pwc-archive/papers-with-abstracts huggingface dataset:
RNN-Guard: Certified Robustness Against Multi-frame Attacks for Recurrent Neural Networks
Authors: Yunruo Zhang, Tianyu Du, Shouling Ji, Peng Tang, Shanqing Guo
Date: 2023-04-17 00:00:00

It is well-known that recurrent neural networks (RNNs), although widely used, are vulnerable to adversarial attacks including one-frame attacks and multi-frame attacks. Though a few certified defenses exist to provide guaranteed robustness against one-frame attacks, we prove that defending against multi-frame attacks remains a challenging problem due to their enormous perturbation space. In this paper, we propose the first certified defense against multi-frame attacks for RNNs called RNN-Guard. To address the above challenge, we adopt the perturb-all-frame strategy to construct perturbation spaces consistent with those in multi-frame attacks. However, the perturb-all-frame strategy causes a precision issue in linear relaxations. To address this issue, we introduce a novel abstract domain called InterZono and design tighter relaxations. We prove that InterZono is more precise than Zonotope yet carries the same time complexity. Experimental evaluations across various datasets and model structures show that the certified robust accuracy calculated by RNN-Guard with InterZono is up to 2.18 times higher than that with Zonotope. In addition, we extend RNN-Guard as the first certified training method against multi-frame attacks to directly enhance RNNs' robustness. The results show that the certified robust accuracy of models trained with RNN-Guard against multi-frame attacks is 15.47 to 67.65 percentage points higher than those with other training methods.
Heterogeneous Domain Adaptation with Positive and Unlabeled Data
Authors: Junki Mori, Ryo Furukawa, Isamu Teranishi, Jun Sakuma
Date: 2023-04-17 00:00:00
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

Heterogeneous unsupervised domain adaptation (HUDA) is the most challenging domain adaptation setting where the feature spaces of source and target domains are heterogeneous, and the target domain has only unlabeled data. Existing HUDA methods assume that both positive and negative examples are available in the source domain, which may not be satisfied in some real applications. This paper addresses a new challenging setting called positive and unlabeled heterogeneous unsupervised domain adaptation (PU-HUDA), a HUDA setting where the source domain only has positives. PU-HUDA can also be viewed as an extension of PU learning where the positive and unlabeled examples are sampled from different domains. A naive combination of existing HUDA and PU learning methods is ineffective in PU-HUDA due to the gap in label distribution between the source and target domains. To overcome this issue, we propose a novel method, predictive adversarial domain adaptation (PADA), which can predict likely positive examples from the unlabeled target data and simultaneously align the feature spaces to reduce the distribution divergence between the whole source data and the likely positive target data. PADA achieves this by a unified adversarial training framework for learning a classifier to predict positive examples and a feature transformer to transform the target feature space to that of the source. Specifically, they are both trained to fool a common discriminator that determines whether the likely positive examples are from the target or source domain. We experimentally show that PADA outperforms several baseline methods, such as the naive combination of HUDA and PU learning.
Metrics for Bayesian Optimal Experiment Design under Model Misspecification
Authors: Tommie A. Catanach, Niladri Das
Date: 2023-04-17 00:00:00

The conventional approach to Bayesian decision-theoretic experiment design involves searching over possible experiments to select a design that maximizes the expected value of a specified utility function. The expectation is over the joint distribution of all unknown variables implied by the statistical model that will be used to analyze the collected data. The utility function defines the objective of the experiment where a common utility function is the information gain. This article introduces an expanded framework for this process, where we go beyond the traditional Expected Information Gain criteria and introduce the Expected General Information Gain which measures robustness to the model discrepancy and Expected Discriminatory Information as a criterion to quantify how well an experiment can detect model discrepancy. The functionality of the framework is showcased through its application to a scenario involving a linearized spring mass damper system and an F-16 model where the model discrepancy is taken into account while doing Bayesian optimal experiment design.
Learning To Rank Resources with GNN
Authors: Ulugbek Ergashev, Eduard C. Dragut, Weiyi Meng
Date: 2023-04-17 00:00:00
Tasks: Graph Neural Network, Information Retrieval, Language Modelling, Learning-To-Rank, Retrieval

