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Title: Variational Kalman Filtering with Hinf-Based Correction for Robust Bayesian Learning in High Dimensions Abstract: In this paper, we address the problem of convergence of sequential variational inference filter (VIF) through the application of a robust variational objective and Hinf-norm based correction for a li... |
Title: FlowGNN: A Dataflow Architecture for Universal Graph Neural Network Inference via Multi-Queue Streaming Abstract: Graph neural networks (GNNs) have recently exploded in popularity thanks to their broad applicability to graph-related problems such as quantum chemistry, drug discovery, and high energy physics. How... |
Title: Worst-Case Dynamic Power Distribution Network Noise Prediction Using Convolutional Neural Network Abstract: Worst-case dynamic PDN noise analysis is an essential step in PDN sign-off to ensure the performance and reliability of chips. However, with the growing PDN size and increasing scenarios to be validated, i... |
Title: Open challenges for Machine Learning based Early Decision-Making research Abstract: More and more applications require early decisions, i.e. taken as soon as possible from partially observed data. However, the later a decision is made, the more its accuracy tends to improve, since the description of the problem ... |
Title: Attention Mechanism in Neural Networks: Where it Comes and Where it Goes Abstract: A long time ago in the machine learning literature, the idea of incorporating a mechanism inspired by the human visual system into neural networks was introduced. This idea is named the attention mechanism, and it has gone through... |
Title: AutoLossGen: Automatic Loss Function Generation for Recommender Systems Abstract: In recommendation systems, the choice of loss function is critical since a good loss may significantly improve the model performance. However, manually designing a good loss is a big challenge due to the complexity of the problem. ... |
Title: R-MBO: A Multi-surrogate Approach for Preference Incorporation in Multi-objective Bayesian Optimisation Abstract: Many real-world multi-objective optimisation problems rely on computationally expensive function evaluations. Multi-objective Bayesian optimisation (BO) can be used to alleviate the computation time ... |
Title: Learning Storm Surge with Gradient Boosting Abstract: Storm surge is a major natural hazard for coastal regions, responsible both for significant property damage and loss of life. Accurate, efficient models of storm surge are needed both to assess long-term risk and to guide emergency management decisions. While... |
Title: FedShuffle: Recipes for Better Use of Local Work in Federated Learning Abstract: The practice of applying several local updates before aggregation across clients has been empirically shown to be a successful approach to overcoming the communication bottleneck in Federated Learning (FL). In this work, we propose ... |
Title: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation Abstract: In Federated Learning (FL), a number of clients or devices collaborate to train a model without sharing their data. Models are optimized locally at each client and further communicated to a central hub called the server for aggr... |
Title: An Adversarial Attack Analysis on Malicious Advertisement URL Detection Framework Abstract: Malicious advertisement URLs pose a security risk since they are the source of cyber-attacks, and the need to address this issue is growing in both industry and academia. Generally, the attacker delivers an attack vector ... |
Title: Interpretable Graph Convolutional Network of Multi-Modality Brain Imaging for Alzheimer's Disease Diagnosis Abstract: Identification of brain regions related to the specific neurological disorders are of great importance for biomarker and diagnostic studies. In this paper, we propose an interpretable Graph Convo... |
Title: Counterfactual Explanations for Natural Language Interfaces Abstract: A key challenge facing natural language interfaces is enabling users to understand the capabilities of the underlying system. We propose a novel approach for generating explanations of a natural language interface based on semantic parsing. We... |
Title: Exploring How Anomalous Model Input and Output Alerts Affect Decision-Making in Healthcare Abstract: An important goal in the field of human-AI interaction is to help users more appropriately trust AI systems' decisions. A situation in which the user may particularly benefit from more appropriate trust is when t... |
Title: Use All The Labels: A Hierarchical Multi-Label Contrastive Learning Framework Abstract: Current contrastive learning frameworks focus on leveraging a single supervisory signal to learn representations, which limits the efficacy on unseen data and downstream tasks. In this paper, we present a hierarchical multi-l... |
Title: ELM: Embedding and Logit Margins for Long-Tail Learning Abstract: Long-tail learning is the problem of learning under skewed label distributions, which pose a challenge for standard learners. Several recent approaches for the problem have proposed enforcing a suitable margin in logit space. Such techniques are i... |
