pwc-archive/papers-with-abstracts huggingface dataset:
Latent Space Representations of Neural Algorithmic Reasoners
Authors: Vladimir V. Mirjanić, Razvan Pascanu, Petar Veličković
Date: 2023-07-17 00:00:00
Tasks: Graph Neural Network, Learning to Execute, Triplet

Neural Algorithmic Reasoning (NAR) is a research area focused on designing neural architectures that can reliably capture classical computation, usually by learning to execute algorithms. A typical approach is to rely on Graph Neural Network (GNN) architectures, which encode inputs in high-dimensional latent spaces that are repeatedly transformed during the execution of the algorithm. In this work we perform a detailed analysis of the structure of the latent space induced by the GNN when executing algorithms. We identify two possible failure modes: (i) loss of resolution, making it hard to distinguish similar values; (ii) inability to deal with values outside the range observed during training. We propose to solve the first issue by relying on a softmax aggregator, and propose to decay the latent space in order to deal with out-of-range values. We show that these changes lead to improvements on the majority of algorithms in the standard CLRS-30 benchmark when using the state-of-the-art Triplet-GMPNN processor. Our code is available at https://github.com/mirjanic/nar-latent-spaces
Meta-Value Learning: a General Framework for Learning with Learning Awareness
Authors: Tim Cooijmans, Milad Aghajohari, Aaron Courville
Date: 2023-07-17 00:00:00
Tasks: Q-Learning

Gradient-based learning in multi-agent systems is difficult because the gradient derives from a first-order model which does not account for the interaction between agents' learning processes. LOLA (arXiv:1709.04326) accounts for this by differentiating through one step of optimization. We propose to judge joint policies by their long-term prospects as measured by the meta-value, a discounted sum over the returns of future optimization iterates. We apply a form of Q-learning to the meta-game of optimization, in a way that avoids the need to explicitly represent the continuous action space of policy updates. The resulting method, MeVa, is consistent and far-sighted, and does not require REINFORCE estimators. We analyze the behavior of our method on a toy game and compare to prior work on repeated matrix games.
An Admissible Shift-Consistent Method for Recommender Systems
Authors: Tung Nguyen, Jeffrey Uhlmann
Date: 2023-07-17 00:00:00
Tasks: Fairness, Imputation, Recommendation Systems

In this paper, we propose a new constraint, called shift-consistency, for solving matrix/tensor completion problems in the context of recommender systems. Our method provably guarantees several key mathematical properties: (1) satisfies a recently established admissibility criterion for recommender systems; (2) satisfies a definition of fairness that eliminates a specific class of potential opportunities for users to maliciously influence system recommendations; and (3) offers robustness by exploiting provable uniqueness of missing-value imputation. We provide a rigorous mathematical description of the method, including its generalization from matrix to tensor form to permit representation and exploitation of complex structural relationships among sets of user and product attributes. We argue that our analysis suggests a structured means for defining latent-space projections that can permit provable performance properties to be established for machine learning methods.
Privacy-preserving patient clustering for personalized federated learning
Authors: Ahmed Elhussein, Gamze Gursoy
Date: 2023-07-17 00:00:00
Tasks: Clustering, Federated Learning, Mortality Prediction, Personalized Federated Learning, Privacy Preserving

Federated Learning (FL) is a machine learning framework that enables multiple organizations to train a model without sharing their data with a central server. However, it experiences significant performance degradation if the data is non-identically independently distributed (non-IID). This is a problem in medical settings, where variations in the patient population contribute significantly to distribution differences across hospitals. Personalized FL addresses this issue by accounting for site-specific distribution differences. Clustered FL, a Personalized FL variant, was used to address this problem by clustering patients into groups across hospitals and training separate models on each group. However, privacy concerns remained as a challenge as the clustering process requires exchange of patient-level information. This was previously solved by forming clusters using aggregated data, which led to inaccurate groups and performance degradation. In this study, we propose Privacy-preserving Community-Based Federated machine Learning (PCBFL), a novel Clustered FL framework that can cluster patients using patient-level data while protecting privacy. PCBFL uses Secure Multiparty Computation, a cryptographic technique, to securely calculate patient-level similarity scores across hospitals. We then evaluate PCBFL by training a federated mortality prediction model using 20 sites from the eICU dataset. We compare the performance gain from PCBFL against traditional and existing Clustered FL frameworks. Our results show that PCBFL successfully forms clinically meaningful cohorts of low, medium, and high-risk patients. PCBFL outperforms traditional and existing Clustered FL frameworks with an average AUC improvement of 4.3% and AUPRC improvement of 7.8%.
Bayesian Safe Policy Learning with Chance Constrained Optimization: Application to Military Security Assessment during the Vietnam War
Authors: Zeyang Jia, Eli Ben-Michael, Kosuke Imai
Date: 2023-07-17 00:00:00
Tasks: Decision Making

