pwc-archive/papers-with-abstracts huggingface dataset:
LEWIS: Levenshtein Editing for Unsupervised Text Style Transfer
Authors: Machel Reid, Victor Zhong
Date: 2021-05-18 00:00:00
Proceeding: Findings (ACL) 2021 8
Tasks: Style Transfer, Text Style Transfer, Unsupervised Text Style Transfer

Many types of text style transfer can be achieved with only small, precise edits (e.g. sentiment transfer from I had a terrible time... to I had a great time...). We propose a coarse-to-fine editor for style transfer that transforms text using Levenshtein edit operations (e.g. insert, replace, delete). Unlike prior single-span edit methods, our method concurrently edits multiple spans in the source text. To train without parallel style text pairs (e.g. pairs of +/- sentiment statements), we propose an unsupervised data synthesis procedure. We first convert text to style-agnostic templates using style classifier attention (e.g. I had a SLOT time...), then fill in slots in these templates using fine-tuned pretrained language models. Our method outperforms existing generation and editing style transfer methods on sentiment (Yelp, Amazon) and politeness (Polite) transfer. In particular, multi-span editing achieves higher performance and more diverse output than single-span editing. Moreover, compared to previous methods on unsupervised data synthesis, our method results in higher quality parallel style pairs and improves model performance.
SA-GAN: Structure-Aware GAN for Organ-Preserving Synthetic CT Generation
Authors: Hajar Emami, Ming Dong, Siamak Nejad-Davarani, Carri Glide-Hurst
Date: 2021-05-14 00:00:00
Tasks: Generative Adversarial Network, Image Generation, Organ Segmentation

In medical image synthesis, model training could be challenging due to the inconsistencies between images of different modalities even with the same patient, typically caused by internal status/tissue changes as different modalities are usually obtained at a different time. This paper proposes a novel deep learning method, Structure-aware Generative Adversarial Network (SA-GAN), that preserves the shapes and locations of in-consistent structures when generating medical images. SA-GAN is employed to generate synthetic computed tomography (synCT) images from magnetic resonance imaging (MRI) with two parallel streams: the global stream translates the input from the MRI to the CT domain while the local stream automatically segments the inconsistent organs, maintains their locations and shapes in MRI, and translates the organ intensities to CT. Through extensive experiments on a pelvic dataset, we demonstrate that SA-GAN provides clinically acceptable accuracy on both synCTs and organ segmentation and supports MR-only treatment planning in disease sites with internal organ status changes.
Exemplar-Based Open-Set Panoptic Segmentation Network
Authors: Jaedong Hwang, Seoung Wug Oh, Joon-Young Lee, Bohyung Han
Date: 2021-05-18 00:00:00
Proceeding: CVPR 2021 1
Tasks: Clustering, Panoptic Segmentation, Segmentation

We extend panoptic segmentation to the open-world and introduce an open-set panoptic segmentation (OPS) task. This task requires performing panoptic segmentation for not only known classes but also unknown ones that have not been acknowledged during training. We investigate the practical challenges of the task and construct a benchmark on top of an existing dataset, COCO. In addition, we propose a novel exemplar-based open-set panoptic segmentation network (EOPSN) inspired by exemplar theory. Our approach identifies a new class based on exemplars, which are identified by clustering and employed as pseudo-ground-truths. The size of each class increases by mining new exemplars based on the similarities to the existing ones associated with the class. We evaluate EOPSN on the proposed benchmark and demonstrate the effectiveness of our proposals. The primary goal of our work is to draw the attention of the community to the recognition in the open-world scenarios. The implementation of our algorithm is available on the project webpage: https://cv.snu.ac.kr/research/EOPSN.
Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional Networks
Authors: Huixuan Chi, Yuying Wang, Qinfen Hao, Hong Xia
Date: 2021-05-18 00:00:00
Tasks: Node Classification, Node Property Prediction

