pwc-archive/papers-with-abstracts huggingface dataset:
Complementary Calibration: Boosting General Continual Learning with Collaborative Distillation and Self-Supervision
Authors: Zhong Ji, Jin Li, Qiang Wang, Zhongfei Zhang
Date: 2021-09-03 00:00:00
Tasks: Continual Learning, Contrastive Learning, Knowledge Distillation, Relation

General Continual Learning (GCL) aims at learning from non independent and identically distributed stream data without catastrophic forgetting of the old tasks that don't rely on task boundaries during both training and testing stages. We reveal that the relation and feature deviations are crucial problems for catastrophic forgetting, in which relation deviation refers to the deficiency of the relationship among all classes in knowledge distillation, and feature deviation refers to indiscriminative feature representations. To this end, we propose a Complementary Calibration (CoCa) framework by mining the complementary model's outputs and features to alleviate the two deviations in the process of GCL. Specifically, we propose a new collaborative distillation approach for addressing the relation deviation. It distills model's outputs by utilizing ensemble dark knowledge of new model's outputs and reserved outputs, which maintains the performance of old tasks as well as balancing the relationship among all classes. Furthermore, we explore a collaborative self-supervision idea to leverage pretext tasks and supervised contrastive learning for addressing the feature deviation problem by learning complete and discriminative features for all classes. Extensive experiments on four popular datasets show that our CoCa framework achieves superior performance against state-of-the-art methods. Code is available at https://github.com/lijincm/CoCa.
Guiding Global Placement With Reinforcement Learning
Authors: Robert Kirby, Kolby Nottingham, Rajarshi Roy, Saad Godil, Bryan Catanzaro
Date: 2021-09-06 00:00:00
Tasks: GPU, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent advances in GPU accelerated global and detail placement have reduced the time to solution by an order of magnitude. This advancement allows us to leverage data driven optimization (such as Reinforcement Learning) in an effort to improve the final quality of placement results. In this work we augment state-of-the-art, force-based global placement solvers with a reinforcement learning agent trained to improve the final detail placed Half Perimeter Wire Length (HPWL). We propose novel control schemes with either global or localized control of the placement process. We then train reinforcement learning agents to use these controls to guide placement to improved solutions. In both cases, the augmented optimizer finds improved placement solutions. Our trained agents achieve an average 1% improvement in final detail place HPWL across a range of academic benchmarks and more than 1% in global place HPWL on real industry designs.
EsmamDS: A more diverse exceptional survival model mining approach
Authors: Juliana Barcellos Mattos, Paulo S. G. de Mattos Neto, Renato Vimieiro
Date: 2021-09-06 00:00:00
Tasks: Descriptive

A variety of works in the literature strive to uncover the factors associated with survival behaviour. However, the computational tools to provide such information are global models designed to predict if or when a (survival) event will occur. When approaching the problem of explaining differences in survival behaviour, those approaches rely on (assumptions of) predictive features followed by risk stratification. In other words, they lack the ability to discover new information on factors related to survival. In contrast, we approach such a problem from the perspective of descriptive supervised pattern mining to discover local patterns associated with different survival behaviours. Hence, we introduce the EsmamDS algorithm: an Exceptional Model Mining framework to provide straightforward characterisations of subgroups presenting unusual survival models -- given by the Kaplan-Meier estimates. This work builds on the Esmam algorithm to address the problem of pattern redundancy and provide a more informative and diverse characterisation of survival behaviour.
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications
Authors: Alexandre Capone, Armin Lederer, Sandra Hirche
Date: 2021-09-06 00:00:00
Tasks: Gaussian Processes

Gaussian processes have become a promising tool for various safety-critical settings, since the posterior variance can be used to directly estimate the model error and quantify risk. However, state-of-the-art techniques for safety-critical settings hinge on the assumption that the kernel hyperparameters are known, which does not apply in general. To mitigate this, we introduce robust Gaussian process uniform error bounds in settings with unknown hyperparameters. Our approach computes a confidence region in the space of hyperparameters, which enables us to obtain a probabilistic upper bound for the model error of a Gaussian process with arbitrary hyperparameters. We do not require to know any bounds for the hyperparameters a priori, which is an assumption commonly found in related work. Instead, we are able to derive bounds from data in an intuitive fashion. We additionally employ the proposed technique to derive performance guarantees for a class of learning-based control problems. Experiments show that the bound performs significantly better than vanilla and fully Bayesian Gaussian processes.
Scikit-dimension: a Python package for intrinsic dimension estimation
Authors: Jonathan Bac, Evgeny M. Mirkes, Alexander N. Gorban, Ivan Tyukin, Andrei Zinovyev
Date: 2021-09-06 00:00:00
Tasks: Benchmarking

