pwc-archive/papers-with-abstracts huggingface dataset:
Causal Interventions for Fairness
Authors: Matt J. Kusner, Chris Russell, Joshua R. Loftus, Ricardo Silva
Date: 2018-06-06 00:00:00
Tasks: Fairness

Most approaches in algorithmic fairness constrain machine learning methods so
the resulting predictions satisfy one of several intuitive notions of fairness.
While this may help private companies comply with non-discrimination laws or
avoid negative publicity, we believe it is often too little, too late. By the
time the training data is collected, individuals in disadvantaged groups have
already suffered from discrimination and lost opportunities due to factors out
of their control. In the present work we focus instead on interventions such as
a new public policy, and in particular, how to maximize their positive effects
while improving the fairness of the overall system. We use causal methods to
model the effects of interventions, allowing for potential interference--each
individual's outcome may depend on who else receives the intervention. We
demonstrate this with an example of allocating a budget of teaching resources
using a dataset of schools in New York City.
Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem
Authors: Andre Wibisono
Date: 2018-02-22 00:00:00

We study sampling as optimization in the space of measures. We focus on
gradient flow-based optimization with the Langevin dynamics as a case study. We
investigate the source of the bias of the unadjusted Langevin algorithm (ULA)
in discrete time, and consider how to remove or reduce the bias. We point out
the difficulty is that the heat flow is exactly solvable, but neither its
forward nor backward method is implementable in general, except for Gaussian
data. We propose the symmetrized Langevin algorithm (SLA), which should have a
smaller bias than ULA, at the price of implementing a proximal gradient step in
space. We show SLA is in fact consistent for Gaussian target measure, whereas
ULA is not. We also illustrate various algorithms explicitly for Gaussian
target measure, including gradient descent, proximal gradient, and
Forward-Backward, and show they are all consistent.
Dempsterian-Shaferian Belief Network From Data
Authors: Mieczysław A. Kłopotek
Date: 2018-06-06 00:00:00

Shenoy and Shafer {Shenoy:90} demonstrated that both for Dempster-Shafer
Theory and probability theory there exists a possibility to calculate
efficiently marginals of joint belief distributions (by so-called local
computations) provided that the joint distribution can be decomposed
(factorized) into a belief network. A number of algorithms exists for
decomposition of probabilistic joint belief distribution into a bayesian
(belief) network from data. For example
  Spirtes, Glymour and Schein{Spirtes:90b} formulated a Conjecture that a
direct dependence test and a head-to-head meeting test would suffice to
construe bayesian network from data in such a way that Pearl's concept of
d-separation {Geiger:90} applies.
  This paper is intended to transfer Spirtes, Glymour and Scheines
{Spirtes:90b} approach onto the ground of the Dempster-Shafer Theory (DST). For
this purpose, a frequentionistic interpretation of the DST developed in
{Klopotek:93b} is exploited. A special notion of conditionality for DST is
introduced and demonstrated to behave with respect to Pearl's d-separation
{Geiger:90} much the same way as conditional probability (though some
differences like non-uniqueness are evident). Based on this, an algorithm
analogous to that from {Spirtes:90b} is developed.
  The notion of a partially oriented graph (pog) is introduced and within this
graph the notion of p-d-separation is defined. If direct dependence test and
head-to-head meeting test are used to orient the pog then its p-d-separation is
shown to be equivalent to the Pearl's d-separation for any compatible dag.
Adversarial Attack on Graph Structured Data
Authors: Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song
Date: 2018-06-06 00:00:00
Proceeding: ICML 2018 7
Tasks: Adversarial Attack, Graph Neural Network, Reinforcement Learning

