pwc-archive/papers-with-abstracts huggingface dataset:
A Sequential Set Generation Method for Predicting Set-Valued Outputs
Authors: Tian Gao, Jie Chen, Vijil Chenthamarakshan, Michael Witbrock
Date: 2019-03-12 00:00:00
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Consider a general machine learning setting where the output is a set of
labels or sequences. This output set is unordered and its size varies with the
input. Whereas multi-label classification methods seem a natural first resort,
they are not readily applicable to set-valued outputs because of the growth
rate of the output space; and because conventional sequence generation doesn't
reflect sets' order-free nature. In this paper, we propose a unified
framework--sequential set generation (SSG)--that can handle output sets of
labels and sequences. SSG is a meta-algorithm that leverages any probabilistic
learning method for label or sequence prediction, but employs a proper
regularization such that a new label or sequence is generated repeatedly until
the full set is produced. Though SSG is sequential in nature, it does not
penalize the ordering of the appearance of the set elements and can be applied
to a variety of set output problems, such as a set of classification labels or
sequences. We perform experiments with both benchmark and synthetic data sets
and demonstrate SSG's strong performance over baseline methods.
On the Pitfalls of Measuring Emergent Communication
Authors: Ryan Lowe, Jakob Foerster, Y-Lan Boureau, Joelle Pineau, Yann Dauphin
Date: 2019-03-12 00:00:00
Tasks: Deep Reinforcement Learning, Fault Detection, Reinforcement Learning

How do we know if communication is emerging in a multi-agent system? The vast
majority of recent papers on emergent communication show that adding a
communication channel leads to an increase in reward or task success. This is a
useful indicator, but provides only a coarse measure of the agent's learned
communication abilities. As we move towards more complex environments, it
becomes imperative to have a set of finer tools that allow qualitative and
quantitative insights into the emergence of communication. This may be
especially useful to allow humans to monitor agents' behaviour, whether for
fault detection, assessing performance, or even building trust. In this paper,
we examine a few intuitive existing metrics for measuring communication, and
show that they can be misleading. Specifically, by training deep reinforcement
learning agents to play simple matrix games augmented with a communication
channel, we find a scenario where agents appear to communicate (their messages
provide information about their subsequent action), and yet the messages do not
impact the environment or other agent in any way. We explain this phenomenon
using ablation studies and by visualizing the representations of the learned
policies. We also survey some commonly used metrics for measuring emergent
communication, and provide recommendations as to when these metrics should be
used.
Exploiting Reuse in Pipeline-Aware Hyperparameter Tuning
Authors: Liam Li, Evan Sparks, Kevin Jamieson, Ameet Talwalkar
Date: 2019-03-12 00:00:00

Hyperparameter tuning of multi-stage pipelines introduces a significant
computational burden. Motivated by the observation that work can be reused
across pipelines if the intermediate computations are the same, we propose a
pipeline-aware approach to hyperparameter tuning. Our approach optimizes both
the design and execution of pipelines to maximize reuse. We design pipelines
amenable for reuse by (i) introducing a novel hybrid hyperparameter tuning
method called gridded random search, and (ii) reducing the average training
time in pipelines by adapting early-stopping hyperparameter tuning approaches.
We then realize the potential for reuse during execution by introducing a novel
caching problem for ML workloads which we pose as a mixed integer linear
program (ILP), and subsequently evaluating various caching heuristics relative
to the optimal solution of the ILP. We conduct experiments on simulated and
real-world machine learning pipelines to show that a pipeline-aware approach to
hyperparameter tuning can offer over an order-of-magnitude speedup over
independently evaluating pipeline configurations.
A Review of Reinforcement Learning for Autonomous Building Energy Management
Authors: Karl Mason, Santiago Grijalva
Date: 2019-03-12 00:00:00
Tasks: BIG-bench Machine Learning, energy management, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The area of building energy management has received a significant amount of
interest in recent years. This area is concerned with combining advancements in
sensor technologies, communications and advanced control algorithms to optimize
energy utilization. Reinforcement learning is one of the most prominent machine
learning algorithms used for control problems and has had many successful
applications in the area of building energy management. This research gives a
comprehensive review of the literature relating to the application of
reinforcement learning to developing autonomous building energy management
systems. The main direction for future research and challenges in reinforcement
learning are also outlined.
Learning Gaussian Policies from Corrective Human Feedback
Authors: Daan Wout, Jan Scholten, Carlos Celemin, Jens Kober
Date: 2019-03-12 00:00:00
Tasks: continuous-control, Continuous Control, Gaussian Processes, OpenAI Gym

