating the Variance of the λ-Return Using Temporal-Difference Methods
Authors: Craig Sherstan, Brendan Bennett, Kenny Young, Dylan R. Ashley, Adam White, Martha White, Richard S. Sutton
Date: 2018-01-25 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper investigates estimating the variance of a temporal-difference
learning agent's update target. Most reinforcement learning methods use an
estimate of the value function, which captures how good it is for the agent to
be in a particular state and is mathematically expressed as the expected sum of
discounted future rewards (called the return). These values can be
straightforwardly estimated by averaging batches of returns using Monte Carlo
methods. However, if we wish to update the agent's value estimates during
learning--before terminal outcomes are observed--we must use a different
estimation target called the {\lambda}-return, which truncates the return with
the agent's own estimate of the value function. Temporal difference learning
methods estimate the expected {\lambda}-return for each state, allowing these
methods to update online and incrementally, and in most cases achieve better
generalization error and faster learning than Monte Carlo methods. Naturally
one could attempt to estimate higher-order moments of the {\lambda}-return.
This paper is about estimating the variance of the {\lambda}-return. Prior work
has shown that given estimates of the variance of the {\lambda}-return,
learning systems can be constructed to (1) mitigate risk in action selection,
and (2) automatically adapt the parameters of the learning process itself to
improve performance. Unfortunately, existing methods for estimating the
variance of the {\lambda}-return are complex and not well understood
empirically. We contribute a method for estimating the variance of the
{\lambda}-return directly using policy evaluation methods from reinforcement
learning. Our approach is significantly simpler than prior methods that
independently estimate the second moment of the {\lambda}-return. Empirically
our new approach behaves at least as well as existing approaches, but is
generally more robust.
Handwritten character recognition using some (anti)-diagonal structural features
Authors: José Manuel Casas, Nick Inassaridze, Manuel Ladra, Susana Ladra
Date: 2017-07-27 00:00:00

In this paper, we present a methodology for off-line handwritten character
recognition. The proposed methodology relies on a new feature extraction
technique based on structural characteristics, histograms and profiles. As
novelty, we propose the extraction of new eight histograms and four profiles
from the $32\times 32$ matrices that represent the characters, creating
256-dimension feature vectors. These feature vectors are then employed in a
classification step that uses a $k$-means algorithm. We performed experiments
using the NIST database to evaluate our proposal. Namely, the recognition
system was trained using 1000 samples and 64 classes for each symbol and was
tested on 500 samples for each symbol. We obtain promising accuracy results
that vary from 81.74\% to 93.75\%, depending on the difficulty of the character
category, showing better accuracy results than other methods from the state of
the art also based on structural characteristics.
Fully Convolutional Network Ensembles for White Matter Hyperintensities Segmentation in MR Images
Authors: Hongwei Li, Gongfa Jiang, Jian-Guo Zhang, Ruixuan Wang, Zhaolei Wang, Wei-Shi Zheng, Bjoern Menze
Date: 2018-02-14 00:00:00
Tasks: Data Augmentation

White matter hyperintensities (WMH) are commonly found in the brains of
healthy elderly individuals and have been associated with various neurological
and geriatric disorders. In this paper, we present a study using deep fully
convolutional network and ensemble models to automatically detect such WMH
using fluid attenuation inversion recovery (FLAIR) and T1 magnetic resonance
(MR) scans. The algorithm was evaluated and ranked 1 st in the WMH Segmentation
Challenge at MICCAI 2017. In the evaluation stage, the implementation of the
algorithm was submitted to the challenge organizers, who then independently
tested it on a hidden set of 110 cases from 5 scanners. Averaged dice score,
precision and robust Hausdorff distance obtained on held-out test datasets were
80%, 84% and 6.30mm respectively. These were the highest achieved in the
challenge, suggesting the proposed method is the state-of-the-art. In this
paper, we provide detailed descriptions and quantitative analysis on key
components of the system. Furthermore, a study of cross-scanner evaluation is
presented to discuss how the combination of modalities and data augmentation
affect the generalization capability of the system. The adaptability of the
system to different scanners and protocols is also investigated. A quantitative
study is further presented to test the effect of ensemble size. Additionally,
software and models of our method are made publicly available. The
effectiveness and generalization capability of the proposed system show its
potential for real-world clinical practice.
Generative Models for Spear Phishing Posts on Social Media
Authors: John Seymour, Philip Tully
Date: 2018-02-14 00:00:00
Tasks: Clustering, Computer Security, Intrusion Detection, Malware Classification

