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33,102 | Geometry-Based Data Generation | cs.LG | Many generative models attempt to replicate the density of their input data.
However, this approach is often undesirable, since data density is highly
affected by sampling biases, noise, and artifacts. We propose a method called
SUGAR (Synthesis Using Geometrically Aligned Random-walks) that uses a
diffusion process to... | computer science |
33,103 | Attack RMSE Leaderboard: An Introduction and Case Study | cs.LG | In this manuscript, we briefly introduce several tricks to climb the
leaderboards which use RMSE for evaluation without exploiting any training
data. | computer science |
33,104 | Graph2Seq: Scalable Learning Dynamics for Graphs | cs.LG | Neural networks have been shown to be an effective tool for learning
algorithms over graph-structured data. However, graph representation
techniques--that convert graphs to real-valued vectors for use with neural
networks--are still in their infancy. Recent works have proposed several
approaches (e.g., graph convolutio... | computer science |
33,105 | DESlib: A Dynamic ensemble selection library in Python | cs.LG | DESlib is an open-source python library providing the implementation of
several dynamic selection techniques. The library is divided into three
modules: (i) dcs, containing the implementation of dynamic classifier selection
methods (DCS); (ii) des, containing the implementation of dynamic ensemble
selection methods (DE... | computer science |
33,106 | Tackling Multilabel Imbalance through Label Decoupling and Data
Resampling Hybridization | cs.LG | The learning from imbalanced data is a deeply studied problem in standard
classification and, in recent times, also in multilabel classification. A
handful of multilabel resampling methods have been proposed in late years,
aiming to balance the labels distribution. However these methods have to face a
new obstacle, spe... | computer science |
33,107 | Dealing with Difficult Minority Labels in Imbalanced Mutilabel Data Sets | cs.LG | Multilabel classification is an emergent data mining task with a broad range
of real world applications. Learning from imbalanced multilabel data is being
deeply studied latterly, and several resampling methods have been proposed in
the literature. The unequal label distribution in most multilabel datasets,
with dispar... | computer science |
33,108 | GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement
Learning Algorithms | cs.LG | In continuous action domains, standard deep reinforcement learning algorithms
like DDPG suffer from inefficient exploration when facing sparse or deceptive
reward problems. Conversely, evolutionary and developmental methods focusing on
exploration like novelty search, quality-diversity or goal exploration
processes are... | computer science |
33,109 | Understanding the Role of Adaptivity in Machine Teaching: The Case of
Version Space Learners | cs.LG | In real-world applications of education and human teaching, an effective
teacher chooses the next example intelligently based on the learner's current
state. However, most of the existing works in algorithmic machine teaching
focus on the batch setting, where adaptivity plays no role. In this paper, we
study the case o... | computer science |
33,110 | Stronger generalization bounds for deep nets via a compression approach | cs.LG | Deep nets generalize well despite having more parameters than the number of
training samples. Recent works try to give an explanation using PAC-Bayes and
Margin-based analyses, but do not as yet result in sample complexity bounds
better than naive parameter counting. The current paper shows generalization
bounds that'r... | computer science |
33,111 | Semi-Supervised Learning on Graphs Based on Local Label Distributions | cs.LG | In this work, we propose a novel approach for the semi-supervised node
classification. Precisely, we propose a method which takes labels in the local
neighborhood of different locality levels into consideration. Most previous
approaches that tackle the problem of node classification consider nodes to be
similar, if the... | computer science |
33,112 | Gradient Boosting With Piece-Wise Linear Regression Trees | cs.LG | Gradient boosting using decision trees as base learners, so called Gradient
Boosted Decision Trees (GBDT), is a very successful ensemble learning algorithm
widely used across a variety of applications. Recently, various GDBT
construction algorithms and implementation have been designed and heavily
optimized in some ver... | computer science |
33,113 | Learning Determinantal Point Processes by Sampling Inferred Negatives | cs.LG | Determinantal Point Processes (DPPs) have attracted significant interest from
the machine-learning community due to their ability to elegantly and tractably
model the delicate balance between quality and diversity of sets. We consider
learning DPPs from data, a key task for DPPs; for this task, we introduce a
novel opt... | computer science |
33,114 | MPC-Inspired Neural Network Policies for Sequential Decision Making | cs.LG | In this paper we investigate the use of MPC-inspired neural network policies
for sequential decision making. We introduce an extension to the DAgger
algorithm for training such policies and show how they have improved training
performance and generalization capabilities. We take advantage of this
extension to show scal... | computer science |
33,115 | Constrained Convolutional-Recurrent Networks to Improve Speech Quality
with Low Impact on Recognition Accuracy | cs.LG | For a speech-enhancement algorithm, it is highly desirable to simultaneously
improve perceptual quality and recognition rate. Thanks to computational costs
and model complexities, it is challenging to train a model that effectively
optimizes both metrics at the same time. In this paper, we propose a method for
speech e... | computer science |
33,116 | Variance-based Gradient Compression for Efficient Distributed Deep
Learning | cs.LG | Due to the substantial computational cost, training state-of-the-art deep
neural networks for large-scale datasets often requires distributed training
using multiple computation workers. However, by nature, workers need to
frequently communicate gradients, causing severe bottlenecks, especially on
lower bandwidth conne... | computer science |
33,117 | An Alternative View: When Does SGD Escape Local Minima? | cs.LG | Stochastic gradient descent (SGD) is widely used in machine learning.
