pwc-archive/papers-with-abstracts huggingface dataset:
Training individually fair ML models with Sensitive Subspace Robustness
Authors: Mikhail Yurochkin, Amanda Bower, Yuekai Sun
Date: 2019-06-28 00:00:00
Proceeding: ICLR 2020 1
Tasks: BIG-bench Machine Learning, Fairness

We consider training machine learning models that are fair in the sense that their performance is invariant under certain sensitive perturbations to the inputs. For example, the performance of a resume screening system should be invariant under changes to the gender and/or ethnicity of the applicant. We formalize this notion of algorithmic fairness as a variant of individual fairness and develop a distributionally robust optimization approach to enforce it during training. We also demonstrate the effectiveness of the approach on two ML tasks that are susceptible to gender and racial biases.
Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models
Authors: Rares-Darius Buhai, Yoni Halpern, Yoon Kim, Andrej Risteski, David Sontag
Date: 2019-06-28 00:00:00
Proceeding: ICML 2020 1
Tasks: Variational Inference

One of the most surprising and exciting discoveries in supervised learning was the benefit of overparameterization (i.e. training a very large model) to improving the optimization landscape of a problem, with minimal effect on statistical performance (i.e. generalization). In contrast, unsupervised settings have been under-explored, despite the fact that it was observed that overparameterization can be helpful as early as Dasgupta & Schulman (2007). We perform an empirical study of different aspects of overparameterization in unsupervised learning of latent variable models via synthetic and semi-synthetic experiments. We discuss benefits to different metrics of success (recovering the parameters of the ground-truth model, held-out log-likelihood), sensitivity to variations of the training algorithm, and behavior as the amount of overparameterization increases. We find that across a variety of models (noisy-OR networks, sparse coding, probabilistic context-free grammars) and training algorithms (variational inference, alternating minimization, expectation-maximization), overparameterization can significantly increase the number of ground truth latent variables recovered.
Cross-product Penalized Component Analysis (XCAN)
Authors: José Camacho, Evrim Acar, Morten A. Rasmussen, Rasmus Bro
Date: 2019-06-28 00:00:00
Tasks: Clustering

Matrix factorization methods are extensively employed to understand complex data. In this paper, we introduce the cross-product penalized component analysis (XCAN), a sparse matrix factorization based on the optimization of a loss function that allows a trade-off between variance maximization and structural preservation. The approach is based on previous developments, notably (i) the Sparse Principal Component Analysis (SPCA) framework based on the LASSO, (ii) extensions of SPCA to constrain both modes of the factorization, like co-clustering or the Penalized Matrix Decomposition (PMD), and (iii) the Group-wise Principal Component Analysis (GPCA) method. The result is a flexible modeling approach that can be used for data exploration in a large variety of problems. We demonstrate its use with applications from different disciplines.
Bayesian Nonparametric Boolean Factor Models
Authors: Tammo Rukat, Christopher Yau
Date: 2019-06-28 00:00:00

We build upon probabilistic models for Boolean Matrix and Boolean Tensor factorisation that have recently been shown to solve these problems with unprecedented accuracy and to enable posterior inference to scale to Billions of observation. Here, we lift the restriction of a pre-specified number of latent dimensions by introducing an Indian Buffet Process prior over factor matrices. Not only does the full factor-conditional take a computationally convenient form due to the logical dependencies in the model, but also the posterior over the number of non-zero latent dimensions is remarkably simple. It amounts to counting the number false and true negative predictions, whereas positive predictions can be ignored. This constitutes a very transparent example of sampling-based posterior inference with an IBP prior and, importantly, lets us maintain extremely efficient inference. We discuss applications to simulated data, as well as to a real world data matrix with 6 Million entries.
Learning to Identify Patients at Risk of Uncontrolled Hypertension Using Electronic Health Records Data
Authors: Ramin Mohammadi, Sarthak Jain, Stephen Agboola, Ramya Palacholla, Sagar Kamarthi, Byron C. Wallace
Date: 2019-06-28 00:00:00
Tasks: Management, regression

