pwc-archive/papers-with-abstracts huggingface dataset:
Provenance detection through learning transformation-resilient watermarking
Authors: Jamie Hayes, Krishnamurthy Dvijotham, Yutian Chen, Sander Dieleman, Pushmeet Kohli, Norman Casagrande
Date: 2019-09-25 00:00:00

Advancements in deep generative models have made it possible to synthesize images, videos and audio signals that are hard to distinguish from natural signals, creating opportunities for potential abuse of these capabilities. This motivates the problem of tracking the provenance of signals, i.e., being able to determine the original source of a signal. Watermarking the signal at the time of signal creation is a potential solution, but current techniques are brittle and watermark detection mechanisms can easily be bypassed by doing some post-processing (cropping images, shifting pitch in the audio etc.). In this paper, we introduce ReSWAT (Resilient Signal Watermarking via Adversarial Training), a framework for learning transformation-resilient watermark detectors that are able to detect a watermark even after a signal has been through several post-processing transformations. Our detection method can be applied to domains with continuous data representations such as images, videos or sound signals. Experiments on watermarking image and audio signals show that our method can reliably detect the provenance of a synthetic signal, even if the signal has been through several post-processing transformations, and improve upon related work in this setting. Furthermore, we show that for specific kinds of transformations (perturbations bounded in the $\ell_2$ norm), we can even get formal guarantees on the ability of our model to detect the watermark.  We provide qualitative examples of watermarked image and audio samples in the anonymous code submission link.
Underwhelming Generalization Improvements From Controlling Feature Attribution
Authors: Joseph D Viviano, Becks Simpson, Francis Dutil, Yoshua Bengio, Joseph Paul Cohen
Date: 2019-09-25 00:00:00

Overfitting is a common issue in machine learning, which can arise when the model learns to predict class membership using convenient but spuriously-correlated image features instead of the true image features that denote a class. These are typically visualized using saliency maps. In some object classification tasks such as for medical images, one may have some images with masks, indicating a region of interest, i.e., which part of the image contains the most relevant information for the classification. We describe a simple method for taking advantage of such auxiliary labels, by training networks to ignore the distracting features which may be extracted outside of the region of interest, on the training images for which such masks are available. This mask information is only used during training and has an impact on generalization accuracy in a dataset-dependent way. We observe an underwhelming relationship between controlling saliency maps and improving generalization performance.
Combining graph and sequence information to learn protein representations
Authors: Hassan Kané, Mohamed Coulibali, Pelkins Ajanoh, Ali Abdalla
Date: 2019-09-25 00:00:00
Tasks: Protein Function Prediction, Representation Learning

Computational methods that infer the function of proteins are key to understanding life at the molecular level. In recent years, representation learning has emerged as a powerful paradigm to discover new patterns among entities as varied as images, words, speech, molecules. In typical representation learning, there is only one source of data or one level of abstraction at which the learned representation occurs. However, proteins can be described by their primary, secondary, tertiary, and quaternary structure or even as nodes in protein-protein interaction networks. Given that protein function is an emergent property of all these levels of interactions in this work, we learn joint representations from both amino acid sequence and multilayer networks representing tissue-specific protein-protein interactions. Using these representations, we train machine learning models that outperform existing methods on the task of tissue-specific protein function prediction on 10 out of 13 tissues. Furthermore, we outperform existing methods by 19% on average.
Sensible adversarial learning
Authors: Jungeum Kim, Xiao Wang
Date: 2019-09-25 00:00:00

The trade-off between robustness and standard accuracy has been consistently reported in the machine learning literature. Although the problem has been widely studied to understand and explain this trade-off, no studies have shown the possibility of a no trade-off solution. In this paper, motivated by the fact that the high dimensional distribution is poorly represented by limited data samples, we introduce sensible adversarial learning and demonstrate the synergistic effect between pursuits of natural accuracy and robustness. Specifically, we define a sensible adversary which is useful for learning a defense model and keeping a high natural accuracy simultaneously. We theoretically establish that the Bayes rule is the most robust multi-class classifier with the 0-1 loss under sensible adversarial learning. We propose a novel and efficient algorithm that trains a robust model with sensible adversarial examples, without a significant drop in natural accuracy. Our model on CIFAR10 yields state-of-the-art results against various attacks with perturbations restricted to l∞ with ε = 8/255, e.g., the robust accuracy 65.17% against PGD attacks as well as the natural accuracy 91.51%.
YaoGAN: Learning Worst-case Competitive Algorithms from Self-generated Inputs
Authors: Goran Zuzic, Di Wang, Aranyak Mehta, D. Sivakumar
Date: 2019-09-25 00:00:00
Tasks: Combinatorial Optimization, Generative Adversarial Network