Deep learning on graph structures has shown exciting results in various
applications. However, few attentions have been paid to the robustness of such
models, in contrast to numerous research work for image or text adversarial
attack and defense. In this paper, we focus on the adversarial attacks that
fool the model by modifying the combinatorial structure of data. We first
propose a reinforcement learning based attack method that learns the
generalizable attack policy, while only requiring prediction labels from the
target classifier. Also, variants of genetic algorithms and gradient methods
are presented in the scenario where prediction confidence or gradients are
available. We use both synthetic and real-world data to show that, a family of
Graph Neural Network models are vulnerable to these attacks, in both
graph-level and node-level classification tasks. We also show such attacks can
be used to diagnose the learned classifiers.
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Authors: Tabish Rashid, Mikayel Samvelyan, Christian Schroeder de Witt, Gregory Farquhar, Jakob Foerster, Shimon Whiteson
Date: 2018-03-30 00:00:00
Proceeding: ICML 2018 7
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), SMAC+, Starcraft, Starcraft II

In many real-world settings, a team of agents must coordinate their behaviour
while acting in a decentralised way. At the same time, it is often possible to
train the agents in a centralised fashion in a simulated or laboratory setting,
where global state information is available and communication constraints are
lifted. Learning joint action-values conditioned on extra state information is
an attractive way to exploit centralised learning, but the best strategy for
then extracting decentralised policies is unclear. Our solution is QMIX, a
novel value-based method that can train decentralised policies in a centralised
end-to-end fashion. QMIX employs a network that estimates joint action-values
as a complex non-linear combination of per-agent values that condition only on
local observations. We structurally enforce that the joint-action value is
monotonic in the per-agent values, which allows tractable maximisation of the
joint action-value in off-policy learning, and guarantees consistency between
the centralised and decentralised policies. We evaluate QMIX on a challenging
set of StarCraft II micromanagement tasks, and show that QMIX significantly
outperforms existing value-based multi-agent reinforcement learning methods.
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
Authors: Seth Neel, Aaron Roth
Date: 2018-06-06 00:00:00
Proceeding: ICML 2018 7
Tasks: Multi-Armed Bandits

Data that is gathered adaptively --- via bandit algorithms, for example ---
exhibits bias. This is true both when gathering simple numeric valued data ---
the empirical means kept track of by stochastic bandit algorithms are biased
downwards --- and when gathering more complicated data --- running hypothesis
tests on complex data gathered via contextual bandit algorithms leads to false
discovery. In this paper, we show that this problem is mitigated if the data
collection procedure is differentially private. This lets us both bound the
bias of simple numeric valued quantities (like the empirical means of
stochastic bandit algorithms), and correct the p-values of hypothesis tests run
on the adaptively gathered data. Moreover, there exist differentially private
bandit algorithms with near optimal regret bounds: we apply existing theorems
in the simple stochastic case, and give a new analysis for linear contextual
bandits. We complement our theoretical results with experiments validating our
theory.
Conditional Linear Regression
Authors: Diego Calderon, Brendan Juba, Sirui Li, Zongyi Li, Lisa Ruan
Date: 2018-06-06 00:00:00
Tasks: regression

Work in machine learning and statistics commonly focuses on building models that capture the vast majority of data, possibly ignoring a segment of the population as outliers. However, there does not often exist a good model on the whole dataset, so we seek to find a small subset where there exists a useful model. We are interested in finding a linear rule capable of achieving more accurate predictions for just a segment of the population. We give an efficient algorithm with theoretical analysis for the conditional linear regression task, which is the joint task of identifying a significant segment of the population, described by a k-DNF, along with its linear regression fit.
Fast and Accurate Online Video Object Segmentation via Tracking Parts
Authors: Jingchun Cheng, Yi-Hsuan Tsai, Wei-Chih Hung, Shengjin Wang, Ming-Hsuan Yang
Date: 2018-06-06 00:00:00
Proceeding: CVPR 2018 6
Tasks: Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation, Visual Object Tracking

基于视频的目标检测算法研究
Learning Kolmogorov Models for Binary Random Variables
Authors: Hadi Ghauch, Mikael Skoglund, Hossein Shokri-Ghadikolaei, Carlo Fischione, Ali H. Sayed
Date: 2018-06-06 00:00:00
Tasks: BIG-bench Machine Learning, Interpretable Machine Learning, Recommendation Systems

