pwc-archive/papers-with-abstracts huggingface dataset:
Minimax-optimal semi-supervised regression on unknown manifolds
Authors: Amit Moscovich, Ariel Jaffe, Boaz Nadler
Date: 2016-11-07 00:00:00
Tasks: Indoor Localization, Pose Estimation, regression

We consider semi-supervised regression when the predictor variables are drawn
from an unknown manifold. A simple two step approach to this problem is to: (i)
estimate the manifold geodesic distance between any pair of points using both
the labeled and unlabeled instances; and (ii) apply a k nearest neighbor
regressor based on these distance estimates. We prove that given sufficiently
many unlabeled points, this simple method of geodesic kNN regression achieves
the optimal finite-sample minimax bound on the mean squared error, as if the
manifold were known. Furthermore, we show how this approach can be efficiently
implemented, requiring only O(k N log N) operations to estimate the regression
function at all N labeled and unlabeled points. We illustrate this approach on
two datasets with a manifold structure: indoor localization using WiFi
fingerprints and facial pose estimation. In both cases, geodesic kNN is more
accurate and much faster than the popular Laplacian eigenvector regressor.
Cheshire: An Online Algorithm for Activity Maximization in Social Networks
Authors: Ali Zarezade, Abir De, Hamid Rabiee, Manuel Gomez Rodriguez
Date: 2017-03-06 00:00:00

User engagement in social networks depends critically on the number of online
actions their users take in the network. Can we design an algorithm that finds
when to incentivize users to take actions to maximize the overall activity in a
social network? In this paper, we model the number of online actions over time
using multidimensional Hawkes processes, derive an alternate representation of
these processes based on stochastic differential equations (SDEs) with jumps
and, exploiting this alternate representation, address the above question from
the perspective of stochastic optimal control of SDEs with jumps. We find that
the optimal level of incentivized actions depends linearly on the current level
of overall actions. Moreover, the coefficients of this linear relationship can
be found by solving a matrix Riccati differential equation, which can be solved
efficiently, and a first order differential equation, which has a closed form
solution. As a result, we are able to design an efficient online algorithm,
Cheshire, to sample the optimal times of the users' incentivized actions.
Experiments on both synthetic and real data gathered from Twitter show that our
algorithm is able to consistently maximize the number of online actions more
effectively than the state of the art.
Measuring Sample Quality with Stein's Method
Authors: Jackson Gorham, Lester Mackey
Date: 2015-06-09 00:00:00
Proceeding: NeurIPS 2015 12

To improve the efficiency of Monte Carlo estimation, practitioners are
turning to biased Markov chain Monte Carlo procedures that trade off asymptotic
exactness for computational speed. The reasoning is sound: a reduction in
variance due to more rapid sampling can outweigh the bias introduced. However,
the inexactness creates new challenges for sampler and parameter selection,
since standard measures of sample quality like effective sample size do not
account for asymptotic bias. To address these challenges, we introduce a new
computable quality measure based on Stein's method that quantifies the maximum
discrepancy between sample and target expectations over a large class of test
functions. We use our tool to compare exact, biased, and deterministic sample
sequences and illustrate applications to hyperparameter selection, convergence
rate assessment, and quantifying bias-variance tradeoffs in posterior
inference.
Performing Stance Detection on Twitter Data using Computational Linguistics Techniques
Authors: Gourav G. Shenoy, Erika H. Dsouza, Sandra Kübler
Date: 2017-03-06 00:00:00
Tasks: General Classification, Stance Detection

