pwc-archive/papers-with-abstracts huggingface dataset:
Unique sparse decomposition of low rank matrices
Authors: Dian Jin, Xin Bing, Yuqian Zhang
Date: 2021-06-14 00:00:00
Proceeding: NeurIPS 2021 12

The problem of finding the unique low dimensional decomposition of a given matrix has been a fundamental and recurrent problem in many areas. In this paper, we study the problem of seeking a unique decomposition of a low rank matrix $Y\in \mathbb{R}^{p\times n}$ that admits a sparse representation. Specifically, we consider $Y = A X\in \mathbb{R}^{p\times n}$ where the matrix $A\in \mathbb{R}^{p\times r}$ has full column rank, with $r < \min\{n,p\}$, and the matrix $X\in \mathbb{R}^{r\times n}$ is element-wise sparse. We prove that this sparse decomposition of $Y$ can be uniquely identified, up to some intrinsic signed permutation. Our approach relies on solving a nonconvex optimization problem constrained over the unit sphere. Our geometric analysis for the nonconvex optimization landscape shows that any {\em strict} local solution is close to the ground truth solution, and can be recovered by a simple data-driven initialization followed with any second order descent algorithm. At last, we corroborate these theoretical results with numerical experiments.
Optimization-friendly generic mechanisms without money
Authors: Mark Braverman
Date: 2021-06-14 00:00:00

The goal of this paper is to develop a generic framework for converting modern optimization algorithms into mechanisms where inputs come from self-interested agents. We focus on aggregating preferences from $n$ players in a context without money. Special cases of this setting include voting, allocation of items by lottery, and matching. Our key technical contribution is a new meta-algorithm we call \apex (Adaptive Pricing Equalizing Externalities). The framework is sufficiently general to be combined with any optimization algorithm that is based on local search. We outline an agenda for studying the algorithm's properties and its applications. As a special case of applying the framework to the problem of one-sided assignment with lotteries, we obtain a strengthening of the 1979 result by Hylland and Zeckhauser on allocation via a competitive equilibrium from equal incomes (CEEI). The [HZ79] result posits that there is a (fractional) allocation and a set of item prices such that the allocation is a competitive equilibrium given prices. We further show that there is always a reweighing of the players' utility values such that running unit-demand VCG with reweighed utilities leads to a HZ-equilibrium prices. Interestingly, not all HZ competitive equilibria come from VCG prices. As part of our proof, we re-prove the [HZ79] result using only Brouwer's fixed point theorem (and not the more general Kakutani's theorem). This may be of independent interest.
Counterfactual Explanations as Interventions in Latent Space
Authors: Riccardo Crupi, Alessandro Castelnovo, Daniele Regoli, Beatriz San Miguel Gonzalez
Date: 2021-06-14 00:00:00
Tasks: counterfactual, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI)

Explainable Artificial Intelligence (XAI) is a set of techniques that allows the understanding of both technical and non-technical aspects of Artificial Intelligence (AI) systems. XAI is crucial to help satisfying the increasingly important demand of \emph{trustworthy} Artificial Intelligence, characterized by fundamental characteristics such as respect of human autonomy, prevention of harm, transparency, accountability, etc. Within XAI techniques, counterfactual explanations aim to provide to end users a set of features (and their corresponding values) that need to be changed in order to achieve a desired outcome. Current approaches rarely take into account the feasibility of actions needed to achieve the proposed explanations, and in particular they fall short of considering the causal impact of such actions. In this paper, we present Counterfactual Explanations as Interventions in Latent Space (CEILS), a methodology to generate counterfactual explanations capturing by design the underlying causal relations from the data, and at the same time to provide feasible recommendations to reach the proposed profile. Moreover, our methodology has the advantage that it can be set on top of existing counterfactuals generator algorithms, thus minimising the complexity of imposing additional causal constrains. We demonstrate the effectiveness of our approach with a set of different experiments using synthetic and real datasets (including a proprietary dataset of the financial domain).
Linear-Time Probabilistic Solutions of Boundary Value Problems
Authors: Nicholas Krämer, Philipp Hennig
Date: 2021-06-14 00:00:00
Tasks: Uncertainty Quantification

