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Title: SWIS: Self-Supervised Representation Learning For Writer Independent Offline Signature Verification Abstract: Writer independent offline signature verification is one of the most challenging tasks in pattern recognition as there is often a scarcity of training data. To handle such data scarcity problem, in this ... |
Title: Continuous Human Action Recognition for Human-Machine Interaction: A Review Abstract: With advances in data-driven machine learning research, a wide variety of prediction models have been proposed to capture spatio-temporal features for the analysis of video streams. Recognising actions and detecting action tran... |
Title: Semantic Supervision: Enabling Generalization over Output Spaces Abstract: In this paper, we propose Semantic Supervision (SemSup) - a unified paradigm for training classifiers that generalize over output spaces. In contrast to standard classification, which treats classes as discrete symbols, SemSup represents ... |
Title: Optimal-er Auctions through Attention Abstract: RegretNet is a recent breakthrough in the automated design of revenue-maximizing auctions. It combines the expressivity of deep learning with the regret-based approach to relax the Incentive Compatibility constraint (that participants benefit from bidding truthfull... |
Title: Analysis of Visual Reasoning on One-Stage Object Detection Abstract: Current state-of-the-art one-stage object detectors are limited by treating each image region separately without considering possible relations of the objects. This causes dependency solely on high-quality convolutional feature representations ... |
Title: Quantum Algorithms for solving Hard Constrained Optimisation Problems Abstract: The thesis deals with Quantum Algorithms for solving Hard Constrained Optimization Problems. It shows how quantum computers can solve difficult everyday problems such as finding the best schedule for social workers or the path of a r... |
Title: Direct data-driven forecast of local turbulent heat flux in Rayleigh-B\'{e}nard convection Abstract: A combined convolutional autoencoder-recurrent neural network machine learning model is presented to analyse and forecast the dynamics and low-order statistics of the local convective heat flux field in a two-dim... |
Title: Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering Abstract: Over the past decades, for One-Class Collaborative Filtering (OCCF), many learning objectives have been researched based on a variety of underlying probabilistic models. From our analysis, we observe that models trai... |
Title: High Dimensional Statistical Estimation under One-bit Quantization Abstract: Compared with data with high precision, one-bit (binary) data are preferable in many applications because of the efficiency in signal storage, processing, transmission, and enhancement of privacy. In this paper, we study three fundament... |
Title: Pix2NeRF: Unsupervised Conditional $\pi$-GAN for Single Image to Neural Radiance Fields Translation Abstract: We propose a pipeline to generate Neural Radiance Fields~(NeRF) of an object or a scene of a specific class, conditioned on a single input image. This is a challenging task, as training NeRF requires mul... |
Title: Statistically Efficient Advantage Learning for Offline Reinforcement Learning in Infinite Horizons Abstract: We consider reinforcement learning (RL) methods in offline domains without additional online data collection, such as mobile health applications. Most of existing policy optimization algorithms in the com... |
Title: BioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves Biomedical Machine Reading Comprehension Task Abstract: Biomedical machine reading comprehension (biomedical-MRC) aims to comprehend complex biomedical narratives and assist healthcare professionals in retrieving information from them. The high ... |
Title: Domain Knowledge-Based Automated Analog Circuit Design with Deep Reinforcement Learning Abstract: The design automation of analog circuits is a longstanding challenge in the integrated circuit field. This paper presents a deep reinforcement learning method to expedite the design of analog circuits at the pre-lay... |
Title: Model-free Reinforcement Learning for Content Caching at the Wireless Edge via Restless Bandits Abstract: An explosive growth in the number of on-demand content requests has imposed significant pressure on current wireless network infrastructure. To enhance the perceived user experience, and support latency-sens... |
Title: Regularized Bilinear Discriminant Analysis for Multivariate Time Series Data Abstract: In recent years, the methods on matrix-based or bilinear discriminant analysis (BLDA) have received much attention. Despite their advantages, it has been reported that the traditional vector-based regularized LDA (RLDA) is sti... |
Title: Relational Surrogate Loss Learning Abstract: Evaluation metrics in machine learning are often hardly taken as loss functions, as they could be non-differentiable and non-decomposable, e.g., average precision and F1 score. This paper aims to address this problem by revisiting the surrogate loss learning, where a ... |
Title: Dropout can Simulate Exponential Number of Models for Sample Selection Techniques Abstract: Following Coteaching, generally in the literature, two models are used in sample selection based approaches for training with noisy labels. Meanwhile, it is also well known that Dropout when present in a network trains an... |