Dealing with uncertainty in applications of machine learning to real-life data critically depends on the knowledge of intrinsic dimensionality (ID). A number of methods have been suggested for the purpose of estimating ID, but no standard package to easily apply them one by one or all at once has been implemented in Python. This technical note introduces \texttt{scikit-dimension}, an open-source Python package for intrinsic dimension estimation. \texttt{scikit-dimension} package provides a uniform implementation of most of the known ID estimators based on scikit-learn application programming interface to evaluate global and local intrinsic dimension, as well as generators of synthetic toy and benchmark datasets widespread in the literature. The package is developed with tools assessing the code quality, coverage, unit testing and continuous integration. We briefly describe the package and demonstrate its use in a large-scale (more than 500 datasets) benchmarking of methods for ID estimation in real-life and synthetic data. The source code is available from https://github.com/j-bac/scikit-dimension , the documentation is available from https://scikit-dimension.readthedocs.io .
Knowledge Graph Enhanced Event Extraction in Financial Documents
Authors: Kaihao Guo, Tianpei Jiang, Haipeng Zhang
Date: 2021-09-06 00:00:00
Tasks: Event Extraction, Graph Neural Network

Event extraction is a classic task in natural language processing with wide use in handling large amount of yet rapidly growing financial, legal, medical, and government documents which often contain multiple events with their elements scattered and mixed across the documents, making the problem much more difficult. Though the underlying relations between event elements to be extracted provide helpful contextual information, they are somehow overlooked in prior studies. We showcase the enhancement to this task brought by utilizing the knowledge graph that captures entity relations and their attributes. We propose a first event extraction framework that embeds a knowledge graph through a Graph Neural Network and integrates the embedding with regular features, all at document-level. Specifically, for extracting events from Chinese financial announcements, our method outperforms the state-of-the-art method by 5.3% in F1-score.
Generation of Synthetic Electronic Health Records Using a Federated GAN
Authors: John Weldon, Tomas Ward, Eoin Brophy
Date: 2021-09-06 00:00:00
Tasks: Federated Learning, Generative Adversarial Network, Synthetic Data Generation

Sensitive medical data is often subject to strict usage constraints. In this paper, we trained a generative adversarial network (GAN) on real-world electronic health records (EHR). It was then used to create a data-set of "fake" patients through synthetic data generation (SDG) to circumvent usage constraints. This real-world data was tabular, binary, intensive care unit (ICU) patient diagnosis data. The entire data-set was split into separate data silos to mimic real-world scenarios where multiple ICU units across different hospitals may have similarly structured data-sets within their own organisations but do not have access to each other's data-sets. We implemented federated learning (FL) to train separate GANs locally at each organisation, using their unique data silo and then combining the GANs into a single central GAN, without any siloed data ever being exposed. This global, central GAN was then used to generate the synthetic patients data-set. We performed an evaluation of these synthetic patients with statistical measures and through a structured review by a group of medical professionals. It was shown that there was no significant reduction in the quality of the synthetic EHR when we moved between training a single central model and training on separate data silos with individual models before combining them into a central model. This was true for both the statistical evaluation (Root Mean Square Error (RMSE) of 0.0154 for single-source vs. RMSE of 0.0169 for dual-source federated) and also for the medical professionals' evaluation (no quality difference between EHR generated from a single source and EHR generated from multiple sources).
Neural Ensemble Search via Bayesian Sampling
Authors: Yao Shu, Yizhou Chen, Zhongxiang Dai, Bryan Kian Hsiang Low
Date: 2021-09-06 00:00:00
Tasks: Adversarial Defense, Neural Architecture Search

Recently, neural architecture search (NAS) has been applied to automate the design of neural networks in real-world applications. A large number of algorithms have been developed to improve the search cost or the performance of the final selected architectures in NAS. Unfortunately, these NAS algorithms aim to select only one single well-performing architecture from their search spaces and thus have overlooked the capability of neural network ensemble (i.e., an ensemble of neural networks with diverse architectures) in achieving improved performance over a single final selected architecture. To this end, we introduce a novel neural ensemble search algorithm, called neural ensemble search via Bayesian sampling (NESBS), to effectively and efficiently select well-performing neural network ensembles from a NAS search space. In our extensive experiments, NESBS algorithm is shown to be able to achieve improved performance over state-of-the-art NAS algorithms while incurring a comparable search cost, thus indicating the superior performance of our NESBS algorithm over these NAS algorithms in practice.
Automated Robustness with Adversarial Training as a Post-Processing Step
Authors: Ambrish Rawat, Mathieu Sinn, Beat Buesser
Date: 2021-09-06 00:00:00
Tasks: Classification, Deep Learning, image-classification, Image Classification, Neural Architecture Search, text-classification, Text Classification