We propose a fast algorithm for the probabilistic solution of boundary value problems (BVPs), which are ordinary differential equations subject to boundary conditions. In contrast to previous work, we introduce a Gauss--Markov prior and tailor it specifically to BVPs, which allows computing a posterior distribution over the solution in linear time, at a quality and cost comparable to that of well-established, non-probabilistic methods. Our model further delivers uncertainty quantification, mesh refinement, and hyperparameter adaptation. We demonstrate how these practical considerations positively impact the efficiency of the scheme. Altogether, this results in a practically usable probabilistic BVP solver that is (in contrast to non-probabilistic algorithms) natively compatible with other parts of the statistical modelling tool-chain.
Tracing Back Music Emotion Predictions to Sound Sources and Intuitive Perceptual Qualities
Authors: Shreyan Chowdhury, Verena Praher, Gerhard Widmer
Date: 2021-06-14 00:00:00
Tasks: Emotion Recognition, Information Retrieval, Music Emotion Recognition, Music Information Retrieval, Prediction, Retrieval

Music emotion recognition is an important task in MIR (Music Information Retrieval) research. Owing to factors like the subjective nature of the task and the variation of emotional cues between musical genres, there are still significant challenges in developing reliable and generalizable models. One important step towards better models would be to understand what a model is actually learning from the data and how the prediction for a particular input is made. In previous work, we have shown how to derive explanations of model predictions in terms of spectrogram image segments that connect to the high-level emotion prediction via a layer of easily interpretable perceptual features. However, that scheme lacks intuitive musical comprehensibility at the spectrogram level. In the present work, we bridge this gap by merging audioLIME -- a source-separation based explainer -- with mid-level perceptual features, thus forming an intuitive connection chain between the input audio and the output emotion predictions. We demonstrate the usefulness of this method by applying it to debug a biased emotion prediction model.
Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction
Authors: Hangrui Bi, Hengyi Wang, Chence Shi, Connor Coley, Jian Tang, Hongyu Guo
Date: 2021-06-08 00:00:00
Tasks: Prediction

Reliably predicting the products of chemical reactions presents a fundamental challenge in synthetic chemistry. Existing machine learning approaches typically produce a reaction product by sequentially forming its subparts or intermediate molecules. Such autoregressive methods, however, not only require a pre-defined order for the incremental construction but preclude the use of parallel decoding for efficient computation. To address these issues, we devise a non-autoregressive learning paradigm that predicts reaction in one shot. Leveraging the fact that chemical reactions can be described as a redistribution of electrons in molecules, we formulate a reaction as an arbitrary electron flow and predict it with a novel multi-pointer decoding network. Experiments on the USPTO-MIT dataset show that our approach has established a new state-of-the-art top-1 accuracy and achieves at least 27 times inference speedup over the state-of-the-art methods. Also, our predictions are easier for chemists to interpret owing to predicting the electron flows.
GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles
Authors: Octavian-Eugen Ganea, Lagnajit Pattanaik, Connor W. Coley, Regina Barzilay, Klavs F. Jensen, William H. Green, Tommi S. Jaakkola
Date: 2021-06-08 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Drug Discovery

Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key role in areas of cheminformatics and drug discovery. Existing generative models have several drawbacks including lack of modeling important molecular geometry elements (e.g. torsion angles), separate optimization stages prone to error accumulation, and the need for structure fine-tuning based on approximate classical force-fields or computationally expensive methods such as metadynamics with approximate quantum mechanics calculations at each geometry. We propose GeoMol--an end-to-end, non-autoregressive and SE(3)-invariant machine learning approach to generate distributions of low-energy molecular 3D conformers. Leveraging the power of message passing neural networks (MPNNs) to capture local and global graph information, we predict local atomic 3D structures and torsion angles, avoiding unnecessary over-parameterization of the geometric degrees of freedom (e.g. one angle per non-terminal bond). Such local predictions suffice both for the training loss computation, as well as for the full deterministic conformer assembly (at test time). We devise a non-adversarial optimal transport based loss function to promote diverse conformer generation. GeoMol predominantly outperforms popular open-source, commercial, or state-of-the-art machine learning (ML) models, while achieving significant speed-ups. We expect such differentiable 3D structure generators to significantly impact molecular modeling and related applications.
Code Integrity Attestation for PLCs using Black Box Neural Network Predictions
Authors: Yuqi Chen, Christopher M. Poskitt, Jun Sun
Date: 2021-06-15 00:00:00
Tasks: Privacy Preserving

