pwc-archive/papers-with-abstracts huggingface dataset:
FLASH: Fast Bayesian Optimization for Data Analytic Pipelines
Authors: Yuyu Zhang, Mohammad Taha Bahadori, Hang Su, Jimeng Sun
Date: 2016-02-20 00:00:00
Tasks: Bayesian Optimization

Modern data science relies on data analytic pipelines to organize
interdependent computational steps. Such analytic pipelines often involve
different algorithms across multiple steps, each with its own hyperparameters.
To achieve the best performance, it is often critical to select optimal
algorithms and to set appropriate hyperparameters, which requires large
computational efforts. Bayesian optimization provides a principled way for
searching optimal hyperparameters for a single algorithm. However, many
challenges remain in solving pipeline optimization problems with
high-dimensional and highly conditional search space. In this work, we propose
Fast LineAr SearcH (FLASH), an efficient method for tuning analytic pipelines.
FLASH is a two-layer Bayesian optimization framework, which firstly uses a
parametric model to select promising algorithms, then computes a nonparametric
model to fine-tune hyperparameters of the promising algorithms. FLASH also
includes an effective caching algorithm which can further accelerate the search
process. Extensive experiments on a number of benchmark datasets have
demonstrated that FLASH significantly outperforms previous state-of-the-art
methods in both search speed and accuracy. Using 50% of the time budget, FLASH
achieves up to 20% improvement on test error rate compared to the baselines.
FLASH also yields state-of-the-art performance on a real-world application for
healthcare predictive modeling.
Translucent Players: Explaining Cooperative Behavior in Social Dilemmas
Authors: Valerio Capraro, Joseph Y. Halpern
Date: 2016-06-24 00:00:00

In the last few decades, numerous experiments have shown that humans do not
always behave so as to maximize their material payoff. Cooperative behavior
when non-cooperation is a dominant strategy (with respect to the material
payoffs) is particularly puzzling. Here we propose a novel approach to explain
cooperation, assuming what Halpern and Pass call translucent players.
Typically, players are assumed to be opaque, in the sense that a deviation by
one player in a normal-form game does not affect the strategies used by other
players. But a player may believe that if he switches from one strategy to
another, the fact that he chooses to switch may be visible to the other
players. For example, if he chooses to defect in Prisoner's Dilemma, the other
player may sense his guilt. We show that by assuming translucent players, we
can recover many of the regularities observed in human behavior in well-studied
games such as Prisoner's Dilemma, Traveler's Dilemma, Bertrand Competition, and
the Public Goods game.
The optimality of coarse categories in decision-making and information storage
Authors: Michael Mandler
Date: 2016-06-24 00:00:00
Tasks: Decision Making

An agent who lacks preferences and instead makes decisions using criteria
that are costly to create should select efficient sets of criteria, where the
cost of making a given number of choice distinctions is minimized. Under mild
conditions, efficiency requires that binary criteria with only two categories
per criterion are chosen. When applied to the problem of determining the
optimal number of digits in an information storage device, this result implies
that binary digits (bits) are the efficient solution, even when the marginal
cost of using additional digits declines rapidly to 0. This short paper pays
particular attention to the symmetry conditions entailed when sets of criteria
are efficient.
A Dynamic Epistemic Framework for Conformant Planning
Authors: Quan Yu, Yanjun Li, Yanjing Wang
Date: 2016-06-24 00:00:00

In this paper, we introduce a lightweight dynamic epistemic logical framework
for automated planning under initial uncertainty. We reduce plan verification
and conformant planning to model checking problems of our logic. We show that
the model checking problem of the iteration-free fragment is PSPACE-complete.
By using two non-standard (but equivalent) semantics, we give novel model
checking algorithms to the full language and the iteration-free language.
Parameterized Complexity Results for a Model of Theory of Mind Based on Dynamic Epistemic Logic
Authors: Iris van de Pol, Iris van Rooij, Jakub Szymanik
Date: 2016-06-24 00:00:00