As humans, we can often detect from a persons utterances if he or she is in
favor of or against a given target entity (topic, product, another person,
etc). But from the perspective of a computer, we need means to automatically
deduce the stance of the tweeter, given just the tweet text. In this paper, we
present our results of performing stance detection on twitter data using a
supervised approach. We begin by extracting bag-of-words to perform
classification using TIMBL, then try and optimize the features to improve
stance detection accuracy, followed by extending the dataset with two sets of
lexicons - arguing, and MPQA subjectivity; next we explore the MALT parser and
construct features using its dependency triples, finally we perform analysis
using Scikit-learn Random Forest implementation.
Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation
Authors: Ashvin Nair, Dian Chen, Pulkit Agrawal, Phillip Isola, Pieter Abbeel, Jitendra Malik, Sergey Levine
Date: 2017-03-06 00:00:00
Tasks: Self-Supervised Learning

Manipulation of deformable objects, such as ropes and cloth, is an important
but challenging problem in robotics. We present a learning-based system where a
robot takes as input a sequence of images of a human manipulating a rope from
an initial to goal configuration, and outputs a sequence of actions that can
reproduce the human demonstration, using only monocular images as input. To
perform this task, the robot learns a pixel-level inverse dynamics model of
rope manipulation directly from images in a self-supervised manner, using about
60K interactions with the rope collected autonomously by the robot. The human
demonstration provides a high-level plan of what to do and the low-level
inverse model is used to execute the plan. We show that by combining the high
and low-level plans, the robot can successfully manipulate a rope into a
variety of target shapes using only a sequence of human-provided images for
direction.
FeUdal Networks for Hierarchical Reinforcement Learning
Authors: Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, Koray Kavukcuoglu
Date: 2017-03-03 00:00:00
Proceeding: ICML 2017 8
Tasks: Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce FeUdal Networks (FuNs): a novel architecture for hierarchical
reinforcement learning. Our approach is inspired by the feudal reinforcement
learning proposal of Dayan and Hinton, and gains power and efficacy by
decoupling end-to-end learning across multiple levels -- allowing it to utilise
different resolutions of time. Our framework employs a Manager module and a
Worker module. The Manager operates at a lower temporal resolution and sets
abstract goals which are conveyed to and enacted by the Worker. The Worker
generates primitive actions at every tick of the environment. The decoupled
structure of FuN conveys several benefits -- in addition to facilitating very
long timescale credit assignment it also encourages the emergence of
sub-policies associated with different goals set by the Manager. These
properties allow FuN to dramatically outperform a strong baseline agent on
tasks that involve long-term credit assignment or memorisation. We demonstrate
the performance of our proposed system on a range of tasks from the ATARI suite
and also from a 3D DeepMind Lab environment.
Neural Episodic Control
Authors: Alexander Pritzel, Benigno Uria, Sriram Srinivasan, Adrià Puigdomènech, Oriol Vinyals, Demis Hassabis, Daan Wierstra, Charles Blundell
Date: 2017-03-06 00:00:00
Proceeding: ICML 2017 8
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning methods attain super-human performance in a wide
range of environments. Such methods are grossly inefficient, often taking
orders of magnitudes more data than humans to achieve reasonable performance.
We propose Neural Episodic Control: a deep reinforcement learning agent that is
able to rapidly assimilate new experiences and act upon them. Our agent uses a
semi-tabular representation of the value function: a buffer of past experience
containing slowly changing state representations and rapidly updated estimates
of the value function. We show across a wide range of environments that our
agent learns significantly faster than other state-of-the-art, general purpose
deep reinforcement learning agents.
Evidential supplier selection based on interval data fusion
Authors: Zichang He, Wen Jiang
Date: 2017-03-06 00:00:00
Tasks: Decision Making

Supplier selection is a typical multi-criteria decision making (MCDM) problem
and lots of uncertain information exist inevitably. To address this issue, a
new method was proposed based on interval data fusion. Our method follows the
original way to generate classical basic probability assignment(BPA) determined
by the distance among the evidences. However, the weights of criteria are kept
as interval numbers to generate interval BPAs and do the fusion of interval
BPAs. Finally, the order is ranked and the decision is made according to the
obtained interval BPAs. In this paper, a numerical example of supplier
selection is applied to verify the feasibility and validity of our method. The
new method is presented aiming at solving multiple-criteria decision-making
problems in which the weights of criteria or experts are described in fuzzy
data like linguistic terms or interval data.
Concentration Bounds for High Sensitivity Functions Through Differential Privacy
Authors: Kobbi Nissim, Uri Stemmer
Date: 2017-03-06 00:00:00
Tasks: Sensitivity, Vocal Bursts Intensity Prediction