Hypertension is a major risk factor for stroke, cardiovascular disease, and end-stage renal disease, and its prevalence is expected to rise dramatically. Effective hypertension management is thus critical. A particular priority is decreasing the incidence of uncontrolled hypertension. Early identification of patients at risk for uncontrolled hypertension would allow targeted use of personalized, proactive treatments. We develop machine learning models (logistic regression and recurrent neural networks) to stratify patients with respect to the risk of exhibiting uncontrolled hypertension within the coming three-month period. We trained and tested models using EHR data from 14,407 and 3,009 patients, respectively. The best model achieved an AUROC of 0.719, outperforming the simple, competitive baseline of relying prediction based on the last BP measure alone (0.634). Perhaps surprisingly, recurrent neural networks did not outperform a simple logistic regression for this task, suggesting that linear models should be included as strong baselines for predictive tasks using EHR
Cellular State Transformations using Generative Adversarial Networks
Authors: Colin Targonski, Benjamin T. Shealy, Melissa C. Smith, F. Alex Feltus
Date: 2019-06-28 00:00:00

We introduce a novel method to unite deep learning with biology by which generative adversarial networks (GANs) generate transcriptome perturbations and reveal condition-defining gene expression patterns. We find that a generator conditioned to perturb any input gene expression profile simulates a realistic transition between source and target RNA expression states. The perturbed samples follow a similar distribution to original samples from the dataset, also suggesting these are biologically meaningful perturbations. Finally, we show that it is possible to identify the genes most positively and negatively perturbed by the generator and that the enriched biological function of the perturbed genes are realistic. We call the framework the Transcriptome State Perturbation Generator (TSPG), which is open source software available at https://github.com/ctargon/TSPG.
Competing Neural Networks for Robust Control of Nonlinear Systems
Authors: Babak Rahmani, Damien Loterie, Eirini Kakkava, Navid Borhani, Uğur Teğin, Demetri Psaltis, Christophe Moser
Date: 2019-06-29 00:00:00

The output of physical systems is often accessible by measurements such as the 3D position of a robotic arm actuated by many actuators or the speckle patterns formed by shining the spot of a laser pointer on a wall. The selection of the input of such a system (actuators and the shape of the laser spot respectively) to obtain a desired output is difficult because it is an ill-posed problem i.e. there are multiple inputs yielding the same output. In this paper, we propose an approach that provides a robust solution to this dilemma for any physical system. We show that it is possible to find the appropriate input of a system that results in a desired output, despite the input-output relation being nonlinear and\or with incomplete measurements of the systems variables. We showcase our approach using an extremely ill-posed problem in imaging. We demonstrate the projection of arbitrary shapes through a multimode fiber (MMF) when a sample of intensity-only measurements are taken at the output. We show image projection fidelity as high as ~90 %, which is on par with the gold standard methods which characterize the system fully by phase and amplitude measurements. The generality as well as simplicity of the proposed approach provides a new way of target-oriented control in real-world applications.
Approximate matrix completion based on cavity method
Authors: Chihiro Noguchi, Yoshiyuki Kabashima
Date: 2019-06-29 00:00:00
Tasks: Matrix Completion, Scheduling

In order to solve large matrix completion problems with practical computational cost, an approximate approach based on matrix factorization has been widely used. Alternating least squares (ALS) and stochastic gradient descent (SGD) are two major algorithms to this end. In this study, we propose a new algorithm, namely cavity-based matrix factorization (CBMF) and approximate cavity-based matrix factorization (ACBMF), which are developed based on the cavity method from statistical mechanics. ALS yields solutions with less iterations when compared to those of SGD. This is because its update rules are described in a closed form although it entails higher computational cost. CBMF can also write its update rules in a closed form, and its computational cost is lower than that of ALS. ACBMF is proposed to compensate a disadvantage of CBMF in terms of relatively high memory cost. We experimentally illustrate that the proposed methods outperform the two existing algorithms in terms of convergence speed per iteration, and it can work under the condition where observed entries are relatively fewer. Additionally, in contrast to SGD, (A)CBMF does not require scheduling of the learning rate.
Streaming Quantiles Algorithms with Small Space and Update Time
Authors: Nikita Ivkin, Edo Liberty, Kevin Lang, Zohar Karnin, Vladimir Braverman
Date: 2019-06-29 00:00:00