Historically, machine learning in computer security has prioritized defense:
think intrusion detection systems, malware classification, and botnet traffic
identification. Offense can benefit from data just as well. Social networks,
with their access to extensive personal data, bot-friendly APIs, colloquial
syntax, and prevalence of shortened links, are the perfect venues for spreading
machine-generated malicious content. We aim to discover what capabilities an
adversary might utilize in such a domain. We present a long short-term memory
(LSTM) neural network that learns to socially engineer specific users into
clicking on deceptive URLs. The model is trained with word vector
representations of social media posts, and in order to make a click-through
more likely, it is dynamically seeded with topics extracted from the target's
timeline. We augment the model with clustering to triage high value targets
based on their level of social engagement, and measure success of the LSTM's
phishing expedition using click-rates of IP-tracked links. We achieve state of
the art success rates, tripling those of historic email attack campaigns, and
outperform humans manually performing the same task.
Adversarial Variational Bayes Methods for Tweedie Compound Poisson Mixed Models
Authors: Yaodong Yang, Rui Luo, Yuanyuan Liu
Date: 2017-06-16 00:00:00
Tasks: Variational Inference

The Tweedie Compound Poisson-Gamma model is routinely used for modeling
non-negative continuous data with a discrete probability mass at zero. Mixed
models with random effects account for the covariance structure related to the
grouping hierarchy in the data. An important application of Tweedie mixed
models is pricing the insurance policies, e.g. car insurance. However, the
intractable likelihood function, the unknown variance function, and the
hierarchical structure of mixed effects have presented considerable challenges
for drawing inferences on Tweedie. In this study, we tackle the Bayesian
Tweedie mixed-effects models via variational inference approaches. In
particular, we empower the posterior approximation by implicit models trained
in an adversarial setting. To reduce the variance of gradients, we
reparameterize random effects, and integrate out one local latent variable of
Tweedie. We also employ a flexible hyper prior to ensure the richness of the
approximation. Our method is evaluated on both simulated and real-world data.
Results show that the proposed method has smaller estimation bias on the random
effects compared to traditional inference methods including MCMC; it also
achieves a state-of-the-art predictive performance, meanwhile offering a richer
estimation of the variance function.
Superposition-Assisted Stochastic Optimization for Hawkes Processes
Authors: Hongteng Xu, Xu Chen, Lawrence Carin
Date: 2018-02-13 00:00:00
Tasks: Diversity, Recommendation Systems, Sequential Recommendation, Stochastic Optimization

We consider the learning of multi-agent Hawkes processes, a model containing
multiple Hawkes processes with shared endogenous impact functions and different
exogenous intensities. In the framework of stochastic maximum likelihood
estimation, we explore the associated risk bound. Further, we consider the
superposition of Hawkes processes within the model, and demonstrate that under
certain conditions such an operation is beneficial for tightening the risk
bound. Accordingly, we propose a stochastic optimization algorithm assisted
with a diversity-driven superposition strategy, achieving better learning
results with improved convergence properties. The effectiveness of the proposed
method is verified on synthetic data, and its potential to solve the cold-start
problem of sequential recommendation systems is demonstrated on real-world
data.
Deep CNNs for HEp-2 Cells Classification : A Cross-specimen Analysis
Authors: Hongwei Li, Wei-Shi Zheng, JianGuo Zhang
Date: 2016-04-20 00:00:00
Tasks: Classification, General Classification

Automatic classification of Human Epithelial Type-2 (HEp-2) cells staining
patterns is an important and yet a challenging problem. Although both shallow
and deep methods have been applied, the study of deep convolutional networks
(CNNs) on this topic is shallow to date, thus failed to claim its top position
for this problem. In this paper, we propose a novel study of using CNNs for
HEp-2 cells classification focusing on cross-specimen analysis, a key
evaluation for generalization. For the first time, our study reveals several
key factors of using CNNs for HEp-2 cells classification. Our proposed system
achieves state-of-the-art classification accuracy on public benchmark dataset.
Comparative experiments on different training data reveals that adding
different specimens,rather than increasing in numbers by affine
transformations, helps to train a good deep model. This opens a new avenue for
adopting deep CNNs to HEp-2 cells classification.
A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets
Authors: Srikrishna Karanam, Mengran Gou, Ziyan Wu, Angels Rates-Borras, Octavia Camps, Richard J. Radke
Date: 2016-05-31 00:00:00
Tasks: Metric Learning, Person Re-Identification

Person re-identification (re-id) is a critical problem in video analytics
applications such as security and surveillance. The public release of several
datasets and code for vision algorithms has facilitated rapid progress in this
area over the last few years. However, directly comparing re-id algorithms
reported in the literature has become difficult since a wide variety of
features, experimental protocols, and evaluation metrics are employed. In order
to address this need, we present an extensive review and performance evaluation
of single- and multi-shot re-id algorithms. The experimental protocol
incorporates the most recent advances in both feature extraction and metric
learning. To ensure a fair comparison, all of the approaches were implemented
using a unified code library that includes 11 feature extraction algorithms and
22 metric learning and ranking techniques. All approaches were evaluated using
a new large-scale dataset that closely mimics a real-world problem setting, in
addition to 16 other publicly available datasets: VIPeR, GRID, CAVIAR,
DukeMTMC4ReID, 3DPeS, PRID, V47, WARD, SAIVT-SoftBio, CUHK01, CHUK02, CUHK03,
RAiD, iLIDSVID, HDA+ and Market1501. The evaluation codebase and results will
be made publicly available for community use.
Benefits from Superposed Hawkes Processes
Authors: Hongteng Xu, Dixin Luo, Xu Chen, Lawrence Carin
Date: 2017-10-14 00:00:00
Tasks: Point Processes, Recommendation Systems