Title: Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization Abstract: We propose a stochastic conditional gradient method (CGM) for minimizing convex finite-sum objectives formed as a sum of smooth and non-smooth terms. Existing CGM variants for this template either suffer from slo... |
Title: Adversarial robustness of sparse local Lipschitz predictors Abstract: This work studies the adversarial robustness of parametric functions composed of a linear predictor and a non-linear representation map. Our analysis relies on sparse local Lipschitzness (SLL), an extension of local Lipschitz continuity that b... |
Title: Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework Abstract: Online learning in large-scale structured bandits is known to be challenging due to the curse of dimensionality. In this paper, we propose a unified meta-learning framework for a general class of structured bandit problems where ... |
Title: Safe Exploration for Efficient Policy Evaluation and Comparison Abstract: High-quality data plays a central role in ensuring the accuracy of policy evaluation. This paper initiates the study of efficient and safe data collection for bandit policy evaluation. We formulate the problem and investigate its several r... |
Title: QOC: Quantum On-Chip Training with Parameter Shift and Gradient Pruning Abstract: Parameterized Quantum Circuits (PQC) are drawing increasing research interest thanks to its potential to achieve quantum advantages on near-term Noisy Intermediate Scale Quantum (NISQ) hardware. In order to achieve scalable PQC lea... |
Title: Automated Data Augmentations for Graph Classification Abstract: Data augmentations are effective in improving the invariance of learning machines. We argue that the corechallenge of data augmentations lies in designing data transformations that preserve labels. This is relativelystraightforward for images, but m... |
Title: Supervising Remote Sensing Change Detection Models with 3D Surface Semantics Abstract: Remote sensing change detection, identifying changes between scenes of the same location, is an active area of research with a broad range of applications. Recent advances in multimodal self-supervised pretraining have resulte... |
Title: Hierarchical Linear Dynamical System for Representing Notes from Recorded Audio Abstract: We seek to develop simultaneous segmentation and classification of notes from audio recordings in presence of outliers. The selected architecture for modeling time series is hierarchical linear dynamical system (HLDS). We p... |
Title: Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform Abstract: Breast cancer is a health problem that affects mainly the female population. An early detection increases the chances of effective treatment, improving the prognosis of the disease. In ... |
Title: Learning the Beauty in Songs: Neural Singing Voice Beautifier Abstract: We are interested in a novel task, singing voice beautifying (SVB). Given the singing voice of an amateur singer, SVB aims to improve the intonation and vocal tone of the voice, while keeping the content and vocal timbre. Current automatic p... |
Title: A Computer Vision-assisted Approach to Automated Real-Time Road Infrastructure Management Abstract: Accurate automated detection of road pavement distresses is critical for the timely identification and repair of potentially accident-inducing road hazards such as potholes and other surface-level asphalt cracks. ... |
Title: Efficient Attribute Unlearning: Towards Selective Removal of Input Attributes from Feature Representations Abstract: Recently, the enactment of privacy regulations has promoted the rise of the machine unlearning paradigm. Existing studies of machine unlearning mainly focus on sample-wise unlearning, such that a ... |
Title: Deep Learning-Based Inverse Design for Engineering Systems: Multidisciplinary Design Optimization of Automotive Brakes Abstract: The braking performance of the brake system is a target performance that must be considered for vehicle development. Apparent piston travel (APT) and drag torque are the most represent... |
Title: Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data Abstract: Practical tools for clustering streaming data must be fast enough to handle the arrival rate of the observations. Typically, they also must adapt on the fly to possible lack of stationarity; i.e., the data statistics may be time... |
Title: Bayesian Robust Tensor Ring Model for Incomplete Multiway Data Abstract: Low-rank tensor completion aims to recover missing entries from the observed data. However, the observed data may be disturbed by noise and outliers. Therefore, robust tensor completion (RTC) is proposed to solve this problem. The recently ... |
Title: Thinking Outside the Ball: Optimal Learning with Gradient Descent for Generalized Linear Stochastic Convex Optimization Abstract: We consider linear prediction with a convex Lipschitz loss, or more generally, stochastic convex optimization problems of generalized linear form, i.e.~where each instantaneous loss i... |
Title: Dual-Branched Spatio-temporal Fusion Network for Multi-horizon Tropical Cyclone Track Forecast Abstract: Tropical cyclone (TC) is an extreme tropical weather system and its trajectory can be described by a variety of spatio-temporal data. Effective mining of these data is the key to accurate TCs track forecastin... |