Although being commonly viewed as a fast but not accurate version of gradient
descent (GD), it always finds better solutions than GD for modern neural
networks.
In order to understand this phenomenon, we take an alternative view that SGD
is working... | computer science |
33,118 | Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM
Encoder-Decoder Architecture | cs.LG | In this paper, we propose a deep learning-based vehicle trajectory prediction
technique which can generate the future trajectory sequence of the surrounding
vehicles in real time. We employ the encoder-decoder architecture which
analyzes the pattern underlying in the past trajectory using the long short
term memory (LS... | computer science |
33,119 | Inductive Framework for Multi-Aspect Streaming Tensor Completion with
Side Information | cs.LG | Low-rank tensor completion is a well-studied problem and has applications in
various fields. However, in many real-world applications the data is dynamic,
i.e., the tensor grows as new data arrives. Besides the tensor, in many
real-world scenarios, side information is also available in the form of
matrices which also g... | computer science |
33,120 | Online Convex Optimization for Cumulative Constraints | cs.LG | We propose an algorithm for online convex optimization which examines a
clipped long-term constraint of the form $\sum\limits_{t=1}^T[g(x_t)]_+$,
encoding the cumulative constraint violation. Previous literature has focused
on long-term constraints of the form $\sum\limits_{t=1}^Tg(x_t)$, for which
strictly feasible so... | computer science |
33,121 | BDA-PCH: Block-Diagonal Approximation of Positive-Curvature Hessian for
Training Neural Networks | cs.LG | We propose a block-diagonal approximation of the positive-curvature Hessian
(BDA-PCH) matrix to measure curvature. Our proposed BDAPCH matrix is memory
efficient and can be applied to any fully-connected neural networks where the
activation and criterion functions are twice differentiable. Particularly, our
BDA-PCH mat... | computer science |
33,122 | On the Optimization of Deep Networks: Implicit Acceleration by
Overparameterization | cs.LG | Conventional wisdom in deep learning states that increasing depth improves
expressiveness but complicates optimization. This paper suggests that,
sometimes, increasing depth can speed up optimization. The effect of depth on
optimization is decoupled from expressiveness by focusing on settings where
additional layers am... | computer science |
33,123 | Deep Echo State Networks for Diagnosis of Parkinson's Disease | cs.LG | In this paper, we introduce a novel approach for diagnosis of Parkinson's
Disease (PD) based on deep Echo State Networks (ESNs). The identification of PD
is performed by analyzing the whole time-series collected from a tablet device
during the sketching of spiral tests, without the need for feature extraction
and data ... | computer science |
33,124 | Tail bounds for volume sampled linear regression | cs.LG | The $n \times d$ design matrix in a linear regression problem is given, but
the response for each point is hidden unless explicitly requested. The goal is
to observe only a small number $k \ll n$ of the responses, and then produce a
weight vector whose sum of square loss over all points is at most $1+\epsilon$
times th... | computer science |
33,125 | Multi-resolution Tensor Learning for Large-Scale Spatial Data | cs.LG | High-dimensional tensor models are notoriously computationally expensive to
train. We present a meta-learning algorithm, MMT, that can significantly speed
up the process for spatial tensor models. MMT leverages the property that
spatial data can be viewed at multiple resolutions, which are related by
coarsening and fin... | computer science |
33,126 | Online Learning with an Unknown Fairness Metric | cs.LG | We consider the problem of online learning in the linear contextual bandits
setting, but in which there are also strong individual fairness constraints
governed by an unknown similarity metric. These constraints demand that we
select similar actions or individuals with approximately equal probability
(arXiv:1104.3913),... | computer science |
33,127 | Constant Regret, Generalized Mixability, and Mirror Descent | cs.LG | We consider the setting of prediction with expert advice; a learner makes
predictions by aggregating those of a group of experts. Under this setting, and
with the right choice of loss function and "mixing" algorithm, it is possible
for the learner to achieve constant regret regardless of the number of
prediction rounds... | computer science |
33,128 | Do Deep Learning Models Have Too Many Parameters? An Information Theory
Viewpoint | cs.LG | Deep learning models often have more parameters than observations, and still
