pwc-archive/papers-with-abstracts huggingface dataset:
Modeling cumulative biological phenomena with Suppes-Bayes Causal Networks
Authors: Daniele Ramazzotti, Alex Graudenzi, Giulio Caravagna, Marco Antoniotti
Date: 2016-02-25 00:00:00
Tasks: Bayesian Inference, Model Selection

Several diseases related to cell proliferation are characterized by the
accumulation of somatic DNA changes, with respect to wildtype conditions.
Cancer and HIV are two common examples of such diseases, where the mutational
load in the cancerous/viral population increases over time. In these cases,
selective pressures are often observed along with competition, cooperation and
parasitism among distinct cellular clones. Recently, we presented a
mathematical framework to model these phenomena, based on a combination of
Bayesian inference and Suppes' theory of probabilistic causation, depicted in
graphical structures dubbed Suppes-Bayes Causal Networks (SBCNs). SBCNs are
generative probabilistic graphical models that recapitulate the potential
ordering of accumulation of such DNA changes during the progression of the
disease. Such models can be inferred from data by exploiting likelihood-based
model-selection strategies with regularization. In this paper we discuss the
theoretical foundations of our approach and we investigate in depth the
influence on the model-selection task of: (i) the poset based on Suppes' theory
and (ii) different regularization strategies. Furthermore, we provide an
example of application of our framework to HIV genetic data highlighting the
valuable insights provided by the inferred.
Bootstrapping with Models: Confidence Intervals for Off-Policy Evaluation
Authors: Josiah P. Hanna, Peter Stone, Scott Niekum
Date: 2016-06-20 00:00:00
Tasks: Off-policy evaluation

For an autonomous agent, executing a poor policy may be costly or even
dangerous. For such agents, it is desirable to determine confidence interval
lower bounds on the performance of any given policy without executing said
policy. Current methods for exact high confidence off-policy evaluation that
use importance sampling require a substantial amount of data to achieve a tight
lower bound. Existing model-based methods only address the problem in discrete
state spaces. Since exact bounds are intractable for many domains we trade off
strict guarantees of safety for more data-efficient approximate bounds. In this
context, we propose two bootstrapping off-policy evaluation methods which use
learned MDP transition models in order to estimate lower confidence bounds on
policy performance with limited data in both continuous and discrete state
spaces. Since direct use of a model may introduce bias, we derive a theoretical
upper bound on model bias for when the model transition function is estimated
with i.i.d. trajectories. This bound broadens our understanding of the
conditions under which model-based methods have high bias. Finally, we
empirically evaluate our proposed methods and analyze the settings in which
different bootstrapping off-policy confidence interval methods succeed and
fail.
Interpretable Structure-Evolving LSTM
Authors: Xiaodan Liang, Liang Lin, Xiaohui Shen, Jiashi Feng, Shuicheng Yan, Eric P. Xing
Date: 2017-03-08 00:00:00
Proceeding: CVPR 2017 7
Tasks: Small Data Image Classification

This paper develops a general framework for learning interpretable data
representation via Long Short-Term Memory (LSTM) recurrent neural networks over
hierarchal graph structures. Instead of learning LSTM models over the pre-fixed
structures, we propose to further learn the intermediate interpretable
multi-level graph structures in a progressive and stochastic way from data
during the LSTM network optimization. We thus call this model the
structure-evolving LSTM. In particular, starting with an initial element-level
graph representation where each node is a small data element, the
structure-evolving LSTM gradually evolves the multi-level graph representations
by stochastically merging the graph nodes with high compatibilities along the
stacked LSTM layers. In each LSTM layer, we estimate the compatibility of two
connected nodes from their corresponding LSTM gate outputs, which is used to
generate a merging probability. The candidate graph structures are accordingly
generated where the nodes are grouped into cliques with their merging
probabilities. We then produce the new graph structure with a
Metropolis-Hasting algorithm, which alleviates the risk of getting stuck in
local optimums by stochastic sampling with an acceptance probability. Once a
graph structure is accepted, a higher-level graph is then constructed by taking
the partitioned cliques as its nodes. During the evolving process,
representation becomes more abstracted in higher-levels where redundant
information is filtered out, allowing more efficient propagation of long-range
data dependencies. We evaluate the effectiveness of structure-evolving LSTM in
the application of semantic object parsing and demonstrate its advantage over
state-of-the-art LSTM models on standard benchmarks.
Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection
Authors: Xiaodan Liang, Lisa Lee, Eric P. Xing
Date: 2017-03-08 00:00:00
Proceeding: CVPR 2017 7
Tasks: Attribute, image-classification, Image Classification, Object, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Relationship Detection, Visual Relationship Detection