We tackle the challenge of using machine learning to find algorithms with strong worst-case guarantees for online combinatorial optimization problems. Whereas the previous approach along this direction (Kong et al., 2018) relies on significant domain expertise to provide hard distributions over input instances at training, we ask whether this can be accomplished from first principles, i.e., without any human-provided data beyond specifying the objective of the optimization problem. To answer this question, we draw insights from classic results in game theory, analysis of algorithms, and online learning to introduce a novel framework. At the high level, similar to a generative adversarial network (GAN), our framework has two components whose respective goals are to learn the optimal algorithm as well as a set of input instances that captures the essential difficulty of the given optimization problem. The two components are trained against each other and evolved simultaneously. We test our ideas on the ski rental problem and the fractional AdWords problem. For these well-studied problems, our preliminary results demonstrate that the framework is capable of finding algorithms as well as difficult input instances that are consistent with known optimal results. We believe our new framework points to a promising direction which can facilitate the research of algorithm design by leveraging ML to improve the state of the art both in theory and in practice.
Self-Induced Curriculum Learning in Neural Machine Translation
Authors: Dana Ruiter, Cristina España-Bonet, Josef van Genabith
Date: 2019-09-25 00:00:00
Tasks: Denoising, Machine Translation, NMT, Translation

Self-supervised neural machine translation (SS-NMT) learns how to extract/select suitable training data from comparable (rather than parallel) corpora and how to translate, in a way that the two tasks support each other in a virtuous circle.  SS-NMT has been shown to be competitive with state-of-the-art unsupervised NMT. In this study we provide an in-depth analysis of the sampling choices the SS-NMT model takes during training. We show that, without it having been told to do so, the model selects samples of increasing (i) complexity and (ii) task-relevance in combination with (iii) a denoising curriculum. We observe that the dynamics of the mutual-supervision of both system internal representation types is vital for the extraction and hence translation performance. We show that in terms of the human Gunning-Fog Readability index (GF), SS-NMT starts by extracting and learning from Wikipedia data suitable for high school (GF=10--11) and quickly moves towards content suitable for first year undergraduate students (GF=13).
Adapt-to-Learn: Policy Transfer in Reinforcement Learning
Authors: Girish Joshi, Girish Chowdhary
Date: 2019-09-25 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Efficient and robust policy transfer remains a key challenge in reinforcement learning. Policy transfer through warm initialization, imitation, or interacting over a large set of agents with randomized instances, have been commonly applied to solve a variety of Reinforcement Learning (RL) tasks. However, this is far from how behavior transfer happens in the biological world: Humans and animals are able to quickly adapt the learned behaviors between similar tasks and learn new skills when presented with new situations. Here we seek to answer the question: Will learning to combine adaptation reward with environmental reward lead to a more efficient transfer of policies between domains? We introduce a principled mechanism that can \textbf{``Adapt-to-Learn"}, that is adapt the source policy to learn to solve a target task with significant transition differences and uncertainties.  We show through theory and experiments that our method leads to a significantly reduced sample complexity of transferring the policies between the tasks.
The Surprising Behavior Of Graph Neural Networks
Authors: Vivek Kothari, Catherine Tong, Nicholas Lane
Date: 2019-09-25 00:00:00
Tasks: Attribute

We highlight a lack of understanding of the behaviour of Graph Neural Networks (GNNs) in various topological contexts. We present 4 experimental studies which counter-intuitively demonstrate that the performance of GNNs is weakly dependent on the topology, sensitive to structural noise and the modality (attributes or edges) of information, and degraded by strong coupling between nodal attributes and structure. We draw on the empirical results to recommend reporting of topological context in GNN evaluation and propose a simple (attribute-structure) decoupling method to improve GNN performance.
On the Reflection of Sensitivity in the Generalization Error
Authors: Mahsa Forouzesh, Farnood Salehi, Patrick Thiran
Date: 2019-09-25 00:00:00
Tasks: Sensitivity

Even though recent works have brought some insight into the performance improvement of techniques used in state-of-the-art deep-learning models, more work is needed to understand the generalization properties of over-parameterized deep neural networks. We shed light on this matter by linking the loss function to the output’s sensitivity to its input. We find a rather strong empirical relation between the output sensitivity and the variance in the bias-variance decomposition of the loss function, which hints on using sensitivity as a metric for comparing generalization performance of networks, without requiring labeled data. We find that sensitivity is decreased by applying popular methods which improve the generalization performance of the model, such as (1) using a deep network rather than a wide one, (2) adding convolutional layers to baseline classifiers instead of adding fully connected layers, (3) using batch normalization, dropout and max-pooling, and (4) applying parameter initialization techniques.
Neural Network Out-of-Distribution Detection for Regression Tasks
Authors: Geoff Pleiss, Amauri Souza, Joseph Kim, Boyi Li, Kilian Q. Weinberger
Date: 2019-09-25 00:00:00
Tasks: Out-of-Distribution Detection, Out of Distribution (OOD) Detection, regression