In this paper we introduce a computational-level model of theory of mind
(ToM) based on dynamic epistemic logic (DEL), and we analyze its computational
complexity. The model is a special case of DEL model checking. We provide a
parameterized complexity analysis, considering several aspects of DEL (e.g.,
number of agents, size of preconditions, etc.) as parameters. We show that
model checking for DEL is PSPACE-hard, also when restricted to single-pointed
models and S5 relations, thereby solving an open problem in the literature. Our
approach is aimed at formalizing current intractability claims in the cognitive
science literature regarding computational models of ToM.
Relating Knowledge and Coordinated Action: The Knowledge of Preconditions Principle
Authors: Yoram Moses
Date: 2016-06-24 00:00:00

The Knowledge of Preconditions principle (KoP) is proposed as a widely
applicable connection between knowledge and action in multi-agent systems.
Roughly speaking, it asserts that if some condition is a necessary condition
for performing a given action A, then knowing that this condition holds is also
a necessary condition for performing A. Since the specifications of tasks often
involve necessary conditions for actions, the KoP principle shows that such
specifications induce knowledge preconditions for the actions. Distributed
protocols or multi-agent plans that satisfy the specifications must ensure that
this knowledge be attained, and that it is detected by the agents as a
condition for action. The knowledge of preconditions principle is formalised in
the runs and systems framework, and is proven to hold in a wide class of
settings. Well-known connections between knowledge and coordinated action are
extended and shown to derive directly from the KoP principle: a "common
knowledge of preconditions" principle is established showing that common
knowledge is a necessary condition for performing simultaneous actions, and a
"nested knowledge of preconditions" principle is proven, showing that
coordinating actions to be performed in linear temporal order requires a
corresponding form of nested knowledge.
Preference at First Sight
Authors: Chanjuan Liu
Date: 2016-06-24 00:00:00
Tasks: Decision Making, Sequential Decision Making

We consider decision-making and game scenarios in which an agent is limited
by his/her computational ability to foresee all the available moves towards the
future - that is, we study scenarios with short sight. We focus on how short
sight affects the logical properties of decision making in multi-agent
settings. We start with single-agent sequential decision making (SSDM)
processes, modeling them by a new structure of "preference-sight trees". Using
this model, we first explore the relation between a new natural solution
concept of Sight-Compatible Backward Induction (SCBI) and the histories
produced by classical Backward Induction (BI). In particular, we find necessary
and sufficient conditions for the two analyses to be equivalent. Next, we study
whether larger sight always contributes to better outcomes. Then we develop a
simple logical special-purpose language to formally express some key properties
of our preference-sight models. Lastly, we show how short-sight SSDM scenarios
call for substantial enrichments of existing fixed-point logics that have been
developed for the classical BI solution concept. We also discuss changes in
earlier modal logics expressing "surface reasoning" about best actions in the
presence of short sight. Our analysis may point the way to logical and
computational analysis of more realistic game models.
An Axiomatic Approach to Routing
Authors: Omer Lev, Moshe Tennenholtz, Aviv Zohar
Date: 2016-06-24 00:00:00

Information delivery in a network of agents is a key issue for large, complex
systems that need to do so in a predictable, efficient manner. The delivery of
information in such multi-agent systems is typically implemented through
routing protocols that determine how information flows through the network.
Different routing protocols exist each with its own benefits, but it is
generally unclear which properties can be successfully combined within a given
algorithm. We approach this problem from the axiomatic point of view, i.e., we
try to establish what are the properties we would seek to see in such a system,
and examine the different properties which uniquely define common routing
algorithms used today.
  We examine several desirable properties, such as robustness, which ensures
adding nodes and edges does not change the routing in a radical, unpredictable
ways; and properties that depend on the operating environment, such as an
"economic model", where nodes choose their paths based on the cost they are
charged to pass information to the next node. We proceed to fully characterize
minimal spanning tree, shortest path, and weakest link routing algorithms,
showing a tight set of axioms for each.
Ceteris paribus logic in counterfactual reasoning
Authors: Patrick Girard, Marcus Anthony Triplett
Date: 2016-06-24 00:00:00
Tasks: counterfactual, Counterfactual Reasoning, Decision Making