perform well. This is sometimes described as a paradox. In this work, we show
experimentally that despite their huge number of parameters, deep neural
networks can compress the data losslessly even when taking the cost of encoding
the paramete... | computer science |
33,129 | Local Differential Privacy for Evolving Data | cs.LG | There are now several large scale deployments of differential privacy used to
track statistical information about users. However, these systems periodically
recollect the data and recompute the statistics using algorithms designed for a
single use and as a result do not provide meaningful privacy guarantees over
long t... | computer science |
33,130 | Scalable Label Propagation for Multi-relational Learning on Tensor
Product Graph | cs.LG | Label propagation on the tensor product of multiple graphs can infer
multi-relations among the entities across the graphs by learning labels in a
tensor. However, the tensor formulation is only empirically scalable up to
three graphs due to the exponential complexity of computing tensors. In this
paper, we propose an o... | computer science |
33,131 | Globally Consistent Algorithms for Mixture of Experts | cs.LG | Mixture-of-Experts (MoE) is a widely popular neural network architecture and
is a basic building block of highly successful modern neural networks, for
example, Gated Recurrent Units (GRU) and Attention networks. However, despite
the empirical success, finding an efficient and provably consistent algorithm
to learn the... | computer science |
33,132 | Active Learning with Partial Feedback | cs.LG | In the large-scale multiclass setting, assigning labels often consists of
answering multiple questions to drill down through a hierarchy of classes.
Here, the labor required per annotation scales with the number of questions
asked. We propose active learning with partial feedback. In this setup, the
learner asks the an... | computer science |
33,133 | Smooth Loss Functions for Deep Top-k Classification | cs.LG | The top-k error is a common measure of performance in machine learning and
computer vision. In practice, top-k classification is typically performed with
deep neural networks trained with the cross-entropy loss. Theoretical results
indeed suggest that cross-entropy is an optimal learning objective for such a
task in th... | computer science |
33,134 | Protecting Sensory Data against Sensitive Inferences | cs.LG | There is growing concern about how personal data are used when users grant
applications direct access to the sensors of their mobile devices. In fact,
high resolution temporal data generated by motion sensors reflect directly the
activities of a user and indirectly physical and demographic attributes. In
this paper, we... | computer science |
33,135 | Diversity regularization in deep ensembles | cs.LG | Calibrating the confidence of supervised learning models is important for a
variety of contexts where the certainty over predictions should be reliable.
However, it has been reported that deep neural network models are often too
poorly calibrated for achieving complex tasks requiring reliable uncertainty
estimates in t... | computer science |
33,136 | Nonlinear Online Learning with Adaptive Nyström Approximation | cs.LG | Use of nonlinear feature maps via kernel approximation has led to success in
many online learning tasks. As a popular kernel approximation method,
Nystr\"{o}m approximation, has been well investigated, and various landmark
points selection methods have been proposed to improve the approximation
quality. However, these ... | computer science |
33,137 | Learning Mixtures of Linear Regressions with Nearly Optimal Complexity | cs.LG | Mixtures of Linear Regressions (MLR) is an important mixture model with many
applications. In this model, each observation is generated from one of the
several unknown linear regression components, where the identity of the
generated component is also unknown. Previous works either assume strong
assumptions on the data... | computer science |
33,138 | Actigraphy-based Sleep/Wake Pattern Detection using Convolutional Neural
Networks | cs.LG | Common medical conditions are often associated with sleep abnormalities.
Patients with medical disorders often suffer from poor sleep quality compared
to healthy individuals, which in turn may worsen the symptoms of the disorder.
Accurate detection of sleep/wake patterns is important in developing
personalized digital ... | computer science |
33,139 | Learning to Route with Sparse Trajectory Sets---Extended Version | cs.LG | Motivated by the increasing availability of vehicle trajectory data, we
propose learn-to-route, a comprehensive trajectory-based routing solution.