Title: Neural network controllers for uncertain linear systems Abstract: We consider the design of reliable neural network (NN)-based approximations of traditional stabilizing controllers for linear systems affected by polytopic uncertainty, including controllers with variable structure and those based on a minimal sel... |
Title: TransHER: Translating Knowledge Graph Embedding with Hyper-Ellipsoidal Restriction Abstract: Knowledge graph embedding methods are important for knowledge graph completion (link prediction) due to their robust performance and efficiency on large-magnitude datasets. One state-of-the-art method, PairRE, leverages ... |
Title: Offline Visual Representation Learning for Embodied Navigation Abstract: How should we learn visual representations for embodied agents that must see and move? The status quo is tabula rasa in vivo, i.e. learning visual representations from scratch while also learning to move, potentially augmented with auxiliar... |
Title: Adversarial Fine-tune with Dynamically Regulated Adversary Abstract: Adversarial training is an effective method to boost model robustness to malicious, adversarial attacks. However, such improvement in model robustness often leads to a significant sacrifice of standard performance on clean images. In many real-... |
Title: BI-GreenNet: Learning Green's functions by boundary integral network Abstract: Green's function plays a significant role in both theoretical analysis and numerical computing of partial differential equations (PDEs). However, in most cases, Green's function is difficult to compute. The troubles arise in the follo... |
Title: Watts: Infrastructure for Open-Ended Learning Abstract: This paper proposes a framework called Watts for implementing, comparing, and recombining open-ended learning (OEL) algorithms. Motivated by modularity and algorithmic flexibility, Watts atomizes the components of OEL systems to promote the study of and dir... |
Title: Covariance-aware Feature Alignment with Pre-computed Source Statistics for Test-time Adaptation Abstract: The accuracy of deep neural networks is degraded when the distribution of features in the test environment (target domain) differs from that of the training (source) environment. To mitigate the degradation,... |
Title: Music Enhancement via Image Translation and Vocoding Abstract: Consumer-grade music recordings such as those captured by mobile devices typically contain distortions in the form of background noise, reverb, and microphone-induced EQ. This paper presents a deep learning approach to enhance low-quality music recor... |
Title: On the Normalizing Constant of the Continuous Categorical Distribution Abstract: Probability distributions supported on the simplex enjoy a wide range of applications across statistics and machine learning. Recently, a novel family of such distributions has been discovered: the continuous categorical. This famil... |
Title: A Decision Model for Federated Learning Architecture Pattern Selection Abstract: Federated learning is growing fast in both academia and industry to resolve data hungriness and privacy issues in machine learning. A federated learning system being widely distributed with different components and stakeholders requ... |
Title: Model Selection, Adaptation, and Combination for Deep Transfer Learning through Neural Networks in Renewable Energies Abstract: There is recent interest in using model hubs, a collection of pre-trained models, in computer vision tasks. To utilize the model hub, we first select a source model and then adapt the m... |
Title: On the Convergence of Momentum-Based Algorithms for Federated Stochastic Bilevel Optimization Problems Abstract: In this paper, we studied the federated stochastic bilevel optimization problem. In particular, we developed two momentum-based algorithms for optimizing this kind of problem. In addition, we establis... |
Title: AlphaZero-Inspired General Board Game Learning and Playing Abstract: Recently, the seminal algorithms AlphaGo and AlphaZero have started a new era in game learning and deep reinforcement learning. While the achievements of AlphaGo and AlphaZero - playing Go and other complex games at super human level - are trul... |
Title: Control-Aware Prediction Objectives for Autonomous Driving Abstract: Autonomous vehicle software is typically structured as a modular pipeline of individual components (e.g., perception, prediction, and planning) to help separate concerns into interpretable sub-tasks. Even when end-to-end training is possible, e... |
Title: Towards Flexible Inference in Sequential Decision Problems via Bidirectional Transformers Abstract: Randomly masking and predicting word tokens has been a successful approach in pre-training language models for a variety of downstream tasks. In this work, we observe that the same idea also applies naturally to s... |
Title: Anomaly Detection by Leveraging Incomplete Anomalous Knowledge with Anomaly-Aware Bidirectional GANs Abstract: The goal of anomaly detection is to identify anomalous samples from normal ones. In this paper, a small number of anomalies are assumed to be available at the training stage, but they are assumed to be ... |
Title: Policy Gradient Stock GAN for Realistic Discrete Order Data Generation in Financial Markets Abstract: This study proposes a new generative adversarial network (GAN) for generating realistic orders in financial markets. In some previous works, GANs for financial markets generated fake orders in continuous spaces ... |
