pwc-archive/papers-with-abstracts huggingface dataset:
Simultaneously Learning Vision and Feature-based Control Policies for Real-world Ball-in-a-Cup
Authors: Devin Schwab, Tobias Springenberg, Murilo F. Martins, Thomas Lampe, Michael Neunert, Abbas Abdolmaleki, Tim Hertweck, Roland Hafner, Francesco Nori, Martin Riedmiller
Date: 2019-02-13 00:00:00
Tasks: Imitation Learning, Reinforcement Learning

We present a method for fast training of vision based control policies on
real robots. The key idea behind our method is to perform multi-task
Reinforcement Learning with auxiliary tasks that differ not only in the reward
to be optimized but also in the state-space in which they operate. In
particular, we allow auxiliary task policies to utilize task features that are
available only at training-time. This allows for fast learning of auxiliary
policies, which subsequently generate good data for training the main,
vision-based control policies. This method can be seen as an extension of the
Scheduled Auxiliary Control (SAC-X) framework. We demonstrate the efficacy of
our method by using both a simulated and real-world Ball-in-a-Cup game
controlled by a robot arm. In simulation, our approach leads to significant
learning speed-ups when compared to standard SAC-X. On the real robot we show
that the task can be learned from-scratch, i.e., with no transfer from
simulation and no imitation learning. Videos of our learned policies running on
the real robot can be found at
https://sites.google.com/view/rss-2019-sawyer-bic/.
Self-adaptive Single and Multi-illuminant Estimation Framework based on Deep Learning
Authors: Yongjie Liu, Sijie Shen
Date: 2019-02-13 00:00:00
Tasks: Clustering, Decoder

Illuminant estimation plays a key role in digital camera pipeline system, it
aims at reducing color casting effect due to the influence of non-white
illuminant. Recent researches handle this task by using Convolution Neural
Network (CNN) as a mapping function from input image to a single illumination
vector. However, global mapping approaches are difficult to deal with scenes
under multi-light-sources. In this paper, we proposed a self-adaptive single
and multi-illuminant estimation framework, which includes the following
novelties: (1) Learning local self-adaptive kernels from the entire image for
illuminant estimation with encoder-decoder CNN structure; (2) Providing
confidence measurement for the prediction; (3) Clustering-based iterative
fitting for computing single and multi-illumination vectors. The proposed
global-to-local aggregation is able to predict multi-illuminant regionally by
utilizing global information instead of training in patches, as well as brings
significant improvement for single illuminant estimation. We outperform the
state-of-the-art methods on standard benchmarks with the largest relative
improvement of 16%. In addition, we collect a dataset contains over 13k images
for illuminant estimation and evaluation. The code and dataset is available on
https://github.com/LiamLYJ/KPF_WB
Neural network models and deep learning - a primer for biologists
Authors: Nikolaus Kriegeskorte, Tal Golan
Date: 2019-02-13 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning

Originally inspired by neurobiology, deep neural network models have become a
powerful tool of machine learning and artificial intelligence, where they are
used to approximate functions and dynamics by learning from examples. Here we
give a brief introduction to neural network models and deep learning for
biologists. We introduce feedforward and recurrent networks and explain the
expressive power of this modeling framework and the backpropagation algorithm
for setting the parameters. Finally, we consider how deep neural networks might
help us understand the brain's computations.
Application of Deep Learning in Fundus Image Processing for Ophthalmic Diagnosis -- A Review
Authors: Sourya Sengupta, Amitojdeep Singh, Henry A. Leopold, Tanmay Gulati, Vasudevan Lakshminarayanan
Date: 2018-12-09 00:00:00
Tasks: Deep Learning, General Classification

An overview of the applications of deep learning in ophthalmic diagnosis using retinal fundus images is presented. We also review various retinal image datasets that can be used for deep learning purposes. Applications of deep learning for segmentation of optic disk, blood vessels and retinal layer as well as detection of lesions are reviewed. Recent deep learning models for classification of diseases such as age-related macular degeneration, glaucoma,diabetic macular edema and diabetic retinopathy are also reported.
Differential Description Length for Hyperparameter Selection in Machine Learning
Authors: Mojtaba Abolfazli, Anders Host-Madsen, June Zhang
Date: 2019-02-13 00:00:00
Tasks: BIG-bench Machine Learning, Model Selection

This paper introduces a new method for model selection and more generally hyperparameter selection in machine learning. Minimum description length (MDL) is an established method for model selection, which is however not directly aimed at minimizing generalization error, which is often the primary goal in machine learning. The paper demonstrates a relationship between generalization error and a difference of description lengths of the training data; we call this difference differential description length (DDL). This allows prediction of generalization error from the training data alone by performing encoding of the training data. DDL can then be used for model selection by choosing the model with the smallest predicted generalization error. We show how this method can be used for linear regression and neural networks and deep learning. Experimental results show that DDL leads to smaller generalization error than cross-validation and traditional MDL and Bayes methods.
Identity Crisis: Memorization and Generalization under Extreme Overparameterization
Authors: Chiyuan Zhang, Samy Bengio, Moritz Hardt, Michael C. Mozer, Yoram Singer
Date: 2019-02-13 00:00:00
Proceeding: ICLR 2020 1
Tasks: Memorization