Despite progress in visual perception tasks such as image classification and
detection, computers still struggle to understand the interdependency of
objects in the scene as a whole, e.g., relations between objects or their
attributes. Existing methods often ignore global context cues capturing the
interactions among different object instances, and can only recognize a handful
of types by exhaustively training individual detectors for all possible
relationships. To capture such global interdependency, we propose a deep
Variation-structured Reinforcement Learning (VRL) framework to sequentially
discover object relationships and attributes in the whole image. First, a
directed semantic action graph is built using language priors to provide a rich
and compact representation of semantic correlations between object categories,
predicates, and attributes. Next, we use a variation-structured traversal over
the action graph to construct a small, adaptive action set for each step based
on the current state and historical actions. In particular, an ambiguity-aware
object mining scheme is used to resolve semantic ambiguity among object
categories that the object detector fails to distinguish. We then make
sequential predictions using a deep RL framework, incorporating global context
cues and semantic embeddings of previously extracted phrases in the state
vector. Our experiments on the Visual Relationship Detection (VRD) dataset and
the large-scale Visual Genome dataset validate the superiority of VRL, which
can achieve significantly better detection results on datasets involving
thousands of relationship and attribute types. We also demonstrate that VRL is
able to predict unseen types embedded in our action graph by learning
correlations on shared graph nodes.
On Quantum Decision Trees
Authors: Subhash Kak
Date: 2017-03-08 00:00:00

Quantum decision systems are being increasingly considered for use in
artificial intelligence applications. Classical and quantum nodes can be
distinguished based on certain correlations in their states. This paper
investigates some properties of the states obtained in a decision tree
structure. How these correlations may be mapped to the decision tree is
considered. Classical tree representations and approximations to quantum states
are provided.
Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks
Authors: Stefano Beretta, Mauro Castelli, Ivo Goncalves, Ivan Merelli, Daniele Ramazzotti
Date: 2017-03-08 00:00:00

Gene and protein networks are very important to model complex large-scale
systems in molecular biology. Inferring or reverseengineering such networks can
be defined as the process of identifying gene/protein interactions from
experimental data through computational analysis. However, this task is
typically complicated by the enormously large scale of the unknowns in a rather
small sample size. Furthermore, when the goal is to study causal relationships
within the network, tools capable of overcoming the limitations of correlation
networks are required. In this work, we make use of Bayesian Graphical Models
to attach this problem and, specifically, we perform a comparative study of
different state-of-the-art heuristics, analyzing their performance in inferring
the structure of the Bayesian Network from breast cancer data.
Parallel Implementation of Efficient Search Schemes for the Inference of Cancer Progression Models
Authors: Daniele Ramazzotti, Marco S. Nobile, Paolo Cazzaniga, Giancarlo Mauri, Marco Antoniotti
Date: 2017-03-08 00:00:00
Tasks: global-optimization, Specificity