Neural network out-of-distribution (OOD) detection aims to identify when a model is unable to generalize to new inputs, either due to covariate shift or anomalous data. Most existing OOD methods only apply to classification tasks, as they assume a discrete set of possible predictions. In this paper, we propose a method for neural network OOD detection that can be applied to regression problems. We demonstrate that the hidden features for in-distribution data can be described by a highly concentrated, low dimensional distribution. Therefore, we can model these in-distribution features with an extremely simple generative model, such as a Gaussian mixture model (GMM) with 4 or fewer components. We demonstrate on several real-world benchmark data sets that GMM-based feature detection achieves state-of-the-art OOD detection results on several regression tasks. Moreover, this approach is simple to implement and computationally efficient.
Mesh-Free Unsupervised Learning-Based PDE Solver of Forward and Inverse problems
Authors: Leah Bar, Nir Sochen
Date: 2019-09-25 00:00:00

We introduce a novel neural network-based partial differential equations solver for forward and inverse problems. The solver is grid free, mesh free and shape free, and the solution is approximated by a neural network. 
We employ an unsupervised approach such that the input to the network is a points set in an arbitrary domain, and the output is the
set of the corresponding function values.  The network is trained to minimize deviations of the learned function from the PDE solution and 
satisfy the boundary conditions. 
The resulting solution in turn is an explicit smooth differentiable function with a known analytical form. 
 
Unlike other numerical methods such as finite differences and finite elements, the derivatives of the desired function can be analytically calculated to any order. This framework therefore, enables the solution of high order non-linear PDEs. The proposed algorithm is a unified formulation of both forward and inverse problems
where the optimized loss function consists of few elements: fidelity terms of L2 and L infinity norms, boundary conditions constraints and additional regularizers. This setting is flexible in the sense that regularizers can be tailored to specific 
problems. We demonstrate our method on a free shape 2D second order elliptical system with application to Electrical Impedance Tomography (EIT).
Gaussian Conditional Random Fields for Classification
Authors: Andrija Petrovic, Mladen Nikolic, Milos Jovanovic, Boris Delibasic
Date: 2019-09-25 00:00:00
Tasks: Binary Classification, Classification

In this paper, a Gaussian conditional random field model for structured binary classification (GCRFBC) is proposed. The model is applicable to classification problems with undirected graphs, intractable for standard classification CRFs. The model representation of GCRFBC is extended by latent variables which yield some appealing properties. Thanks to the GCRF latent structure, the model becomes tractable, efficient, and open to improvements previously applied to GCRF regression. Two different forms of the algorithm are presented: GCRFBCb (GCRGBC - Bayesian) and GCRFBCnb (GCRFBC - non-Bayesian). The extended method of local variational approximation of sigmoid function is used for solving empirical Bayes in GCRFBCb variant, whereas MAP value of latent variables is the basis for learning and inference in the GCRFBCnb variant. The inference in GCRFBCb is solved by Newton-Cotes formulas for one-dimensional integration. Both models are evaluated on synthetic data and real-world data. It was shown that both models achieve better prediction performance than relevant baselines. Advantages and disadvantages of the proposed models are discussed.
Supervised learning with incomplete data via sparse representations
Authors: Cesar F. Caiafa, Ziyao Wang, Jordi Solé-Casals, Qibin Zhao
Date: 2019-09-25 00:00:00
Tasks: Imputation

This paper addresses the problem of training a classifier on incomplete data and its application to a complete or incomplete test dataset. A supervised learning method is developed to train a general classifier, such as a logistic regression or a deep neural network, using only a limited number of observed entries, assuming sparse representations of data vectors on an unknown dictionary. The proposed method simultaneously learns the classifier, the dictionary and the corresponding sparse representations of each input data sample. A theoretical analysis is also provided comparing this method with the standard imputation approach, which consists on performing data completion followed by training the classifier based on their reconstructions. The limitations of this last "sequential" approach are identified, and a description of how the proposed new "simultaneous" method can overcome the problem of indiscernible observations is provided. Additionally, it is shown that, if it is possible to train a classifier on incomplete observations so that its reconstructions are well separated by a hyperplane, then the same classifier also correctly separates the original (unobserved) data samples. Extensive simulation results are presented on synthetic and well-known reference datasets that demonstrate the effectiveness of the proposed method compared to traditional data imputation methods.
Transfer Active Learning For Graph Neural Networks
Authors: Shengding Hu, Meng Qu, Zhiyuan Liu, Jian Tang
Date: 2019-09-25 00:00:00
Tasks: Active Learning, Node Classification, Transfer Learning