Graph Convolutional Networks (GCNs) and subsequent variants have been proposed to solve tasks on graphs, especially node classification tasks. In the literature, however, most tricks or techniques are either briefly mentioned as implementation details or only visible in source code. In this paper, we first summarize some existing effective tricks used in GCNs mini-batch training. Based on this, two novel tricks named GCN_res Framework and Embedding Usage are proposed by leveraging residual network and pre-trained embedding to improve baseline's test accuracy in different datasets. Experiments on Open Graph Benchmark (OGB) show that, by combining these techniques, the test accuracy of various GCNs increases by 1.21%~2.84%. We open source our implementation at https://github.com/ytchx1999/PyG-OGB-Tricks.
The Computational Complexity of ReLU Network Training Parameterized by Data Dimensionality
Authors: Vincent Froese, Christoph Hertrich, Rolf Niedermeier
Date: 2021-05-18 00:00:00

Understanding the computational complexity of training simple neural networks with rectified linear units (ReLUs) has recently been a subject of intensive research. Closing gaps and complementing results from the literature, we present several results on the parameterized complexity of training two-layer ReLU networks with respect to various loss functions. After a brief discussion of other parameters, we focus on analyzing the influence of the dimension $d$ of the training data on the computational complexity. We provide running time lower bounds in terms of W[1]-hardness for parameter $d$ and prove that known brute-force strategies are essentially optimal (assuming the Exponential Time Hypothesis). In comparison with previous work, our results hold for a broad(er) range of loss functions, including $\ell^p$-loss for all $p\in[0,\infty]$. In particular, we extend a known polynomial-time algorithm for constant $d$ and convex loss functions to a more general class of loss functions, matching our running time lower bounds also in these cases.
BBE: Simulating the Microstructural Dynamics of an In-Play Betting Exchange via Agent-Based Modelling
Authors: Dave Cliff
Date: 2021-05-18 00:00:00

I describe the rationale for, and design of, an agent-based simulation model of a contemporary online sports-betting exchange: such exchanges, closely related to the exchange mechanisms at the heart of major financial markets, have revolutionized the gambling industry in the past 20 years, but gathering sufficiently large quantities of rich and temporally high-resolution data from real exchanges - i.e., the sort of data that is needed in large quantities for Deep Learning - is often very expensive, and sometimes simply impossible; this creates a need for a plausibly realistic synthetic data generator, which is what this simulation now provides. The simulator, named the "Bristol Betting Exchange" (BBE), is intended as a common platform, a data-source and experimental test-bed, for researchers studying the application of AI and machine learning (ML) techniques to issues arising in betting exchanges; and, as far as I have been able to determine, BBE is the first of its kind: a free open-source agent-based simulation model consisting not only of a sports-betting exchange, but also a minimal simulation model of racetrack sporting events (e.g., horse-races or car-races) about which bets may be made, and a population of simulated bettors who each form their own private evaluation of odds and place bets on the exchange before and - crucially - during the race itself (i.e., so-called "in-play" betting) and whose betting opinions change second-by-second as each race event unfolds. BBE is offered as a proof-of-concept system that enables the generation of large high-resolution data-sets for automated discovery or improvement of profitable strategies for betting on sporting events via the application of AI/ML and advanced data analytics techniques. This paper offers an extensive survey of relevant literature and explains the motivation and design of BBE, and presents brief illustrative results.
Deep Graph Convolutional Reinforcement Learning for Financial Portfolio Management -- DeepPocket
Authors: Farzan Soleymani, Eric Paquet
Date: 2021-05-06 00:00:00
Tasks: Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Portfolio management aims at maximizing the return on investment while minimizing risk by continuously reallocating the assets forming the portfolio. These assets are not independent but correlated during a short time period. A graph convolutional reinforcement learning framework called DeepPocket is proposed whose objective is to exploit the time-varying interrelations between financial instruments. These interrelations are represented by a graph whose nodes correspond to the financial instruments while the edges correspond to a pair-wise correlation function in between assets. DeepPocket consists of a restricted, stacked autoencoder for feature extraction, a convolutional network to collect underlying local information shared among financial instruments, and an actor-critic reinforcement learning agent. The actor-critic structure contains two convolutional networks in which the actor learns and enforces an investment policy which is, in turn, evaluated by the critic in order to determine the best course of action by constantly reallocating the various portfolio assets to optimize the expected return on investment. The agent is initially trained offline with online stochastic batching on historical data. As new data become available, it is trained online with a passive concept drift approach to handle unexpected changes in their distributions. DeepPocket is evaluated against five real-life datasets over three distinct investment periods, including during the Covid-19 crisis, and clearly outperformed market indexes.
Fighting Gradients with Gradients: Dynamic Defenses against Adversarial Attacks
Authors: Dequan Wang, An Ju, Evan Shelhamer, David Wagner, Trevor Darrell
Date: 2021-05-18 00:00:00
Proceeding: NeurIPS 2021 12