Specifically, we first construct a graph-like structure from trajectories as
the routing infrastructure. Second, we enable trajectory-based routing given an
arbitrary (sourc... | computer science |
33,140 | Unicorn: Continual Learning with a Universal, Off-policy Agent | cs.LG | Some real-world domains are best characterized as a single task, but for
others this perspective is limiting. Instead, some tasks continually grow in
complexity, in tandem with the agent's competence. In continual learning, also
referred to as lifelong learning, there are no explicit task boundaries or
curricula. As le... | computer science |
33,141 | Diverse Exploration for Fast and Safe Policy Improvement | cs.LG | We study an important yet under-addressed problem of quickly and safely
improving policies in online reinforcement learning domains. As its solution,
we propose a novel exploration strategy - diverse exploration (DE), which
learns and deploys a diverse set of safe policies to explore the environment.
We provide DE theo... | computer science |
33,142 | Loss-aware Weight Quantization of Deep Networks | cs.LG | The huge size of deep networks hinders their use in small computing devices.
In this paper, we consider compressing the network by weight quantization. We
extend a recently proposed loss-aware weight binarization scheme to
ternarization, with possibly different scaling parameters for the positive and
negative weights, ... | computer science |
33,143 | Asynchronous Stochastic Proximal Methods for Nonconvex Nonsmooth
Optimization | cs.LG | We study stochastic algorithms for solving non-convex optimization problems
with a convex yet possibly non-smooth regularizer, which find wide applications
in many practical machine learning applications. However, compared to
asynchronous parallel stochastic gradient descent (AsynSGD), an algorithm
targeting smooth opt... | computer science |
33,144 | A Block-wise, Asynchronous and Distributed ADMM Algorithm for General
Form Consensus Optimization | cs.LG | Many machine learning models, including those with non-smooth regularizers,
can be formulated as consensus optimization problems, which can be solved by
the alternating direction method of multipliers (ADMM). Many recent efforts
have been made to develop asynchronous distributed ADMM to handle large amounts
of training... | computer science |
33,145 | Time Series Learning using Monotonic Logical Properties | cs.LG | We propose a new paradigm for time-series learning where users implicitly
specify families of signal shapes by choosing monotonic parameterized signal
predicates. These families of predicates (also called specifications) can be
seen as infinite Boolean feature vectors, that are able to leverage a user's
domain expertis... | computer science |
33,146 | Temporal Difference Models: Model-Free Deep RL for Model-Based Control | cs.LG | Model-free reinforcement learning (RL) is a powerful, general tool for
learning complex behaviors. However, its sample efficiency is often
impractically large for solving challenging real-world problems, even with
off-policy algorithms such as Q-learning. A limiting factor in classic
model-free RL is that the learning ... | computer science |
33,147 | Max-Mahalanobis Linear Discriminant Analysis Networks | cs.LG | A deep neural network (DNN) consists of a nonlinear transformation from an
input to a feature representation, followed by a common softmax linear
classifier. Though many efforts have been devoted to designing a proper
architecture for nonlinear transformation, little investigation has been done
on the classifier part. ... | computer science |
33,148 | Stochastic Hyperparameter Optimization through Hypernetworks | cs.LG | Machine learning models are often tuned by nesting optimization of model
weights inside the optimization of hyperparameters. We give a method to
collapse this nested optimization into joint stochastic optimization of weights
and hyperparameters. Our process trains a neural network to output
approximately optimal weight... | computer science |
33,149 | Retrieval-Augmented Convolutional Neural Networks for Improved
Robustness against Adversarial Examples | cs.LG | We propose a retrieval-augmented convolutional network and propose to train
it with local mixup, a novel variant of the recently proposed mixup algorithm.
The proposed hybrid architecture combining a convolutional network and an
off-the-shelf retrieval engine was designed to mitigate the adverse effect of
off-manifold ... | computer science |
33,150 | Multi-Observation Regression | cs.LG | Recent work introduced loss functions which measure the error of a prediction
based on multiple simultaneous observations or outcomes. In this paper, we
explore the theoretical and practical questions that arise when using such
multi-observation losses for regression on data sets of $(x,y)$ pairs. When a
loss depends o... | computer science |
33,151 | Robust Actor-Critic Contextual Bandit for Mobile Health (mHealth)
Interventions | cs.LG | We consider the actor-critic contextual bandit for the mobile health
(mHealth) intervention. State-of-the-art decision-making algorithms generally
ignore the outliers in the dataset. In this paper, we propose a novel robust
contextual bandit method for the mHealth. It can achieve the conflicting goal
of reducing the in... | computer science |
33,152 | L1-Norm Batch Normalization for Efficient Training of Deep Neural
Networks | cs.LG | Batch Normalization (BN) has been proven to be quite effective at
accelerating and improving the training of deep neural networks (DNNs).
