pwc-archive/papers-with-abstracts huggingface dataset:
Improving the compromise between accuracy, interpretability and personalization of rule-based machine learning in medical problems
Authors: Francisco Valente, Jorge Henriques, Simão Paredes, Teresa Rocha, Paulo de Carvalho, João Morais
Date: 2021-06-15 00:00:00

One of the key challenges when developing a predictive model is the capability to describe the domain knowledge and the cause-effect relationships in a simple way. Decision rules are a useful and important methodology in this context, justifying their application in several areas, particularly in clinical practice. Several machine-learning classifiers have exploited the advantageous properties of decision rules to build intelligent prediction models, namely decision trees and ensembles of trees (ETs). However, such methodologies usually suffer from a trade-off between interpretability and predictive performance. Some procedures consider a simplification of ETs, using heuristic approaches to select an optimal reduced set of decision rules. In this paper, we introduce a novel step to those methodologies. We create a new component to predict if a given rule will be correct or not for a particular patient, which introduces personalization into the procedure. Furthermore, the validation results using three public clinical datasets suggest that it also allows to increase the predictive performance of the selected set of rules, improving the mentioned trade-off.
On the Convergence and Calibration of Deep Learning with Differential Privacy
Authors: Zhiqi Bu, Hua Wang, Zongyu Dai, Qi Long
Date: 2021-06-15 00:00:00

Differentially private (DP) training preserves the data privacy usually at the cost of slower convergence (and thus lower accuracy), as well as more severe mis-calibration than its non-private counterpart. To analyze the convergence of DP training, we formulate a continuous time analysis through the lens of neural tangent kernel (NTK), which characterizes the per-sample gradient clipping and the noise addition in DP training, for arbitrary network architectures and loss functions. Interestingly, we show that the noise addition only affects the privacy risk but not the convergence or calibration, whereas the per-sample gradient clipping (under both flat and layerwise clipping styles) only affects the convergence and calibration. Furthermore, we observe that while DP models trained with small clipping norm usually achieve the best accurate, but are poorly calibrated and thus unreliable. In sharp contrast, DP models trained with large clipping norm enjoy the same privacy guarantee and similar accuracy, but are significantly more \textit{calibrated}. Our code can be found at \url{https://github.com/woodyx218/opacus_global_clipping}.
Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent
Authors: Priyank Jaini, Lars Holdijk, Max Welling
Date: 2021-06-15 00:00:00
Proceeding: NeurIPS 2021 12

We focus on the problem of efficient sampling and learning of probability densities by incorporating symmetries in probabilistic models. We first introduce Equivariant Stein Variational Gradient Descent algorithm -- an equivariant sampling method based on Stein's identity for sampling from densities with symmetries. Equivariant SVGD explicitly incorporates symmetry information in a density through equivariant kernels which makes the resultant sampler efficient both in terms of sample complexity and the quality of generated samples. Subsequently, we define equivariant energy based models to model invariant densities that are learned using contrastive divergence. By utilizing our equivariant SVGD for training equivariant EBMs, we propose new ways of improving and scaling up training of energy based models. We apply these equivariant energy models for modelling joint densities in regression and classification tasks for image datasets, many-body particle systems and molecular structure generation.
Improved Regret Bounds for Online Submodular Maximization
Authors: Omid Sadeghi, Prasanna Raut, Maryam Fazel
Date: 2021-06-15 00:00:00