The semantics for counterfactuals due to David Lewis has been challenged on
the basis of unlikely, or impossible, events. Such events may skew a given
similarity order in favour of those possible worlds which exhibit them. By
updating the relational structure of a model according to a ceteris paribus
clause one forces out, in a natural manner, those possible worlds which do not
satisfy the requirements of the clause. We develop a ceteris paribus logic for
counterfactual reasoning capable of performing such actions, and offer several
alternative (relaxed) interpretations of ceteris paribus. We apply this
framework in a way which allows us to reason counterfactually without having
our similarity order skewed by unlikely events. This continues the
investigation of formal ceteris paribus reasoning, which has previously been
applied to preferences, logics of game forms, and questions in decision-making,
among other areas.
Standard State Space Models of Unawareness (Extended Abstract)
Authors: Peter Fritz, Harvey Lederman
Date: 2016-06-24 00:00:00
Tasks: State Space Models

The impossibility theorem of Dekel, Lipman and Rustichini has been thought to
demonstrate that standard state-space models cannot be used to represent
unawareness. We first show that Dekel, Lipman and Rustichini do not establish
this claim. We then distinguish three notions of awareness, and argue that
although one of them may not be adequately modeled using standard state spaces,
there is no reason to think that standard state spaces cannot provide models of
the other two notions. In fact, standard space models of these forms of
awareness are attractively simple. They allow us to prove completeness and
decidability results with ease, to carry over standard techniques from decision
theory, and to add propositional quantifiers straightforwardly.
On the Solvability of Inductive Problems: A Study in Epistemic Topology
Authors: Alexandru Baltag, Nina Gierasimczuk, Sonja Smets
Date: 2016-06-24 00:00:00

We investigate the issues of inductive problem-solving and learning by
doxastic agents. We provide topological characterizations of solvability and
learnability, and we use them to prove that AGM-style belief revision is
"universal", i.e., that every solvable problem is solvable by AGM conditioning.
Epistemic Protocols for Distributed Gossiping
Authors: Krzysztof R. Apt, Davide Grossi, Wiebe van der Hoek
Date: 2016-06-24 00:00:00

Gossip protocols aim at arriving, by means of point-to-point or group
communications, at a situation in which all the agents know each other's
secrets. We consider distributed gossip protocols which are expressed by means
of epistemic logic. We provide an operational semantics of such protocols and
set up an appropriate framework to argue about their correctness. Then we
analyze specific protocols for complete graphs and for directed rings.
Resolving Distributed Knowledge
Authors: Thomas Ågotnes, Yì N. Wáng
Date: 2016-06-24 00:00:00

Distributed knowledge is the sum of the knowledge in a group; what someone
who is able to discern between two possible worlds whenever any member of the
group can discern between them, would know. Sometimes distributed knowledge is
referred to as the potential knowledge of a group, or the joint knowledge they
could obtain if they had unlimited means of communication. In epistemic logic,
the formula D_G{\phi} is intended to express the fact that group G has
distributed knowledge of {\phi}, that there is enough information in the group
to infer {\phi}. But this is not the same as reasoning about what happens if
the members of the group share their information. In this paper we introduce an
operator R_G, such that R_G{\phi} means that {\phi} is true after G have shared
all their information with each other - after G's distributed knowledge has
been resolved. The R_G operators are called resolution operators. Semantically,
we say that an expression R_G{\phi} is true iff {\phi} is true in what van
Benthem [11, p. 249] calls (G's) communication core; the model update obtained
by removing links to states for members of G that are not linked by all members
of G. We study logics with different combinations of resolution operators and
operators for common and distributed knowledge. Of particular interest is the
relationship between distributed and common knowledge. The main results are
sound and complete axiomatizations.
Human-Agent Decision-making: Combining Theory and Practice
Authors: Sarit Kraus
Date: 2016-06-24 00:00:00
Tasks: Decision Making