Approximating quantiles and distributions over streaming data has been studied for roughly two decades now. Recently, Karnin, Lang, and Liberty proposed the first asymptotically optimal algorithm for doing so. This manuscript complements their theoretical result by providing a practical variants of their algorithm with improved constants. For a given sketch size, our techniques provably reduce the upper bound on the sketch error by a factor of two. These improvements are verified experimentally. Our modified quantile sketch improves the latency as well by reducing the worst case update time from $O(1/\varepsilon)$ down to $O(\log (1/\varepsilon))$. We also suggest two algorithms for weighted item streams which offer improved asymptotic update times compared to na\"ive extensions. Finally, we provide a specialized data structure for these sketches which reduces both their memory footprints and update times.
Identification In Missing Data Models Represented By Directed Acyclic Graphs
Authors: Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser, James M. Robins
Date: 2019-06-29 00:00:00
Tasks: Causal Inference

Missing data is a pervasive problem in data analyses, resulting in datasets that contain censored realizations of a target distribution. Many approaches to inference on the target distribution using censored observed data, rely on missing data models represented as a factorization with respect to a directed acyclic graph. In this paper we consider the identifiability of the target distribution within this class of models, and show that the most general identification strategies proposed so far retain a significant gap in that they fail to identify a wide class of identifiable distributions. To address this gap, we propose a new algorithm that significantly generalizes the types of manipulations used in the ID algorithm, developed in the context of causal inference, in order to obtain identification.
On Training Flexible Robots using Deep Reinforcement Learning
Authors: Zach Dwiel, Madhavun Candadai, Mariano Phielipp
Date: 2019-06-29 00:00:00
Tasks: Deep Reinforcement Learning, Industrial Robots, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The use of robotics in controlled environments has flourished over the last several decades and training robots to perform tasks using control strategies developed from dynamical models of their hardware have proven very effective. However, in many real-world settings, the uncertainties of the environment, the safety requirements and generalized capabilities that are expected of robots make rigid industrial robots unsuitable. This created great research interest into developing control strategies for flexible robot hardware for which building dynamical models are challenging. In this paper, inspired by the success of deep reinforcement learning (DRL) in other areas, we systematically study the efficacy of policy search methods using DRL in training flexible robots. Our results indicate that DRL is successfully able to learn efficient and robust policies for complex tasks at various degrees of flexibility. We also note that DRL using Deep Deterministic Policy Gradients can be sensitive to the choice of sensors and adding more informative sensors does not necessarily make the task easier to learn.
Conjugate Gradients and Accelerated Methods Unified: The Approximate Duality Gap View
Authors: Jelena Diakonikolas, Lorenzo Orecchia
Date: 2019-06-29 00:00:00

This note provides a novel, simple analysis of the method of conjugate gradients for the minimization of convex quadratic functions. In contrast with standard arguments, our proof is entirely self-contained and does not rely on the existence of Chebyshev polynomials. Another advantage of our development is that it clarifies the relation between the method of conjugate gradients and general accelerated methods for smooth minimization by unifying their analyses within the framework of the Approximate Duality Gap Technique that was introduced by the authors.
Signed Laplacian Deep Learning with Adversarial Augmentation for Improved Mammography Diagnosis
Authors: Heyi Li, Dong-Dong Chen, William H. Nailon, Mike E. Davies, David I. Laurenson
Date: 2019-06-30 00:00:00

Computer-aided breast cancer diagnosis in mammography is limited by inadequate data and the similarity between benign and cancerous masses. To address this, we propose a signed graph regularized deep neural network with adversarial augmentation, named \textsc{DiagNet}. Firstly, we use adversarial learning to generate positive and negative mass-contained mammograms for each mass class. After that, a signed similarity graph is built upon the expanded data to further highlight the discrimination. Finally, a deep convolutional neural network is trained by jointly optimizing the signed graph regularization and classification loss. Experiments show that the \textsc{DiagNet} framework outperforms the state-of-the-art in breast mass diagnosis in mammography.
Competitive Algorithms for Online Budget-Constrained Continuous DR-Submodular Problems
Authors: Omid Sadeghi, Reza Eghbali, Maryam Fazel
Date: 2019-06-30 00:00:00