In this paper, we consider an online optimization problem over $T$ rounds where at each step $t\in[T]$, the algorithm chooses an action $x_t$ from the fixed convex and compact domain set $\mathcal{K}$. A utility function $f_t(\cdot)$ is then revealed and the algorithm receives the payoff $f_t(x_t)$. This problem has been previously studied under the assumption that the utilities are adversarially chosen monotone DR-submodular functions and $\mathcal{O}(\sqrt{T})$ regret bounds have been derived. We first characterize the class of strongly DR-submodular functions and then, we derive regret bounds for the following new online settings: $(1)$ $\{f_t\}_{t=1}^T$ are monotone strongly DR-submodular and chosen adversarially, $(2)$ $\{f_t\}_{t=1}^T$ are monotone submodular (while the average $\frac{1}{T}\sum_{t=1}^T f_t$ is strongly DR-submodular) and chosen by an adversary but they arrive in a uniformly random order, $(3)$ $\{f_t\}_{t=1}^T$ are drawn i.i.d. from some unknown distribution $f_t\sim \mathcal{D}$ where the expected function $f(\cdot)=\mathbb{E}_{f_t\sim\mathcal{D}}[f_t(\cdot)]$ is monotone DR-submodular. For $(1)$, we obtain the first logarithmic regret bounds. In terms of the second framework, we show that it is possible to obtain similar logarithmic bounds with high probability. Finally, for the i.i.d. model, we provide algorithms with $\tilde{\mathcal{O}}(\sqrt{T})$ stochastic regret bound, both in expectation and with high probability. Experimental results demonstrate that our algorithms outperform the previous techniques in the aforementioned three settings.
Randomized Exploration for Reinforcement Learning with General Value Function Approximation
Authors: Haque Ishfaq, Qiwen Cui, Viet Nguyen, Alex Ayoub, Zhuoran Yang, Zhaoran Wang, Doina Precup, Lin F. Yang
Date: 2021-06-15 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a model-free reinforcement learning algorithm inspired by the popular randomized least squares value iteration (RLSVI) algorithm as well as the optimism principle. Unlike existing upper-confidence-bound (UCB) based approaches, which are often computationally intractable, our algorithm drives exploration by simply perturbing the training data with judiciously chosen i.i.d. scalar noises. To attain optimistic value function estimation without resorting to a UCB-style bonus, we introduce an optimistic reward sampling procedure. When the value functions can be represented by a function class $\mathcal{F}$, our algorithm achieves a worst-case regret bound of $\widetilde{O}(\mathrm{poly}(d_EH)\sqrt{T})$ where $T$ is the time elapsed, $H$ is the planning horizon and $d_E$ is the $\textit{eluder dimension}$ of $\mathcal{F}$. In the linear setting, our algorithm reduces to LSVI-PHE, a variant of RLSVI, that enjoys an $\widetilde{\mathcal{O}}(\sqrt{d^3H^3T})$ regret. We complement the theory with an empirical evaluation across known difficult exploration tasks.
Voting for the right answer: Adversarial defense for speaker verification
Authors: Haibin Wu, Yang Zhang, Zhiyong Wu, Dong Wang, Hung-Yi Lee
Date: 2021-06-15 00:00:00
Tasks: Adversarial Defense, Speaker Verification

Automatic speaker verification (ASV) is a well developed technology for biometric identification, and has been ubiquitous implemented in security-critic applications, such as banking and access control. However, previous works have shown that ASV is under the radar of adversarial attacks, which are very similar to their original counterparts from human's perception, yet will manipulate the ASV render wrong prediction. Due to the very late emergence of adversarial attacks for ASV, effective countermeasures against them are limited. Given that the security of ASV is of high priority, in this work, we propose the idea of "voting for the right answer" to prevent risky decisions of ASV in blind spot areas, by employing random sampling and voting. Experimental results show that our proposed method improves the robustness against both the limited-knowledge attackers by pulling the adversarial samples out of the blind spots, and the perfect-knowledge attackers by introducing randomness and increasing the attackers' budgets.
Credit Assignment in Neural Networks through Deep Feedback Control
Authors: Alexander Meulemans, Matilde Tristany Farinha, Javier García Ordóñez, Pau Vilimelis Aceituno, João Sacramento, Benjamin F. Grewe
Date: 2021-06-15 00:00:00
Proceeding: NeurIPS 2021 12