Title: Towards Robust Off-policy Learning for Runtime Uncertainty Abstract: Off-policy learning plays a pivotal role in optimizing and evaluating policies prior to the online deployment. However, during the real-time serving, we observe varieties of interventions and constraints that cause inconsistency between the onl... |
Title: Data Overlap: A Prerequisite For Disentanglement Abstract: Learning disentangled representations with variational autoencoders (VAEs) is often attributed to the regularisation component of the loss. In this work, we highlight the interaction between data and the reconstruction term of the loss as the main contri... |
Title: Split HE: Fast Secure Inference Combining Split Learning and Homomorphic Encryption Abstract: This work presents a novel protocol for fast secure inference of neural networks applied to computer vision applications. It focuses on improving the overall performance of the online execution by deploying a subset of ... |
Title: Benign Underfitting of Stochastic Gradient Descent Abstract: We study to what extent may stochastic gradient descent (SGD) be understood as a "conventional" learning rule that achieves generalization performance by obtaining a good fit to training data. We consider the fundamental stochastic convex optimization ... |
Title: Robust Continual Learning through a Comprehensively Progressive Bayesian Neural Network Abstract: This work proposes a comprehensively progressive Bayesian neural network for robust continual learning of a sequence of tasks. A Bayesian neural network is progressively pruned and grown such that there are sufficie... |
Title: A Multimodal German Dataset for Automatic Lip Reading Systems and Transfer Learning Abstract: Large datasets as required for deep learning of lip reading do not exist in many languages. In this paper we present the dataset GLips (German Lips) consisting of 250,000 publicly available videos of the faces of speake... |
Title: Taming the Long Tail of Deep Probabilistic Forecasting Abstract: Deep probabilistic forecasting is gaining attention in numerous applications ranging from weather prognosis, through electricity consumption estimation, to autonomous vehicle trajectory prediction. However, existing approaches focus on improvements... |
Title: Bayesian Active Learning for Discrete Latent Variable Models Abstract: Active learning seeks to reduce the number of samples required to estimate the parameters of a model, thus forming an important class of techniques in modern machine learning. However, past work on active learning has largely overlooked laten... |
Title: Meta-path Analysis on Spatio-Temporal Graphs for Pedestrian Trajectory Prediction Abstract: Spatio-temporal graphs (ST-graphs) have been used to model time series tasks such as traffic forecasting, human motion modeling, and action recognition. The high-level structure and corresponding features from ST-graphs h... |
Title: Distribution Preserving Graph Representation Learning Abstract: Graph neural network (GNN) is effective to model graphs for distributed representations of nodes and an entire graph. Recently, research on the expressive power of GNN attracted growing attention. A highly-expressive GNN has the ability to generate ... |
Title: Neural-Progressive Hedging: Enforcing Constraints in Reinforcement Learning with Stochastic Programming Abstract: We propose a framework, called neural-progressive hedging (NP), that leverages stochastic programming during the online phase of executing a reinforcement learning (RL) policy. The goal is to ensure ... |
Title: A Unified Wasserstein Distributional Robustness Framework for Adversarial Training Abstract: It is well-known that deep neural networks (DNNs) are susceptible to adversarial attacks, exposing a severe fragility of deep learning systems. As the result, adversarial training (AT) method, by incorporating adversaria... |
Title: Stability vs Implicit Bias of Gradient Methods on Separable Data and Beyond Abstract: An influential line of recent work has focused on the generalization properties of unregularized gradient-based learning procedures applied to separable linear classification with exponentially-tailed loss functions. The abilit... |
Title: Graph-Assisted Communication-Efficient Ensemble Federated Learning Abstract: Communication efficiency arises as a necessity in federated learning due to limited communication bandwidth. To this end, the present paper develops an algorithmic framework where an ensemble of pre-trained models is learned. At each le... |
Title: Federated Online Sparse Decision Making Abstract: This paper presents a novel federated linear contextual bandits model, where individual clients face different K-armed stochastic bandits with high-dimensional decision context and coupled through common global parameters. By leveraging the sparsity structure of ... |
Title: Conditional Simulation Using Diffusion Schr\"odinger Bridges Abstract: Denoising diffusion models have recently emerged as a powerful class of generative models. They provide state-of-the-art results, not only for unconditional simulation, but also when used to solve conditional simulation problems arising in a ... |