Learning from human feedback is a viable alternative to control design that
does not require modelling or control expertise. Particularly, learning from
corrective advice garners advantages over evaluative feedback as it is a more
intuitive and scalable format. The current state-of-the-art in this field,
COACH, has proven to be a effective approach for confined problems. However, it
parameterizes the policy with Radial Basis Function networks, which require
meticulous feature space engineering for higher order systems. We introduce
Gaussian Process Coach (GPC), where feature space engineering is avoided by
employing Gaussian Processes. In addition, we use the available policy
uncertainty to 1) inquire feedback samples of maximal utility and 2) to adapt
the learning rate to the teacher's learning phase. We demonstrate that the
novel algorithm outperforms the current state-of-the-art in final performance,
convergence rate and robustness to erroneous feedback in OpenAI Gym continuous
control benchmarks, both for simulated and real human teachers.
Confident Kernel Sparse Coding and Dictionary Learning
Authors: Babak Hosseini, Barbara Hammer
Date: 2019-03-12 00:00:00
Tasks: Dictionary Learning, Time Series, Time Series Analysis

In recent years, kernel-based sparse coding (K-SRC) has received particular
attention due to its efficient representation of nonlinear data structures in
the feature space. Nevertheless, the existing K-SRC methods suffer from the
lack of consistency between their training and test optimization frameworks. In
this work, we propose a novel confident K-SRC and dictionary learning algorithm
(CKSC) which focuses on the discriminative reconstruction of the data based on
its representation in the kernel space. CKSC focuses on reconstructing each
data sample via weighted contributions which are confident in its corresponding
class of data. We employ novel discriminative terms to apply this scheme to
both training and test frameworks in our algorithm. This specific design
increases the consistency of these optimization frameworks and improves the
discriminative performance in the recall phase. In addition, CKSC directly
employs the supervised information in its dictionary learning framework to
enhance the discriminative structure of the dictionary. For empirical
evaluations, we implement our CKSC algorithm on multivariate time-series
benchmarks such as DynTex++ and UTKinect. Our claims regarding the superior
performance of the proposed algorithm are justified throughout comparing its
classification results to the state-of-the-art K-SRC algorithms.
Non-Negative Local Sparse Coding for Subspace Clustering
Authors: Babak Hosseini, Barbara Hammer
Date: 2019-03-12 00:00:00
Tasks: Clustering

Subspace sparse coding (SSC) algorithms have proven to be beneficial to
clustering problems. They provide an alternative data representation in which
the underlying structure of the clusters can be better captured. However, most
of the research in this area is mainly focused on enhancing the sparse coding
part of the problem. In contrast, we introduce a novel objective term in our
proposed SSC framework which focuses on the separability of data points in the
coding space. We also provide mathematical insights into how this
local-separability term improves the clustering result of the SSC framework.
Our proposed non-linear local SSC algorithm (NLSSC) also benefits from the
efficient choice of its sparsity terms and constraints. The NLSSC algorithm is
also formulated in the kernel-based framework (NLKSSC) which can represent the
nonlinear structure of data. In addition, we address the possibility of having
redundancies in sparse coding results and its negative effect on graph-based
clustering problems. We introduce the link-restore post-processing step to
improve the representation graph of non-negative SSC algorithms such as ours.
Empirical evaluations on well-known clustering benchmarks show that our
proposed NLSSC framework results in better clusterings compared to the
state-of-the-art baselines and demonstrate the effectiveness of the
link-restore post-processing in improving the clustering accuracy via
correcting the broken links of the representation graph.
AutoML @ NeurIPS 2018 challenge: Design and Results
Authors: Hugo Jair Escalante, Wei-Wei Tu, Isabelle Guyon, Daniel L. Silver, Evelyne Viegas, Yuqiang Chen, Wenyuan Dai, Qiang Yang
Date: 2019-03-12 00:00:00
Tasks: AutoML, BIG-bench Machine Learning, Lifelong learning

We organized a competition on Autonomous Lifelong Machine Learning with Drift
that was part of the competition program of NeurIPS 2018. This data driven
competition asked participants to develop computer programs capable of solving
supervised learning problems where the i.i.d. assumption did not hold. Large
data sets were arranged in a lifelong learning and evaluation scenario and
CodaLab was used as the challenge platform. The challenge attracted more than
300 participants in its two month duration. This chapter describes the design
of the challenge and summarizes its main results.
Task-oriented Design through Deep Reinforcement Learning
Authors: Junyoung Choi, Minsung Hyun, Nojun Kwak
Date: 2019-03-13 00:00:00
Tasks: BIG-bench Machine Learning, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a new low-cost machine-learning-based methodology which assists
designers in reducing the gap between the problem and the solution in the
design process. Our work applies reinforcement learning (RL) to find the
optimal task-oriented design solution through the construction of the design
action for each task. For this task-oriented design, the 3D design process in
product design is assigned to an action space in Deep RL, and the desired 3D
model is obtained by training each design action according to the task. By
showing that this method achieves satisfactory design even when applied to a
task pursuing multiple goals, we suggest the direction of how machine learning
can contribute to the design process. Also, we have validated with product
designers that this methodology can assist the creative part in the process of
design.
Augment-Reinforce-Merge Policy Gradient for Binary Stochastic Policy
Authors: Yunhao Tang, Mingzhang Yin, Mingyuan Zhou
Date: 2019-03-13 00:00:00