Cyber-physical systems (CPSs) are widespread in critical domains, and significant damage can be caused if an attacker is able to modify the code of their programmable logic controllers (PLCs). Unfortunately, traditional techniques for attesting code integrity (i.e. verifying that it has not been modified) rely on firmware access or roots-of-trust, neither of which proprietary or legacy PLCs are likely to provide. In this paper, we propose a practical code integrity checking solution based on privacy-preserving black box models that instead attest the input/output behaviour of PLC programs. Using faithful offline copies of the PLC programs, we identify their most important inputs through an information flow analysis, execute them on multiple combinations to collect data, then train neural networks able to predict PLC outputs (i.e. actuator commands) from their inputs. By exploiting the black box nature of the model, our solution maintains the privacy of the original PLC code and does not assume that attackers are unaware of its presence. The trust instead comes from the fact that it is extremely hard to attack the PLC code and neural networks at the same time and with consistent outcomes. We evaluated our approach on a modern six-stage water treatment plant testbed, finding that it could predict actuator states from PLC inputs with near-100% accuracy, and thus could detect all 120 effective code mutations that we subjected the PLCs to. Finally, we found that it is not practically possible to simultaneously modify the PLC code and apply discreet adversarial noise to our attesters in a way that leads to consistent (mis-)predictions.
Evading Malware Classifiers via Monte Carlo Mutant Feature Discovery
Authors: John Boutsikas, Maksim E. Eren, Charles Varga, Edward Raff, Cynthia Matuszek, Charles Nicholas
Date: 2021-06-15 00:00:00
Tasks: BIG-bench Machine Learning, Malware Analysis, Malware Detection

The use of Machine Learning has become a significant part of malware detection efforts due to the influx of new malware, an ever changing threat landscape, and the ability of Machine Learning methods to discover meaningful distinctions between malicious and benign software. Antivirus vendors have also begun to widely utilize malware classifiers based on dynamic and static malware analysis features. Therefore, a malware author might make evasive binary modifications against Machine Learning models as part of the malware development life cycle to execute an attack successfully. This makes the studying of possible classifier evasion strategies an essential part of cyber defense against malice. To this extent, we stage a grey box setup to analyze a scenario where the malware author does not know the target classifier algorithm, and does not have access to decisions made by the classifier, but knows the features used in training. In this experiment, a malicious actor trains a surrogate model using the EMBER-2018 dataset to discover binary mutations that cause an instance to be misclassified via a Monte Carlo tree search. Then, mutated malware is sent to the victim model that takes the place of an antivirus API to test whether it can evade detection.
S-LIME: Stabilized-LIME for Model Explanation
Authors: Zhengze Zhou, Giles Hooker, Fei Wang
Date: 2021-06-15 00:00:00
Tasks: BIG-bench Machine Learning, model

An increasing number of machine learning models have been deployed in domains with high stakes such as finance and healthcare. Despite their superior performances, many models are black boxes in nature which are hard to explain. There are growing efforts for researchers to develop methods to interpret these black-box models. Post hoc explanations based on perturbations, such as LIME, are widely used approaches to interpret a machine learning model after it has been built. This class of methods has been shown to exhibit large instability, posing serious challenges to the effectiveness of the method itself and harming user trust. In this paper, we propose S-LIME, which utilizes a hypothesis testing framework based on central limit theorem for determining the number of perturbation points needed to guarantee stability of the resulting explanation. Experiments on both simulated and real world data sets are provided to demonstrate the effectiveness of our method.
Learning Revenue-Maximizing Auctions With Differentiable Matching
Authors: Michael J. Curry, Uro Lyi, Tom Goldstein, John Dickerson
Date: 2021-06-15 00:00:00