Title: ONE-NAS: An Online NeuroEvolution based Neural Architecture Search for Time Series Forecasting Abstract: Time series forecasting (TSF) is one of the most important tasks in data science, as accurate time series (TS) predictions can drive and advance a wide variety of domains including finance, transportation, he... |
Title: The Spectral Bias of Polynomial Neural Networks Abstract: Polynomial neural networks (PNNs) have been recently shown to be particularly effective at image generation and face recognition, where high-frequency information is critical. Previous studies have revealed that neural networks demonstrate a $\textit{spec... |
Title: Interpretable Concept-based Prototypical Networks for Few-Shot Learning Abstract: Few-shot learning aims at recognizing new instances from classes with limited samples. This challenging task is usually alleviated by performing meta-learning on similar tasks. However, the resulting models are black-boxes. There h... |
Title: PARIS and ELSA: An Elastic Scheduling Algorithm for Reconfigurable Multi-GPU Inference Servers Abstract: In cloud machine learning (ML) inference systems, providing low latency to end-users is of utmost importance. However, maximizing server utilization and system throughput is also crucial for ML service provid... |
Title: Causal Domain Adaptation with Copula Entropy based Conditional Independence Test Abstract: Domain Adaptation (DA) is a typical problem in machine learning that aims to transfer the model trained on source domain to target domain with different distribution. Causal DA is a special case of DA that solves the probl... |
Title: Architectural Optimization and Feature Learning for High-Dimensional Time Series Datasets Abstract: As our ability to sense increases, we are experiencing a transition from data-poor problems, in which the central issue is a lack of relevant data, to data-rich problems, in which the central issue is to identify ... |
Title: Point Label Aware Superpixels for Multi-species Segmentation of Underwater Imagery Abstract: Monitoring coral reefs using underwater vehicles increases the range of marine surveys and availability of historical ecological data by collecting significant quantities of images. Analysis of this imagery can be automa... |
Title: Limitations of Deep Learning for Inverse Problems on Digital Hardware Abstract: Deep neural networks have seen tremendous success over the last years. Since the training is performed on digital hardware, in this paper, we analyze what actually can be computed on current hardware platforms modeled as Turing machi... |
Title: Sparsity-aware neural user behavior modeling in online interaction platforms Abstract: Modern online platforms offer users an opportunity to participate in a variety of content-creation, social networking, and shopping activities. With the rapid proliferation of such online services, learning data-driven user be... |
Title: Machine learning techniques to identify antibiotic resistance in patients diagnosed with various skin and soft tissue infections Abstract: Skin and soft tissue infections (SSTIs) are among the most frequently observed diseases in ambulatory and hospital settings. Resistance of diverse bacterial pathogens to anti... |
Title: Variational Interpretable Learning from Multi-view Data Abstract: The main idea of canonical correlation analysis (CCA) is to map different views onto a common latent space with maximum correlation. We propose a deep interpretable variational canonical correlation analysis (DICCA) for multi-view learning. The de... |
Title: Evaluating High-Order Predictive Distributions in Deep Learning Abstract: Most work on supervised learning research has focused on marginal predictions. In decision problems, joint predictive distributions are essential for good performance. Previous work has developed methods for assessing low-order predictive ... |
Title: Risk-Aware Scene Sampling for Dynamic Assurance of Autonomous Systems Abstract: Autonomous Cyber-Physical Systems must often operate under uncertainties like sensor degradation and shifts in the operating conditions, which increases its operational risk. Dynamic Assurance of these systems requires designing runt... |
Title: Sparse Graph Learning with Eigen-gap for Spectral Filter Training in Graph Convolutional Networks Abstract: It is now known that the expressive power of graph convolutional neural nets (GCN) does not grow infinitely with the number of layers. Instead, the GCN output approaches a subspace spanned by the first eig... |
Title: LobsDICE: Offline Imitation Learning from Observation via Stationary Distribution Correction Estimation Abstract: We consider the problem of imitation from observation (IfO), in which the agent aims to mimic the expert's behavior from the state-only demonstrations by experts. We additionally assume that the agen... |
Title: Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning Abstract: Subgraph-based graph representation learning (SGRL) has been recently proposed to deal with some fundamental challenges encountered by canonical graph neural networks (GNNs), and has demonstrated advantages in man... |
Title: Pattern Based Multivariable Regression using Deep Learning (PBMR-DP) Abstract: We propose a deep learning methodology for multivariate regression that is based on pattern recognition that triggers fast learning over sensor data. We used a conversion of sensors-to-image which enables us to take advantage of Compu... |