As the content on the Internet continues to grow, many new dynamically changing and heterogeneous sources of data constantly emerge. A conventional search engine cannot crawl and index at the same pace as the expansion of the Internet. Moreover, a large portion of the data on the Internet is not accessible to traditional search engines. Distributed Information Retrieval (DIR) is a viable solution to this as it integrates multiple shards (resources) and provides a unified access to them. Resource selection is a key component of DIR systems. There is a rich body of literature on resource selection approaches for DIR. A key limitation of the existing approaches is that they primarily use term-based statistical features and do not generally model resource-query and resource-resource relationships. In this paper, we propose a graph neural network (GNN) based approach to learning-to-rank that is capable of modeling resource-query and resource-resource relationships. Specifically, we utilize a pre-trained language model (PTLM) to obtain semantic information from queries and resources. Then, we explicitly build a heterogeneous graph to preserve structural information of query-resource relationships and employ GNN to extract structural information. In addition, the heterogeneous graph is enriched with resource-resource type of edges to further enhance the ranking accuracy. Extensive experiments on benchmark datasets show that our proposed approach is highly effective in resource selection. Our method outperforms the state-of-the-art by 6.4% to 42% on various performance metrics.
Reclaimer: A Reinforcement Learning Approach to Dynamic Resource Allocation for Cloud Microservices
Authors: Quintin Fettes, Avinash Karanth, Razvan Bunescu, Brandon Beckwith, Sreenivas Subramoney
Date: 2023-04-17 00:00:00
Tasks: CPU, Deep Reinforcement Learning, reinforcement-learning

Many cloud applications are migrated from the monolithic model to a microservices framework in which hundreds of loosely-coupled microservices run concurrently, with significant benefits in terms of scalability, rapid development, modularity, and isolation. However, dependencies among microservices with uneven execution time may result in longer queues, idle resources, or Quality-of-Service (QoS) violations. In this paper we introduce Reclaimer, a deep reinforcement learning model that adapts to runtime changes in the number and behavior of microservices in order to minimize CPU core allocation while meeting QoS requirements. When evaluated with two benchmark microservice-based applications, Reclaimer reduces the mean CPU core allocation by 38.4% to 74.4% relative to the industry-standard scaling solution, and by 27.5% to 58.1% relative to a current state-of-the art method.
Leveraging sparse and shared feature activations for disentangled representation learning
Authors: Marco Fumero, Florian Wenzel, Luca Zancato, Alessandro Achille, Emanuele Rodolà, Stefano Soatto, Bernhard Schölkopf, Francesco Locatello
Date: 2023-04-17 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Representation Learning

Recovering the latent factors of variation of high dimensional data has so far focused on simple synthetic settings. Mostly building on unsupervised and weakly-supervised objectives, prior work missed out on the positive implications for representation learning on real world data. In this work, we propose to leverage knowledge extracted from a diversified set of supervised tasks to learn a common disentangled representation. Assuming each supervised task only depends on an unknown subset of the factors of variation, we disentangle the feature space of a supervised multi-task model, with features activating sparsely across different tasks and information being shared as appropriate. Importantly, we never directly observe the factors of variations but establish that access to multiple tasks is sufficient for identifiability under sufficiency and minimality assumptions. We validate our approach on six real world distribution shift benchmarks, and different data modalities (images, text), demonstrating how disentangled representations can be transferred to real settings.
A Randomized Approach for Tight Privacy Accounting
Authors: Jiachen T. Wang, Saeed Mahloujifar, Tong Wu, Ruoxi Jia, Prateek Mittal
Date: 2023-04-17 00:00:00
Tasks: Privacy Preserving

