pwc-archive/papers-with-abstracts huggingface dataset:
Improving Missing Data Imputation with Deep Generative Models
Authors: Ramiro D. Camino, Christian A. Hammerschmidt, Radu State
Date: 2019-02-27 00:00:00
Tasks: Imputation, Missing Values

Datasets with missing values are very common on industry applications, and
they can have a negative impact on machine learning models. Recent studies
introduced solutions to the problem of imputing missing values based on deep
generative models. Previous experiments with Generative Adversarial Networks
and Variational Autoencoders showed interesting results in this domain, but it
is not clear which method is preferable for different use cases. The goal of
this work is twofold: we present a comparison between missing data imputation
solutions based on deep generative models, and we propose improvements over
those methodologies. We run our experiments using known real life datasets with
different characteristics, removing values at random and reconstructing them
with several imputation techniques. Our results show that the presence or
absence of categorical variables can alter the selection of the best model, and
that some models are more stable than others after similar runs with different
random number generator seeds.
Local Function Complexity for Active Learning via Mixture of Gaussian Processes
Authors: Danny Panknin, Stefan Chmiela, Klaus-Robert Müller, Shinichi Nakajima
Date: 2019-02-27 00:00:00
Tasks: Active Learning, Gaussian Processes, GPR, regression

Inhomogeneities in real-world data, e.g., due to changes in the observation noise level or variations in the structural complexity of the source function, pose a unique set of challenges for statistical inference. Accounting for them can greatly improve predictive power when physical resources or computation time is limited. In this paper, we draw on recent theoretical results on the estimation of local function complexity (LFC), derived from the domain of local polynomial smoothing (LPS), to establish a notion of local structural complexity, which is used to develop a model-agnostic active learning (AL) framework. Due to its reliance on pointwise estimates, the LPS model class is not robust and scalable concerning large input space dimensions that typically come along with real-world problems. Here, we derive and estimate the Gaussian process regression (GPR)-based analog of the LPS-based LFC and use it as a substitute in the above framework to make it robust and scalable. We assess the effectiveness of our LFC estimate in an AL application on a prototypical low-dimensional synthetic dataset, before taking on the challenging real-world task of reconstructing a quantum chemical force field for a small organic molecule and demonstrating state-of-the-art performance with a significantly reduced training demand.
Robust Decision Trees Against Adversarial Examples
Authors: Hongge Chen, huan zhang, Duane Boning, Cho-Jui Hsieh
Date: 2019-02-27 00:00:00
Tasks: Adversarial Attack, Adversarial Defense

Although adversarial examples and model robustness have been extensively studied in the context of linear models and neural networks, research on this issue in tree-based models and how to make tree-based models robust against adversarial examples is still limited. In this paper, we show that tree based models are also vulnerable to adversarial examples and develop a novel algorithm to learn robust trees. At its core, our method aims to optimize the performance under the worst-case perturbation of input features, which leads to a max-min saddle point problem. Incorporating this saddle point objective into the decision tree building procedure is non-trivial due to the discrete nature of trees --- a naive approach to finding the best split according to this saddle point objective will take exponential time. To make our approach practical and scalable, we propose efficient tree building algorithms by approximating the inner minimizer in this saddle point problem, and present efficient implementations for classical information gain based trees as well as state-of-the-art tree boosting models such as XGBoost. Experimental results on real world datasets demonstrate that the proposed algorithms can substantially improve the robustness of tree-based models against adversarial examples.
Unsupervised Attention Mechanism across Neural Network Layers
Authors: Baihan Lin
Date: 2019-02-27 00:00:00
Tasks: Few-Shot Learning, Image Classification, Model Selection, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Inspired by the adaptation phenomenon of neuronal firing, we propose an unsupervised attention mechanism (UAM) which computes the statistical regularity in the implicit space of neural networks under the Minimum Description Length (MDL) principle. Treating the neural network optimization process as a partially observable model selection problem, UAM constrained the implicit space by a normalization factor, the universal code length. We compute this universal code incrementally across neural network layers and demonstrated the flexibility to include data priors such as top-down attention and other oracle information. Empirically, our approach outperforms existing normalization methods in tackling limited, imbalanced and non-stationary input distribution in computer vision and reinforcement learning tasks. Lastly, UAM tracks dependency and critical learning stages across layers and recurrent time steps of deep networks.
From explanation to synthesis: Compositional program induction for learning from demonstration
Authors: Michael Burke, Svetlin Penkov, Subramanian Ramamoorthy
Date: 2019-02-27 00:00:00
Tasks: Program induction