However, BN brings additional computation, consumes more memory and generally
slows down the training process by a large margin, which aggravates the
training effort. Furthermore, th... | computer science |
33,153 | Time-sensitive Customer Churn Prediction based on PU Learning | cs.LG | With the fast development of Internet companies throughout the world,
customer churn has become a serious concern. To better help the companies
retain their customers, it is important to build a customer churn prediction
model to identify the customers who are most likely to churn ahead of time. In
this paper, we propo... | computer science |
33,154 | Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data | cs.LG | Learning inter-domain mappings from unpaired data can improve performance in
structured prediction tasks, such as image segmentation, by reducing the need
for paired data. CycleGAN was recently proposed for this problem, but
critically assumes the underlying inter-domain mapping is approximately
deterministic and one-t... | computer science |
33,155 | Clustering of Naturalistic Driving Encounters Using Unsupervised
Learning | cs.LG | Deep understanding of driving encounters could help self-driving cars make
appropriate decisions when driving in complex settings with surrounding
vehicles engaged. This paper develops an unsupervised classifier to group
naturalistic driving encounters into several distinguishable clusters by
combining an auto-encoder ... | computer science |
33,156 | Tensor Decomposition for Compressing Recurrent Neural Network | cs.LG | In the machine learning fields, Recurrent Neural Network (RNN) has become a
popular algorithm for sequential data modeling. However, behind the impressive
performance, RNNs require a large number of parameters for both training and
inference. In this paper, we are trying to reduce the number of parameters and
maintain ... | computer science |
33,157 | Diversity and degrees of freedom in regression ensembles | cs.LG | Ensemble methods are a cornerstone of modern machine learning. The
performance of an ensemble depends crucially upon the level of diversity
between its constituent learners. This paper establishes a connection between
diversity and degrees of freedom (i.e. the capacity of the model), showing that
diversity may be viewe... | computer science |
33,158 | Reinforcement Learning to Rank in E-Commerce Search Engine:
Formalization, Analysis, and Application | cs.LG | In e-commerce platforms such as Amazon and TaoBao, ranking items in a search
session is a typical multi-step decision-making problem. Learning to rank (LTR)
methods have been widely applied to ranking problems. However, such methods
often consider different ranking steps in a session to be independent, which
conversely... | computer science |
33,159 | A more globally accurate dimensionality reduction method using triplets | cs.LG | We first show that the commonly used dimensionality reduction (DR) methods
such as t-SNE and LargeVis poorly capture the global structure of the data in
the low dimensional embedding. We show this via a number of tests for the DR
methods that can be easily applied by any practitioner to the dataset at hand.
Surprisingl... | computer science |
33,160 | Impact of Biases in Big Data | cs.LG | The underlying paradigm of big data-driven machine learning reflects the
desire of deriving better conclusions from simply analyzing more data, without
the necessity of looking at theory and models. Is having simply more data
always helpful? In 1936, The Literary Digest collected 2.3M filled in
questionnaires to predic... | computer science |
33,161 | Distributed Prioritized Experience Replay | cs.LG | We propose a distributed architecture for deep reinforcement learning at
scale, that enables agents to learn effectively from orders of magnitude more
data than previously possible. The algorithm decouples acting from learning:
the actors interact with their own instances of the environment by selecting
actions accordi... | computer science |
33,162 | Not All Samples Are Created Equal: Deep Learning with Importance
Sampling | cs.LG | Deep neural network training spends most of the computation on examples that
are properly handled, and could be ignored.
We propose to mitigate this phenomenon with a principled importance sampling
scheme that focuses computation on "informative" examples, and reduces the
variance of the stochastic gradients during t... | computer science |
33,163 | Quantitatively Evaluating GANs With Divergences Proposed for Training | cs.LG | Generative adversarial networks (GANs) have been extremely effective in
approximating complex distributions of high-dimensional, input data samples,
and substantial progress has been made in understanding and improving GAN
performance in terms of both theory and application. However, we currently lack
quantitative meth... | computer science |
33,164 | Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with
Adversarial Examples | cs.LG | Crafting adversarial examples has become an important technique to evaluate
the robustness of deep neural networks (DNNs). However, most existing works
focus on attacking the image classification problem since its input space is
continuous and output space is finite.