Title: BAGNet: Bidirectional Aware Guidance Network for Malignant Breast lesions Segmentation Abstract: Breast lesions segmentation is an important step of computer-aided diagnosis system, and it has attracted much attention. However, accurate segmentation of malignant breast lesions is a challenging task due to the ef... |
Title: Actor-Critic Scheduling for Path-Aware Air-to-Ground Multipath Multimedia Delivery Abstract: Reinforcement Learning (RL) has recently found wide applications in network traffic management and control because some of its variants do not require prior knowledge of network models. In this paper, we present a novel ... |
Title: Continual Learning with Bayesian Model based on a Fixed Pre-trained Feature Extractor Abstract: Deep learning has shown its human-level performance in various applications. However, current deep learning models are characterised by catastrophic forgetting of old knowledge when learning new classes. This poses a ... |
Title: It's DONE: Direct ONE-shot learning with Hebbian weight imprinting Abstract: Learning a new concept from one example is a superior function of human brain and it is drawing attention in the field of machine learning as one-shot learning task. In this paper, we propose the simplest method for this task with a non... |
Title: Semantic Communication: An Information Bottleneck View Abstract: Motivated by recent success of machine learning tools at the PHY layer and driven by high bandwidth demands of the next wireless communication standard 6G, the old idea of semantic communication by Weaver from 1949 has received considerable attenti... |
Title: Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods Abstract: Reconfigurable intelligent surfaces (RISs) have a revolutionary capability to customize the radio propagation environment for wireless networks. To fully exploit the advantages of RISs in wireless systems, the ... |
Title: Machine learning for knowledge acquisition and accelerated inverse-design for non-Hermitian systems Abstract: Non-Hermitian systems offer new platforms for unusual physical properties that can be flexibly manipulated by redistribution of the real and imaginary parts of refractive indices, whose presence breaks c... |
Title: Learning General Inventory Management Policy for Large Supply Chain Network Abstract: Inventory management in warehouses directly affects profits made by manufacturers. Particularly, large manufacturers produce a very large variety of products that are handled by a significantly large number of retailers. In suc... |
Title: Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features Abstract: Federated learning (FL) provides a privacy-preserving solution for distributed machine learning tasks. One challenging problem that severely damages the performance of FL models is the co-occurre... |
Title: List-Mode PET Image Reconstruction Using Deep Image Prior Abstract: List-mode positron emission tomography (PET) image reconstruction is an important tool for PET scanners with many lines-of-response (LORs) and additional information such as time-of-flight and depth-of-interaction. Deep learning is one possible ... |
Title: WeaNF: Weak Supervision with Normalizing Flows Abstract: A popular approach to decrease the need for costly manual annotation of large data sets is weak supervision, which introduces problems of noisy labels, coverage and bias. Methods for overcoming these problems have either relied on discriminative models, tr... |
Title: Improving the Robustness of Federated Learning for Severely Imbalanced Datasets Abstract: With the ever increasing data deluge and the success of deep neural networks, the research of distributed deep learning has become pronounced. Two common approaches to achieve this distributed learning is synchronous and as... |
Title: Autoencoder based Hybrid Multi-Task Predictor Network for Daily Open-High-Low-Close Prices Prediction of Indian Stocks Abstract: Stock prices are highly volatile and sudden changes in trends are often very problematic for traditional forecasting models to handle. The standard Long Short Term Memory (LSTM) networ... |
Title: DOTIN: Dropping Task-Irrelevant Nodes for GNNs Abstract: Scalability is an important consideration for deep graph neural networks. Inspired by the conventional pooling layers in CNNs, many recent graph learning approaches have introduced the pooling strategy to reduce the size of graphs for learning, such that t... |
Title: Regotron: Regularizing the Tacotron2 architecture via monotonic alignment loss Abstract: Recent deep learning Text-to-Speech (TTS) systems have achieved impressive performance by generating speech close to human parity. However, they suffer from training stability issues as well as incorrect alignment of the int... |
Title: Cumulative Stay-time Representation for Electronic Health Records in Medical Event Time Prediction Abstract: We address the problem of predicting when a disease will develop, i.e., medical event time (MET), from a patient's electronic health record (EHR). The MET of non-communicable diseases like diabetes is hig... |
Title: Fuzzy Cognitive Maps and Hidden Markov Models: Comparative Analysis of Efficiency within the Confines of the Time Series Classification Task Abstract: Time series classification is one of the very popular machine learning tasks. In this paper, we explore the application of Hidden Markov Model (HMM) for time seri... |