Algorithmic decisions and recommendations are used in many high-stakes decision-making settings such as criminal justice, medicine, and public policy. We investigate whether it would have been possible to improve a security assessment algorithm employed during the Vietnam War, using outcomes measured immediately after its introduction in late 1969. This empirical application raises several methodological challenges that frequently arise in high-stakes algorithmic decision-making. First, before implementing a new algorithm, it is essential to characterize and control the risk of yielding worse outcomes than the existing algorithm. Second, the existing algorithm is deterministic, and learning a new algorithm requires transparent extrapolation. Third, the existing algorithm involves discrete decision tables that are difficult to optimize over. To address these challenges, we introduce the Average Conditional Risk (ACRisk), which first quantifies the risk that a new algorithmic policy leads to worse outcomes for subgroups of individual units and then averages this over the distribution of subgroups. We also propose a Bayesian policy learning framework that maximizes the posterior expected value while controlling the posterior expected ACRisk. This framework separates the estimation of heterogeneous treatment effects from policy optimization, enabling flexible estimation of effects and optimization over complex policy classes. We characterize the resulting chance-constrained optimization problem as a constrained linear programming problem. Our analysis shows that compared to the actual algorithm used during the Vietnam War, the learned algorithm assesses most regions as more secure and emphasizes economic and political factors over military factors.
A Meta-Learning Based Precoder Optimization Framework for Rate-Splitting Multiple Access
Authors: Rafael Cerna Loli, Bruno Clerckx
Date: 2023-07-17 00:00:00
Tasks: Meta-Learning

In this letter, we propose the use of a meta-learning based precoder optimization framework to directly optimize the Rate-Splitting Multiple Access (RSMA) precoders with partial Channel State Information at the Transmitter (CSIT). By exploiting the overfitting of the compact neural network to maximize the explicit Average Sum-Rate (ASR) expression, we effectively bypass the need for any other training data while minimizing the total running time. Numerical results reveal that the meta-learning based solution achieves similar ASR performance to conventional precoder optimization in medium-scale scenarios, and significantly outperforms sub-optimal low complexity precoder algorithms in the large-scale regime.
Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge
Authors: Gilchan Park, Byung-Jun Yoon, Xihaier Luo, Vanessa López-Marrero, Shinjae Yoo, Shantenu Jha
Date: 2023-07-17 00:00:00

Understanding protein interactions and pathway knowledge is crucial for unraveling the complexities of living systems and investigating the underlying mechanisms of biological functions and complex diseases. While existing databases provide curated biological data from literature and other sources, they are often incomplete and their maintenance is labor-intensive, necessitating alternative approaches. In this study, we propose to harness the capabilities of large language models to address these issues by automatically extracting such knowledge from the relevant scientific literature. Toward this goal, in this work, we investigate the effectiveness of different large language models in tasks that involve recognizing protein interactions, identifying genes associated with pathways affected by low-dose radiation, and gene regulatory relations. We thoroughly evaluate the performance of various models, highlight the significant findings, and discuss both the future opportunities and the remaining challenges associated with this approach. The code and data are available at: https://github.com/boxorange/BioIE-LLM
Co(ve)rtex: ML Models as storage channels and their (mis-)applications
Authors: Md Abdullah Al Mamun, Quazi Mishkatul Alam, Erfan Shayegani, Pedram Zaree, Ihsen Alouani, Nael Abu-Ghazaleh
Date: 2023-07-17 00:00:00
Tasks: Data Augmentation

Machine learning (ML) models are overparameterized to support generality and avoid overfitting. The state of these parameters is essentially a "don't-care" with respect to the primary model provided that this state does not interfere with the primary model. In both hardware and software systems, don't-care states and undefined behavior have been shown to be sources of significant vulnerabilities. In this paper, we propose a new information theoretic perspective of the problem; we consider the ML model as a storage channel with a capacity that increases with overparameterization. Specifically, we consider a sender that embeds arbitrary information in the model at training time, which can be extracted by a receiver with a black-box access to the deployed model. We derive an upper bound on the capacity of the channel based on the number of available unused parameters. We then explore black-box write and read primitives that allow the attacker to:(i) store data in an optimized way within the model by augmenting the training data at the transmitter side, and (ii) to read it by querying the model after it is deployed. We also consider a new version of the problem which takes information storage covertness into account. Specifically, to obtain storage covertness, we introduce a new constraint such that the data augmentation used for the write primitives minimizes the distribution shift with the initial (baseline task) distribution. This constraint introduces a level of "interference" with the initial task, thereby limiting the channel's effective capacity. Therefore, we develop optimizations to improve the capacity in this case, including a novel ML-specific substitution based error correction protocol. We believe that the proposed modeling of the problem offers new tools to better understand and mitigate potential vulnerabilities of ML, especially in the context of increasingly large models.
Anomaly Detection with Selective Dictionary Learning
Authors: Denis C. Ilie-Ablachim, Bogdan Dumitrescu
Date: 2023-07-17 00:00:00
Tasks: Anomaly Detection, Dictionary Learning, Outlier Detection