Extensive work has been conducted both in game theory and logic to model
strategic interaction. An important question is whether we can use these
theories to design agents for interacting with people? On the one hand, they
provide a formal design specification for agent strategies. On the other hand,
people do not necessarily adhere to playing in accordance with these
strategies, and their behavior is affected by a multitude of social and
psychological factors. In this paper we will consider the question of whether
strategies implied by theories of strategic behavior can be used by automated
agents that interact proficiently with people. We will focus on automated
agents that we built that need to interact with people in two negotiation
settings: bargaining and deliberation. For bargaining we will study game-theory
based equilibrium agents and for argumentation we will discuss logic-based
argumentation theory. We will also consider security games and persuasion games
and will discuss the benefits of using equilibrium based agents.
Disjunctive Normal Level Set: An Efficient Parametric Implicit Method
Authors: Fitsum Mesadi, Mujdat Cetin, Tolga Tasdizen
Date: 2016-06-24 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Level set methods are widely used for image segmentation because of their
capability to handle topological changes. In this paper, we propose a novel
parametric level set method called Disjunctive Normal Level Set (DNLS), and
apply it to both two phase (single object) and multiphase (multi-object) image
segmentations. The DNLS is formed by union of polytopes which themselves are
formed by intersections of half-spaces. The proposed level set framework has
the following major advantages compared to other level set methods available in
the literature. First, segmentation using DNLS converges much faster. Second,
the DNLS level set function remains regular throughout its evolution. Third,
the proposed multiphase version of the DNLS is less sensitive to
initialization, and its computational cost and memory requirement remains
almost constant as the number of objects to be simultaneously segmented grows.
The experimental results show the potential of the proposed method.
Convex Decomposition And Efficient Shape Representation Using Deformable Convex Polytopes
Authors: Fitsum Mesadi, Tolga Tasdizen
Date: 2016-06-23 00:00:00

Decomposition of shapes into (approximate) convex parts is essential for
applications such as part-based shape representation, shape matching, and
collision detection. In this paper, we propose a novel convex decomposition
using a parametric implicit shape model called Disjunctive Normal Shape Model
(DNSM). The DNSM is formed as a union of polytopes which themselves are formed
by intersections of halfspaces. The key idea is by deforming the polytopes,
which naturally remain convex during the evolution, the polytopes capture
convex parts without the need to compute convexity. The major contributions of
this paper include a robust convex decomposition which also results in an
efficient part-based shape representation, and a novel shape convexity measure.
The experimental results show the potential of the proposed method.
Is a Picture Worth Ten Thousand Words in a Review Dataset?
Authors: Roberto Camacho Barranco, Laura M. Rodriguez, Rebecca Urbina, M. Shahriar Hossain
Date: 2016-06-23 00:00:00
Tasks: TAG

While textual reviews have become prominent in many recommendation-based
systems, automated frameworks to provide relevant visual cues against text
reviews where pictures are not available is a new form of task confronted by
data mining and machine learning researchers. Suggestions of pictures that are
relevant to the content of a review could significantly benefit the users by
increasing the effectiveness of a review. We propose a deep learning-based
framework to automatically: (1) tag the images available in a review dataset,
(2) generate a caption for each image that does not have one, and (3) enhance
each review by recommending relevant images that might not be uploaded by the
corresponding reviewer. We evaluate the proposed framework using the Yelp
Challenge Dataset. While a subset of the images in this particular dataset are
correctly captioned, the majority of the pictures do not have any associated
text. Moreover, there is no mapping between reviews and images. Each image has
a corresponding business-tag where the picture was taken, though. The overall
data setting and unavailability of crucial pieces required for a mapping make
the problem of recommending images for reviews a major challenge. Qualitative
and quantitative evaluations indicate that our proposed framework provides high
quality enhancements through automatic captioning, tagging, and recommendation
for mapping reviews and images.
CUNI System for WMT16 Automatic Post-Editing and Multimodal Translation Tasks
Authors: Jindřich Libovický, Jindřich Helcl, Marek Tlustý, Pavel Pecina, Ondřej Bojar
Date: 2016-06-23 00:00:00
Proceeding: WS 2016 8
Tasks: Automatic Post-Editing, Machine Translation, Multimodal Machine Translation, Translation