We propose a new architecture to approximately learn incentive compatible, revenue-maximizing auctions from sampled valuations. Our architecture uses the Sinkhorn algorithm to perform a differentiable bipartite matching which allows the network to learn strategyproof revenue-maximizing mechanisms in settings not learnable by the previous RegretNet architecture. In particular, our architecture is able to learn mechanisms in settings without free disposal where each bidder must be allocated exactly some number of items. In experiments, we show our approach successfully recovers multiple known optimal mechanisms and high-revenue, low-regret mechanisms in larger settings where the optimal mechanism is unknown.
S2Engine: A Novel Systolic Architecture for Sparse Convolutional Neural Networks
Authors: Jianlei Yang, Wenzhi Fu, Xingzhou Cheng, Xucheng Ye, Pengcheng Dai, Weisheng Zhao
Date: 2021-06-15 00:00:00

Convolutional neural networks (CNNs) have achieved great success in performing cognitive tasks. However, execution of CNNs requires a large amount of computing resources and generates heavy memory traffic, which imposes a severe challenge on computing system design. Through optimizing parallel executions and data reuse in convolution, systolic architecture demonstrates great advantages in accelerating CNN computations. However, regular internal data transmission path in traditional systolic architecture prevents the systolic architecture from completely leveraging the benefits introduced by neural network sparsity. Deployment of fine-grained sparsity on the existing systolic architectures is greatly hindered by the incurred computational overheads. In this work, we propose S2Engine $-$ a novel systolic architecture that can fully exploit the sparsity in CNNs with maximized data reuse. S2Engine transmits compressed data internally and allows each processing element to dynamically select an aligned data from the compressed dataflow in convolution. Compared to the naive systolic array, S2Engine achieves about $3.2\times$ and about $3.0\times$ improvements on speed and energy efficiency, respectively.
CAN-LOC: Spoofing Detection and Physical Intrusion Localization on an In-Vehicle CAN Bus Based on Deep Features of Voltage Signals
Authors: Efrat Levy, Asaf Shabtai, Bogdan Groza, Pal-Stefan Murvay, Yuval Elovici
Date: 2021-06-15 00:00:00
Tasks: Data Augmentation

The Controller Area Network (CAN) is used for communication between in-vehicle devices. The CAN bus has been shown to be vulnerable to remote attacks. To harden vehicles against such attacks, vehicle manufacturers have divided in-vehicle networks into sub-networks, logically isolating critical devices. However, attackers may still have physical access to various sub-networks where they can connect a malicious device. This threat has not been adequately addressed, as methods proposed to determine physical intrusion points have shown weak results, emphasizing the need to develop more advanced techniques. To address this type of threat, we propose a security hardening system for in-vehicle networks. The proposed system includes two mechanisms that process deep features extracted from voltage signals measured on the CAN bus. The first mechanism uses data augmentation and deep learning to detect and locate physical intrusions when the vehicle starts; this mechanism can detect and locate intrusions, even when the connected malicious devices are silent. This mechanism's effectiveness (100% accuracy) is demonstrated in a wide variety of insertion scenarios on a CAN bus prototype. The second mechanism is a continuous device authentication mechanism, which is also based on deep learning; this mechanism's robustness (99.8% accuracy) is demonstrated on a real moving vehicle.
Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals
Authors: Lang Liu, Krishna Pillutla, Sean Welleck, Sewoong Oh, Yejin Choi, Zaid Harchaoui
Date: 2021-06-15 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Diversity, Quantization