In this paper we present new methods of anomaly detection based on Dictionary Learning (DL) and Kernel Dictionary Learning (KDL). The main contribution consists in the adaption of known DL and KDL algorithms in the form of unsupervised methods, used for outlier detection. We propose a reduced kernel version (RKDL), which is useful for problems with large data sets, due to the large kernel matrix. We also improve the DL and RKDL methods by the use of a random selection of signals, which aims to eliminate the outliers from the training procedure. All our algorithms are introduced in an anomaly detection toolbox and are compared to standard benchmark results.
Towards Automated Design of Riboswitches
Authors: Frederic Runge, Jörg K. H. Franke, Frank Hutter
Date: 2023-07-17 00:00:00

Experimental screening and selection pipelines for the discovery of novel riboswitches are expensive, time-consuming, and inefficient. Using computational methods to reduce the number of candidates for the screen could drastically decrease these costs. However, existing computational approaches do not fully satisfy all requirements for the design of such initial screening libraries. In this work, we present a new method, libLEARNA, capable of providing RNA focus libraries of diverse variable-length qualified candidates. Our novel structure-based design approach considers global properties as well as desired sequence and structure features. We demonstrate the benefits of our method by designing theophylline riboswitch libraries, following a previously published protocol, and yielding 30% more unique high-quality candidates.
regulAS: A Bioinformatics Tool for the Integrative Analysis of Alternative Splicing Regulome using RNA-Seq data
Authors: Sofya Lipnitskaya
Date: 2023-07-17 00:00:00
Tasks: Experimental Design

The regulAS software package is a bioinformatics tool designed to support computational biology researchers in investigating regulatory mechanisms of splicing alterations through integrative analysis of large-scale RNA-Seq data from cancer and healthy human donors, characterized by TCGA and GTEx projects. This technical report provides a comprehensive overview of regulAS, focusing on its core functionality, basic modules, experiment configuration, further extensibility and customisation. The core functionality of regulAS enables the automation of computational experiments, efficient results storage and processing, and streamlined workflow management. Integrated basic modules extend regulAS with features such as RNA-Seq data retrieval from the public multi-omics UCSC Xena data repository, predictive modeling and feature ranking capabilities using the scikit-learn package, and flexible reporting generation for analysing gene expression profiles and relevant modulations of alternative splicing aberrations across tissues and cancer types. Experiment configuration is handled through YAML files with the Hydra and OmegaConf libraries, offering a user-friendly approach. Additionally, regulAS allows for the development and integration of custom modules to handle specialized tasks. In conclusion, regulAS provides an automated solution for alternative splicing and cancer biology studies, enhancing efficiency, reproducibility, and customization of experimental design, while the extensibility of the pipeline enables researchers to further tailor the software package to their specific needs. Source code is available under the MIT license at https://github.com/slipnitskaya/regulAS.
Reduced Kernel Dictionary Learning
Authors: Denis C. Ilie-Ablachim, Bogdan Dumitrescu
Date: 2023-07-17 00:00:00
Tasks: Dictionary Learning

In this paper we present new algorithms for training reduced-size nonlinear representations in the Kernel Dictionary Learning (KDL) problem. Standard KDL has the drawback of a large size of the kernel matrix when the data set is large. There are several ways of reducing the kernel size, notably Nystr\"om sampling. We propose here a method more in the spirit of dictionary learning, where the kernel vectors are obtained with a trained sparse representation of the input signals. Moreover, we optimize directly the kernel vectors in the KDL process, using gradient descent steps. We show with three data sets that our algorithms are able to provide better representations, despite using a small number of kernel vectors, and also decrease the execution time with respect to KDL.
Classification with Incoherent Kernel Dictionary Learning
Authors: Denis C. Ilie-Ablachim, Bogdan Dumitrescu
Date: 2023-07-17 00:00:00
Tasks: Classification, Dictionary Learning

In this paper we present a new classification method based on Dictionary Learning (DL). The main contribution consists of a kernel version of incoherent DL, derived from its standard linear counterpart. We also propose an improvement of the AK-SVD algorithm concerning the representation update. Our algorithms are tested on several popular databases of classification problems.
A Novel Application of Conditional Normalizing Flows: Stellar Age Inference with Gyrochronology
Authors: Phil Van-Lane, Joshua S. Speagle, Stephanie Douglas
Date: 2023-07-17 00:00:00