We summarize our recent findings, where we proposed a framework for learning
a Kolmogorov model, for a collection of binary random variables. More
specifically, we derive conditions that link outcomes of specific random
variables, and extract valuable relations from the data. We also propose an
algorithm for computing the model and show its first-order optimality, despite
the combinatorial nature of the learning problem. We apply the proposed
algorithm to recommendation systems, although it is applicable to other
scenarios. We believe that the work is a significant step toward interpretable
machine learning.
Adaptive feature recombination and recalibration for semantic segmentation: application to brain tumor segmentation in MRI
Authors: Sérgio Pereira, Victor Alves, Carlos A. Silva
Date: 2018-06-06 00:00:00
Tasks: Brain Tumor Segmentation, Segmentation, Semantic Segmentation, Tumor Segmentation

Convolutional neural networks (CNNs) have been successfully used for brain
tumor segmentation, specifically, fully convolutional networks (FCNs). FCNs can
segment a set of voxels at once, having a direct spatial correspondence between
units in feature maps (FMs) at a given location and the corresponding
classified voxels. In convolutional layers, FMs are merged to create new FMs,
so, channel combination is crucial. However, not all FMs have the same
relevance for a given class. Recently, in classification problems,
Squeeze-and-Excitation (SE) blocks have been proposed to re-calibrate FMs as a
whole, and suppress the less informative ones. However, this is not optimal in
FCN due to the spatial correspondence between units and voxels. In this
article, we propose feature recombination through linear expansion and
compression to create more complex features for semantic segmentation.
Additionally, we propose a segmentation SE (SegSE) block for feature
recalibration that collects contextual information, while maintaining the
spatial meaning. Finally, we evaluate the proposed methods in brain tumor
segmentation, using publicly available data.
Randomized Value Functions via Multiplicative Normalizing Flows
Authors: Ahmed Touati, Harsh Satija, Joshua Romoff, Joelle Pineau, Pascal Vincent
Date: 2018-06-06 00:00:00
Tasks: Efficient Exploration, Thompson Sampling

Randomized value functions offer a promising approach towards the challenge of efficient exploration in complex environments with high dimensional state and action spaces. Unlike traditional point estimate methods, randomized value functions maintain a posterior distribution over action-space values. This prevents the agent's behavior policy from prematurely exploiting early estimates and falling into local optima. In this work, we leverage recent advances in variational Bayesian neural networks and combine these with traditional Deep Q-Networks (DQN) and Deep Deterministic Policy Gradient (DDPG) to achieve randomized value functions for high-dimensional domains. In particular, we augment DQN and DDPG with multiplicative normalizing flows in order to track a rich approximate posterior distribution over the parameters of the value function. This allows the agent to perform approximate Thompson sampling in a computationally efficient manner via stochastic gradient methods. We demonstrate the benefits of our approach through an empirical comparison in high dimensional environments.
SCAN: Learning Hierarchical Compositional Visual Concepts
Authors: Irina Higgins, Nicolas Sonnerat, Loic Matthey, Arka Pal, Christopher P. Burgess, Matko Bosnjak, Murray Shanahan, Matthew Botvinick, Demis Hassabis, Alexander Lerchner
Date: 2017-07-11 00:00:00
Proceeding: ICLR 2018 1

The seemingly infinite diversity of the natural world arises from a
relatively small set of coherent rules, such as the laws of physics or
chemistry. We conjecture that these rules give rise to regularities that can be
discovered through primarily unsupervised experiences and represented as
abstract concepts. If such representations are compositional and hierarchical,
they can be recombined into an exponentially large set of new concepts. This
paper describes SCAN (Symbol-Concept Association Network), a new framework for
learning such abstractions in the visual domain. SCAN learns concepts through
fast symbol association, grounding them in disentangled visual primitives that
are discovered in an unsupervised manner. Unlike state of the art multimodal
generative model baselines, our approach requires very few pairings between
symbols and images and makes no assumptions about the form of symbol
representations. Once trained, SCAN is capable of multimodal bi-directional
inference, generating a diverse set of image samples from symbolic descriptions
and vice versa. It also allows for traversal and manipulation of the implicit
hierarchy of visual concepts through symbolic instructions and learnt logical
recombination operations. Such manipulations enable SCAN to break away from its
training data distribution and imagine novel visual concepts through
symbolically instructed recombination of previously learnt concepts.
Unsupervised Attention-guided Image to Image Translation
Authors: Youssef A. Mejjati, Christian Richardt, James Tompkin, Darren Cosker, Kwang In Kim
Date: 2018-06-06 00:00:00
Tasks: Image-to-Image Translation, Translation, Unsupervised Image-To-Image Translation