Neural sequence to sequence learning recently became a very promising
paradigm in machine translation, achieving competitive results with statistical
phrase-based systems. In this system description paper, we attempt to utilize
several recently published methods used for neural sequential learning in order
to build systems for WMT 2016 shared tasks of Automatic Post-Editing and
Multimodal Machine Translation.
NN-grams: Unifying neural network and n-gram language models for Speech Recognition
Authors: Babak Damavandi, Shankar Kumar, Noam Shazeer, Antoine Bruguier
Date: 2016-06-23 00:00:00
Tasks: Binary Classification, Language Modeling, Language Modelling, Memorization, speech-recognition, Speech Recognition

We present NN-grams, a novel, hybrid language model integrating n-grams and
neural networks (NN) for speech recognition. The model takes as input both word
histories as well as n-gram counts. Thus, it combines the memorization capacity
and scalability of an n-gram model with the generalization ability of neural
networks. We report experiments where the model is trained on 26B words.
NN-grams are efficient at run-time since they do not include an output soft-max
layer. The model is trained using noise contrastive estimation (NCE), an
approach that transforms the estimation problem of neural networks into one of
binary classification between data samples and noise samples. We present
results with noise samples derived from either an n-gram distribution or from
speech recognition lattices. NN-grams outperforms an n-gram model on an Italian
speech recognition dictation task.
Structured Prediction Energy Networks
Authors: David Belanger, Andrew McCallum
Date: 2015-11-19 00:00:00
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Prediction, Structured Prediction

We introduce structured prediction energy networks (SPENs), a flexible
framework for structured prediction. A deep architecture is used to define an
energy function of candidate labels, and then predictions are produced by using
back-propagation to iteratively optimize the energy with respect to the labels.
This deep architecture captures dependencies between labels that would lead to
intractable graphical models, and performs structure learning by automatically
learning discriminative features of the structured output. One natural
application of our technique is multi-label classification, which traditionally
has required strict prior assumptions about the interactions between labels to
ensure tractable learning and prediction. We are able to apply SPENs to
multi-label problems with substantially larger label sets than previous
applications of structured prediction, while modeling high-order interactions
using minimal structural assumptions. Overall, deep learning provides
remarkable tools for learning features of the inputs to a prediction problem,
and this work extends these techniques to learning features of structured
outputs. Our experiments provide impressive performance on a variety of
benchmark multi-label classification tasks, demonstrate that our technique can
be used to provide interpretable structure learning, and illuminate fundamental
trade-offs between feed-forward and iterative structured prediction.
Crowdsourcing scoring of immunohistochemistry images: Evaluating Performance of the Crowd and an Automated Computational Method
Authors: Humayun Irshad, Eun-Yeong Oh, Daniel Schmolze, Liza M Quintana, Laura Collins, Rulla M. Tamimi, Andrew H. Beck
Date: 2016-06-21 00:00:00
Tasks: Diagnostic

The assessment of protein expression in immunohistochemistry (IHC) images
provides important diagnostic, prognostic and predictive information for
guiding cancer diagnosis and therapy. Manual scoring of IHC images represents a
logistical challenge, as the process is labor intensive and time consuming.
Since the last decade, computational methods have been developed to enable the
application of quantitative methods for the analysis and interpretation of
protein expression in IHC images. These methods have not yet replaced manual
scoring for the assessment of IHC in the majority of diagnostic laboratories
and in many large-scale research studies. An alternative approach is
crowdsourcing the quantification of IHC images to an undefined crowd. The aim
of this study is to quantify IHC images for labeling of ER status with two
different crowdsourcing approaches, image labeling and nuclei labeling, and
compare their performance with automated methods. Crowdsourcing-derived scores
obtained greater concordance with the pathologist interpretations for both
image labeling and nuclei labeling tasks (83% and 87%), as compared to the
pathologist concordance achieved by the automated method (81%) on 5,483 TMA
images from 1,909 breast cancer patients. This analysis shows that
crowdsourcing the scoring of protein expression in IHC images is a promising
new approach for large scale cancer molecular pathology studies.