Hybrid systems are a compact and natural mechanism with which to address problems in robotics. This work introduces an approach to learning hybrid systems from demonstrations, with an emphasis on extracting models that are explicitly verifiable and easily interpreted by robot operators. We fit a sequence of controllers using sequential importance sampling under a generative switching proportional controller task model. Here, we parameterise controllers using a proportional gain and a visually verifiable joint angle goal. Inference under this model is challenging, but we address this by introducing an attribution prior extracted from a neural end-to-end visuomotor control model. Given the sequence of controllers comprising a task, we simplify the trace using grammar parsing strategies, taking advantage of the sequence compositionality, before grounding the controllers by training perception networks to predict goals given images. Using this approach, we are successfully able to induce a program for a visuomotor reaching task involving loops and conditionals from a single demonstration and a neural end-to-end model. In addition, we are able to discover the program used for a tower building task. We argue that computer program-like control systems are more interpretable than alternative end-to-end learning approaches, and that hybrid systems inherently allow for better generalisation across task configurations.
Linear Time Visualization and Search in Big Data using Pixellated Factor Space Mapping
Authors: Fionn Murtagh
Date: 2019-02-27 00:00:00
Tasks: Clustering

It is demonstrated how linear computational time and storage efficient
approaches can be adopted when analyzing very large data sets. More
importantly, interpretation is aided and furthermore, basic processing is
easily supported. Such basic processing can be the use of supplementary, i.e.
contextual, elements, or particular associations. Furthermore pixellated grid
cell contents can be utilized as a basic form of imposed clustering. For a
given resolution level, here related to an associated m-adic ($m$ here is a
non-prime integer) or p-adic ($p$ is prime) number system encoding, such
pixellated mapping results in partitioning. The association of a range of
m-adic and p-adic representations leads naturally to an imposed hierarchical
clustering, with partition levels corresponding to the m-adic-based and
p-adic-based representations and displays. In these clustering embedding and
imposed cluster structures, some analytical visualization and search
applications are described
F10-SGD: Fast Training of Elastic-net Linear Models for Text Classification and Named-entity Recognition
Authors: Stanislav Peshterliev, Alexander Hsieh, Imre Kiss
Date: 2019-02-27 00:00:00
Tasks: Classification, General Classification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, text-classification, Text Classification

Voice-assistants text classification and named-entity recognition (NER)
models are trained on millions of example utterances. Because of the large
datasets, long training time is one of the bottlenecks for releasing improved
models. In this work, we develop F10-SGD, a fast optimizer for text
classification and NER elastic-net linear models. On internal datasets, F10-SGD
provides 4x reduction in training time compared to the OWL-QN optimizer without
loss of accuracy or increase in model size. Furthermore, we incorporate biased
sampling that prioritizes harder examples towards the end of the training. As a
result, in addition to faster training, we were able to obtain statistically
significant accuracy improvements for NER.
  On public datasets, F10-SGD obtains 22% faster training time compared to
FastText for text classification. And, 4x reduction in training time compared
to CRFSuite OWL-QN for NER.
Unifying Ensemble Methods for Q-learning via Social Choice Theory
Authors: Rishav Chourasia, Adish Singla
Date: 2019-02-27 00:00:00
Tasks: Diversity, Q-Learning, Reinforcement Learning, Reinforcement Learning (RL)