A new line of work [Dwork et al. STOC 2015], [Hardt and Ullman FOCS 2014],
[Steinke and Ullman COLT 2015], [Bassily et al. STOC 2016] demonstrates how
differential privacy [Dwork et al. TCC 2006] can be used as a mathematical tool
for guaranteeing generalization in adaptive data analysis. Specifically, if a
differentially private analysis is applied on a sample S of i.i.d. examples to
select a low-sensitivity function f, then w.h.p. f(S) is close to its
expectation, although f is being chosen based on the data.
  Very recently, Steinke and Ullman observed that these generalization
guarantees can be used for proving concentration bounds in the non-adaptive
setting, where the low-sensitivity function is fixed beforehand. In particular,
they obtain alternative proofs for classical concentration bounds for
low-sensitivity functions, such as the Chernoff bound and McDiarmid's
Inequality.
  In this work, we set out to examine the situation for functions with
high-sensitivity, for which differential privacy does not imply generalization
guarantees under adaptive analysis. We show that differential privacy can be
used to prove concentration bounds for such functions in the non-adaptive
setting.
A quantum dynamic belief model to explain the interference effects of categorization on decision making
Authors: Zichang He, Wen Jiang
Date: 2017-03-06 00:00:00
Tasks: Decision Making

Categorization is necessary for many decision making tasks. However, the
categorization process may interfere the decision making result and the law of
total probability can be violated in some situations. To predict the
interference effect of categorization, some model based on quantum probability
has been proposed. In this paper, a new quantum dynamic belief (QDB) model is
proposed. Considering the precise decision may not be made during the process,
the concept of uncertainty is introduced in our model to simulate real human
thinking process. Then the interference effect categorization can be predicted
by handling the uncertain information. The proposed model is applied to a
categorization decision-making experiment to explain the interference effect of
categorization. Compared with other models, our model is relatively more
succinct and the result shows the correctness and effectiveness of our model.
A new belief Markov chain model and its application in inventory prediction
Authors: Zichang He, Wen Jiang
Date: 2017-03-06 00:00:00
Tasks: Prediction

Markov chain model is widely applied in many fields, especially the field of
prediction. The classical Discrete-time Markov chain(DTMC) is a widely used
method for prediction. However, the classical DTMC model has some limitation
when the system is complex with uncertain information or state space is not
discrete. To address it, a new belief Markov chain model is proposed by
combining Dempster-Shafer evidence theory with Markov chain. In our model, the
uncertain data is allowed to be handle in the form of interval number and the
basic probability assignment(BPA) is generated based on the distance between
interval numbers. The new belief Markov chain model overcomes the shortcomings
of classical Markov chain and has an efficient ability in dealing with
uncertain information. Moreover, an example of inventory prediction and the
comparison between our model and classical DTMC model can show the
effectiveness and rationality of our proposed model.
Probabilistic Reduced-Order Modeling for Stochastic Partial Differential Equations
Authors: Constantin Grigo, Phaedon-Stelios Koutsourelakis
Date: 2017-03-06 00:00:00