Adversarial attacks optimize against models to defeat defenses. Existing defenses are static, and stay the same once trained, even while attacks change. We argue that models should fight back, and optimize their defenses against attacks at test time. We propose dynamic defenses, to adapt the model and input during testing, by defensive entropy minimization (dent). Dent alters testing, but not training, for compatibility with existing models and train-time defenses. Dent improves the robustness of adversarially-trained defenses and nominally-trained models against white-box, black-box, and adaptive attacks on CIFAR-10/100 and ImageNet. In particular, dent boosts state-of-the-art defenses by 20+ points absolute against AutoAttack on CIFAR-10 at $\epsilon_\infty$ = 8/255.
Learning and Certification under Instance-targeted Poisoning
Authors: Ji Gao, Amin Karbasi, Mohammad Mahmoody
Date: 2021-05-18 00:00:00
Tasks: PAC learning

In this paper, we study PAC learnability and certification of predictions under instance-targeted poisoning attacks, where the adversary who knows the test instance may change a fraction of the training set with the goal of fooling the learner at the test instance. Our first contribution is to formalize the problem in various settings and to explicitly model subtle aspects such as the proper or improper nature of the learning, learner's randomness, and whether (or not) adversary's attack can depend on it. Our main result shows that when the budget of the adversary scales sublinearly with the sample complexity, (improper) PAC learnability and certification are achievable; in contrast, when the adversary's budget grows linearly with the sample complexity, the adversary can potentially drive up the expected 0-1 loss to one. We also study distribution-specific PAC learning in the same attack model and show that proper learning with certification is possible for learning half spaces under natural distributions. Finally, we empirically study the robustness of K nearest neighbour, logistic regression, multi-layer perceptron, and convolutional neural network on real data sets against targeted-poisoning attacks. Our experimental results show that many models, especially state-of-the-art neural networks, are indeed vulnerable to these strong attacks. Interestingly, we observe that methods with high standard accuracy might be more vulnerable to instance-targeted poisoning attacks.
DID-eFed: Facilitating Federated Learning as a Service with Decentralized Identities
Authors: Jiahui Geng, Neel Kanwal, Martin Gilje Jaatun, Chunming Rong
Date: 2021-05-18 00:00:00
Tasks: Federated Learning, Management

We have entered the era of big data, and it is considered to be the "fuel" for the flourishing of artificial intelligence applications. The enactment of the EU General Data Protection Regulation (GDPR) raises concerns about individuals' privacy in big data. Federated learning (FL) emerges as a functional solution that can help build high-performance models shared among multiple parties while still complying with user privacy and data confidentiality requirements. Although FL has been intensively studied and used in real applications, there is still limited research related to its prospects and applications as a FLaaS (Federated Learning as a Service) to interested 3rd parties. In this paper, we present a FLaaS system: DID-eFed, where FL is facilitated by decentralized identities (DID) and a smart contract. DID enables a more flexible and credible decentralized access management in our system, while the smart contract offers a frictionless and less error-prone process. We describe particularly the scenario where our DID-eFed enables the FLaaS among hospitals and research institutions.
On the Robustness of Domain Constraints
Authors: Ryan Sheatsley, Blaine Hoak, Eric Pauley, Yohan Beugin, Michael J. Weisman, Patrick McDaniel
Date: 2021-05-18 00:00:00
Tasks: valid