Ensemble methods have been widely applied in Reinforcement Learning (RL) in order to enhance stability, increase convergence speed, and improve exploration. These methods typically work by employing an aggregation mechanism over actions of different RL algorithms. We show that a variety of these methods can be unified by drawing parallels from committee voting rules in Social Choice Theory. We map the problem of designing an action aggregation mechanism in an ensemble method to a voting problem which, under different voting rules, yield popular ensemble-based RL algorithms like Majority Voting Q-learning or Bootstrapped Q-learning. Our unification framework, in turn, allows us to design new ensemble-RL algorithms with better performance. For instance, we map two diversity-centered committee voting rules, namely Single Non-Transferable Voting Rule and Chamberlin-Courant Rule, into new RL algorithms that demonstrate excellent exploratory behavior in our experiments.
Provable Guarantees for Gradient-Based Meta-Learning
Authors: Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar
Date: 2019-02-27 00:00:00
Tasks: Deep Learning, Generalization Bounds, Meta-Learning

We study the problem of meta-learning through the lens of online convex optimization, developing a meta-algorithm bridging the gap between popular gradient-based meta-learning and classical regularization-based multi-task transfer methods. Our method is the first to simultaneously satisfy good sample efficiency guarantees in the convex setting, with generalization bounds that improve with task-similarity, while also being computationally scalable to modern deep learning architectures and the many-task setting. Despite its simplicity, the algorithm matches, up to a constant factor, a lower bound on the performance of any such parameter-transfer method under natural task similarity assumptions. We use experiments in both convex and deep learning settings to verify and demonstrate the applicability of our theory.
Learning Dynamic-Objective Policies from a Class of Optimal Trajectories
Authors: Christopher Iliffe Sprague, Dario Izzo, Petter Ögren
Date: 2019-02-27 00:00:00
Tasks: Imitation Learning

Optimal state-feedback controllers, capable of changing between different objective functions, are advantageous to systems in which unexpected situations may arise. However, synthesising such controllers, even for a single objective, is a demanding process. In this paper, we present a novel and straightforward approach to synthesising these policies through a combination of trajectory optimisation, homotopy continuation, and imitation learning. We use numerical continuation to efficiently generate optimal demonstrations across several objectives and boundary conditions, and use these to train our policies. Additionally, we demonstrate the ability of our policies to effectively learn families of optimal state-feedback controllers, which can be used to change objective functions online. We illustrate this approach across two trajectory optimisation problems, an inverted pendulum swingup and a spacecraft orbit transfer, and show that the synthesised policies, when evaluated in simulation, produce trajectories that are near-optimal. These results indicate the benefit of trajectory optimisation and homotopy continuation to the synthesis of controllers in dynamic-objective contexts.
Alternating Synthetic and Real Gradients for Neural Language Modeling
Authors: Fangxin Shang, Hao Zhang
Date: 2019-02-27 00:00:00
Tasks: Language Modeling, Language Modelling

Training recurrent neural networks (RNNs) with backpropagation through time (BPTT) has known drawbacks such as being difficult to capture longterm dependencies in sequences. Successful alternatives to BPTT have not yet been discovered. Recently, BP with synthetic gradients by a decoupled neural interface module has been proposed to replace BPTT for training RNNs. On the other hand, it has been shown that the representations learned with synthetic and real gradients are different though they are functionally identical. In this project, we explore ways of combining synthetic and real gradients with application to neural language modeling tasks. Empirically, we demonstrate the effectiveness of alternating training with synthetic and real gradients after periodic warm restarts on language modeling tasks.
Zoho at SemEval-2019 Task 9: Semi-supervised Domain Adaptation using Tri-training for Suggestion Mining
Authors: Sai Prasanna, Sri Ananda Seelan
Date: 2019-02-27 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Domain Adaptation, General Classification, Language Modeling, Language Modelling, Semi-supervised Domain Adaptation, Sentence, Sentence Classification, Suggestion mining