We discuss a Bayesian formulation to coarse-graining (CG) of PDEs where the
coefficients (e.g. material parameters) exhibit random, fine scale variability.
The direct solution to such problems requires grids that are small enough to
resolve this fine scale variability which unavoidably requires the repeated
solution of very large systems of algebraic equations. We establish a
physically inspired, data-driven coarse-grained model which learns a low-
dimensional set of microstructural features that are predictive of the
fine-grained model (FG) response. Once learned, those features provide a sharp
distribution over the coarse scale effec- tive coefficients of the PDE that are
most suitable for prediction of the fine scale model output. This ultimately
allows to replace the computationally expensive FG by a generative proba-
bilistic model based on evaluating the much cheaper CG several times. Sparsity
enforcing pri- ors further increase predictive efficiency and reveal
microstructural features that are important in predicting the FG response.
Moreover, the model yields probabilistic rather than single-point predictions,
which enables the quantification of the unavoidable epistemic uncertainty that
is present due to the information loss that occurs during the coarse-graining
process.
On parameters transformations for emulating sparse priors using variational-Laplace inference
Authors: Jean Daunizeau
Date: 2017-03-06 00:00:00

So-called sparse estimators arise in the context of model fitting, when one a
priori assumes that only a few (unknown) model parameters deviate from zero.
Sparsity constraints can be useful when the estimation problem is
under-determined, i.e. when number of model parameters is much higher than the
number of data points. Typically, such constraints are enforced by minimizing
the L1 norm, which yields the so-called LASSO estimator. In this work, we
propose a simple parameter transform that emulates sparse priors without
sacrificing the simplicity and robustness of L2-norm regularization schemes. We
show how L1 regularization can be obtained with a "sparsify" remapping of
parameters under normal Bayesian priors, and we demonstrate the ensuing
variational Laplace approach using Monte-Carlo simulations.
Cellulyzer - Automated analysis and interactive visualization/simulation of select cellular processes
Authors: Aliakbar Jafarpour, Holger Lorenz
Date: 2017-03-06 00:00:00

Here we report on a set of programs developed at the ZMBH Bio-Imaging
Facility for tracking real-life images of cellular processes. These programs
perform 1) automated tracking; 2) quantitative and comparative track analyses
of different images in different groups; 3) different interactive visualization
schemes; and 4) interactive realistic simulation of different cellular
processes for validation and optimal problem-specific adjustment of image
acquisition parameters (tradeoff between speed, resolution, and quality with
feedback from the very final results). The collection of programs is primarily
developed for the common bio-image analysis software ImageJ (as a single Java
Plugin). Some programs are also available in other languages (C++ and
Javascript) and may be run simply with a web-browser; even on a low-end Tablet
or Smartphone. The programs are available at
https://github.com/nurlicht/CellulyzerDemo
Trusting SVM for Piecewise Linear CNNs
Authors: Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
Date: 2016-11-07 00:00:00
Tasks: parameter estimation

We present a novel layerwise optimization algorithm for the learning
objective of Piecewise-Linear Convolutional Neural Networks (PL-CNNs), a large
class of convolutional neural networks. Specifically, PL-CNNs employ piecewise
linear non-linearities such as the commonly used ReLU and max-pool, and an SVM
classifier as the final layer. The key observation of our approach is that the
problem corresponding to the parameter estimation of a layer can be formulated
as a difference-of-convex (DC) program, which happens to be a latent structured
SVM. We optimize the DC program using the concave-convex procedure, which
requires us to iteratively solve a structured SVM problem. This allows to
design an optimization algorithm with an optimal learning rate that does not
require any tuning. Using the MNIST, CIFAR and ImageNet data sets, we show that
our approach always improves over the state of the art variants of
backpropagation and scales to large data and large network settings.
OpenNMT: Open-Source Toolkit for Neural Machine Translation
Authors: Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, Alexander M. Rush
Date: 2017-01-10 00:00:00
Proceeding: ACL 2017 7
Tasks: Machine Translation, NMT, Translation

We describe an open-source toolkit for neural machine translation (NMT). The
toolkit prioritizes efficiency, modularity, and extensibility with the goal of
supporting NMT research into model architectures, feature representations, and
source modalities, while maintaining competitive performance and reasonable
training requirements. The toolkit consists of modeling and translation
support, as well as detailed pedagogical documentation about the underlying
techniques.
Neural Decision Trees
Authors: Randall Balestriero
Date: 2017-02-23 00:00:00
Tasks: Clustering, global-optimization