We study the interplay between memorization and generalization of overparameterized networks in the extreme case of a single training example and an identity-mapping task. We examine fully-connected and convolutional networks (FCN and CNN), both linear and nonlinear, initialized randomly and then trained to minimize the reconstruction error. The trained networks stereotypically take one of two forms: the constant function (memorization) and the identity function (generalization). We formally characterize generalization in single-layer FCNs and CNNs. We show empirically that different architectures exhibit strikingly different inductive biases. For example, CNNs of up to 10 layers are able to generalize from a single example, whereas FCNs cannot learn the identity function reliably from 60k examples. Deeper CNNs often fail, but nonetheless do astonishing work to memorize the training output: because CNN biases are location invariant, the model must progressively grow an output pattern from the image boundaries via the coordination of many layers. Our work helps to quantify and visualize the sensitivity of inductive biases to architectural choices such as depth, kernel width, and number of channels.
Rethinking Generative Mode Coverage: A Pointwise Guaranteed Approach
Authors: Peilin Zhong, Yuchen Mo, Chang Xiao, Peng-Yu Chen, Changxi Zheng
Date: 2019-02-13 00:00:00
Proceeding: NeurIPS 2019 12

Many generative models have to combat $\textit{missing modes}$. The conventional wisdom to this end is by reducing through training a statistical distance (such as $f$-divergence) between the generated distribution and provided data distribution. But this is more of a heuristic than a guarantee. The statistical distance measures a $\textit{global}$, but not $\textit{local}$, similarity between two distributions. Even if it is small, it does not imply a plausible mode coverage. Rethinking this problem from a game-theoretic perspective, we show that a complete mode coverage is firmly attainable. If a generative model can approximate a data distribution moderately well under a global statistical distance measure, then we will be able to find a mixture of generators that collectively covers $\textit{every}$ data point and thus $\textit{every}$ mode, with a lower-bounded generation probability. Constructing the generator mixture has a connection to the multiplicative weights update rule, upon which we propose our algorithm. We prove that our algorithm guarantees complete mode coverage. And our experiments on real and synthetic datasets confirm better mode coverage over recent approaches, ones that also use generator mixtures but rely on global statistical distances.
Analysis of Baseline Evolutionary Algorithms for the Packing While Travelling Problem
Authors: Vahid Roostapour, Mojgan Pourhassan, Frank Neumann
Date: 2019-02-13 00:00:00
Tasks: Evolutionary Algorithms

The performance of base-line Evolutionary Algorithms (EAs) on combinatorial problems has been studied rigorously. From the theoretical viewpoint, the literature extensively investigates the linear problems, while the theoretical analysis of the non-linear problems is still far behind. In this paper, variations of the Packing While Travelling (PWT) -- also known as the non-linear knapsack problem -- are studied as an attempt to analyse the behaviour of EAs on non-linear problems from theoretical perspective. We investigate PWT for two cities and $n$ items with correlated weights and profits, using single-objective and multi-objective algorithms. Our results show that RLS\_swap, which differs from the classical RLS by having the ability to swap two bits in one iteration, finds the optimal solution in $O(n^3)$ expected time. We also study an enhanced version of GSEMO, which a specific selection operator to deal with exponential population size, and prove that it finds the Pareto front in the same asymptotic expected time. In the case of uniform weights, (1+1)~EA is able to find the optimal solution in expected time $O(n^2\log{(\max\{n,p_{\max}\})})$, where $p_{\max}$ is the largest profit of the given items. We also perform an experimental analysis to complement our theoretical investigations and provide additional insights into the runtime behavior.
Privacy-Utility Trade-off of Linear Regression under Random Projections and Additive Noise
Authors: Mehrdad Showkatbakhsh, Can Karakus, Suhas Diggavi
Date: 2019-02-13 00:00:00
Tasks: BIG-bench Machine Learning, regression