The emergence and development of cancer is a consequence of the accumulation
over time of genomic mutations involving a specific set of genes, which
provides the cancer clones with a functional selective advantage. In this work,
we model the order of accumulation of such mutations during the progression,
which eventually leads to the disease, by means of probabilistic graphic
models, i.e., Bayesian Networks (BNs). We investigate how to perform the task
of learning the structure of such BNs, according to experimental evidence,
adopting a global optimization meta-heuristics. In particular, in this work we
rely on Genetic Algorithms, and to strongly reduce the execution time of the
inference -- which can also involve multiple repetitions to collect
statistically significant assessments of the data -- we distribute the
calculations using both multi-threading and a multi-node architecture. The
results show that our approach is characterized by good accuracy and
specificity; we also demonstrate its feasibility, thanks to a 84x reduction of
the overall execution time with respect to a traditional sequential
implementation.
Exposing the Probabilistic Causal Structure of Discrimination
Authors: Francesco Bonchi, Sara Hajian, Bud Mishra, Daniele Ramazzotti
Date: 2015-10-02 00:00:00
Tasks: valid

Discrimination discovery from data is an important task aiming at identifying
patterns of illegal and unethical discriminatory activities against
protected-by-law groups, e.g., ethnic minorities. While any legally-valid proof
of discrimination requires evidence of causality, the state-of-the-art methods
are essentially correlation-based, albeit, as it is well known, correlation
does not imply causation.
  In this paper we take a principled causal approach to the data mining problem
of discrimination detection in databases. Following Suppes' probabilistic
causation theory, we define a method to extract, from a dataset of historical
decision records, the causal structures existing among the attributes in the
data. The result is a type of constrained Bayesian network, which we dub
Suppes-Bayes Causal Network (SBCN). Next, we develop a toolkit of methods based
on random walks on top of the SBCN, addressing different anti-discrimination
legal concepts, such as direct and indirect discrimination, group and
individual discrimination, genuine requirement, and favoritism. Our experiments
on real-world datasets confirm the inferential power of our approach in all
these different tasks.
A Manifold Approach to Learning Mutually Orthogonal Subspaces
Authors: Stephen Giguere, Francisco Garcia, Sridhar Mahadevan
Date: 2017-03-08 00:00:00
Tasks: Domain Adaptation, Riemannian optimization

Although many machine learning algorithms involve learning subspaces with
particular characteristics, optimizing a parameter matrix that is constrained
to represent a subspace can be challenging. One solution is to use Riemannian
optimization methods that enforce such constraints implicitly, leveraging the
fact that the feasible parameter values form a manifold. While Riemannian
methods exist for some specific problems, such as learning a single subspace,
there are more general subspace constraints that offer additional flexibility
when setting up an optimization problem, but have not been formulated as a
manifold.
  We propose the partitioned subspace (PS) manifold for optimizing matrices
that are constrained to represent one or more subspaces. Each point on the
manifold defines a partitioning of the input space into mutually orthogonal
subspaces, where the number of partitions and their sizes are defined by the
user. As a result, distinct groups of features can be learned by defining
different objective functions for each partition. We illustrate the properties
of the manifold through experiments on multiple dataset analysis and domain
adaptation.
Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment
Authors: Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, Krishna P. Gummadi
Date: 2016-10-26 00:00:00
Tasks: Decision Making, Fairness, General Classification

Automated data-driven decision making systems are increasingly being used to
assist, or even replace humans in many settings. These systems function by
learning from historical decisions, often taken by humans. In order to maximize
the utility of these systems (or, classifiers), their training involves
minimizing the errors (or, misclassifications) over the given historical data.
However, it is quite possible that the optimally trained classifier makes
decisions for people belonging to different social groups with different
misclassification rates (e.g., misclassification rates for females are higher
than for males), thereby placing these groups at an unfair disadvantage. To
account for and avoid such unfairness, in this paper, we introduce a new notion
of unfairness, disparate mistreatment, which is defined in terms of
misclassification rates. We then propose intuitive measures of disparate
mistreatment for decision boundary-based classifiers, which can be easily
incorporated into their formulation as convex-concave constraints. Experiments
on synthetic as well as real world datasets show that our methodology is
effective at avoiding disparate mistreatment, often at a small cost in terms of
accuracy.
Unsupervised Ensemble Regression
Authors: Omer Dror, Boaz Nadler, Erhan Bilal, Yuval Kluger
Date: 2017-03-08 00:00:00
Tasks: regression