Stellar ages are critical building blocks of evolutionary models, but challenging to measure for low mass main sequence stars. An unexplored solution in this regime is the application of probabilistic machine learning methods to gyrochronology, a stellar dating technique that is uniquely well suited for these stars. While accurate analytical gyrochronological models have proven challenging to develop, here we apply conditional normalizing flows to photometric data from open star clusters, and demonstrate that a data-driven approach can constrain gyrochronological ages with a precision comparable to other standard techniques. We evaluate the flow results in the context of a Bayesian framework, and show that our inferred ages recover literature values well. This work demonstrates the potential of a probabilistic data-driven solution to widen the applicability of gyrochronological stellar dating.
Certifying the Fairness of KNN in the Presence of Dataset Bias
Authors: Yannan Li, Jingbo Wang, Chao Wang
Date: 2023-07-17 00:00:00
Tasks: Fairness

We propose a method for certifying the fairness of the classification result of a widely used supervised learning algorithm, the k-nearest neighbors (KNN), under the assumption that the training data may have historical bias caused by systematic mislabeling of samples from a protected minority group. To the best of our knowledge, this is the first certification method for KNN based on three variants of the fairness definition: individual fairness, $\epsilon$-fairness, and label-flipping fairness. We first define the fairness certification problem for KNN and then propose sound approximations of the complex arithmetic computations used in the state-of-the-art KNN algorithm. This is meant to lift the computation results from the concrete domain to an abstract domain, to reduce the computational cost. We show effectiveness of this abstract interpretation based technique through experimental evaluation on six datasets widely used in the fairness research literature. We also show that the method is accurate enough to obtain fairness certifications for a large number of test inputs, despite the presence of historical bias in the datasets.
Efficient Prediction of Peptide Self-assembly through Sequential and Graphical Encoding
Authors: Zihan Liu, Jiaqi Wang, Yun Luo, Shuang Zhao, Wenbin Li, Stan Z. Li
Date: 2023-07-17 00:00:00
Tasks: Benchmarking, Deep Learning, Prediction

In recent years, there has been an explosion of research on the application of deep learning to the prediction of various peptide properties, due to the significant development and market potential of peptides. Molecular dynamics has enabled the efficient collection of large peptide datasets, providing reliable training data for deep learning. However, the lack of systematic analysis of the peptide encoding, which is essential for AI-assisted peptide-related tasks, makes it an urgent problem to be solved for the improvement of prediction accuracy. To address this issue, we first collect a high-quality, colossal simulation dataset of peptide self-assembly containing over 62,000 samples generated by coarse-grained molecular dynamics (CGMD). Then, we systematically investigate the effect of peptide encoding of amino acids into sequences and molecular graphs using state-of-the-art sequential (i.e., RNN, LSTM, and Transformer) and structural deep learning models (i.e., GCN, GAT, and GraphSAGE), on the accuracy of peptide self-assembly prediction, an essential physiochemical process prior to any peptide-related applications. Extensive benchmarking studies have proven Transformer to be the most powerful sequence-encoding-based deep learning model, pushing the limit of peptide self-assembly prediction to decapeptides. In summary, this work provides a comprehensive benchmark analysis of peptide encoding with advanced deep learning models, serving as a guide for a wide range of peptide-related predictions such as isoelectric points, hydration free energy, etc.
Joint Microseismic Event Detection and Location with a Detection Transformer
Authors: Yuanyuan Yang, Claire Birnie, Tariq Alkhalifah
Date: 2023-07-16 00:00:00
Tasks: Decoder, Event Detection

Microseismic event detection and location are two primary components in microseismic monitoring, which offers us invaluable insights into the subsurface during reservoir stimulation and evolution. Conventional approaches for event detection and location often suffer from manual intervention and/or heavy computation, while current machine learning-assisted approaches typically address detection and location separately; such limitations hinder the potential for real-time microseismic monitoring. We propose an approach to unify event detection and source location into a single framework by adapting a Convolutional Neural Network backbone and an encoder-decoder Transformer with a set-based Hungarian loss, which is applied directly to recorded waveforms. The proposed network is trained on synthetic data simulating multiple microseismic events corresponding to random source locations in the area of suspected microseismic activities. A synthetic test on a 2D profile of the SEAM Time Lapse model illustrates the capability of the proposed method in detecting the events properly and locating them in the subsurface accurately; while, a field test using the Arkoma Basin data further proves its practicability, efficiency, and its potential in paving the way for real-time monitoring of microseismic events.