Adversarial training is a computationally expensive task and hence searching for neural network architectures with robustness as the criterion can be challenging. As a step towards practical automation, this work explores the efficacy of a simple post processing step in yielding robust deep learning model. To achieve this, we adopt adversarial training as a post-processing step for optimised network architectures obtained from a neural architecture search algorithm. Specific policies are adopted for tuning the hyperparameters of the different steps, resulting in a fully automated pipeline for generating adversarially robust deep learning models. We evidence the usefulness of the proposed pipeline with extensive experimentation across 11 image classification and 9 text classification tasks.
Error Controlled Actor-Critic
Authors: Xingen Gao, Fei Chao, Changle Zhou, Zhen Ge, Chih-Min Lin, Longzhi Yang, Xiang Chang, Changjing Shang
Date: 2021-09-06 00:00:00
Tasks: continuous-control, Continuous Control

On error of value function inevitably causes an overestimation phenomenon and has a negative impact on the convergence of the algorithms. To mitigate the negative effects of the approximation error, we propose Error Controlled Actor-critic which ensures confining the approximation error in value function. We present an analysis of how the approximation error can hinder the optimization process of actor-critic methods.Then, we derive an upper boundary of the approximation error of Q function approximator and find that the error can be lowered by restricting on the KL-divergence between every two consecutive policies when training the policy. The results of experiments on a range of continuous control tasks demonstrate that the proposed actor-critic algorithm apparently reduces the approximation error and significantly outperforms other model-free RL algorithms.
Data-Driven Wind Turbine Wake Modeling via Probabilistic Machine Learning
Authors: S. Ashwin Renganathan, Romit Maulik, Stefano Letizia, Giacomo Valerio Iungo
Date: 2021-09-06 00:00:00
Tasks: Active Learning, BIG-bench Machine Learning

Wind farm design primarily depends on the variability of the wind turbine wake flows to the atmospheric wind conditions, and the interaction between wakes. Physics-based models that capture the wake flow-field with high-fidelity are computationally very expensive to perform layout optimization of wind farms, and, thus, data-driven reduced order models can represent an efficient alternative for simulating wind farms. In this work, we use real-world light detection and ranging (LiDAR) measurements of wind-turbine wakes to construct predictive surrogate models using machine learning. Specifically, we first demonstrate the use of deep autoencoders to find a low-dimensional \emph{latent} space that gives a computationally tractable approximation of the wake LiDAR measurements. Then, we learn the mapping between the parameter space and the (latent space) wake flow-fields using a deep neural network. Additionally, we also demonstrate the use of a probabilistic machine learning technique, namely, Gaussian process modeling, to learn the parameter-space-latent-space mapping in addition to the epistemic and aleatoric uncertainty in the data. Finally, to cope with training large datasets, we demonstrate the use of variational Gaussian process models that provide a tractable alternative to the conventional Gaussian process models for large datasets. Furthermore, we introduce the use of active learning to adaptively build and improve a conventional Gaussian process model predictive capability. Overall, we find that our approach provides accurate approximations of the wind-turbine wake flow field that can be queried at an orders-of-magnitude cheaper cost than those generated with high-fidelity physics-based simulations.
Statistical Privacy Guarantees of Machine Learning Preprocessing Techniques
Authors: Ashly Lau, Jonathan Passerat-Palmbach
Date: 2021-09-06 00:00:00
Tasks: BIG-bench Machine Learning

Differential privacy provides strong privacy guarantees for machine learning applications. Much recent work has been focused on developing differentially private models, however there has been a gap in other stages of the machine learning pipeline, in particular during the preprocessing phase. Our contributions are twofold: we adapt a privacy violation detection framework based on statistical methods to empirically measure privacy levels of machine learning pipelines, and apply the newly created framework to show that resampling techniques used when dealing with imbalanced datasets cause the resultant model to leak more privacy. These results highlight the need for developing private preprocessing techniques.
Learning Interpretable Representations of Entanglement in Quantum Optics Experiments using Deep Generative Models
Authors: Daniel Flam-Shepherd, Tony Wu, Xuemei Gu, Alba Cervera-Lierta, Mario Krenn, Alan Aspuru-Guzik
Date: 2021-09-06 00:00:00