The superposition of temporal point processes has been studied for many
years, although the usefulness of such models for practical applications has
not be fully developed. We investigate superposed Hawkes process as an
important class of such models, with properties studied in the framework of
least squares estimation. The superposition of Hawkes processes is demonstrated
to be beneficial for tightening the upper bound of excess risk under certain
conditions, and we show the feasibility of the benefit in typical situations.
The usefulness of superposed Hawkes processes is verified on synthetic data,
and its potential to solve the cold-start problem of recommendation systems is
demonstrated on real-world data.
On the Blindspots of Convolutional Networks
Authors: Elad Hoffer, Shai Fine, Daniel Soudry
Date: 2018-02-14 00:00:00

Deep convolutional network has been the state-of-the-art approach for a wide
variety of tasks over the last few years. Its successes have, in many cases,
turned it into the default model in quite a few domains. In this work, we will
demonstrate that convolutional networks have limitations that may, in some
cases, hinder it from learning properties of the data, which are easily
recognizable by traditional, less demanding, models. To this end, we present a
series of competitive analysis studies on image recognition and text analysis
tasks, for which convolutional networks are known to provide state-of-the-art
results. In our studies, we inject a truth-revealing signal, indiscernible for
the network, thus hitting time and again the network's blind spots. The signal
does not impair the network's existing performances, but it does provide an
opportunity for a significant performance boost by models that can capture it.
The various forms of the carefully designed signals shed a light on the
strengths and weaknesses of convolutional network, which may provide insights
for both theoreticians that study the power of deep architectures, and for
practitioners that consider applying convolutional networks to the task at
hand.
Theoretical Foundations of Forward Feature Selection Methods based on Mutual Information
Authors: Francisco Macedo, M. Rosário Oliveira, António Pacheco, Rui Valadas
Date: 2017-01-26 00:00:00
Tasks: Face Recognition, feature selection, General Classification, image-classification, Image Classification, Multi-Label Learning, Text Categorization

Feature selection problems arise in a variety of applications, such as
microarray analysis, clinical prediction, text categorization, image
classification and face recognition, multi-label learning, and classification
of internet traffic. Among the various classes of methods, forward feature
selection methods based on mutual information have become very popular and are
widely used in practice. However, comparative evaluations of these methods have
been limited by being based on specific datasets and classifiers. In this
paper, we develop a theoretical framework that allows evaluating the methods
based on their theoretical properties. Our framework is grounded on the
properties of the target objective function that the methods try to
approximate, and on a novel categorization of features, according to their
contribution to the explanation of the class; we derive upper and lower bounds
for the target objective function and relate these bounds with the feature
types. Then, we characterize the types of approximations taken by the methods,
and analyze how these approximations cope with the good properties of the
target objective function. Additionally, we develop a distributional setting
designed to illustrate the various deficiencies of the methods, and provide
several examples of wrong feature selections. Based on our work, we identify
clearly the methods that should be avoided, and the methods that currently have
the best performance.
On Fast Convergence of Proximal Algorithms for SQRT-Lasso Optimization: Don't Worry About Its Nonsmooth Loss Function
Authors: Xingguo Li, Haoming Jiang, Jarvis Haupt, Raman Arora, Han Liu, Mingyi Hong, Tuo Zhao
Date: 2016-05-25 00:00:00
Tasks: regression

Many machine learning techniques sacrifice convenient computational
structures to gain estimation robustness and modeling flexibility. However, by
exploring the modeling structures, we find these "sacrifices" do not always
require more computational efforts. To shed light on such a "free-lunch"
phenomenon, we study the square-root-Lasso (SQRT-Lasso) type regression
problem. Specifically, we show that the nonsmooth loss functions of SQRT-Lasso
type regression ease tuning effort and gain adaptivity to inhomogeneous noise,
but is not necessarily more challenging than Lasso in computation. We can
directly apply proximal algorithms (e.g. proximal gradient descent, proximal
Newton, and proximal Quasi-Newton algorithms) without worrying the
nonsmoothness of the loss function. Theoretically, we prove that the proximal
algorithms combined with the pathwise optimization scheme enjoy fast
convergence guarantees with high probability. Numerical results are provided to
support our theory.