Bounding privacy leakage over compositions, i.e., privacy accounting, is a key challenge in differential privacy (DP). The privacy parameter ($\eps$ or $\delta$) is often easy to estimate but hard to bound. In this paper, we propose a new differential privacy paradigm called estimate-verify-release (EVR), which addresses the challenges of providing a strict upper bound for privacy parameter in DP compositions by converting an estimate of privacy parameter into a formal guarantee. The EVR paradigm first estimates the privacy parameter of a mechanism, then verifies whether it meets this guarantee, and finally releases the query output based on the verification result. The core component of the EVR is privacy verification. We develop a randomized privacy verifier using Monte Carlo (MC) technique. Furthermore, we propose an MC-based DP accountant that outperforms existing DP accounting techniques in terms of accuracy and efficiency. Our empirical evaluation shows the newly proposed EVR paradigm improves the utility-privacy tradeoff for privacy-preserving machine learning.
A Neural Network Transformer Model for Composite Microstructure Homogenization
Authors: Emil Pitz, Kishore Pochiraju
Date: 2023-04-16 00:00:00
Tasks: Dimensionality Reduction

Heterogeneity and uncertainty in a composite microstructure lead to either computational bottlenecks if modeled rigorously or to solution inaccuracies in the stress field and failure predictions if approximated. Although methods suitable for analyzing arbitrary and non-linear microstructures exist, their computational cost makes them impractical to use in large-scale structural analysis. Surrogate models or Reduced Order Models (ROMs) commonly enhance efficiencies but are typically calibrated with a single microstructure. Homogenization methods, such as the Mori-Tanaka method, offer rapid homogenization for a wide range of constituent properties. However, simplifying assumptions, like stress and strain averaging in phases, render the consideration of both deterministic and stochastic variations in microstructure infeasible. This paper illustrates a transformer neural network architecture that captures the knowledge of various microstructures and constituents, enabling it to function as a computationally efficient homogenization surrogate model. Given an image or an abstraction of an arbitrary composite microstructure of linearly elastic fibers in an elastoplastic matrix, the transformer network predicts the history-dependent, non-linear, and homogenized stress-strain response. Two methods for encoding microstructure features were tested: calculating two-point statistics using Principal Component Analysis (PCA) for dimensionality reduction and employing an autoencoder with a Convolutional Neural Network (CNN). Both methods accurately predict the homogenized material response. The developed transformer neural network offers an efficient means for microstructure-to-property translation, generalizable and extendable to a variety of microstructures. The paper describes the network architecture, training and testing data generation, and performance under cycling and random loadings.
CEBoosting: Online Sparse Identification of Dynamical Systems with Regime Switching by Causation Entropy Boosting
Authors: Chuanqi Chen, Nan Chen, Jin-Long Wu
Date: 2023-04-16 00:00:00
Tasks: parameter estimation

Regime switching is ubiquitous in many complex dynamical systems with multiscale features, chaotic behavior, and extreme events. In this paper, a causation entropy boosting (CEBoosting) strategy is developed to facilitate the detection of regime switching and the discovery of the dynamics associated with the new regime via online model identification. The causation entropy, which can be efficiently calculated, provides a logic value of each candidate function in a pre-determined library. The reversal of one or a few such causation entropy indicators associated with the model calibrated for the current regime implies the detection of regime switching. Despite the short length of each batch formed by the sequential data, the accumulated value of causation entropy corresponding to a sequence of data batches leads to a robust indicator. With the detected rectification of the model structure, the subsequent parameter estimation becomes a quadratic optimization problem, which is solved using closed analytic formulae. Using the Lorenz 96 model, it is shown that the causation entropy indicator can be efficiently calculated, and the method applies to moderately large dimensional systems. The CEBoosting algorithm is also adaptive to the situation with partial observations. It is shown via a stochastic parameterized model that the CEBoosting strategy can be combined with data assimilation to identify regime switching triggered by the unobserved latent processes. In addition, the CEBoosting method is applied to a nonlinear paradigm model for topographic mean flow interaction, demonstrating the online detection of regime switching in the presence of strong intermittency and extreme events.
Enhancing Automated Program Repair through Fine-tuning and Prompt Engineering
Authors: Rishov Paul, Md. Mohib Hossain, Mohammed Latif Siddiq, Masum Hasan, Anindya Iqbal, Joanna C. S. Santos
Date: 2023-04-16 00:00:00
Tasks: Program Repair, Prompt Engineering