Due to the high variance of policy gradients, on-policy optimization
algorithms are plagued with low sample efficiency. In this work, we propose
Augment-Reinforce-Merge (ARM) policy gradient estimator as an unbiased
low-variance alternative to previous baseline estimators on tasks with binary
action space, inspired by the recent ARM gradient estimator for discrete random
variable models. We show that the ARM policy gradient estimator achieves
variance reduction with theoretical guarantees, and leads to significantly more
stable and faster convergence of policies parameterized by neural networks.
Zero-shot Domain Adaptation Based on Attribute Information
Authors: Masato Ishii, Takashi Takenouchi, Masashi Sugiyama
Date: 2019-03-13 00:00:00
Tasks: Attribute, Domain Adaptation

In this paper, we propose a novel domain adaptation method that can be
applied without target data. We consider the situation where domain shift is
caused by a prior change of a specific factor and assume that we know how the
prior changes between source and target domains. We call this factor an
attribute, and reformulate the domain adaptation problem to utilize the
attribute prior instead of target data. In our method, the source data are
reweighted with the sample-wise weight estimated by the attribute prior and the
data themselves so that they are useful in the target domain. We theoretically
reveal that our method provides more precise estimation of sample-wise
transferability than a straightforward attribute-based reweighting approach.
Experimental results with both toy datasets and benchmark datasets show that
our method can perform well, though it does not use any target data.
DeepCount: Crowd Counting with WiFi via Deep Learning
Authors: Shangqing Liu, Yanchao Zhao, Fanggang Xue, Bing Chen, Xiang Chen
Date: 2019-03-13 00:00:00
Tasks: Activity Recognition, Crowd Counting, Deep Learning

Recently, the research of wireless sensing has achieved more intelligent
results, and the intelligent sensing of human location and activity can be
realized by means of WiFi devices. However, most of the current human
environment perception work is limited to a single person's environment,
because the environment in which multiple people exist is more complicated than
the environment in which a single person exists. In order to solve the problem
of human behavior perception in a multi-human environment, we first proposed a
solution to achieve crowd counting (inferred population) using deep learning in
a closed environment with WIFI signals - DeepCout, which is the first in a
multi-human environment. step. Since the use of WiFi to directly count the
crowd is too complicated, we use deep learning to solve this problem, use
Convolutional Neural Network(CNN) to automatically extract the relationship
between the number of people and the channel, and use Long Short Term
Memory(LSTM) to resolve the dependencies of number of people and Channel State
Information(CSI) . To overcome the massive labelled data required by deep
learning method, we add an online learning mechanism to determine whether or
not someone is entering/leaving the room by activity recognition model, so as
to correct the deep learning model in the fine-tune stage, which, in turn,
reduces the required training data and make our method evolving over time. The
system of DeepCount is performed and evaluated on the commercial WiFi devices.
By massive training samples, our end-to-end learning approach can achieve an
average of 86.4% prediction accuracy in an environment of up to 5 people.
Meanwhile, by the amendment mechanism of the activity recognition model to
judge door switch to get the variance of crowd to amend deep learning predicted
results, the accuracy is up to 90%.
What relations are reliably embeddable in Euclidean space?
Authors: Robi Bhattacharjee, Sanjoy Dasgupta
Date: 2019-03-13 00:00:00
Tasks: Knowledge Graphs, Relation

We consider the problem of embedding a relation, represented as a directed graph, into Euclidean space. For three types of embeddings motivated by the recent literature on knowledge graphs, we obtain characterizations of which relations they are able to capture, as well as bounds on the minimal dimensionality and precision needed.
Personal Dynamic Cost-Aware Sensing for Latent Context Detection
Authors: Saar Tal, Bracha Shapira, Lior Rokach
Date: 2019-03-13 00:00:00