Consider a regression problem where there is no labeled data and the only
observations are the predictions $f_i(x_j)$ of $m$ experts $f_{i}$ over many
samples $x_j$. With no knowledge on the accuracy of the experts, is it still
possible to accurately estimate the unknown responses $y_{j}$? Can one still
detect the least or most accurate experts? In this work we propose a framework
to study these questions, based on the assumption that the $m$ experts have
uncorrelated deviations from the optimal predictor. Assuming the first two
moments of the response are known, we develop methods to detect the best and
worst regressors, and derive U-PCR, a novel principal components approach for
unsupervised ensemble regression. We provide theoretical support for U-PCR and
illustrate its improved accuracy over the ensemble mean and median on a variety
of regression problems.
Distribution-Free Predictive Inference For Regression
Authors: Jing Lei, Max G'Sell, Alessandro Rinaldo, Ryan J. Tibshirani, Larry Wasserman
Date: 2016-04-14 00:00:00
Tasks: Computational Efficiency, Prediction, Prediction Intervals, regression, valid

We develop a general framework for distribution-free predictive inference in
regression, using conformal inference. The proposed methodology allows for the
construction of a prediction band for the response variable using any estimator
of the regression function. The resulting prediction band preserves the
consistency properties of the original estimator under standard assumptions,
while guaranteeing finite-sample marginal coverage even when these assumptions
do not hold. We analyze and compare, both empirically and theoretically, the
two major variants of our conformal framework: full conformal inference and
split conformal inference, along with a related jackknife method. These methods
offer different tradeoffs between statistical accuracy (length of resulting
prediction intervals) and computational efficiency. As extensions, we develop a
method for constructing valid in-sample prediction intervals called {\it
rank-one-out} conformal inference, which has essentially the same computational
efficiency as split conformal inference. We also describe an extension of our
procedures for producing prediction bands with locally varying length, in order
to adapt to heteroskedascity in the data. Finally, we propose a model-free
notion of variable importance, called {\it leave-one-covariate-out} or LOCO
inference. Accompanying this paper is an R package {\tt conformalInference}
that implements all of the proposals we have introduced. In the spirit of
reproducibility, all of our empirical results can also be easily (re)generated
using this package.
Filtrated Algebraic Subspace Clustering
Authors: Manolis C. Tsakiris, Rene Vidal
Date: 2015-06-20 00:00:00
Tasks: Clustering

Subspace clustering is the problem of clustering data that lie close to a
union of linear subspaces. In the abstract form of the problem, where no noise
or other corruptions are present, the data are assumed to lie in general
position inside the algebraic variety of a union of subspaces, and the
objective is to decompose the variety into its constituent subspaces. Prior
algebraic-geometric approaches to this problem require the subspaces to be of
equal dimension, or the number of subspaces to be known. Subspaces of arbitrary
dimensions can still be recovered in closed form, in terms of all homogeneous
polynomials of degree $m$ that vanish on their union, when an upper bound m on
the number of the subspaces is given. In this paper, we propose an alternative,
provably correct, algorithm for addressing a union of at most $m$
arbitrary-dimensional subspaces, based on the idea of descending filtrations of
subspace arrangements. Our algorithm uses the gradient of a vanishing
polynomial at a point in the variety to find a hyperplane containing the
subspace S passing through that point. By intersecting the variety with this
hyperplane, we obtain a subvariety that contains S, and recursively applying
the procedure until no non-trivial vanishing polynomial exists, our algorithm
eventually identifies S. By repeating this procedure for other points, our
algorithm eventually identifies all the subspaces by returning a basis for
their orthogonal complement. Finally, we develop a variant of the abstract
algorithm, suitable for computations with noisy data. We show by experiments on
synthetic and real data that the proposed algorithm outperforms
state-of-the-art methods on several occasions, thus demonstrating the merit of
the idea of filtrations.
A Statistical Learning Approach to Modal Regression
Authors: Yunlong Feng, Jun Fan, Johan A. K. Suykens
Date: 2017-02-20 00:00:00
Tasks: quantile regression, regression

