Fix PDF wrap hyphens in _posts abstracts

_posts/2018-08-06-adams18a.md

@@ -1,14 +1,14 @@
 ---
 abstract: This paper considers the problem of learning time series segmentation models
-  when the la- beled data are subject to temporal uncertainty or noise. Our approach
+  when the labeled data are subject to temporal uncertainty or noise. Our approach
   augments the semi- Markov conditional random field (semi-CRF) model with a probabilistic
   model of the label observation process. This augmentation allows us to estimate
   the parameters of the semi-CRF from timestamps corresponding roughly to the occurrence
   of transitions between segments. We show how exact marginal inference can be performed
-  in the augmented model in polyno- mial time, enabling learning based on marginal
+  in the augmented model in polynomial time, enabling learning based on marginal
   likelihood maximization. Our experiments on two activity detection problems show
-  that the proposed approach can learn models from tem- porally imprecise labels,
-  and can successfully refine imprecise segmentations through poste- rior inference.
+  that the proposed approach can learn models from temporally imprecise labels,
+  and can successfully refine imprecise segmentations through posterior inference.
   Finally, we show how inference complexity can be reduced by a factor of 40 using
   static and model-based pruning of the inference dynamic program.
 title: Learning Time Series Segmentation Models from Temporally Imprecise Labels

_posts/2018-08-06-aditya18a.md

@@ -1,20 +1,19 @@
 ---
-abstract: The uncertainty associated with human per- ception is often reduced by one’s
+abstract: The uncertainty associated with human perception is often reduced by one’s
   extensive prior experience and knowledge. Current datasets and systems do not emphasize
-  the ne- cessity and benefit of using such knowledge. In this work, we propose the
+  the necessity and benefit of using such knowledge. In this work, we propose the
   task of solving a genre of image-puzzles (“image riddles”) that require both capabilities
-  involving visual de- tection (including object, activity recognition) and, knowledge-based
-  or commonsense rea- soning. Each puzzle involves a set of images and the question
+  involving visual detection (including object, activity recognition) and, knowledge-based
+  or commonsense reasoning. Each puzzle involves a set of images and the question
   “what word connects these images?”. We compile a dataset of over 3k riddles where
-  each riddle consists of 4 im- ages and a groundtruth answer. The annota- tions are
-  validated using crowd-sourced eval- uation. We also define an automatic evalua-
-  tion metric to track future progress. Our task bears similarity with the commonly
-  known IQ tasks such as analogy solving, sequence fill- ing that are often used to
-  test intelligence. We develop a Probabilistic Reasoning-based ap- proach that utilizes
-  commonsense knowledge about words and phrases to answer these rid- dles with a reasonable
-  accuracy. Our approach achieves some promising results for these rid- dles and provides
-  a strong baseline for future attempts. We make the entire dataset and re- lated
-  materials publicly available to the com- munity (bit.ly/22f9Ala).
+  each riddle consists of 4 images and a groundtruth answer. The annotations are
+  validated using crowd-sourced evaluation. We also define an automatic evaluation metric to track future progress. Our task bears similarity with the commonly
+  known IQ tasks such as analogy solving, sequence filling that are often used to
+  test intelligence. We develop a Probabilistic Reasoning-based approach that utilizes
+  commonsense knowledge about words and phrases to answer these riddles with a reasonable
+  accuracy. Our approach achieves some promising results for these riddles and provides
+  a strong baseline for future attempts. We make the entire dataset and related
+  materials publicly available to the community (bit.ly/22f9Ala).
 title: Combining Knowledge and Reasoning through Probabilistic Soft Logic for Image
   Puzzle Solving
 year: '2018'