Title: COSTI: a New Classifier for Sequences of Temporal Intervals Abstract: Classification of sequences of temporal intervals is a part of time series analysis which concerns series of events. We propose a new method of transforming the problem to a task of multivariate series classification. We use one of the state-o... |
Title: EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification Abstract: Knowledge-based authentication is crucial for task-oriented spoken dialogue systems that offer personalised and privacy-focused services. Such systems should be able to enrol (E), verify ... |
Title: Unsupervised Spatial-spectral Hyperspectral Image Reconstruction and Clustering with Diffusion Geometry Abstract: Hyperspectral images, which store a hundred or more spectral bands of reflectance, have become an important data source in natural and social sciences. Hyperspectral images are often generated in lar... |
Title: Multi-Player Multi-Armed Bandits with Finite Shareable Resources Arms: Learning Algorithms & Applications Abstract: Multi-player multi-armed bandits (MMAB) study how decentralized players cooperatively play the same multi-armed bandit so as to maximize their total cumulative rewards. Existing MMAB models mostly ... |
Title: On tuning a mean-field model for semi-supervised classification Abstract: Semi-supervised learning (SSL) has become an interesting research area due to its capacity for learning in scenarios where both labeled and unlabeled data are available. In this work, we focus on the task of transduction - when the objecti... |
Title: Machine Learning for Violence Risk Assessment Using Dutch Clinical Notes Abstract: Violence risk assessment in psychiatric institutions enables interventions to avoid violence incidents. Clinical notes written by practitioners and available in electronic health records are valuable resources capturing unique inf... |
Title: Predicting batch queue job wait times for informed scheduling of urgent HPC workloads Abstract: There is increasing interest in the use of HPC machines for urgent workloads to help tackle disasters as they unfold. Whilst batch queue systems are not ideal in supporting such workloads, many disadvantages can be wo... |
Title: Predicting single-cell perturbation responses for unseen drugs Abstract: Single-cell transcriptomics enabled the study of cellular heterogeneity in response to perturbations at the resolution of individual cells. However, scaling high-throughput screens (HTSs) to measure cellular responses for many drugs remains... |
Title: Mixup-based Deep Metric Learning Approaches for Incomplete Supervision Abstract: Deep learning architectures have achieved promising results in different areas (e.g., medicine, agriculture, and security). However, using those powerful techniques in many real applications becomes challenging due to the large labe... |
Title: An Explainable Regression Framework for Predicting Remaining Useful Life of Machines Abstract: Prediction of a machine's Remaining Useful Life (RUL) is one of the key tasks in predictive maintenance. The task is treated as a regression problem where Machine Learning (ML) algorithms are used to predict the RUL of... |
Title: Prescriptive and Descriptive Approaches to Machine-Learning Transparency Abstract: Specialized documentation techniques have been developed to communicate key facts about machine-learning (ML) systems and the datasets and models they rely on. Techniques such as Datasheets, FactSheets, and Model Cards have taken ... |
Title: Predicting Sleeping Quality using Convolutional Neural Networks Abstract: Identifying sleep stages and patterns is an essential part of diagnosing and treating sleep disorders. With the advancement of smart technologies, sensor data related to sleeping patterns can be captured easily. In this paper, we propose a... |
Title: Nonbacktracking spectral clustering of nonuniform hypergraphs Abstract: Spectral methods offer a tractable, global framework for clustering in graphs via eigenvector computations on graph matrices. Hypergraph data, in which entities interact on edges of arbitrary size, poses challenges for matrix representations... |
Title: Supervised machine learning classification for short straddles on the S&P500 Abstract: In this working paper we present our current progress in the training of machine learning models to execute short option strategies on the S&P500. As a first step, this paper is breaking this problem down to a supervised class... |
Title: Computer Vision for Road Imaging and Pothole Detection: A State-of-the-Art Review of Systems and Algorithms Abstract: Computer vision algorithms have been prevalently utilized for 3-D road imaging and pothole detection for over two decades. Nonetheless, there is a lack of systematic survey articles on state-of-t... |
Title: Continual Learning for Peer-to-Peer Federated Learning: A Study on Automated Brain Metastasis Identification Abstract: Due to data privacy constraints, data sharing among multiple centers is restricted. Continual learning, as one approach to peer-to-peer federated learning, can promote multicenter collaboration ... |
Title: Poisoning Deep Learning Based Recommender Model in Federated Learning Scenarios Abstract: Various attack methods against recommender systems have been proposed in the past years, and the security issues of recommender systems have drawn considerable attention. Traditional attacks attempt to make target items rec... |