The success of deep learning sparked interest in whether the brain learns by using similar techniques for assigning credit to each synaptic weight for its contribution to the network output. However, the majority of current attempts at biologically-plausible learning methods are either non-local in time, require highly specific connectivity motives, or have no clear link to any known mathematical optimization method. Here, we introduce Deep Feedback Control (DFC), a new learning method that uses a feedback controller to drive a deep neural network to match a desired output target and whose control signal can be used for credit assignment. The resulting learning rule is fully local in space and time and approximates Gauss-Newton optimization for a wide range of feedback connectivity patterns. To further underline its biological plausibility, we relate DFC to a multi-compartment model of cortical pyramidal neurons with a local voltage-dependent synaptic plasticity rule, consistent with recent theories of dendritic processing. By combining dynamical system theory with mathematical optimization theory, we provide a strong theoretical foundation for DFC that we corroborate with detailed results on toy experiments and standard computer-vision benchmarks.
Probabilistic Margins for Instance Reweighting in Adversarial Training
Authors: Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama
Date: 2021-06-15 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Adversarial Robustness

Reweighting adversarial data during training has been recently shown to improve adversarial robustness, where data closer to the current decision boundaries are regarded as more critical and given larger weights. However, existing methods measuring the closeness are not very reliable: they are discrete and can take only a few values, and they are path-dependent, i.e., they may change given the same start and end points with different attack paths. In this paper, we propose three types of probabilistic margin (PM), which are continuous and path-independent, for measuring the aforementioned closeness and reweighting adversarial data. Specifically, a PM is defined as the difference between two estimated class-posterior probabilities, e.g., such the probability of the true label minus the probability of the most confusing label given some natural data. Though different PMs capture different geometric properties, all three PMs share a negative correlation with the vulnerability of data: data with larger/smaller PMs are safer/riskier and should have smaller/larger weights. Experiments demonstrate that PMs are reliable measurements and PM-based reweighting methods outperform state-of-the-art methods.
Machine learning-based conditional mean filter: a generalization of the ensemble Kalman filter for nonlinear data assimilation
Authors: Truong-Vinh Hoang, Sebastian Krumscheid, Hermann G. Matthies, Raúl Tempone
Date: 2021-06-15 00:00:00
Tasks: Model Selection

This paper presents the machine learning-based ensemble conditional mean filter (ML-EnCMF) -- a filtering method based on the conditional mean filter (CMF) previously introduced in the literature. The updated mean of the CMF matches that of the posterior, obtained by applying Bayes' rule on the filter's forecast distribution. Moreover, we show that the CMF's updated covariance coincides with the expected conditional covariance. Implementing the EnCMF requires computing the conditional mean (CM). A likelihood-based estimator is prone to significant errors for small ensemble sizes, causing the filter divergence. We develop a systematical methodology for integrating machine learning into the EnCMF based on the CM's orthogonal projection property. First, we use a combination of an artificial neural network (ANN) and a linear function, obtained based on the ensemble Kalman filter (EnKF), to approximate the CM, enabling the ML-EnCMF to inherit EnKF's advantages. Secondly, we apply a suitable variance reduction technique to reduce statistical errors when estimating loss function. Lastly, we propose a model selection procedure for element-wisely selecting the applied filter, i.e., either the EnKF or ML-EnCMF, at each updating step. We demonstrate the ML-EnCMF performance using the Lorenz-63 and Lorenz-96 systems and show that the ML-EnCMF outperforms the EnKF and the likelihood-based EnCMF.
Control Variates for Slate Off-Policy Evaluation
Authors: Nikos Vlassis, Ashok Chandrashekar, Fernando Amat Gil, Nathan Kallus
Date: 2021-06-15 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Off-policy evaluation, Recommendation Systems

We study the problem of off-policy evaluation from batched contextual bandit data with multidimensional actions, often termed slates. The problem is common to recommender systems and user-interface optimization, and it is particularly challenging because of the combinatorially-sized action space. Swaminathan et al. (2017) have proposed the pseudoinverse (PI) estimator under the assumption that the conditional mean rewards are additive in actions. Using control variates, we consider a large class of unbiased estimators that includes as specific cases the PI estimator and (asymptotically) its self-normalized variant. By optimizing over this class, we obtain new estimators with risk improvement guarantees over both the PI and the self-normalized PI estimators. Experiments with real-world recommender data as well as synthetic data validate these improvements in practice.
Machine Learning with Electronic Health Records is vulnerable to Backdoor Trigger Attacks
Authors: Byunggill Joe, Akshay Mehra, Insik Shin, Jihun Hamm
Date: 2021-06-15 00:00:00
Tasks: BIG-bench Machine Learning, Diagnostic, Management, Mortality Prediction