_posts/2018-08-06-agrawal18a.md

@@ -1,19 +1,19 @@
 ---
 abstract: Decentralized planning under uncertainty for agent teams is a problem of
-  interest in many domains including (but not limited to) disas- ter rescue, sensor
-  networks and security pa- trolling. Decentralized MDPs, Dec-MDPs have traditionally
-  been used to represent such decen- tralized planning under uncertainty problems.
+  interest in many domains including (but not limited to) disaster rescue, sensor
+  networks and security patrolling. Decentralized MDPs, Dec-MDPs have traditionally
+  been used to represent such decentralized planning under uncertainty problems.
   However, in many domains, agents may not be dedicated to the team for the entire
-  time horizon. For instance, due to limited availabil- ity of resources, it is quite
+  time horizon. For instance, due to limited availability of resources, it is quite
   common for police personnel leaving patrolling teams to attend to accidents. Such
   non-dedication can arise due to the emergence of higher priority tasks or damage
   to existing agents. However, there is very limited literature dealing with handling
   of non-dedication in decentralized settings. To that end, we provide a general model
-  to rep- resent problems dealing with cooperative and decentralized planning for
+  to represent problems dealing with cooperative and decentralized planning for
   non-dedicated agent teams. We also provide two greedy approaches (an offline one
   and an offline-online one) that are able to deal with agents leaving the team in
   an effective and efficient way by exploiting the submodularity property. Finally,
-  we demon- strate that our approaches are able to obtain more than 90% of optimal
+  we demonstrate that our approaches are able to obtain more than 90% of optimal
   solution quality on benchmark problems from the literature.
 title: Decentralized Planning for Non-dedicated Agent Teams with Submodular Rewards
   in Uncertain Environments

_posts/2018-08-06-atzmon18a.md

@@ -1,18 +1,18 @@
 ---
 abstract: 'In zero-shot learning (ZSL), a classifier is trained to recognize visual
-  classes without any image samples. Instead, it is given seman- tic information about
+  classes without any image samples. Instead, it is given semantic information about
   the class, like a textual description or a set of attributes. Learning from attributes
-  could benefit from explicitly modeling structure of the attribute space. Un- fortunately,
+  could benefit from explicitly modeling structure of the attribute space. Unfortunately,
   learning of general structure from empirical samples is hard with typical dataset
   sizes. Here we describe LAGO1, a probabilistic model designed to capture natural
-  soft and- or relations across groups of attributes. We show how this model can be
-  learned end-to- end with a deep attribute-detection model. The soft group structure
-  can be learned from data jointly as part of the model, and can also read- ily incorporate
-  prior knowledge about groups if available. The soft and-or structure suc- ceeds
+  soft and or relations across groups of attributes. We show how this model can be
+  learned end-to-end with a deep attribute-detection model. The soft group structure
+  can be learned from data jointly as part of the model, and can also readily incorporate
+  prior knowledge about groups if available. The soft and-or structure succeeds
   to capture meaningful and predictive structures, improving the accuracy of zero-shot
   learning on two of three benchmarks. Finally, LAGO reveals a unified formulation
   over two ZSL approaches: DAP (Lampert et al., 2009) and ESZSL (Romera-Paredes &
-  Torr, 2015). Interestingly, taking only one sin- gleton group for each attribute,
+  Torr, 2015). Interestingly, taking only one singleton group for each attribute,
   introduces a new soft-relaxation of DAP, that outperforms DAP by $\tilde$40%.'
 title: Probabilistic AND-OR Attribute Grouping for Zero-Shot Learning
 year: '2018'

_posts/2018-08-06-babichev18a.md

@@ -1,9 +1,9 @@
 ---
 abstract: Stochastic gradient methods enable learning probabilistic models from large
   amounts of data. While large step-sizes (learning rates) have shown to be best for
-  least-squares (e.g., Gaussian noise) once combined with param- eter averaging, these
-  are not leading to con- vergent algorithms in general. In this pa- per, we consider
-  generalized linear models, that is, conditional models based on exponen- tial families.
+  least-squares (e.g., Gaussian noise) once combined with parameter averaging, these
+  are not leading to convergent algorithms in general. In this paper, we consider
+  generalized linear models, that is, conditional models based on exponential families.
   We propose averaging moment parameters instead of natural parameters for constant-step-size
   stochastic gradient descent. For finite-dimensional models, we show that this can
   sometimes (and surprisingly) lead to better predictions than the best linear model.