In this paper we propose a synergistic melting of neural networks and
decision trees (DT) we call neural decision trees (NDT). NDT is an architecture
a la decision tree where each splitting node is an independent multilayer
perceptron allowing oblique decision functions or arbritrary nonlinear decision
function if more than one layer is used. This way, each MLP can be seen as a
node of the tree. We then show that with the weight sharing asumption among
those units, we end up with a Hashing Neural Network (HNN) which is a
multilayer perceptron with sigmoid activation function for the last layer as
opposed to the standard softmax. The output units then jointly represent the
probability to be in a particular region. The proposed framework allows for
global optimization as opposed to greedy in DT and differentiability w.r.t. all
parameters and the input, allowing easy integration in any learnable pipeline,
for example after CNNs for computer vision tasks. We also demonstrate the
modeling power of HNN allowing to learn union of disjoint regions for final
clustering or classification making it more general and powerful than standard
softmax MLP requiring linear separability thus reducing the need on the inner
layer to perform complex data transformations. We finally show experiments for
supervised, semi-suppervised and unsupervised tasks and compare results with
standard DTs and MLPs.
Grammar Variational Autoencoder
Authors: Matt J. Kusner, Brooks Paige, José Miguel Hernández-Lobato
Date: 2017-03-06 00:00:00
Proceeding: ICML 2017 8
Tasks: Bayesian Optimization, Symbolic Regression, valid

Deep generative models have been wildly successful at learning coherent
latent representations for continuous data such as video and audio. However,
generative modeling of discrete data such as arithmetic expressions and
molecular structures still poses significant challenges. Crucially,
state-of-the-art methods often produce outputs that are not valid. We make the
key observation that frequently, discrete data can be represented as a parse
tree from a context-free grammar. We propose a variational autoencoder which
encodes and decodes directly to and from these parse trees, ensuring the
generated outputs are always valid. Surprisingly, we show that not only does
our model more often generate valid outputs, it also learns a more coherent
latent space in which nearby points decode to similar discrete outputs. We
demonstrate the effectiveness of our learned models by showing their improved
performance in Bayesian optimization for symbolic regression and molecular
synthesis.
Exchangeable choice functions
Authors: Arthur Van Camp, Gert de Cooman
Date: 2017-03-06 00:00:00

We investigate how to model exchangeability with choice functions.
Exchangeability is a structural assessment on a sequence of uncertain
variables. We show how such assessments are a special indifference assessment,
and how that leads to a counterpart of de Finetti's Representation Theorem,
both in a finite and a countable context.
A time series distance measure for efficient clustering of input output signals by their underlying dynamics
Authors: Oliver Lauwers, Bart De Moor
Date: 2017-03-06 00:00:00
Tasks: Clustering, Dynamic Time Warping, Time Series, Time Series Analysis

Starting from a dataset with input/output time series generated by multiple
deterministic linear dynamical systems, this paper tackles the problem of
automatically clustering these time series. We propose an extension to the
so-called Martin cepstral distance, that allows to efficiently cluster these
time series, and apply it to simulated electrical circuits data. Traditionally,
two ways of handling the problem are used. The first class of methods employs a
distance measure on time series (e.g. Euclidean, Dynamic Time Warping) and a
clustering technique (e.g. k-means, k-medoids, hierarchical clustering) to find
natural groups in the dataset. It is, however, often not clear whether these
distance measures effectively take into account the specific temporal
correlations in these time series. The second class of methods uses the
input/output data to identify a dynamic system using an identification scheme,
and then applies a model norm-based distance (e.g. H2, H-infinity) to find out
which systems are similar. This, however, can be very time consuming for large
amounts of long time series data. We show that the new distance measure
presented in this paper performs as good as when every input/output pair is
modelled explicitly, but remains computationally much less complex. The
complexity of calculating this distance between two time series of length N is
O(N logN).