Data privacy is an important concern in machine learning, and is
fundamentally at odds with the task of training useful learning models, which
typically require the acquisition of large amounts of private user data. One
possible way of fulfilling the machine learning task while preserving user
privacy is to train the model on a transformed, noisy version of the data,
which does not reveal the data itself directly to the training procedure. In
this work, we analyze the privacy-utility trade-off of two such schemes for the
problem of linear regression: additive noise, and random projections. In
contrast to previous work, we consider a recently proposed notion of
differential privacy that is based on conditional mutual information (MI-DP),
which is stronger than the conventional $(\epsilon, \delta)$-differential
privacy, and use relative objective error as the utility metric. We find that
projecting the data to a lower-dimensional subspace before adding noise attains
a better trade-off in general. We also make a connection between privacy
problem and (non-coherent) SIMO, which has been extensively studied in wireless
communication, and use tools from there for the analysis. We present numerical
results demonstrating the performance of the schemes.
The Complexity of Making the Gradient Small in Stochastic Convex Optimization
Authors: Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake Woodworth
Date: 2019-02-13 00:00:00
Tasks: Stochastic Optimization

We give nearly matching upper and lower bounds on the oracle complexity of
finding $\epsilon$-stationary points ($\| \nabla F(x) \| \leq\epsilon$) in
stochastic convex optimization. We jointly analyze the oracle complexity in
both the local stochastic oracle model and the global oracle (or, statistical
learning) model. This allows us to decompose the complexity of finding
near-stationary points into optimization complexity and sample complexity, and
reveals some surprising differences between the complexity of stochastic
optimization versus learning. Notably, we show that in the global
oracle/statistical learning model, only logarithmic dependence on smoothness is
required to find a near-stationary point, whereas polynomial dependence on
smoothness is necessary in the local stochastic oracle model. In other words,
the separation in complexity between the two models can be exponential, and
that the folklore understanding that smoothness is required to find stationary
points is only weakly true for statistical learning.
  Our upper bounds are based on extensions of a recent "recursive
regularization" technique proposed by Allen-Zhu (2018). We show how to extend
the technique to achieve near-optimal rates, and in particular show how to
leverage the extra information available in the global oracle model. Our
algorithm for the global model can be implemented efficiently through finite
sum methods, and suggests an interesting new computational-statistical
tradeoff.
Forensic Similarity for Digital Images
Authors: Owen Mayer, Matthew C. Stamm
Date: 2019-02-13 00:00:00
Tasks: Image Forensics, Image Manipulation Detection

In this paper we introduce a new digital image forensics approach called forensic similarity, which determines whether two image patches contain the same forensic trace or different forensic traces. One benefit of this approach is that prior knowledge, e.g. training samples, of a forensic trace are not required to make a forensic similarity decision on it in the future. To do this, we propose a two part deep-learning system composed of a CNN-based feature extractor and a three-layer neural network, called the similarity network. This system maps pairs of image patches to a score indicating whether they contain the same or different forensic traces. We evaluated system accuracy of determining whether two image patches were 1) captured by the same or different camera model, 2) manipulated by the same or different editing operation, and 3) manipulated by the same or different manipulation parameter, given a particular editing operation. Experiments demonstrate applicability to a variety of forensic traces, and importantly show efficacy on "unknown" forensic traces that were not used to train the system. Experiments also show that the proposed system significantly improves upon prior art, reducing error rates by more than half. Furthermore, we demonstrated the utility of the forensic similarity approach in two practical applications: forgery detection and localization, and database consistency verification.
Towards moderate overparameterization: global convergence guarantees for training shallow neural networks
Authors: Samet Oymak, Mahdi Soltanolkotabi
Date: 2019-02-12 00:00:00

Many modern neural network architectures are trained in an overparameterized
regime where the parameters of the model exceed the size of the training
dataset. Sufficiently overparameterized neural network architectures in
principle have the capacity to fit any set of labels including random noise.
However, given the highly nonconvex nature of the training landscape it is not
clear what level and kind of overparameterization is required for first order
methods to converge to a global optima that perfectly interpolate any labels. A
number of recent theoretical works have shown that for very wide neural
networks where the number of hidden units is polynomially large in the size of
the training data gradient descent starting from a random initialization does
indeed converge to a global optima. However, in practice much more moderate
levels of overparameterization seems to be sufficient and in many cases
overparameterized models seem to perfectly interpolate the training data as
soon as the number of parameters exceed the size of the training data by a
constant factor. Thus there is a huge gap between the existing theoretical
literature and practical experiments. In this paper we take a step towards
closing this gap. Focusing on shallow neural nets and smooth activations, we
show that (stochastic) gradient descent when initialized at random converges at
a geometric rate to a nearby global optima as soon as the square-root of the
number of network parameters exceeds the size of the training data. Our results
also benefit from a fast convergence rate and continue to hold for
non-differentiable activations such as Rectified Linear Units (ReLUs).
Learning Generative Models of Structured Signals from Their Superposition Using GANs with Application to Denoising and Demixing
Authors: Mohammadreza Soltani, Swayambhoo Jain, Abhinav Sambasivan
Date: 2019-02-12 00:00:00
Tasks: Compressive Sensing, Denoising