Electronic Health Records (EHRs) provide a wealth of information for machine learning algorithms to predict the patient outcome from the data including diagnostic information, vital signals, lab tests, drug administration, and demographic information. Machine learning models can be built, for example, to evaluate patients based on their predicted mortality or morbidity and to predict required resources for efficient resource management in hospitals. In this paper, we demonstrate that an attacker can manipulate the machine learning predictions with EHRs easily and selectively at test time by backdoor attacks with the poisoned training data. Furthermore, the poison we create has statistically similar features to the original data making it hard to detect, and can also attack multiple machine learning models without any knowledge of the models. With less than 5% of the raw EHR data poisoned, we achieve average attack success rates of 97% on mortality prediction tasks with MIMIC-III database against Logistic Regression, Multilayer Perceptron, and Long Short-term Memory models simultaneously.
Simple GNN Regularisation for 3D Molecular Property Prediction & Beyond
Authors: Jonathan Godwin, Michael Schaarschmidt, Alexander Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Veličković, James Kirkpatrick, Peter Battaglia
Date: 2021-06-15 00:00:00
Tasks: Denoising, Diversity, Graph Property Prediction, Initial Structure to Relaxed Energy (IS2RE), Molecular Property Prediction, Prediction, Property Prediction, Structured Prediction

In this paper we show that simple noise regularisation can be an effective way to address GNN oversmoothing. First we argue that regularisers addressing oversmoothing should both penalise node latent similarity and encourage meaningful node representations. From this observation we derive "Noisy Nodes", a simple technique in which we corrupt the input graph with noise, and add a noise correcting node-level loss. The diverse node level loss encourages latent node diversity, and the denoising objective encourages graph manifold learning. Our regulariser applies well-studied methods in simple, straightforward ways which allow even generic architectures to overcome oversmoothing and achieve state of the art results on quantum chemistry tasks, and improve results significantly on Open Graph Benchmark (OGB) datasets. Our results suggest Noisy Nodes can serve as a complementary building block in the GNN toolkit.
Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection
Authors: Tuo Zhang, Chaoyang He, Tianhao Ma, Lei Gao, Mark Ma, Salman Avestimehr
Date: 2021-06-15 00:00:00
Tasks: Anomaly Detection, Federated Learning, Time Series, Time Series Analysis

Federated learning can be a promising solution for enabling IoT cybersecurity (i.e., anomaly detection in the IoT environment) while preserving data privacy and mitigating the high communication/storage overhead (e.g., high-frequency data from time-series sensors) of centralized over-the-cloud approaches. In this paper, to further push forward this direction with a comprehensive study in both algorithm and system design, we build FedIoT platform that contains FedDetect algorithm for on-device anomaly data detection and a system design for realistic evaluation of federated learning on IoT devices. Furthermore, the proposed FedDetect learning framework improves the performance by utilizing a local adaptive optimizer (e.g., Adam) and a cross-round learning rate scheduler. In a network of realistic IoT devices (Raspberry PI), we evaluate FedIoT platform and FedDetect algorithm in both model and system performance. Our results demonstrate the efficacy of federated learning in detecting a wider range of attack types occurred at multiple devices. The system efficiency analysis indicates that both end-to-end training time and memory cost are affordable and promising for resource-constrained IoT devices. The source code is publicly available at https://github.com/FedML-AI/FedIoT.
Compression Implies Generalization
Authors: Allan Grønlund, Mikael Høgsgaard, Lior Kamma, Kasper Green Larsen
Date: 2021-06-15 00:00:00
Tasks: BIG-bench Machine Learning, Generalization Bounds