Quantum physics experiments produce interesting phenomena such as interference or entanglement, which are core properties of numerous future quantum technologies. The complex relationship between the setup structure of a quantum experiment and its entanglement properties is essential to fundamental research in quantum optics but is difficult to intuitively understand. We present a deep generative model of quantum optics experiments where a variational autoencoder is trained on a dataset of quantum optics experimental setups. In a series of computational experiments, we investigate the learned representation of our Quantum Optics Variational Auto Encoder (QOVAE) and its internal understanding of the quantum optics world. We demonstrate that the QOVAE learns an interpretable representation of quantum optics experiments and the relationship between experiment structure and entanglement. We show the QOVAE is able to generate novel experiments for highly entangled quantum states with specific distributions that match its training data. The QOVAE can learn to generate specific entangled states and efficiently search the space of experiments that produce highly entangled quantum states. Importantly, we are able to interpret how the QOVAE structures its latent space, finding curious patterns that we can explain in terms of quantum physics. The results demonstrate how we can use and understand the internal representations of deep generative models in a complex scientific domain. The QOVAE and the insights from our investigations can be immediately applied to other physical systems.
Learning Neural Causal Models with Active Interventions
Authors: Nino Scherrer, Olexa Bilaniuk, Yashas Annadani, Anirudh Goyal, Patrick Schwab, Bernhard Schölkopf, Michael C. Mozer, Yoshua Bengio, Stefan Bauer, Nan Rosemary Ke
Date: 2021-09-06 00:00:00
Tasks: Causal Discovery

Discovering causal structures from data is a challenging inference problem of fundamental importance in all areas of science. The appealing properties of neural networks have recently led to a surge of interest in differentiable neural network-based methods for learning causal structures from data. So far, differentiable causal discovery has focused on static datasets of observational or fixed interventional origin. In this work, we introduce an active intervention targeting (AIT) method which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting and is applicable for both discrete and continuous optimization formulations of learning the underlying directed acyclic graph (DAG) from data. We examine the proposed method across multiple frameworks in a wide range of settings and demonstrate superior performance on multiple benchmarks from simulated to real-world data.
Byzantine-Robust Federated Learning via Credibility Assessment on Non-IID Data
Authors: Kun Zhai, Qiang Ren, Junli Wang, Chungang Yan
Date: 2021-09-06 00:00:00
Tasks: Anomaly Detection, Federated Learning

Federated learning is a novel framework that enables resource-constrained edge devices to jointly learn a model, which solves the problem of data protection and data islands. However, standard federated learning is vulnerable to Byzantine attacks, which will cause the global model to be manipulated by the attacker or fail to converge. On non-iid data, the current methods are not effective in defensing against Byzantine attacks. In this paper, we propose a Byzantine-robust framework for federated learning via credibility assessment on non-iid data (BRCA). Credibility assessment is designed to detect Byzantine attacks by combing adaptive anomaly detection model and data verification. Specially, an adaptive mechanism is incorporated into the anomaly detection model for the training and prediction of the model. Simultaneously, a unified update algorithm is given to guarantee that the global model has a consistent direction. On non-iid data, our experiments demonstrate that the BRCA is more robust to Byzantine attacks compared with conventional methods
On Second-order Optimization Methods for Federated Learning
Authors: Sebastian Bischoff, Stephan Günnemann, Martin Jaggi, Sebastian U. Stich
Date: 2021-09-06 00:00:00
Tasks: Federated Learning, Specificity

We consider federated learning (FL), where the training data is distributed across a large number of clients. The standard optimization method in this setting is Federated Averaging (FedAvg), which performs multiple local first-order optimization steps between communication rounds. In this work, we evaluate the performance of several second-order distributed methods with local steps in the FL setting which promise to have favorable convergence properties. We (i) show that FedAvg performs surprisingly well against its second-order competitors when evaluated under fair metrics (equal amount of local computations)-in contrast to the results of previous work. Based on our numerical study, we propose (ii) a novel variant that uses second-order local information for updates and a global line search to counteract the resulting local specificity.
Explicit construction of the minimum error variance estimator for stochastic LTI state-space systems
Authors: Deividas Eringis, John Leth, Zheng-Hua Tan, Rafal Wisniewski, Mihaly Petreczky
Date: 2021-09-06 00:00:00

In this short article, we showcase the derivation of the optimal (minimum error variance) estimator, when one part of the stochastic LTI system output is not measured but is able to be predicted from the measured system outputs. Similar derivations have been done before but not using state-space representation.
Backdoor Attack and Defense for Deep Regression
Authors: Xi Li, George Kesidis, David J. Miller, Vladimir Lucic
Date: 2021-09-06 00:00:00
Tasks: Backdoor Attack, backdoor defense, Data Poisoning, regression

We demonstrate a backdoor attack on a deep neural network used for regression. The backdoor attack is localized based on training-set data poisoning wherein the mislabeled samples are surrounded by correctly labeled ones. We demonstrate how such localization is necessary for attack success. We also study the performance of a backdoor defense using gradient-based discovery of local error maximizers. Local error maximizers which are associated with significant (interpolation) error, and are proximal to many training samples, are suspicious. This method is also used to accurately train for deep regression in the first place by active (deep) learning leveraging an "oracle" capable of providing real-valued supervision (a regression target) for samples. Such oracles, including traditional numerical solvers of PDEs or SDEs using finite difference or Monte Carlo approximations, are far more computationally costly compared to deep regression.