Title: RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network Abstract: Graph Convolutional Network (GCN) plays pivotal roles in many real-world applications. Despite the successes of GCN deployment, GCN often exhibits performance disparity with respect to node degrees, resulting in worse predic... |
Title: Machine Learning Empowered Intelligent Data Center Networking: A Survey Abstract: To support the needs of ever-growing cloud-based services, the number of servers and network devices in data centers is increasing exponentially, which in turn results in high complexities and difficulties in network optimization. ... |
Title: Towards A Device-Independent Deep Learning Approach for the Automated Segmentation of Sonographic Fetal Brain Structures: A Multi-Center and Multi-Device Validation Abstract: Quality assessment of prenatal ultrasonography is essential for the screening of fetal central nervous system (CNS) anomalies. The interpr... |
Title: A Machine Learning Method for Material Property Prediction: Example Polymer Compatibility Abstract: Prediction of material property is a key problem because of its significance to material design and screening. We present a brand-new and general machine learning method for material property prediction. As a repr... |
Title: Towards Machine Learning for Placement and Routing in Chip Design: a Methodological Overview Abstract: Placement and routing are two indispensable and challenging (NP-hard) tasks in modern chip design flows. Compared with traditional solvers using heuristics or expert-well-designed algorithms, machine learning h... |
Title: Learning Parameters for a Generalized Vidale-Wolfe Response Model with Flexible Ad Elasticity and Word-of-Mouth Abstract: In this research, we investigate a generalized form of Vidale-Wolfe (GVW) model. One key element of our modeling work is that the GVW model contains two useful indexes representing advertiser... |
Title: KL Divergence Estimation with Multi-group Attribution Abstract: Estimating the Kullback-Leibler (KL) divergence between two distributions given samples from them is well-studied in machine learning and information theory. Motivated by considerations of multi-group fairness, we seek KL divergence estimates that a... |
Title: Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgotten Abstract: As the use of machine learning (ML) models is becoming increasingly popular in many real-world applications, there are practical challenges that need to be addressed for model maintenance. One such challenge is to ... |
Title: LCP-dropout: Compression-based Multiple Subword Segmentation for Neural Machine Translation Abstract: In this study, we propose a simple and effective preprocessing method for subword segmentation based on a data compression algorithm. Compression-based subword segmentation has recently attracted significant att... |
Title: Rectified Max-Value Entropy Search for Bayesian Optimization Abstract: Although the existing max-value entropy search (MES) is based on the widely celebrated notion of mutual information, its empirical performance can suffer due to two misconceptions whose implications on the exploration-exploitation trade-off a... |
Title: Bandit Learning with General Function Classes: Heteroscedastic Noise and Variance-dependent Regret Bounds Abstract: We consider learning a stochastic bandit model, where the reward function belongs to a general class of uniformly bounded functions, and the additive noise can be heteroscedastic. Our model capture... |
Title: Asynchronous Decentralized Federated Learning for Collaborative Fault Diagnosis of PV Stations Abstract: Due to the different losses caused by various photovoltaic (PV) array faults, accurate diagnosis of fault types is becoming increasingly important. Compared with a single one, multiple PV stations collect suf... |
Title: Semi-supervised Learning on Large Graphs: is Poisson Learning a Game-Changer? Abstract: We explain Poisson learning on graph-based semi-supervised learning to see if it could avoid the problem of global information loss problem as Laplace-based learning methods on large graphs. From our analysis, Poisson learnin... |
Title: WSLRec: Weakly Supervised Learning for Neural Sequential Recommendation Models Abstract: Learning the user-item relevance hidden in implicit feedback data plays an important role in modern recommender systems. Neural sequential recommendation models, which formulates learning the user-item relevance as a sequent... |
Title: Deep learning enhanced Rydberg multifrequency microwave recognition Abstract: Recognition of multifrequency microwave (MW) electric fields is challenging because of the complex interference of multifrequency fields in practical applications. Rydberg atom-based measurements for multifrequency MW electric fields i... |
Title: Enhance transferability of adversarial examples with model architecture Abstract: Transferability of adversarial examples is of critical importance to launch black-box adversarial attacks, where attackers are only allowed to access the output of the target model. However, under such a challenging but practical s... |
Title: Improving Response Time of Home IoT Services in Federated Learning Abstract: For intelligent home IoT services with sensors and machine learning, we need to upload IoT data to the cloud server which cannot share private data for training. A recent machine learning approach, called federated learning, keeps user ... |