Explaining the surprising generalization performance of deep neural networks is an active and important line of research in theoretical machine learning. Influential work by Arora et al. (ICML'18) showed that, noise stability properties of deep nets occurring in practice can be used to provably compress model representations. They then argued that the small representations of compressed networks imply good generalization performance albeit only of the compressed nets. Extending their compression framework to yield generalization bounds for the original uncompressed networks remains elusive. Our main contribution is the establishment of a compression-based framework for proving generalization bounds. The framework is simple and powerful enough to extend the generalization bounds by Arora et al. to also hold for the original network. To demonstrate the flexibility of the framework, we also show that it allows us to give simple proofs of the strongest known generalization bounds for other popular machine learning models, namely Support Vector Machines and Boosting.
Time Series Anomaly Detection for Cyber-Physical Systems via Neural System Identification and Bayesian Filtering
Authors: Cheng Feng, Pengwei Tian
Date: 2021-06-15 00:00:00
Tasks: Anomaly Detection, Time Series, Time Series Analysis, Time Series Anomaly Detection

Recent advances in AIoT technologies have led to an increasing popularity of utilizing machine learning algorithms to detect operational failures for cyber-physical systems (CPS). In its basic form, an anomaly detection module monitors the sensor measurements and actuator states from the physical plant, and detects anomalies in these measurements to identify abnormal operation status. Nevertheless, building effective anomaly detection models for CPS is rather challenging as the model has to accurately detect anomalies in presence of highly complicated system dynamics and unknown amount of sensor noise. In this work, we propose a novel time series anomaly detection method called Neural System Identification and Bayesian Filtering (NSIBF) in which a specially crafted neural network architecture is posed for system identification, i.e., capturing the dynamics of CPS in a dynamical state-space model; then a Bayesian filtering algorithm is naturally applied on top of the "identified" state-space model for robust anomaly detection by tracking the uncertainty of the hidden state of the system recursively over time. We provide qualitative as well as quantitative experiments with the proposed method on a synthetic and three real-world CPS datasets, showing that NSIBF compares favorably to the state-of-the-art methods with considerable improvements on anomaly detection in CPS.
Multivariate Business Process Representation Learning utilizing Gramian Angular Fields and Convolutional Neural Networks
Authors: Peter Pfeiffer, Johannes Lahann, Peter Fettke
Date: 2021-06-15 00:00:00
Tasks: Anomaly Detection, Clustering, Representation Learning, Retrieval

Learning meaningful representations of data is an important aspect of machine learning and has recently been successfully applied to many domains like language understanding or computer vision. Instead of training a model for one specific task, representation learning is about training a model to capture all useful information in the underlying data and make it accessible for a predictor. For predictive process analytics, it is essential to have all explanatory characteristics of a process instance available when making predictions about the future, as well as for clustering and anomaly detection. Due to the large variety of perspectives and types within business process data, generating a good representation is a challenging task. In this paper, we propose a novel approach for representation learning of business process instances which can process and combine most perspectives in an event log. In conjunction with a self-supervised pre-training method, we show the capabilities of the approach through a visualization of the representation space and case retrieval. Furthermore, the pre-trained model is fine-tuned to multiple process prediction tasks and demonstrates its effectiveness in comparison with existing approaches.
Residual Reinforcement Learning from Demonstrations
Authors: Minttu Alakuijala, Gabriel Dulac-Arnold, Julien Mairal, Jean Ponce, Cordelia Schmid
Date: 2021-06-15 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Residual reinforcement learning (RL) has been proposed as a way to solve challenging robotic tasks by adapting control actions from a conventional feedback controller to maximize a reward signal. We extend the residual formulation to learn from visual inputs and sparse rewards using demonstrations. Learning from images, proprioceptive inputs and a sparse task-completion reward relaxes the requirement of accessing full state features, such as object and target positions. In addition, replacing the base controller with a policy learned from demonstrations removes the dependency on a hand-engineered controller in favour of a dataset of demonstrations, which can be provided by non-experts. Our experimental evaluation on simulated manipulation tasks on a 6-DoF UR5 arm and a 28-DoF dexterous hand demonstrates that residual RL from demonstrations is able to generalize to unseen environment conditions more flexibly than either behavioral cloning or RL fine-tuning, and is capable of solving high-dimensional, sparse-reward tasks out of reach for RL from scratch.