Current unsupervised image-to-image translation techniques struggle to focus
their attention on individual objects without altering the background or the
way multiple objects interact within a scene. Motivated by the important role
of attention in human perception, we tackle this limitation by introducing
unsupervised attention mechanisms that are jointly adversarialy trained with
the generators and discriminators. We demonstrate qualitatively and
quantitatively that our approach is able to attend to relevant regions in the
image without requiring supervision, and that by doing so it achieves more
realistic mappings compared to recent approaches.
Model-free, Model-based, and General Intelligence
Authors: Hector Geffner
Date: 2018-06-06 00:00:00
Tasks: model

During the 60s and 70s, AI researchers explored intuitions about intelligence
by writing programs that displayed intelligent behavior. Many good ideas came
out from this work but programs written by hand were not robust or general.
After the 80s, research increasingly shifted to the development of learners
capable of inferring behavior and functions from experience and data, and
solvers capable of tackling well-defined but intractable models like SAT,
classical planning, Bayesian networks, and POMDPs. The learning approach has
achieved considerable success but results in black boxes that do not have the
flexibility, transparency, and generality of their model-based counterparts.
Model-based approaches, on the other hand, require models and scalable
algorithms. Model-free learners and model-based solvers have close parallels
with Systems 1 and 2 in current theories of the human mind: the first, a fast,
opaque, and inflexible intuitive mind; the second, a slow, transparent, and
flexible analytical mind. In this paper, I review developments in AI and draw
on these theories to discuss the gap between model-free learners and
model-based solvers, a gap that needs to be bridged in order to have
intelligent systems that are robust and general.
Dilatation of Lateral Ventricles with Brain Volumes in Infants with 3D Transfontanelle US
Authors: Marc-Antoine Boucher, Sarah Lippe, Amelie Damphousse, Ramy El-Jalbout, Samuel Kadoury
Date: 2018-06-06 00:00:00
Tasks: Segmentation

Ultrasound (US) can be used to assess brain development in newborns, as MRI
is challenging due to immobilization issues, and may require sedation.
Dilatation of the lateral ventricles in the brain is a risk factor for poorer
neurodevelopment outcomes in infants. Hence, 3D US has the ability to assess
the volume of the lateral ventricles similar to clinically standard MRI, but
manual segmentation is time consuming. The objective of this study is to
develop an approach quantifying the ratio of lateral ventricular dilatation
with respect to total brain volume using 3D US, which can assess the severity
of macrocephaly. Automatic segmentation of the lateral ventricles is achieved
with a multi-atlas deformable registration approach using locally linear
correlation metrics for US-MRI fusion, followed by a refinement step using
deformable mesh models. Total brain volume is estimated using a 3D ellipsoid
modeling approach. Validation was performed on a cohort of 12 infants, ranging
from 2 to 8.5 months old, where 3D US and MRI were used to compare brain
volumes and segmented lateral ventricles. Automatically extracted volumes from
3D US show a high correlation and no statistically significant difference when
compared to ground truth measurements. Differences in volume ratios was 6.0 +/-
4.8% compared to MRI, while lateral ventricular segmentation yielded a mean
Dice coefficient of 70.8 +/- 3.6% and a mean absolute distance (MAD) of 0.88
+/- 0.2mm, demonstrating the clinical benefit of this tool in paediatric
ultrasound.
Data-driven Probabilistic Atlases Capture Whole-brain Individual Variation
Authors: Yuankai Huo, Katherine Swett, Susan M. Resnick, Laurie E. Cutting, Bennett A. Landman
Date: 2018-06-06 00:00:00
Tasks: Specificity