Machine learning is vulnerable to adversarial examples-inputs designed to cause models to perform poorly. However, it is unclear if adversarial examples represent realistic inputs in the modeled domains. Diverse domains such as networks and phishing have domain constraints-complex relationships between features that an adversary must satisfy for an attack to be realized (in addition to any adversary-specific goals). In this paper, we explore how domain constraints limit adversarial capabilities and how adversaries can adapt their strategies to create realistic (constraint-compliant) examples. In this, we develop techniques to learn domain constraints from data, and show how the learned constraints can be integrated into the adversarial crafting process. We evaluate the efficacy of our approach in network intrusion and phishing datasets and find: (1) up to 82% of adversarial examples produced by state-of-the-art crafting algorithms violate domain constraints, (2) domain constraints are robust to adversarial examples; enforcing constraints yields an increase in model accuracy by up to 34%. We observe not only that adversaries must alter inputs to satisfy domain constraints, but that these constraints make the generation of valid adversarial examples far more challenging.
Adaptive ABAC Policy Learning: A Reinforcement Learning Approach
Authors: Leila Karimi, Mai Abdelhakim, James Joshi
Date: 2021-05-18 00:00:00
Tasks: Attribute, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

With rapid advances in computing systems, there is an increasing demand for more effective and efficient access control (AC) approaches. Recently, Attribute Based Access Control (ABAC) approaches have been shown to be promising in fulfilling the AC needs of such emerging complex computing environments. An ABAC model grants access to a requester based on attributes of entities in a system and an authorization policy; however, its generality and flexibility come with a higher cost. Further, increasing complexities of organizational systems and the need for federated accesses to their resources make the task of AC enforcement and management much more challenging. In this paper, we propose an adaptive ABAC policy learning approach to automate the authorization management task. We model ABAC policy learning as a reinforcement learning problem. In particular, we propose a contextual bandit system, in which an authorization engine adapts an ABAC model through a feedback control loop; it relies on interacting with users/administrators of the system to receive their feedback that assists the model in making authorization decisions. We propose four methods for initializing the learning model and a planning approach based on attribute value hierarchy to accelerate the learning process. We focus on developing an adaptive ABAC policy learning model for a home IoT environment as a running example. We evaluate our proposed approach over real and synthetic data. We consider both complete and sparse datasets in our evaluations. Our experimental results show that the proposed approach achieves performance that is comparable to ones based on supervised learning in many scenarios and even outperforms them in several situations.
Oneshot Differentially Private Top-k Selection
Authors: Gang Qiao, Weijie J. Su, Li Zhang
Date: 2021-05-18 00:00:00

Being able to efficiently and accurately select the top-$k$ elements with differential privacy is an integral component of various private data analysis tasks. In this paper, we present the oneshot Laplace mechanism, which generalizes the well-known Report Noisy Max mechanism to reporting noisy top-$k$ elements. We show that the oneshot Laplace mechanism with a noise level of $\widetilde{O}(\sqrt{k}/\eps)$ is approximately differentially private. Compared to the previous peeling approach of running Report Noisy Max $k$ times, the oneshot Laplace mechanism only adds noises and computes the top $k$ elements once, hence much more efficient for large $k$. In addition, our proof of privacy relies on a novel coupling technique that bypasses the use of composition theorems. Finally, we present a novel application of efficient top-$k$ selection in the classical problem of ranking from pairwise comparisons.
Itsy Bitsy SpiderNet: Fully Connected Residual Network for Fraud Detection
Authors: Sergey Afanasiev, Anastasiya Smirnova, Diana Kotereva
Date: 2021-05-17 00:00:00
Tasks: Feature Engineering, Fraud Detection