In this paper, we study a certain class of online optimization problems, where the goal is to maximize a function that is not necessarily concave and satisfies the Diminishing Returns (DR) property under budget constraints. We analyze a primal-dual algorithm, called the Generalized Sequential algorithm, and we obtain the first bound on the competitive ratio of online monotone DR-submodular function maximization subject to linear packing constraints which matches the known tight bound in the special case of linear objective function.
Online Continuous DR-Submodular Maximization with Long-Term Budget Constraints
Authors: Omid Sadeghi, Maryam Fazel
Date: 2019-06-30 00:00:00

In this paper, we study a class of online optimization problems with long-term budget constraints where the objective functions are not necessarily concave (nor convex) but they instead satisfy the Diminishing Returns (DR) property. Specifically, a sequence of monotone DR-submodular objective functions $\{f_t(x)\}_{t=1}^T$ and monotone linear budget functions $\{\langle p_t,x \rangle \}_{t=1}^T$ arrive over time and assuming a total targeted budget $B_T$, the goal is to choose points $x_t$ at each time $t\in\{1,\dots,T\}$, without knowing $f_t$ and $p_t$ on that step, to achieve sub-linear regret bound while the total budget violation $\sum_{t=1}^T \langle p_t,x_t \rangle -B_T$ is sub-linear as well. Prior work has shown that achieving sub-linear regret is impossible if the budget functions are chosen adversarially. Therefore, we modify the notion of regret by comparing the agent against a $(1-\frac{1}{e})$-approximation to the best fixed decision in hindsight which satisfies the budget constraint proportionally over any window of length $W$. We propose the Online Saddle Point Hybrid Gradient (OSPHG) algorithm to solve this class of online problems. For $W=T$, we recover the aforementioned impossibility result. However, when $W=o(T)$, we show that it is possible to obtain sub-linear bounds for both the $(1-\frac{1}{e})$-regret and the total budget violation.
Mechanisms of Artistic Creativity in Deep Learning Neural Networks
Authors: Lonce Wyse
Date: 2019-06-30 00:00:00
Tasks: Deep Learning

The generative capabilities of deep learning neural networks (DNNs) have been attracting increasing attention for both the remarkable artifacts they produce, but also because of the vast conceptual difference between how they are programmed and what they do. DNNs are 'black boxes' where high-level behavior is not explicitly programmed, but emerges from the complex interactions of thousands or millions of simple computational elements. Their behavior is often described in anthropomorphic terms that can be misleading, seem magical, or stoke fears of an imminent singularity in which machines become 'more' than human. In this paper, we examine 5 distinct behavioral characteristics associated with creativity, and provide an example of a mechanisms from generative deep learning architectures that give rise to each these characteristics. All 5 emerge from machinery built for purposes other than the creative characteristics they exhibit, mostly classification. These mechanisms of creative generative capabilities thus demonstrate a deep kinship to computational perceptual processes. By understanding how these different behaviors arise, we hope to on one hand take the magic out of anthropomorphic descriptions, but on the other, to build a deeper appreciation of machinic forms of creativity on their own terms that will allow us to nurture their further development.
Prediction of Small Molecule Kinase Inhibitors for Chemotherapy Using Deep Learning
Authors: Niranjan Balachandar, Christine Liu, Winston Wang
Date: 2019-06-30 00:00:00