Title: PhysioGAN: Training High Fidelity Generative Model for Physiological Sensor Readings Abstract: Generative models such as the variational autoencoder (VAE) and the generative adversarial networks (GAN) have proven to be incredibly powerful for the generation of synthetic data that preserves statistical properties... |
Title: A unified theory of information transfer and causal relation Abstract: Information transfer between coupled stochastic dynamics, measured by transfer entropy and information flow, is suggested as a physical process underlying the causal relation of systems. While information transfer analysis has booming applica... |
Title: Signal Recovery with Non-Expansive Generative Network Priors Abstract: We study compressive sensing with a deep generative network prior. Initial theoretical guarantees for efficient recovery from compressed linear measurements have been developed for signals in the range of a ReLU network with Gaussian weights ... |
Title: Bona fide Riesz projections for density estimation Abstract: The projection of sample measurements onto a reconstruction space represented by a basis on a regular grid is a powerful and simple approach to estimate a probability density function. In this paper, we focus on Riesz bases and propose a projection ope... |
Title: Process-BERT: A Framework for Representation Learning on Educational Process Data Abstract: Educational process data, i.e., logs of detailed student activities in computerized or online learning platforms, has the potential to offer deep insights into how students learn. One can use process data for many downstr... |
Title: Representative period selection for power system planning using autoencoder-based dimensionality reduction Abstract: Power sector capacity expansion models (CEMs) that are used for studying future low-carbon grid scenarios must incorporate detailed representation of grid operations. Often CEMs are formulated to ... |
Title: Personalized Federated Learning with Multiple Known Clusters Abstract: We consider the problem of personalized federated learning when there are known cluster structures within users. An intuitive approach would be to regularize the parameters so that users in the same cluster share similar model weights. The di... |
Title: Standardized Evaluation of Machine Learning Methods for Evolving Data Streams Abstract: Due to the unspecified and dynamic nature of data streams, online machine learning requires powerful and flexible solutions. However, evaluating online machine learning methods under realistic conditions is difficult. Existin... |
Title: Russian Texts Detoxification with Levenshtein Editing Abstract: Text detoxification is a style transfer task of creating neutral versions of toxic texts. In this paper, we use the concept of text editing to build a two-step tagging-based detoxification model using a parallel corpus of Russian texts. With this mo... |
Title: Unlocking High-Accuracy Differentially Private Image Classification through Scale Abstract: Differential Privacy (DP) provides a formal privacy guarantee preventing adversaries with access to a machine learning model from extracting information about individual training points. Differentially Private Stochastic ... |
Title: Toward Compositional Generalization in Object-Oriented World Modeling Abstract: Compositional generalization is a critical ability in learning and decision-making. We focus on the setting of reinforcement learning in object-oriented environments to study compositional generalization in world modeling. We (1) for... |
Title: Schr\"odinger's FP: Dynamic Adaptation of Floating-Point Containers for Deep Learning Training Abstract: We introduce a software-hardware co-design approach to reduce memory traffic and footprint during training with BFloat16 or FP32 boosting energy efficiency and execution time performance. We introduce methods... |
Title: Unaligned Supervision For Automatic Music Transcription in The Wild Abstract: Multi-instrument Automatic Music Transcription (AMT), or the decoding of a musical recording into semantic musical content, is one of the holy grails of Music Information Retrieval. Current AMT approaches are restricted to piano and (s... |
Title: Unified Simulation, Perception, and Generation of Human Behavior Abstract: Understanding and modeling human behavior is fundamental to almost any computer vision and robotics applications that involve humans. In this thesis, we take a holistic approach to human behavior modeling and tackle its three essential as... |
Title: Curriculum Learning for Dense Retrieval Distillation Abstract: Recent work has shown that more effective dense retrieval models can be obtained by distilling ranking knowledge from an existing base re-ranking model. In this paper, we propose a generic curriculum learning based optimization framework called CL-DR... |
Title: KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradients Abstract: Simulators offer the possibility of safe, low-cost development of self-driving systems. However, current driving simulators exhibit na\"ive behavior models for background traffic. Hand-tuned scenarios are t... |