This paper describes our submission for the SemEval-2019 Suggestion Mining
task. A simple Convolutional Neural Network (CNN) classifier with contextual
word representations from a pre-trained language model was used for sentence
classification. The model is trained using tri-training, a semi-supervised
bootstrapping mechanism for labelling unseen data. Tri-training proved to be an
effective technique to accommodate domain shift for cross-domain suggestion
mining (Subtask B) where there is no hand labelled training data. For in-domain
evaluation (Subtask A), we use the same technique to augment the training set.
Our system ranks thirteenth in Subtask A with an $F_1$-score of 68.07 and third
in Subtask B with an $F_1$-score of 81.94.
Learning Factored Markov Decision Processes with Unawareness
Authors: Craig Innes, Alex Lascarides
Date: 2019-02-27 00:00:00

Methods for learning and planning in sequential decision problems often
assume the learner is aware of all possible states and actions in advance. This
assumption is sometimes untenable. In this paper, we give a method to learn
factored markov decision problems from both domain exploration and expert
assistance, which guarantees convergence to near-optimal behaviour, even when
the agent begins unaware of factors critical to success. Our experiments show
our agent learns optimal behaviour on small and large problems, and that
conserving information on discovering new possibilities results in faster
convergence.
Architecting Dependable Learning-enabled Autonomous Systems: A Survey
Authors: Chih-Hong Cheng, Dhiraj Gulati, Rongjie Yan
Date: 2019-02-27 00:00:00
Tasks: Survey

We provide a summary over architectural approaches that can be used to
construct dependable learning-enabled autonomous systems, with a focus on
automated driving. We consider three technology pillars for architecting
dependable autonomy, namely diverse redundancy, information fusion, and runtime
monitoring. For learning-enabled components, we additionally summarize recent
architectural approaches to increase the dependability beyond standard
convolutional neural networks. We conclude the study with a list of promising
research directions addressing the challenges of existing approaches.
Road is Enough! Extrinsic Calibration of Non-overlapping Stereo Camera and LiDAR using Road Information
Authors: Jinyong Jeong, Lucas Y. Cho, Ayoung Kim
Date: 2019-02-27 00:00:00
Tasks: global-optimization

This paper presents a framework for the targetless extrinsic calibration of
stereo cameras and Light Detection and Ranging (LiDAR) sensors with a
non-overlapping Field of View (FOV). In order to solve the extrinsic
calibrations problem under such challenging configuration, the proposed
solution exploits road markings as static and robust features among the various
dynamic objects that are present in urban environment. First, this study
utilizes road markings that are commonly captured by the two sensor modalities
to select informative images for estimating the extrinsic parameters. In order
to accomplish stable optimization, multiple cost functions are defined,
including Normalized Information Distance (NID), edge alignment and, plane
fitting cost. Therefore a smooth cost curve is formed for global optimization
to prevent convergence to the local optimal point. We further evaluate each
cost function by examining parameter sensitivity near the optimal point.
Another key characteristic of extrinsic calibration, repeatability, is analyzed
by conducting the proposed method multiple times with varying randomly
perturbed initial points.
When a Tweet is Actually Sexist. A more Comprehensive Classification of Different Online Harassment Categories and The Challenges in NLP
Authors: Sima Sharifirad, Stan Matwin
Date: 2019-02-27 00:00:00
Tasks: General Classification

Sexism is very common in social media and makes the boundaries of freedom
tighter for feminist and female users. There is still no comprehensive
classification of sexism attracting natural language processing techniques.
Categorizing sexism in social media in the categories of hostile or benevolent
sexism are so general that simply ignores the other types of sexism happening
in these media. This paper proposes a more comprehensive and in-depth
categories of online harassment in social media e.g. twitter into the following
categories, "Indirect harassment", "Information threat", "sexual harassment",
"Physical harassment" and "Not sexist" and address the challenge of labeling
them along with presenting the classification result of the categories. It is
preliminary work applying machine learning to learn the concept of sexism and
distinguishes itself by looking at more precise categories of sexism in social
media.
Polynomial-time Algorithms for Multiple-arm Identification with Full-bandit Feedback
Authors: Yuko Kuroki, Liyuan Xu, Atsushi Miyauchi, Junya Honda, Masashi Sugiyama
Date: 2019-02-27 00:00:00