In this paper, we study the much more challenging ... | computer science |
33,165 | Modeling Spatial-Temporal Dynamics for Traffic Prediction | cs.LG | Spatial-temporal prediction has many applications such as climate forecasting
and urban planning. In particular, traffic prediction has drawn increasing
attention in data mining research field for the growing traffic related
datasets and for its impacts in real-world applications. For example, an
accurate taxi demand p... | computer science |
33,166 | Accelerating Natural Gradient with Higher-Order Invariance | cs.LG | An appealing property of the natural gradient is that it is invariant to
arbitrary differentiable reparameterizations of the model. However, this
invariance property requires infinitesimal steps and is lost in practical
implementations with small but finite step sizes. In this paper, we study
invariance properties from... | computer science |
33,167 | An Optimal Control Approach to Deep Learning and Applications to
Discrete-Weight Neural Networks | cs.LG | Deep learning is formulated as a discrete-time optimal control problem. This
allows one to characterize necessary conditions for optimality and develop
training algorithms that do not rely on gradients with respect to the trainable
parameters. In particular, we introduce the discrete-time method of successive
approxima... | computer science |
33,168 | An Analysis of the t-SNE Algorithm for Data Visualization | cs.LG | A first line of attack in exploratory data analysis is data visualization,
i.e., generating a 2-dimensional representation of data that makes clusters of
similar points visually identifiable. Standard Johnson-Lindenstrauss
dimensionality reduction does not produce data visualizations. The t-SNE
heuristic of van der Maa... | computer science |
33,169 | Relative Pairwise Relationship Constrained Non-negative Matrix
Factorisation | cs.LG | Non-negative Matrix Factorisation (NMF) has been extensively used in machine
learning and data analytics applications. Most existing variations of NMF only
consider how each row/column vector of factorised matrices should be shaped,
and ignore the relationship among pairwise rows or columns. In many cases, such
pairwis... | computer science |
33,170 | Deep Information Networks | cs.LG | We describe a novel classifier with a tree structure, designed using
information theory concepts. This Information Network is made of information
nodes, that compress the input data, and multiplexers, that connect two or more
input nodes to an output node. Each information node is trained, independently
of the others, ... | computer science |
33,171 | Learning SMaLL Predictors | cs.LG | We present a new machine learning technique for training small
resource-constrained predictors. Our algorithm, the Sparse Multiprototype
Linear Learner (SMaLL), is inspired by the classic machine learning problem of
learning $k$-DNF Boolean formulae. We present a formal derivation of our
algorithm and demonstrate the b... | computer science |
33,172 | Arbitrary Discrete Sequence Anomaly Detection with Zero Boundary LSTM | cs.LG | We propose a simple mathematical definition and new neural architecture for
finding anomalies within discrete sequence datasets. Our model comprises of a
modified LSTM autoencoder and an array of One-Class SVMs. The LSTM takes in
elements from a sequence and creates context vectors that are used to predict
the probabil... | computer science |
33,173 | A Reductions Approach to Fair Classification | cs.LG | We present a systematic approach for achieving fairness in a binary
classification setting. While we focus on two well-known quantitative
definitions of fairness, our approach encompasses many other previously studied
definitions as special cases. Our approach works by reducing fair
classification to a sequence of cost... | computer science |
33,174 | Multiple Kernel $k$-means Clustering using Min-Max Optimization with
$l_2$ Regularization | cs.LG | As various types of biomedical data become available, multiple kernel
learning approaches have been proposed to incorporate abundant yet diverse
information collected from multiple sources (or views) to facilitate disease
prediction and pattern recognition. Although supervised multiple kernel
learning has been extensiv... | computer science |
33,175 | A Neural Network Approach to Missing Marker Reconstruction | cs.LG | Optical motion capture systems have become a widely used technology in
various fields, such as augmented reality, robotics, movie production, etc.