Graph neural networks have been proved very effective for a variety of prediction tasks on graphs such as node classification. Generally, a large number of labeled data are required to train these networks. However, in reality it could be very expensive to obtain a large number of labeled data on large-scale graphs. In this paper, we studied active learning for graph neural networks, i.e., how to effectively label the nodes on a graph for training graph neural networks. We formulated the problem as a sequential decision process, which sequentially label informative nodes, and trained a policy network to maximize the performance of graph neural networks for a specific task. Moreover, we also studied how to learn a universal policy for labeling nodes on graphs with multiple training graphs and then transfer the learned policy to unseen graphs. Experimental results on both settings of a single graph and multiple training graphs (transfer learning setting) prove the effectiveness of our proposed approaches over many competitive baselines.
Assessing Generalization in TD methods for Deep Reinforcement Learning
Authors: Emmanuel Bengio, Doina Precup, Joelle Pineau
Date: 2019-09-25 00:00:00
Tasks: Deep Reinforcement Learning, Memorization, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Current Deep Reinforcement Learning (DRL) methods  can exhibit both data inefficiency and brittleness, which seem to indicate that they generalize poorly. In this work, we experimentally analyze this issue through the lens of memorization, and show that it can be observed directly during training. More precisely, we find that Deep Neural Networks (DNNs) trained with supervised tasks on trajectories capture temporal structure well, but DNNs trained with TD(0) methods struggle to do so, while using TD(lambda) targets leads to better generalization.
Chordal-GCN: Exploiting sparsity in training large-scale graph convolutional networks
Authors: Xin Jiang*, Kewei Cheng*, Song Jiang*, Yizhou Sun
Date: 2019-09-25 00:00:00
Tasks: Node Classification

Despite the impressive success of graph convolutional networks (GCNs) on numerous applications, training on large-scale sparse networks remains challenging. Current algorithms require large memory space for storing GCN outputs as well as all the intermediate embeddings. Besides, most of these algorithms involves either random sampling or an approximation of the adjacency matrix, which might unfortunately lose important structure information. In this paper, we propose Chordal-GCN for semi-supervised node classification. The proposed model utilizes the exact graph structure (i.e., without sampling or approximation), while requires limited memory resources compared with the original GCN. Moreover, it leverages the sparsity pattern as well as the clustering structure of the graph. The proposed model first decomposes a large-scale sparse network into several small dense subgraphs (called cliques), and constructs a clique tree. By traversing the tree, GCN training is performed clique by clique, and connections between cliques are exploited via the tree hierarchy. Furthermore, we implement Chordal-GCN on large-scale datasets and demonstrate superior performance.
Certifying Distributional Robustness using Lipschitz Regularisation
Authors: Zac Cranko, Zhan Shi, Xinhua Zhang, Simon Kornblith, Richard Nock
Date: 2019-09-25 00:00:00

Distributional robust risk (DRR) minimisation has arisen as a flexible and effective framework for machine learning. Approximate solutions based on dualisation have become particularly favorable in addressing the semi-infinite optimisation, and they also provide a certificate of the robustness for the worst-case population loss. However existing methods are restricted to either linear models or very small perturbations, and cannot find the globally optimal solution for restricted nonlinear models such as kernel methods. In this paper we resolved these limitations by upper bounding DRRs with an empirical risk regularised by the Lipschitz constant of the model, including deep neural networks and kernel methods. As an application, we showed that it also provides a certificate for adversarial training, and global solutions can be achieved on product kernel machines in polynomial time.
Solving single-objective tasks by preference multi-objective reinforcement learning
Authors: Jinsheng Ren, Shangqi Guo, Feng Chen
Date: 2019-09-25 00:00:00
Tasks: Multi-Objective Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

There ubiquitously exist many single-objective tasks in the real world that are inevitably related to some other objectives and influenced by them. We call such task as the objective-constrained task, which is inherently a multi-objective problem. Due to the conflict among different objectives, a trade-off is needed. A common compromise is to design a scalar reward function through clarifying the relationship among these objectives using the prior knowledge of experts. However, reward engineering is extremely cumbersome. This will result in behaviors that optimize our reward function without actually satisfying our preferences. In this paper, we explicitly cast the objective-constrained task as preference multi-objective reinforcement learning, with the overall goal of finding a Pareto optimal policy. Combined with Trajectory Preference Domination we propose, a weight vector that reflects the agent's preference for each objective can be learned. We analyzed the feasibility of our algorithm in theory, and further proved in experiments its better performance compared to those that design the reward function by experts.