We study the problem of stochastic combinatorial pure exploration (CPE), where an agent sequentially pulls a set of single arms (a.k.a. a super arm) and tries to find the best super arm. Among a variety of problem settings of the CPE, we focus on the full-bandit setting, where we cannot observe the reward of each single arm, but only the sum of the rewards. Although we can regard the CPE with full-bandit feedback as a special case of pure exploration in linear bandits, an approach based on linear bandits is not computationally feasible since the number of super arms may be exponential. In this paper, we first propose a polynomial-time bandit algorithm for the CPE under general combinatorial constraints and provide an upper bound of the sample complexity. Second, we design an approximation algorithm for the 0-1 quadratic maximization problem, which arises in many bandit algorithms with confidence ellipsoids. Based on our approximation algorithm, we propose novel bandit algorithms for the top-k selection problem, and prove that our algorithms run in polynomial time. Finally, we conduct experiments on synthetic and real-world datasets, and confirm the validity of our theoretical analysis in terms of both the computation time and the sample complexity.
Multiresolution Graph Attention Networks for Relevance Matching
Authors: Ting Zhang, Bang Liu, Di Niu, Kunfeng Lai, Yu Xu
Date: 2019-02-27 00:00:00
Tasks: Graph Attention, Information Retrieval, Paraphrase Identification, Question Answering, Retrieval, Text Matching

A large number of deep learning models have been proposed for the text
matching problem, which is at the core of various typical natural language
processing (NLP) tasks. However, existing deep models are mainly designed for
the semantic matching between a pair of short texts, such as paraphrase
identification and question answering, and do not perform well on the task of
relevance matching between short-long text pairs. This is partially due to the
fact that the essential characteristics of short-long text matching have not
been well considered in these deep models. More specifically, these methods
fail to handle extreme length discrepancy between text pieces and neither can
they fully characterize the underlying structural information in long text
documents. In this paper, we are especially interested in relevance matching
between a piece of short text and a long document, which is critical to
problems like query-document matching in information retrieval and web
searching. To extract the structural information of documents, an undirected
graph is constructed, with each vertex representing a keyword and the weight of
an edge indicating the degree of interaction between keywords. Based on the
keyword graph, we further propose a Multiresolution Graph Attention Network to
learn multi-layered representations of vertices through a Graph Convolutional
Network (GCN), and then match the short text snippet with the graphical
representation of the document with the attention mechanisms applied over each
layer of the GCN. Experimental results on two datasets demonstrate that our
graph approach outperforms other state-of-the-art deep matching models.
Distributed Edge Caching via Reinforcement Learning in Fog Radio Access Networks
Authors: Liuyang Lu, Yanxiang Jiang, Mehdi Bennis, Zhiguo Ding, Fu-Chun Zheng, Xiaohu You
Date: 2019-02-27 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, the distributed edge caching problem in fog radio access
networks (F-RANs) is investigated. By considering the unknown spatio-temporal
content popularity and user preference, a user request model based on hidden
Markov process is proposed to characterize the fluctuant spatio-temporal
traffic demands in F-RANs. Then, the Q-learning method based on the
reinforcement learning (RL) framework is put forth to seek the optimal caching
policy in a distributed manner, which enables fog access points (F-APs) to
learn and track the potential dynamic process without extra communications
cost. Furthermore, we propose a more efficient Q-learning method with value
function approximation (Q-VFA-learning) to reduce complexity and accelerate
convergence. Simulation results show that the performance of our proposed
method is superior to those of the traditional methods.
TrIK-SVM : an alternative decomposition for kernel methods in Krein spaces
Authors: Gaëlle Loosli
Date: 2019-02-27 00:00:00

The proposed work aims at proposing a alternative kernel decomposition in the
context of kernel machines with indefinite kernels. The original paper of KSVM
(SVM in Kre\v{i}n spaces) uses the eigen-decomposition, our proposition avoids
this decompostion. We explain how it can help in designing an algorithm that
won't require to compute the full kernel matrix. Finally we illustrate the good
behavior of the proposed method compared to KSVM.