Such systems use a large number of cameras to triangulate the position of
optical markers. These are then used to reconstruct the motion of rigid objects
or human articulate... | computer science |
33,176 | The Advantage of Doubling: A Deep Reinforcement Learning Approach to
Studying the Double Team in the NBA | cs.LG | During the 2017 NBA playoffs, Celtics coach Brad Stevens was faced with a
difficult decision when defending against the Cavaliers: "Do you double and
risk giving up easy shots, or stay at home and do the best you can?" It's a
tough call, but finding a good defensive strategy that effectively incorporates
doubling can m... | computer science |
33,177 | Some Approximation Bounds for Deep Networks | cs.LG | In this paper we introduce new bounds on the approximation of functions in
deep networks and in doing so introduce some new deep network architectures for
function approximation. These results give some theoretical insight into the
success of autoencoders and ResNets. | computer science |
33,178 | A Deep Generative Model for Disentangled Representations of Sequential
Data | cs.LG | We present a VAE architecture for encoding and generating high dimensional
sequential data, such as video or audio. Our deep generative model learns a
latent representation of the data which is split into a static and dynamic
part, allowing us to approximately disentangle latent time-dependent features
(dynamics) from ... | computer science |
33,179 | Reptile: a Scalable Metalearning Algorithm | cs.LG | This paper considers metalearning problems, where there is a distribution of
tasks, and we would like to obtain an agent that performs well (i.e., learns
quickly) when presented with a previously unseen task sampled from this
distribution. We present a remarkably simple metalearning algorithm called
Reptile, which lear... | computer science |
33,180 | Learning with Rules | cs.LG | Complex classifiers may exhibit "embarassing" failures in cases that would be
easily classified and justified by a human. Avoiding such failures is obviously
paramount, particularly in domains where we cannot accept this unexplained
behavior. In this work, we focus on one such setting, where a label is
perfectly predic... | computer science |
33,181 | Fast Decoding in Sequence Models using Discrete Latent Variables | cs.LG | Autoregressive sequence models based on deep neural networks, such as RNNs,
Wavenet and the Transformer attain state-of-the-art results on many tasks.
However, they are difficult to parallelize and are thus slow at processing long
sequences. RNNs lack parallelism both during training and decoding, while
architectures l... | computer science |
33,182 | Construction of neural networks for realization of localized deep
learning | cs.LG | The subject of deep learning has recently attracted users of machine learning
from various disciplines, including: medical diagnosis and bioinformatics,
financial market analysis and online advertisement, speech and handwriting
recognition, computer vision and natural language processing, time series
forecasting, and s... | computer science |
33,183 | Sequential Outlier Detection based on Incremental Decision Trees | cs.LG | We introduce an online outlier detection algorithm to detect outliers in a
sequentially observed data stream. For this purpose, we use a two-stage
filtering and hedging approach. In the first stage, we construct a multi-modal
probability density function to model the normal samples. In the second stage,
given a new obs... | computer science |
33,184 | Generalization and Expressivity for Deep Nets | cs.LG | Along with the rapid development of deep learning in practice, the
theoretical explanations for its success become urgent. Generalization and
expressivity are two widely used measurements to quantify theoretical behaviors
of deep learning. The expressivity focuses on finding functions expressible by
deep nets but canno... | computer science |
33,185 | Kickstarting Deep Reinforcement Learning | cs.LG | We present a method for using previously-trained 'teacher' agents to
kickstart the training of a new 'student' agent. To this end, we leverage ideas
from policy distillation and population based training. Our method places no
constraints on the architecture of the teacher or student agents, and it
regulates itself to a... | computer science |
33,186 | Detecting Adversarial Examples via Neural Fingerprinting | cs.LG | Deep neural networks are vulnerable to adversarial examples, which
dramatically alter model output using small input changes. We propose Neural
Fingerprinting, a simple, yet effective method to detect adversarial examples
by verifying whether model behavior is consistent with a set of secret
fingerprints, inspired by t... | computer science |
33,187 | Incentives in the Dark: Multi-armed Bandits for Evolving Users with
Unknown Type | cs.LG | Design of incentives or recommendations to users is becoming more common as
platform providers continually emerge. We propose a multi-armed bandit approach
to the problem in which users types are unknown a priori and evolve dynamically
in time. Unlike the traditional bandit setting, observed rewards are generated
by a ... | computer science |
33,188 | Sales forecasting using WaveNet within the framework of the Kaggle
competition | cs.LG | We took part in the Corporacion Favorita Grocery Sales Forecasting
competition hosted on Kaggle and achieved the 2nd place. In this abstract
paper, we present an overall analysis and solution to the underlying
machine-learning problem based on time series data, where major challenges are
identified and corresponding pr... | computer science |
33,189 | Combinatorial Multi-Objective Multi-Armed Bandit Problem | cs.LG | In this paper, we introduce the COmbinatorial Multi-Objective Multi-Armed
Bandit (COMO-MAB) problem that captures the challenges of combinatorial and
multi-objective online learning simultaneously. In this setting, the goal of
the learner is to choose an action at each time, whose reward vector is a
linear combination ... | computer science |
33,190 | The Everlasting Database: Statistical Validity at a Fair Price | cs.LG | The problem of handling adaptivity in data analysis, intentional or not,
permeates a variety of fields, including test-set overfitting in ML challenges
and the accumulation of invalid scientific discoveries. We propose a mechanism
for answering an arbitrarily long sequence of potentially adaptive statistical
queries, b... | computer science |
33,191 | Spatial Graph Convolutions for Drug Discovery | cs.LG | Predicting the binding free energy, or affinity, of a small molecule for a
protein target is frequently the first step along the arc of drug discovery.