Recently, Generative Adversarial Networks (GANs) have emerged as a popular
alternative for modeling complex high dimensional distributions. Most of the
existing works implicitly assume that the clean samples from the target
distribution are easily available. However, in many applications, this
assumption is violated. In this paper, we consider the observation setting when
the samples from target distribution are given by the superposition of two
structured components and leverage GANs for learning the structure of the
components. We propose two novel frameworks: denoising-GAN and demixing-GAN.
The denoising-GAN assumes access to clean samples from the second component and
try to learn the other distribution, whereas demixing-GAN learns the
distribution of the components at the same time. Through extensive numerical
experiments, we demonstrate that proposed frameworks can generate clean samples
from unknown distributions, and provide competitive performance in tasks such
as denoising, demixing, and compressive sensing.
Confidence regions and minimax rates in outlier-robust estimation on the probability simplex
Authors: Amir-Hossein Bateni, Arnak S. Dalalyan
Date: 2019-02-12 00:00:00

We consider the problem of estimating the mean of a distribution supported by the $k$-dimensional probability simplex in the setting where an $\varepsilon$ fraction of observations are subject to adversarial corruption. A simple particular example is the problem of estimating the distribution of a discrete random variable. Assuming that the discrete variable takes $k$ values, the unknown parameter $\boldsymbol \theta$ is a $k$-dimensional vector belonging to the probability simplex. We first describe various settings of contamination and discuss the relation between these settings. We then establish minimax rates when the quality of estimation is measured by the total-variation distance, the Hellinger distance, or the $\mathbb L^2$-distance between two probability measures. We also provide confidence regions for the unknown mean that shrink at the minimax rate. Our analysis reveals that the minimax rates associated to these three distances are all different, but they are all attained by the sample average. Furthermore, we show that the latter is adaptive to the possible sparsity of the unknown vector. Some numerical experiments illustrating our theoretical findings are reported.
Weighted Tensor Completion for Time-Series Causal Inference
Authors: Debmalya Mandal, David Parkes
Date: 2019-02-12 00:00:00
Tasks: Causal Inference, Time Series, Time Series Analysis

Marginal Structural Models (MSM) are the most popular models for causal inference from time-series observational data. However, they have two main drawbacks: (a) they do not capture subject heterogeneity, and (b) they only consider fixed time intervals and do not scale gracefully with longer intervals. In this work, we propose a new family of MSMs to address these two concerns. We model the potential outcomes as a three-dimensional tensor of low rank, where the three dimensions correspond to the agents, time periods and the set of possible histories. Unlike the traditional MSM, we allow the dimensions of the tensor to increase with the number of agents and time periods. We set up a weighted tensor completion problem as our estimation procedure, and show that the solution to this problem converges to the true model in an appropriate sense. Then we show how to solve the estimation problem, providing conditions under which we can approximately and efficiently solve the estimation problem. Finally we propose an algorithm based on projected gradient descent, which is easy to implement, and evaluate its performance on a simulated dataset.
A Tunable Loss Function for Binary Classification
Authors: Tyler Sypherd, Mario Diaz, Lalitha Sankar, Peter Kairouz
Date: 2019-02-12 00:00:00
Tasks: Binary Classification, Classification, General Classification, regression

We present $\alpha$-loss, $\alpha \in [1,\infty]$, a tunable loss function
for binary classification that bridges log-loss ($\alpha=1$) and $0$-$1$ loss
($\alpha = \infty$). We prove that $\alpha$-loss has an equivalent margin-based
form and is classification-calibrated, two desirable properties for a good
surrogate loss function for the ideal yet intractable $0$-$1$ loss. For
logistic regression-based classification, we provide an upper bound on the
difference between the empirical and expected risk at the empirical risk
minimizers for $\alpha$-loss by exploiting its Lipschitzianity along with
recent results on the landscape features of empirical risk functions. Finally,
we show that $\alpha$-loss with $\alpha = 2$ performs better than log-loss on
MNIST for logistic regression.
Crowdsourced PAC Learning under Classification Noise
Authors: Shelby Heinecke, Lev Reyzin
Date: 2019-02-12 00:00:00
Tasks: Classification, General Classification, PAC learning

In this paper, we analyze PAC learnability from labels produced by
crowdsourcing. In our setting, unlabeled examples are drawn from a distribution
and labels are crowdsourced from workers who operate under classification
noise, each with their own noise parameter. We develop an end-to-end
crowdsourced PAC learning algorithm that takes unlabeled data points as input
and outputs a trained classifier. Our three-step algorithm incorporates
majority voting, pure-exploration bandits, and noisy-PAC learning. We prove
several guarantees on the number of tasks labeled by workers for PAC learning
in this setting and show that our algorithm improves upon the baseline by
reducing the total number of tasks given to workers. We demonstrate the
robustness of our algorithm by exploring its application to additional
realistic crowdsourcing settings.