_posts/2018-08-06-berg18a.md

@@ -1,10 +1,10 @@
 ---
-abstract: Variational inference relies on flexible ap- proximate posterior distributions.
-  Normaliz- ing flows provide a general recipe to con- struct flexible variational
-  posteriors. We in- troduce Sylvester normalizing flows, which can be seen as a generalization
+abstract: Variational inference relies on flexible approximate posterior distributions.
+  Normalizing flows provide a general recipe to construct flexible variational
+  posteriors. We introduce Sylvester normalizing flows, which can be seen as a generalization
   of planar flows. Sylvester normalizing flows remove the well-known single-unit bottleneck
   from planar flows, making a single transformation much more flexible. We compare
-  the performance of Sylvester normalizing flows against pla- nar flows and inverse
+  the performance of Sylvester normalizing flows against planar flows and inverse
   autoregressive flows and demonstrate that they compare favorably on several datasets.
 title: Sylvester Normalizing Flows for Variational Inference
 year: '2018'

_posts/2018-08-06-bixler18a.md

@@ -1,12 +1,12 @@
 ---
 abstract: We propose sparse-matrix belief propagation, which executes loopy belief
-  propagation in pair- wise Markov random fields by replacing in- dexing over graph
-  neighborhoods with sparse- matrix operations. This abstraction allows for seamless
-  integration with optimized sparse lin- ear algebra libraries, including those that
-  per- form matrix and tensor operations on modern hardware such as graphical processing
+  propagation in pairwise Markov random fields by replacing indexing over graph
+  neighborhoods with sparse-matrix operations. This abstraction allows for seamless
+  integration with optimized sparse linear algebra libraries, including those that
+  per-form matrix and tensor operations on modern hardware such as graphical processing
   units (GPUs). The sparse-matrix abstraction allows the implementation of belief
   propagation in a high-level language (e.g., Python) that is also able to leverage
-  the power of GPU paralleliza- tion. We demonstrate sparse-matrix belief prop- agation
+  the power of GPU parallelization. We demonstrate sparse-matrix belief propagation
   by implementing it in a modern deep learning framework (PyTorch), measuring the
   resulting massive improvement in running time, and facilitating future integration
   into deep learning models.

_posts/2018-08-06-bloem-reddy18a.md

@@ -1,17 +1,17 @@
 ---
 abstract: Empirical evidence suggests that heavy-tailed degree distributions occurring
-  in many real net- works are well-approximated by power laws with exponents $\eta$
+  in many real networks are well-approximated by power laws with exponents $\eta$
   that may take values either less than and greater than two. Models based on various
   forms of exchangeability are able to capture power laws with $\eta$ < 2, and admit
-  tractable inference algorithms; we draw on pre- vious results to show that $\eta$
-  > 2 cannot be gen- erated by the forms of exchangeability used in existing random
-  graph models. Preferential at- tachment models generate power law exponents greater
+  tractable inference algorithms; we draw on previous results to show that $\eta$
+  > 2 cannot be generated by the forms of exchangeability used in existing random
+  graph models. Preferential attachment models generate power law exponents greater
   than two, but have been of limited use as statistical models due to the inherent
   difficulty of performing inference in non-exchangeable models. Motivated by this
   gap, we design and implement inference algorithms for a recently proposed class
   of models that generates $\eta$ of all possible values. We show that although they
-  are not exchangeable, these models have prob- abilistic structure amenable to inference.
-  Our methods make a large class of previously in- tractable models useful for statistical
+  are not exchangeable, these models have probabilistic structure amenable to inference.
+  Our methods make a large class of previously intractable models useful for statistical
   inference.
 title: Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks
 year: '2018'

_posts/2018-08-06-blom18a.md

@@ -1,10 +1,10 @@
 ---
-abstract: Causal discovery algorithms infer causal re- lations from data based on
-  several assump- tions, including notably the absence of mea- surement error. However,
+abstract: Causal discovery algorithms infer causal relations from data based on
+  several assumptions, including notably the absence of measurement error. However,
   this assumption is most likely violated in practical applications, which may result
   in erroneous, irreproducible results. In this work we show how to obtain an upper
-  bound for the variance of random mea- surement error from the covariance matrix
-  of measured variables and how to use this up- per bound as a correction for constraint-based
+  bound for the variance of random measurement error from the covariance matrix
+  of measured variables and how to use this upper bound as a correction for constraint-based
   causal discovery. We demonstrate a practical application of our approach on both
   simulated data and real-world protein signaling data.
 title: Causal Discovery in the Presence of Measurement Error