Sequence-to-sequence models have been used to transform erroneous programs into correct ones when trained with a large enough dataset. Some recent studies also demonstrated strong empirical evidence that code review could improve the program repair further. Large language models, trained with Natural Language (NL) and Programming Language (PL), can contain inherent knowledge of both. In this study, we investigate if this inherent knowledge of PL and NL can be utilized to improve automated program repair. We applied PLBART and CodeT5, two state-of-the-art language models that are pre-trained with both PL and NL, on two such natural language-based program repair datasets and found that the pre-trained language models fine-tuned with datasets containing both code review and subsequent code changes notably outperformed each of the previous models. With the advent of code generative models like Codex and GPT-3.5-Turbo, we also performed zero-shot and few-shots learning-based prompt engineering to assess their performance on these datasets. However, the practical application of using LLMs in the context of automated program repair is still a long way off based on our manual analysis of the generated repaired codes by the learning models.
An Interpretable Approach to Load Profile Forecasting in Power Grids using Galerkin-Approximated Koopman Pseudospectra
Authors: Ali Tavasoli, Behnaz MoradiJamei, Heman Shakeri
Date: 2023-04-16 00:00:00
Tasks: Computational Efficiency, Interpretable Machine Learning, Load Forecasting

This paper presents an interpretable machine learning approach that characterizes load dynamics within an operator-theoretic framework for electricity load forecasting in power grids. We represent the dynamics of load data using the Koopman operator, which provides a linear, infinite-dimensional representation of the nonlinear dynamics, and approximate a finite version that remains robust against spectral pollutions due to truncation. By computing $\epsilon$-approximate Koopman eigenfunctions using dynamics-adapted kernels in delay coordinates, we decompose the load dynamics into coherent spatiotemporal patterns that evolve quasi-independently. Our approach captures temporal coherent patterns due to seasonal changes and finer time scales, such as time of day and day of the week. This method allows for a more nuanced understanding of the complex interactions within power grids and their response to various exogenous factors. We assess our method using a large-scale dataset from a renewable power system in the continental European electricity system. The results indicate that our Koopman-based method surpasses a separately optimized deep learning (LSTM) architecture in both accuracy and computational efficiency, while providing deeper insights into the underlying dynamics of the power grid\footnote{The code is available at \href{https://github.com/Shakeri-Lab/Power-Grids}{github.com/Shakeri-Lab/Power-Grids}.
Time-dependent Iterative Imputation for Multivariate Longitudinal Clinical Data
Authors: Omer Noy, Ron Shamir
Date: 2023-04-16 00:00:00
Tasks: Imputation, Missing Values

Missing data is a major challenge in clinical research. In electronic medical records, often a large fraction of the values in laboratory tests and vital signs are missing. The missingness can lead to biased estimates and limit our ability to draw conclusions from the data. Additionally, many machine learning algorithms can only be applied to complete datasets. A common solution is data imputation, the process of filling-in the missing values. However, some of the popular imputation approaches perform poorly on clinical data. We developed a simple new approach, Time-Dependent Iterative imputation (TDI), which offers a practical solution for imputing time-series data. It addresses both multivariate and longitudinal data, by integrating forward-filling and Iterative Imputer. The integration employs a patient, variable, and observation-specific dynamic weighting strategy, based on the clinical patterns of the data, including missing rates and measurement frequency. We tested TDI on randomly masked clinical datasets. When applied to a cohort consisting of more than 500,000 patient observations from MIMIC III, our approach outperformed state-of-the-art imputation methods for 25 out of 30 clinical variables, with an overall root-mean-squared-error of 0.63, compared to 0.85 for SoftImpute, the second best method. MIMIC III and COVID-19 inpatient datasets were used to perform prediction tasks. Importantly, these tests demonstrated that TDI imputation can lead to improved risk prediction.
Comparative Study of MPPT and Parameter Estimation of PV cells
Authors: Sahil Kumar, Sahitya Gupta, Vajayant Pratik, Pascal Brunet
Date: 2023-04-16 00:00:00
Tasks: Computational Efficiency, parameter estimation