The spectacular success of deep generative models calls for quantitative tools to measure their statistical performance. Divergence frontiers have recently been proposed as an evaluation framework for generative models, due to their ability to measure the quality-diversity trade-off inherent to deep generative modeling. We establish non-asymptotic bounds on the sample complexity of divergence frontiers. We also introduce frontier integrals which provide summary statistics of divergence frontiers. We show how smoothed estimators such as Good-Turing or Krichevsky-Trofimov can overcome the missing mass problem and lead to faster rates of convergence. We illustrate the theoretical results with numerical examples from natural language processing and computer vision.
ATD: Augmenting CP Tensor Decomposition by Self Supervision
Authors: Chaoqi Yang, Cheng Qian, Navjot Singh, Cao Xiao, M Brandon Westover, Edgar Solomonik, Jimeng Sun
Date: 2021-06-15 00:00:00
Tasks: Data Augmentation, Dimensionality Reduction, Self-Supervised Learning, Stochastic Optimization, Tensor Decomposition

Tensor decompositions are powerful tools for dimensionality reduction and feature interpretation of multidimensional data such as signals. Existing tensor decomposition objectives (e.g., Frobenius norm) are designed for fitting raw data under statistical assumptions, which may not align with downstream classification tasks. In practice, raw input tensors can contain irrelevant information while data augmentation techniques may be used to smooth out class-irrelevant noise in samples. This paper addresses the above challenges by proposing augmented tensor decomposition (ATD), which effectively incorporates data augmentations and self-supervised learning (SSL) to boost downstream classification. To address the non-convexity of the new augmented objective, we develop an iterative method that enables the optimization to follow an alternating least squares (ALS) fashion. We evaluate our proposed ATD on multiple datasets. It can achieve 0.8% - 2.5% accuracy gain over tensor-based baselines. Also, our ATD model shows comparable or better performance (e.g., up to 15% in accuracy) over self-supervised and autoencoder baselines while using less than 5% of learnable parameters of these baseline models
Learning to Compensate: A Deep Neural Network Framework for 5G Power Amplifier Compensation
Authors: Po-Yu Chen, Hao Chen, Yi-Min Tsai, Hsien-Kai Kuo, Hantao Huang, Hsin-Hung Chen, Sheng-Hong Yan, Wei-Lun Ou, Chia-Ming Cheng
Date: 2021-06-15 00:00:00

Owing to the complicated characteristics of 5G communication system, designing RF components through mathematical modeling becomes a challenging obstacle. Moreover, such mathematical models need numerous manual adjustments for various specification requirements. In this paper, we present a learning-based framework to model and compensate Power Amplifiers (PAs) in 5G communication. In the proposed framework, Deep Neural Networks (DNNs) are used to learn the characteristics of the PAs, while, correspondent Digital Pre-Distortions (DPDs) are also learned to compensate for the nonlinear and memory effects of PAs. On top of the framework, we further propose two frequency domain losses to guide the learning process to better optimize the target, compared to naive time domain Mean Square Error (MSE). The proposed framework serves as a drop-in replacement for the conventional approach. The proposed approach achieves an average of 56.7% reduction of nonlinear and memory effects, which converts to an average of 16.3% improvement over a carefully-designed mathematical model, and even reaches 34% enhancement in severe distortion scenarios.
CatBoost model with synthetic features in application to loan risk assessment of small businesses
Authors: Haoxue Wang, Liexin Cheng
Date: 2021-06-15 00:00:00

Loan risk for small businesses has long been a complex problem worthy of exploring. Predicting the loan risk can benefit entrepreneurship by developing more jobs for the society. CatBoost (Categorical Boosting) is a powerful machine learning algorithm suitable for dataset with many categorical variables like the dataset for forecasting loan risk. In this paper, we identify the important risk factors that contribute to loan status classification problem. Then we compare the performance between boosting-type algorithms(especially CatBoost) with other traditional yet popular ones. The dataset we adopt in the research comes from the U.S. Small Business Administration (SBA) and holds a very large sample size (899,164 observations and 27 features). In order to make the best use of the important features in the dataset, we propose a technique named "synthetic generation" to develop more combined features based on arithmetic operation, which ends up improving the accuracy and AUC of the original CatBoost model. We obtain a high accuracy of 95.84% and well-performed AUC of 98.80% compared with the existent literature of related research.