Title: GPU-Accelerated Policy Optimization via Batch Automatic Differentiation of Gaussian Processes for Real-World Control Abstract: The ability of Gaussian processes (GPs) to predict the behavior of dynamical systems as a more sample-efficient alternative to parametric models seems promising for real-world robotics r... |
Title: Hierarchical Multi-Agent DRL-Based Framework for Joint Multi-RAT Assignment and Dynamic Resource Allocation in Next-Generation HetNets Abstract: This paper considers the problem of cost-aware downlink sum-rate maximization via joint optimal radio access technologies (RATs) assignment and power allocation in next... |
Title: Avalanche RL: a Continual Reinforcement Learning Library Abstract: Continual Reinforcement Learning (CRL) is a challenging setting where an agent learns to interact with an environment that is constantly changing over time (the stream of experiences). In this paper, we describe Avalanche RL, a library for Contin... |
Title: Restless Multi-Armed Bandits under Exogenous Global Markov Process Abstract: We consider an extension to the restless multi-armed bandit (RMAB) problem with unknown arm dynamics, where an unknown exogenous global Markov process governs the rewards distribution of each arm. Under each global state, the rewards pr... |
Title: Recent Advances and Challenges in Deep Audio-Visual Correlation Learning Abstract: Audio-visual correlation learning aims to capture essential correspondences and understand natural phenomena between audio and video. With the rapid growth of deep learning, an increasing amount of attention has been paid to this ... |
Title: Estimating Model Performance on External Samples from Their Limited Statistical Characteristics Abstract: Methods that address data shifts usually assume full access to multiple datasets. In the healthcare domain, however, privacy-preserving regulations as well as commercial interests limit data availability and... |
Title: Online Learning with Knapsacks: the Best of Both Worlds Abstract: We study online learning problems in which a decision maker wants to maximize their expected reward without violating a finite set of $m$ resource constraints. By casting the learning process over a suitably defined space of strategy mixtures, we ... |
Title: Evaluating the Adversarial Robustness of Adaptive Test-time Defenses Abstract: Adaptive defenses that use test-time optimization promise to improve robustness to adversarial examples. We categorize such adaptive test-time defenses and explain their potential benefits and drawbacks. In the process, we evaluate so... |
Title: Fast Feature Selection with Fairness Constraints Abstract: We study the fundamental problem of selecting optimal features for model construction. This problem is computationally challenging on large datasets, even with the use of greedy algorithm variants. To address this challenge, we extend the adaptive query ... |
Title: On the Benefits of Large Learning Rates for Kernel Methods Abstract: This paper studies an intriguing phenomenon related to the good generalization performance of estimators obtained by using large learning rates within gradient descent algorithms. First observed in the deep learning literature, we show that a p... |
Title: Missing Value Estimation using Clustering and Deep Learning within Multiple Imputation Framework Abstract: Missing values in tabular data restrict the use and performance of machine learning, requiring the imputation of missing values. The most popular imputation algorithm is arguably multiple imputations using ... |
Title: On the Robustness of CountSketch to Adaptive Inputs Abstract: CountSketch is a popular dimensionality reduction technique that maps vectors to a lower dimension using randomized linear measurements. The sketch supports recovering $\ell_2$-heavy hitters of a vector (entries with $v[i]^2 \geq \frac{1}{k}\|\boldsym... |
Title: Logical Fallacy Detection Abstract: Reasoning is central to human intelligence. However, fallacious arguments are common, and some exacerbate problems such as spreading misinformation about climate change. In this paper, we propose the task of logical fallacy detection, and provide a new dataset (Logic) of logic... |
Title: Path-Aware Graph Attention for HD Maps in Motion Prediction Abstract: The success of motion prediction for autonomous driving relies on integration of information from the HD maps. As maps are naturally graph-structured, investigation on graph neural networks (GNNs) for encoding HD maps is burgeoning in recent y... |
Title: Selection, Ignorability and Challenges With Causal Fairness Abstract: In this paper we look at popular fairness methods that use causal counterfactuals. These methods capture the intuitive notion that a prediction is fair if it coincides with the prediction that would have been made if someone's race, gender or ... |
Title: Learning the nonlinear dynamics of soft mechanical metamaterials with graph networks Abstract: The dynamics of soft mechanical metamaterials provides opportunities for many exciting engineering applications. Previous studies often use discrete systems, composed of rigid elements and nonlinear springs, to model t... |
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