High throughput experimental and virtual screening both suffer from low
accuracy, whereas more accurate approaches in both domains suffer from lack of
scale due to eith... | computer science |
33,192 | Thompson Sampling for Combinatorial Semi-Bandits | cs.LG | We study the application of the Thompson Sampling (TS) methodology to the
stochastic combinatorial multi-armed bandit (CMAB) framework. We analyze the
standard TS algorithm for the general CMAB, and obtain the first
distribution-dependent regret bound of $O(m\log T / \Delta_{\min}) $ for TS
under general CMAB, where $m... | computer science |
33,193 | Policy Search in Continuous Action Domains: an Overview | cs.LG | Continuous action policy search, the search for efficient policies in
continuous control tasks, is currently the focus of intensive research driven
both by the recent success of deep reinforcement learning algorithms and by the
emergence of competitors based on evolutionary algorithms. In this paper, we
present a broad... | computer science |
33,194 | Model-Agnostic Private Learning via Stability | cs.LG | We design differentially private learning algorithms that are agnostic to the
learning model. Our algorithms are interactive in nature, i.e., instead of
outputting a model based on the training data, they provide predictions for a
set of $m$ feature vectors that arrive online. We show that, for the feature
vectors on w... | computer science |
33,195 | Latent Tree Variational Autoencoder for Joint Representation Learning
and Multidimensional Clustering | cs.LG | Recently, deep learning based clustering methods are shown superior to
traditional ones by jointly conducting representation learning and clustering.
These methods rely on the assumptions that the number of clusters is known, and
that there is one single partition over the data and all attributes define that
partition.... | computer science |
33,196 | Building Sparse Deep Feedforward Networks using Tree Receptive Fields | cs.LG | Sparse connectivity is an important factor behind the success of
convolutional neural networks and recurrent neural networks. In this paper, we
consider the problem of learning sparse connectivity for feedforward neural
networks (FNNs). The key idea is that a unit should be connected to a small
number of units at the n... | computer science |
33,197 | LSH Microbatches for Stochastic Gradients: Value in Rearrangement | cs.LG | Metric embeddings are immensely useful representation of interacting entities
such as videos, users, search queries, online resources, words, and more.
Embeddings are computed by optimizing a loss function of the form of a sum over
provided associations so that relation of embedding vectors reflects strength
of associa... | computer science |
33,198 | SUSTain: Scalable Unsupervised Scoring for Tensors and its Application
to Phenotyping | cs.LG | This paper presents a new method, which we call SUSTain, that extends
real-valued matrix and tensor factorizations to data where values are integers.
Such data are common when the values correspond to event counts or ordinal
measures. The conventional approach is to treat integer data as real, and then
apply real-value... | computer science |
33,199 | Theory and Algorithms for Forecasting Time Series | cs.LG | We present data-dependent learning bounds for the general scenario of
non-stationary non-mixing stochastic processes. Our learning guarantees are
expressed in terms of a data-dependent measure of sequential complexity and a
discrepancy measure that can be estimated from data under some mild
assumptions. We also also pr... | computer science |
33,200 | Learning Sparse Deep Feedforward Networks via Tree Skeleton Expansion | cs.LG | Despite the popularity of deep learning, structure learning for deep models
remains a relatively under-explored area. In contrast, structure learning has
been studied extensively for probabilistic graphical models (PGMs). In
particular, an efficient algorithm has been developed for learning a class of
tree-structured P... | computer science |
33,201 | Distributed Computation as Hierarchy | cs.DC | This paper presents a new distributed computational model of distributed
systems called the phase web that extends V. Pratt's orthocurrence relation
from 1986. The model uses mutual-exclusion to express sequence, and a new kind
of hierarchy to replace event sequences, posets, and pomsets. The model
explicitly connects ... | computer science |
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