The current state of cancer therapeutics has been moving away from one-size-fits-all cytotoxic chemotherapy, and towards a more individualized and specific approach involving the targeting of each tumor's genetic vulnerabilities. Different tumors, even of the same type, may be more reliant on certain cellular pathways more than others. With modern advancements in our understanding of cancer genome sequencing, these pathways can be discovered. Investigating each of the millions of possible small molecule inhibitors for each kinase in vitro, however, would be extremely expensive and time consuming. This project focuses on predicting the inhibition activity of small molecules targeting 8 different kinases using multiple deep learning models. We trained fingerprint-based MLPs and simplified molecular-input line-entry specification (SMILES)-based recurrent neural networks (RNNs) and molecular graph convolutional networks (GCNs) to accurately predict inhibitory activity targeting these 8 kinases.
An Enhanced Electrocardiogram Biometric Authentication System Using Machine Learning
Authors: Ebrahim Al Alkeem, Song-Kyoo Kim, Chan Yeob Yeun, M. Jamal Zemerly, Kin Poon, Paul D. Yoo
Date: 2019-06-30 00:00:00
Tasks: BIG-bench Machine Learning

Traditional authentication systems use alphanumeric or graphical passwords, or token-based techniques that require "something you know and something you have". The disadvantages of these systems include the risks of forgetfulness, loss, and theft. To address these shortcomings, biometric authentication is rapidly replacing traditional authentication methods and is becoming a part of everyday life. The electrocardiogram (ECG) is one of the most recent traits considered for biometric purposes. In this work we describe an ECG-based authentication system suitable for security checks and hospital environments. The proposed system will help investigators studying ECG-based biometric authentication techniques to define dataset boundaries and to acquire high-quality training data. We evaluated the performance of the proposed system and found that it could achieve up to the 92 percent identification accuracy. In addition, by applying the Amang ECG (amgecg) toolbox within MATLAB, we investigated the two parameters that directly affect the accuracy of authentication: the ECG slicing time (sliding window) and the sampling time period, and found their optimal values.
Machine Learning for Intelligent Authentication in 5G-and-Beyond Wireless Networks
Authors: He Fang, Xianbin Wang, Stefano Tomasin
Date: 2019-06-30 00:00:00
Tasks: BIG-bench Machine Learning, Reinforcement Learning, Unsupervised Reinforcement Learning

The fifth generation (5G) and beyond wireless networks are critical to support diverse vertical applications by connecting heterogeneous devices and machines, which directly increase vulnerability for various spoofing attacks. Conventional cryptographic and physical layer authentication techniques are facing some challenges in complex dynamic wireless environments, including significant security overhead, low reliability, as well as difficulty in pre-designing authentication model, providing continuous protections, and learning time-varying attributes. In this article, we envision new authentication approaches based on machine learning techniques by opportunistically leveraging physical layer attributes, and introduce intelligence to authentication for more efficient security provisioning. Machine learning paradigms for intelligent authentication design are presented, namely for parametric/non-parametric and supervised/unsupervised/reinforcement learning algorithms. In a nutshell, the machine learning-based intelligent authentication approaches utilize specific features in the multi-dimensional domain for achieving cost-effective, more reliable, model-free, continuous and situation-aware device validation under unknown network conditions and unpredictable dynamics.
Directivity Modes of Earthquake Populations with Unsupervised Learning
Authors: Zachary E. Ross, Daniel T. Trugman, Kamyar Azizzadenesheli, Anima Anandkumar
Date: 2019-06-30 00:00:00

We present a novel approach for resolving modes of rupture directivity in large populations of earthquakes. A seismic spectral decomposition technique is used to first produce relative measurements of radiated energy for earthquakes in a spatially-compact cluster. The azimuthal distribution of energy for each earthquake is then assumed to result from one of several distinct modes of rupture propagation. Rather than fitting a kinematic rupture model to determine the most likely mode of rupture propagation, we instead treat the modes as latent variables and learn them with a Gaussian mixture model. The mixture model simultaneously determines the number of events that best identify with each mode. The technique is demonstrated on four datasets in California with several thousand earthquakes. We show that the datasets naturally decompose into distinct rupture propagation modes that correspond to different rupture directions, and the fault plane is unambiguously identified for all cases. We find that these small earthquakes exhibit unilateral ruptures 53-74% of the time on average. The results provide important observational constraints on the physics of earthquakes and faults.