In the past decade, the usage of mobile devices has gone far beyond simple
activities like calling and texting. Today, smartphones contain multiple
embedded sensors and are able to collect useful sensing data about the user and
infer the user's context. The more frequent the sensing, the more accurate the
context. However, continuous sensing results in huge energy consumption,
decreasing the battery's lifetime. We propose a novel approach for cost-aware
sensing when performing continuous latent context detection. The suggested
method dynamically determines user's sensors sampling policy based on three
factors: (1) User's last known context; (2) Predicted information loss using
KL-Divergence; and (3) Sensors' sampling costs. The objective function aims at
minimizing both sampling cost and information loss. The method is based on
various machine learning techniques including autoencoder neural networks for
latent context detection, linear regression for information loss prediction,
and convex optimization for determining the optimal sampling policy. To
evaluate the suggested method, we performed a series of tests on real-world
data recorded at a high-frequency rate; the data was collected from six mobile
phone sensors of twenty users over the course of a week. Results show that by
applying a dynamic sampling policy, our method naturally balances information
loss and energy consumption and outperforms the static approach.% We compared
the performance of our method with another state of the art dynamic sampling
method and demonstrate its consistent superiority in various measures. %Our
methods outperformed, and were able to improve we achieved better results in
either sampling cost or information loss, and in some cases we improved both.
Online Budgeted Learning for Classifier Induction
Authors: Eran Fainman, Bracha Shapira, Lior Rokach, Yisroel Mirsky
Date: 2019-03-13 00:00:00

In real-world machine learning applications, there is a cost associated with
sampling of different features. Budgeted learning can be used to select which
feature-values to acquire from each instance in a dataset, such that the best
model is induced under a given constraint. However, this approach is not
possible in the domain of online learning since one may not retroactively
acquire feature-values from past instances. In online learning, the challenge
is to find the optimum set of features to be acquired from each instance upon
arrival from a data stream. In this paper we introduce the issue of online
budgeted learning and describe a general framework for addressing this
challenge. We propose two types of feature value acquisition policies based on
the multi-armed bandit problem: random and adaptive. Adaptive policies perform
online adjustments according to new information coming from a data stream,
while random policies are not sensitive to the information that arrives from
the data stream. Our comparative study on five real-world datasets indicates
that adaptive policies outperform random policies for most budget limitations
and datasets. Furthermore, we found that in some cases adaptive policies
achieve near-optimal results.
DeepOBS: A Deep Learning Optimizer Benchmark Suite
Authors: Frank Schneider, Lukas Balles, Philipp Hennig
Date: 2019-03-13 00:00:00
Proceeding: ICLR 2019 5
Tasks: Benchmarking, Deep Learning, image-classification, Image Classification, Stochastic Optimization

Because the choice and tuning of the optimizer affects the speed, and
ultimately the performance of deep learning, there is significant past and
recent research in this area. Yet, perhaps surprisingly, there is no generally
agreed-upon protocol for the quantitative and reproducible evaluation of
optimization strategies for deep learning. We suggest routines and benchmarks
for stochastic optimization, with special focus on the unique aspects of deep
learning, such as stochasticity, tunability and generalization. As the primary
contribution, we present DeepOBS, a Python package of deep learning
optimization benchmarks. The package addresses key challenges in the
quantitative assessment of stochastic optimizers, and automates most steps of
benchmarking. The library includes a wide and extensible set of ready-to-use
realistic optimization problems, such as training Residual Networks for image
classification on ImageNet or character-level language prediction models, as
well as popular classics like MNIST and CIFAR-10. The package also provides
realistic baseline results for the most popular optimizers on these test
problems, ensuring a fair comparison to the competition when benchmarking new
optimizers, and without having to run costly experiments. It comes with output
back-ends that directly produce LaTeX code for inclusion in academic
publications. It supports TensorFlow and is available open source.
Improving Transparency of Deep Neural Inference Process
Authors: Hiroshi Kuwajima, Masayuki Tanaka, Masatoshi Okutomi
Date: 2019-03-13 00:00:00

Deep learning techniques are rapidly advanced recently, and becoming a
necessity component for widespread systems. However, the inference process of
deep learning is black-box, and not very suitable to safety-critical systems
which must exhibit high transparency. In this paper, to address this black-box
limitation, we develop a simple analysis method which consists of 1) structural
feature analysis: lists of the features contributing to inference process, 2)
linguistic feature analysis: lists of the natural language labels describing
the visual attributes for each feature contributing to inference process, and
3) consistency analysis: measuring consistency among input data, inference
(label), and the result of our structural and linguistic feature analysis. Our
analysis is simplified to reflect the actual inference process for high
transparency, whereas it does not include any additional black-box mechanisms
such as LSTM for highly human readable results. We conduct experiments and
discuss the results of our analysis qualitatively and quantitatively, and come
to believe that our work improves the transparency of neural networks.
Evaluated through 12,800 human tasks, 75% workers answer that input data and
result of our feature analysis are consistent, and 70% workers answer that
inference (label) and result of our feature analysis are consistent. In
addition to the evaluation of the proposed analysis, we find that our analysis
also provide suggestions, or possible next actions such as expanding neural
network complexity or collecting training data to improve a neural network.