With the development of high technology, the scope of fraud is increasing, resulting in annual losses of billions of dollars worldwide. The preventive protection measures become obsolete and vulnerable over time, so effective detective tools are needed. In this paper, we propose a convolutional neural network architecture SpiderNet designed to solve fraud detection problems. We noticed that the principles of pooling and convolutional layers in neural networks are very similar to the way antifraud analysts work when conducting investigations. Moreover, the skip-connections used in neural networks make the usage of features of various power in antifraud models possible. Our experiments have shown that SpiderNet provides better quality compared to Random Forest and adapted for antifraud modeling problems 1D-CNN, 1D-DenseNet, F-DenseNet neural networks. We also propose new approaches for fraud feature engineering called B-tests and W-tests, which generalize the concepts of Benford's Law for fraud anomalies detection. Our results showed that B-tests and W-tests give a significant increase to the quality of our anti-fraud models. The SpiderNet code is available at https://github.com/aasmirnova24/SpiderNet
Privacy-Preserving Constrained Domain Generalization via Gradient Alignment
Authors: Chris Xing Tian, Haoliang Li, YuFei Wang, Shiqi Wang
Date: 2021-05-14 00:00:00
Tasks: Classification, Domain Generalization, Federated Learning, Image Classification, Medical Image Classification, Privacy Preserving

Deep neural networks (DNN) have demonstrated unprecedented success for medical imaging applications. However, due to the issue of limited dataset availability and the strict legal and ethical requirements for patient privacy protection, the broad applications of medical imaging classification driven by DNN with large-scale training data have been largely hindered. For example, when training the DNN from one domain (e.g., with data only from one hospital), the generalization capability to another domain (e.g., data from another hospital) could be largely lacking. In this paper, we aim to tackle this problem by developing the privacy-preserving constrained domain generalization method, aiming to improve the generalization capability under the privacy-preserving condition. In particular, We propose to improve the information aggregation process on the centralized server-side with a novel gradient alignment loss, expecting that the trained model can be better generalized to the "unseen" but related medical images. The rationale and effectiveness of our proposed method can be explained by connecting our proposed method with the Maximum Mean Discrepancy (MMD) which has been widely adopted as the distribution distance measurement. Experimental results on two challenging medical imaging classification tasks indicate that our method can achieve better cross-domain generalization capability compared to the state-of-the-art federated learning methods.
Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency
Authors: Kyra Yee, Uthaipon Tantipongpipat, Shubhanshu Mishra
Date: 2021-05-18 00:00:00
Tasks: Fairness, Image Cropping

Twitter uses machine learning to crop images, where crops are centered around the part predicted to be the most salient. In fall 2020, Twitter users raised concerns that the automated image cropping system on Twitter favored light-skinned over dark-skinned individuals, as well as concerns that the system favored cropping woman's bodies instead of their heads. In order to address these concerns, we conduct an extensive analysis using formalized group fairness metrics. We find systematic disparities in cropping and identify contributing factors, including the fact that the cropping based on the single most salient point can amplify the disparities because of an effect we term argmax bias. However, we demonstrate that formalized fairness metrics and quantitative analysis on their own are insufficient for capturing the risk of representational harm in automatic cropping. We suggest the removal of saliency-based cropping in favor of a solution that better preserves user agency. For developing a new solution that sufficiently address concerns related to representational harm, our critique motivates a combination of quantitative and qualitative methods that include human-centered design.
AI and Shared Prosperity
Authors: Katya Klinova, Anton Korinek
Date: 2021-05-18 00:00:00

Future advances in AI that automate away human labor may have stark implications for labor markets and inequality. This paper proposes a framework to analyze the effects of specific types of AI systems on the labor market, based on how much labor demand they will create versus displace, while taking into account that productivity gains also make society wealthier and thereby contribute to additional labor demand. This analysis enables ethically-minded companies creating or deploying AI systems as well as researchers and policymakers to take into account the effects of their actions on labor markets and inequality, and therefore to steer progress in AI in a direction that advances shared prosperity and an inclusive economic future for all of humanity.