Title: Foundations for learning from noisy quantum experiments Abstract: Understanding what can be learned from experiments is central to scientific progress. In this work, we use a learning-theoretic perspective to study the task of learning physical operations in a quantum machine when all operations (state preparati... |
Title: Bilinear value networks Abstract: The dominant framework for off-policy multi-goal reinforcement learning involves estimating goal conditioned Q-value function. When learning to achieve multiple goals, data efficiency is intimately connected with the generalization of the Q-function to new goals. The de-facto pa... |
Title: Federated Learning: Balancing the Thin Line Between Data Intelligence and Privacy Abstract: Federated learning holds great promise in learning from fragmented sensitive data and has revolutionized how machine learning models are trained. This article provides a systematic overview and detailed taxonomy of federa... |
Title: Identifying Critical LMS Features for Predicting At-risk Students Abstract: Learning management systems (LMSs) have become essential in higher education and play an important role in helping educational institutions to promote student success. Traditionally, LMSs have been used by postsecondary institutions in a... |
Title: Neighbor-Based Optimized Logistic Regression Machine Learning Model For Electric Vehicle Occupancy Detection Abstract: This paper presents an optimized logistic regression machine learning model that predicts the occupancy of an Electric Vehicle (EV) charging station given the occupancy of neighboring stations. ... |
Title: Hyperbolic Hierarchical Knowledge Graph Embeddings for Link Prediction in Low Dimensions Abstract: Knowledge graph embeddings (KGE) have been validated as powerful methods for inferring missing links in knowledge graphs (KGs) since they map entities into Euclidean space and treat relations as transformations of ... |
Title: Coupling Deep Imputation with Multitask Learning for Downstream Tasks on Genomics Data Abstract: Genomics data such as RNA gene expression, methylation and micro RNA expression are valuable sources of information for various clinical predictive tasks. For example, predicting survival outcomes, cancer histology t... |
Title: Tag-assisted Multimodal Sentiment Analysis under Uncertain Missing Modalities Abstract: Multimodal sentiment analysis has been studied under the assumption that all modalities are available. However, such a strong assumption does not always hold in practice, and most of multimodal fusion models may fail when par... |
Title: Learning cosmology and clustering with cosmic graphs Abstract: We train deep learning models on thousands of galaxy catalogues from the state-of-the-art hydrodynamic simulations of the CAMELS project to perform regression and inference. We employ Graph Neural Networks (GNNs), architectures designed to work with ... |
Title: An Intriguing Property of Geophysics Inversion Abstract: Inversion techniques are widely used to reconstruct subsurface physical properties (e.g., velocity, conductivity) from surface-based geophysical measurements (e.g., seismic, electric/magnetic (EM) data). The problems are governed by partial differential eq... |
Title: GCN-FFNN: A Two-Stream Deep Model for Learning Solution to Partial Differential Equations Abstract: This paper introduces a novel two-stream deep model based on graph convolutional network (GCN) architecture and feed-forward neural networks (FFNN) for learning the solution of nonlinear partial differential equat... |
Title: CAVES: A Dataset to facilitate Explainable Classification and Summarization of Concerns towards COVID Vaccines Abstract: Convincing people to get vaccinated against COVID-19 is a key societal challenge in the present times. As a first step towards this goal, many prior works have relied on social media analysis ... |
Title: Learning to Split for Automatic Bias Detection Abstract: Classifiers are biased when trained on biased datasets. As a remedy, we propose Learning to Split (ls), an algorithm for automatic bias detection. Given a dataset with input-label pairs, ls learns to split this dataset so that predictors trained on the tra... |
Title: BEINIT: Avoiding Barren Plateaus in Variational Quantum Algorithms Abstract: Barren plateaus are a notorious problem in the optimization of variational quantum algorithms and pose a critical obstacle in the quest for more efficient quantum machine learning algorithms. Many potential reasons for barren plateaus h... |
Title: High Dimensional Bayesian Optimization with Kernel Principal Component Analysis Abstract: Bayesian Optimization (BO) is a surrogate-based global optimization strategy that relies on a Gaussian Process regression (GPR) model to approximate the objective function and an acquisition function to suggest candidate po... |
Title: Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Ranking Abstract: There are several measures for fairness in ranking, based on different underlying assumptions and perspectives. PL optimization with the REINFORCE algorithm can be used for optimizing black-box objective functions ov... |
Title: Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting--Full Version Abstract: A variety of real-world applications rely on far future information to make decisions, thus calling for efficient and accurate long sequence multivariate time series forecasting. Whi... |
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