Probabilistic atlases provide essential spatial contextual information for
image interpretation, Bayesian modeling, and algorithmic processing. Such
atlases are typically constructed by grouping subjects with similar demographic
information. Importantly, use of the same scanner minimizes inter-group
variability. However, generalizability and spatial specificity of such
approaches is more limited than one might like. Inspired by Commowick
"Frankenstein's creature paradigm" which builds a personal specific anatomical
atlas, we propose a data-driven framework to build a personal specific
probabilistic atlas under the large-scale data scheme. The data-driven
framework clusters regions with similar features using a point distribution
model to learn different anatomical phenotypes. Regional structural atlases and
corresponding regional probabilistic atlases are used as indices and targets in
the dictionary. By indexing the dictionary, the whole brain probabilistic
atlases adapt to each new subject quickly and can be used as spatial priors for
visualization and processing. The novelties of this approach are (1) it
provides a new perspective of generating personal specific whole brain
probabilistic atlases (132 regions) under data-driven scheme across sites. (2)
The framework employs the large amount of heterogeneous data (2349 images). (3)
The proposed framework achieves low computational cost since only one affine
registration and Pearson correlation operation are required for a new subject.
Our method matches individual regions better with higher Dice similarity value
when testing the probabilistic atlases. Importantly, the advantage the
large-scale scheme is demonstrated by the better performance of using
large-scale training data (1888 images) than smaller training set (720 images).
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning
Authors: Irina Higgins, Arka Pal, Andrei A. Rusu, Loic Matthey, Christopher P. Burgess, Alexander Pritzel, Matthew Botvinick, Charles Blundell, Alexander Lerchner
Date: 2017-07-26 00:00:00
Proceeding: ICML 2017 8
Tasks: Deep Reinforcement Learning, Domain Adaptation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

Domain adaptation is an important open problem in deep reinforcement learning
(RL). In many scenarios of interest data is hard to obtain, so agents may learn
a source policy in a setting where data is readily available, with the hope
that it generalises well to the target domain. We propose a new multi-stage RL
agent, DARLA (DisentAngled Representation Learning Agent), which learns to see
before learning to act. DARLA's vision is based on learning a disentangled
representation of the observed environment. Once DARLA can see, it is able to
acquire source policies that are robust to many domain shifts - even with no
access to the target domain. DARLA significantly outperforms conventional
baselines in zero-shot domain adaptation scenarios, an effect that holds across
a variety of RL environments (Jaco arm, DeepMind Lab) and base RL algorithms
(DQN, A3C and EC).
Regularization by Denoising: Clarifications and New Interpretations
Authors: Edward T. Reehorst, Philip Schniter
Date: 2018-06-06 00:00:00
Tasks: Denoising, Density Estimation, Image Denoising

Regularization by Denoising (RED), as recently proposed by Romano, Elad, and
Milanfar, is powerful image-recovery framework that aims to minimize an
explicit regularization objective constructed from a plug-in image-denoising
function. Experimental evidence suggests that the RED algorithms are
state-of-the-art. We claim, however, that explicit regularization does not
explain the RED algorithms. In particular, we show that many of the expressions
in the paper by Romano et al. hold only when the denoiser has a symmetric
Jacobian, and we demonstrate that such symmetry does not occur with practical
denoisers such as non-local means, BM3D, TNRD, and DnCNN. To explain the RED
algorithms, we propose a new framework called Score-Matching by Denoising
(SMD), which aims to match a "score" (i.e., the gradient of a log-prior). We
then show tight connections between SMD, kernel density estimation, and
constrained minimum mean-squared error denoising. Furthermore, we interpret the
RED algorithms from Romano et al. and propose new algorithms with acceleration
and convergence guarantees. Finally, we show that the RED algorithms seek a
consensus equilibrium solution, which facilitates a comparison to plug-and-play
ADMM.