The presented work focuses on utilising machine learning techniques to accurately estimate accurate values for known and unknown parameters of the PVLIB model for solar cells and photovoltaic modules.Finding accurate model parameters of circuits for photovoltaic (PV) cells is important for a variety of tasks. An Artificial Neural Network (ANN) algorithm was employed, which outperformed other metaheuristic and machine learning algorithms in terms of computational efficiency. To validate the consistency of the data and output, the results were compared against other machine learning algorithms based on irradiance and temperature. A Bland Altman test was conducted that resulted in more than 95 percent accuracy rate. Upon validation, the ANN algorithm was utilised to estimate the parameters and their respective values.
Harnessing Digital Pathology And Causal Learning To Improve Eosinophilic Esophagitis Dietary Treatment Assignment
Authors: Eliel Aknin, Ariel Larey, Julie M. Caldwell, Margaret H. Collins, Juan P. Abonia, Seema S. Aceves, Nicoleta C. Arva, Mirna Chehade, Evan S. Dellon, Nirmala Gonsalves, Sandeep K. Gupta, John Leung, Kathryn A. Peterson, Tetsuo Shoda, Jonathan M. Spergel, Marc E. Rothenberg, Yonatan Savir
Date: 2023-04-16 00:00:00

Eosinophilic esophagitis (EoE) is a chronic, food antigen-driven, allergic inflammatory condition of the esophagus associated with elevated esophageal eosinophils. EoE is a top cause of chronic dysphagia after GERD. Diagnosis of EoE relies on counting eosinophils in histological slides, a manual and time-consuming task that limits the ability to extract complex patient-dependent features. The treatment of EoE includes medication and food elimination. A personalized food elimination plan is crucial for engagement and efficiency, but previous attempts failed to produce significant results. In this work, on the one hand, we utilize AI for inferring histological features from the entire biopsy slide, features that cannot be extracted manually. On the other hand, we develop causal learning models that can process this wealth of data. We applied our approach to the 'Six-Food vs. One-Food Eosinophilic Esophagitis Diet Study', where 112 symptomatic adults aged 18-60 years with active EoE were assigned to either a six-food elimination diet (6FED) or a one-food elimination diet (1FED) for six weeks. Our results show that the average treatment effect (ATE) of the 6FED treatment compared with the 1FED treatment is not significant, that is, neither diet was superior to the other. We examined several causal models and show that the best treatment strategy was obtained using T-learner with two XGBoost modules. While 1FED only and 6FED only provide improvement for 35%-38% of the patients, which is not significantly different from a random treatment assignment, our causal model yields a significantly better improvement rate of 58.4%. This study illustrates the significance of AI in enhancing treatment planning by analyzing molecular features' distribution in histological slides through causal learning. Our approach can be harnessed for other conditions that rely on histology for diagnosis and treatment.
A Comprehensive Evaluation of Neural SPARQL Query Generation from Natural Language Questions
Authors: Papa Abdou Karim Karou Diallo, Samuel Reyd, Amal Zouaq
Date: 2023-04-16 00:00:00
Tasks: Decoder, Machine Translation, NMT, TAG, Translation

In recent years, the field of neural machine translation (NMT) for SPARQL query generation has witnessed significant growth. Incorporating the copy mechanism with traditional encoder-decoder architectures and using pre-trained encoder-decoders and large language models have set new performance benchmarks. This paper presents various experiments that replicate and expand upon recent NMT-based SPARQL generation studies, comparing pre-trained language models (PLMs), non-pre-trained language models (NPLMs), and large language models (LLMs), highlighting the impact of question annotation and the copy mechanism and testing various fine-tuning methods using LLMs. In particular, we provide a systematic error analysis of the models and test their generalization ability. Our study demonstrates that the copy mechanism yields significant performance enhancements for most PLMs and NPLMs. Annotating the data is pivotal to generating correct URIs, with the "tag-within" strategy emerging as the most effective approach. Additionally, our findings reveal that the primary source of errors stems from incorrect URIs in SPARQL queries that are sometimes replaced with hallucinated URIs when using base models. This does not happen using the copy mechanism, but it sometimes leads to selecting wrong URIs among candidates. Finally, the performance of the tested LLMs fell short of achieving the desired outcomes.