_posts/2018-08-06-broka18a.md

@@ -1,13 +1,13 @@
 ---
-abstract: We present a new sampling scheme for approx- imating hard to compute queries
-  over graphical models, such as computing the partition func- tion. The scheme builds
+abstract: We present a new sampling scheme for approximating hard to compute queries
+  over graphical models, such as computing the partition function. The scheme builds
   upon exact algorithms that traverse a weighted directed state-space graph representing
   a global function over a graphical model (e.g., probability distribution). With
-  the aid of an abstraction function and ran- domization, the state space can be compacted
-  (or trimmed) to facilitate tractable computa- tion, yielding a Monte Carlo Estimate
+  the aid of an abstraction function and randomization, the state space can be compacted
+  (or trimmed) to facilitate tractable computation, yielding a Monte Carlo Estimate
   that is unbiased. We present the general scheme and analyze its properties analytically
-  and empiri- cally, investigating two specific ideas for pick- ing abstractions -
-  targeting reduction of vari- ance or search space size.
+  and empirically, investigating two specific ideas for picking abstractions -
+  targeting reduction of variance or search space size.
 title: Abstraction Sampling in Graphical Models
 year: '2018'
 layout: inproceedings

_posts/2018-08-06-callaway18a.md

@@ -1,16 +1,16 @@
 ---
-abstract: 'The efficient use of limited computational re- sources is an essential
-  ingredient of intel- ligence. Selecting computations optimally according to rational
-  metareasoning would achieve this, but this is computationally in- tractable. Inspired
-  by psychology and neu- roscience, we propose the first concrete and domain-general
-  learning algorithm for approx- imating the optimal selection of computations: Bayesian
+abstract: 'The efficient use of limited computational resources is an essential
+  ingredient of intelligence. Selecting computations optimally according to rational
+  metareasoning would achieve this, but this is computationally intractable. Inspired
+  by psychology and neuroscience, we propose the first concrete and domain-general
+  learning algorithm for approximating the optimal selection of computations: Bayesian
   metalevel policy search (BMPS). We derive this general, sample-efficient search
-  al- gorithm for a computation-selecting metalevel policy based on the insight that
+  algorithm for a computation-selecting metalevel policy based on the insight that
   the value of information lies between the myopic value of information and the value
-  of perfect in- formation. We evaluate BMPS on three in- creasingly difficult metareasoning
-  problems: when to terminate computation, how to allo- cate computation between competing
+  of perfect information. We evaluate BMPS on three increasingly difficult metareasoning
+  problems: when to terminate computation, how to allocate computation between competing
   options, and planning. Across all three domains, BMPS achieved near-optimal performance
-  and com- pared favorably to previously proposed metar- easoning heuristics. Finally,
+  and compared favorably to previously proposed metareasoning heuristics. Finally,
   we demonstrate the practical utility of BMPS in an emergency management scenario,
   even accounting for the overhead of metareasoning.'
 title: Learning to select computations

_posts/2018-08-06-chaudhuri18a.md

@@ -1,10 +1,10 @@
 ---
-abstract: 'The stochastic multi-armed bandit is a well- studied abstraction of decision
+abstract: 'The stochastic multi-armed bandit is a well studied abstraction of decision
   making in the face of uncertainty. We consider the setting in which the number of
   bandit arms is much larger than the possible number of pulls, and can even be infinite.
   With the aim of minimising regret with respect to an optimal arm, existing methods
-  for this set- ting either assume some structure over the set of arms (Kleinberg
-  et al., 2008, Ray Chowdhury and Gopalan, 2017), or some property of the re- ward
+  for this setting either assume some structure over the set of arms (Kleinberg
+  et al., 2008, Ray Chowdhury and Gopalan, 2017), or some property of the reward
   distribution (Wang et al., 2008). Invariably, the validity of such assumptions—and
   therefore the performance of the corresponding methods— depends on instance-specific
   parameters, which might not be known beforehand. We propose a conceptually simple,
@@ -14,7 +14,7 @@ abstract: 'The stochastic multi-armed bandit is a well- studied abstraction of d
   that achieves sublinear “quantile-regret”, both (1) when it is specified a quantile,
   and (2) when the quantile can be any (unknown) positive value. The algorithm needs
   no side information about the arms or about the structure of their reward distributions:
-  it re- lies on random sampling to reach arms in the top quantile. Experiments show
+  it relies on random sampling to reach arms in the top quantile. Experiments show
   that our algorithm outperforms several previous methods (in terms of conventional
   regret) when the latter are not tuned well, and often even when they are.'
 title: Quantile-Regret Minimisation in Infinitely Many-Armed Bandits