This paper studies the nonparametric modal regression problem systematically from a statistical learning view. Originally motivated by pursuing a theoretical understanding of the maximum correntropy criterion based regression (MCCR), our study reveals that MCCR with a tending-to-zero scale parameter is essentially modal regression. We show that nonparametric modal regression problem can be approached via the classical empirical risk minimization. Some efforts are then made to develop a framework for analyzing and implementing modal regression. For instance, the modal regression function is described, the modal regression risk is defined explicitly and its \textit{Bayes} rule is characterized; for the sake of computational tractability, the surrogate modal regression risk, which is termed as the generalization risk in our study, is introduced. On the theoretical side, the excess modal regression risk, the excess generalization risk, the function estimation error, and the relations among the above three quantities are studied rigorously. It turns out that under mild conditions, function estimation consistency and convergence may be pursued in modal regression as in vanilla regression protocols, such as mean regression, median regression, and quantile regression. However, it outperforms these regression models in terms of robustness as shown in our study from a re-descending M-estimation view. This coincides with and in return explains the merits of MCCR on robustness. On the practical side, the implementation issues of modal regression including the computational algorithm and the tuning parameters selection are discussed. Numerical assessments on modal regression are also conducted to verify our findings empirically.
Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning
Authors: Abhishek Gupta, Coline Devin, Yuxuan Liu, Pieter Abbeel, Sergey Levine
Date: 2017-03-08 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

People can learn a wide range of tasks from their own experience, but can
also learn from observing other creatures. This can accelerate acquisition of
new skills even when the observed agent differs substantially from the learning
agent in terms of morphology. In this paper, we examine how reinforcement
learning algorithms can transfer knowledge between morphologically different
agents (e.g., different robots). We introduce a problem formulation where two
agents are tasked with learning multiple skills by sharing information. Our
method uses the skills that were learned by both agents to train invariant
feature spaces that can then be used to transfer other skills from one agent to
another. The process of learning these invariant feature spaces can be viewed
as a kind of "analogy making", or implicit learning of partial correspondences
between two distinct domains. We evaluate our transfer learning algorithm in
two simulated robotic manipulation skills, and illustrate that we can transfer
knowledge between simulated robotic arms with different numbers of links, as
well as simulated arms with different actuation mechanisms, where one robot is
torque-driven while the other is tendon-driven.
Algebraic Clustering of Affine Subspaces
Authors: Manolis C. Tsakiris, Rene Vidal
Date: 2015-09-22 00:00:00
Tasks: Clustering, Position

Subspace clustering is an important problem in machine learning with many
applications in computer vision and pattern recognition. Prior work has studied
this problem using algebraic, iterative, statistical, low-rank and sparse
representation techniques. While these methods have been applied to both linear
and affine subspaces, theoretical results have only been established in the
case of linear subspaces. For example, algebraic subspace clustering (ASC) is
guaranteed to provide the correct clustering when the data points are in
general position and the union of subspaces is transversal. In this paper we
study in a rigorous fashion the properties of ASC in the case of affine
subspaces. Using notions from algebraic geometry, we prove that the
homogenization trick, which embeds points in a union of affine subspaces into
points in a union of linear subspaces, preserves the general position of the
points and the transversality of the union of subspaces in the embedded space,
thus establishing the correctness of ASC for affine subpaces.