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Title: In the Service of Online Order: Tackling Cyber-Bullying with Machine Learning and Affect Analysis Abstract: One of the burning problems lately in Japan has been cyber-bullying, or slandering and bullying people online. The problem has been especially noticed on unofficial Web sites of Japanese schools. Volunteer... |
Title: Adversarial Patterns: Building Robust Android Malware Classifiers Abstract: Deep learning-based classifiers have substantially improved recognition of malware samples. However, these classifiers can be vulnerable to adversarial input perturbations. Any vulnerability in malware classifiers poses significant threa... |
Title: Abuse and Fraud Detection in Streaming Services Using Heuristic-Aware Machine Learning Abstract: This work presents a fraud and abuse detection framework for streaming services by modeling user streaming behavior. The goal is to discover anomalous and suspicious incidents and scale the investigation efforts by c... |
Title: Distributionally Robust Bayesian Optimization with $\phi$-divergences Abstract: The study of robustness has received much attention due to its inevitability in data-driven settings where many systems face uncertainty. One such example of concern is Bayesian Optimization (BO), where uncertainty is multi-faceted, ... |
Title: Continual Few-shot Relation Learning via Embedding Space Regularization and Data Augmentation Abstract: Existing continual relation learning (CRL) methods rely on plenty of labeled training data for learning a new task, which can be hard to acquire in real scenario as getting large and representative labeled dat... |
Title: Analysis of closed-loop inertial gradient dynamics Abstract: In this paper, we analyse the performance of the closed-loop Whiplash gradient descent algorithm for L-smooth convex cost functions. Using numerical experiments, we study the algorithm's performance for convex cost functions, for different condition nu... |
Title: Training language models to follow instructions with human feedback Abstract: Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user. In other words, the... |
Title: MF-Hovernet: An Extension of Hovernet for Colon Nuclei Identification and Counting (CoNiC) Challenge Abstract: Nuclei Identification and Counting is the most important morphological feature of cancers, especially in the colon. Many deep learning-based methods have been proposed to deal with this problem. In this... |
Title: GCNet: Graph Completion Network for Incomplete Multimodal Learning in Conversation Abstract: Conversations have become a critical data format on social media platforms. Understanding conversation from emotion, content, and other aspects also attracts increasing attention from researchers due to its widespread ap... |
Title: Passive and Active Learning of Driver Behavior from Electric Vehicles Abstract: Modeling driver behavior provides several advantages in the automotive industry, including prediction of electric vehicle energy consumption. Studies have shown that aggressive driving can consume up to 30% more energy than moderate ... |
Title: Matrix Completion via Non-Convex Relaxation and Adaptive Correlation Learning Abstract: The existing matrix completion methods focus on optimizing the relaxation of rank function such as nuclear norm, Schatten-p norm, etc. They usually need many iterations to converge. Moreover, only the low-rank property of mat... |
Title: Rethinking Reconstruction Autoencoder-Based Out-of-Distribution Detection Abstract: In some scenarios, classifier requires detecting out-of-distribution samples far from its training data. With desirable characteristics, reconstruction autoencoder-based methods deal with this problem by using input reconstructio... |
Title: Neural Simulated Annealing Abstract: Simulated annealing (SA) is a stochastic global optimisation technique applicable to a wide range of discrete and continuous variable problems. Despite its simplicity, the development of an effective SA optimiser for a given problem hinges on a handful of carefully handpicked... |
Title: Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis Abstract: The increasing energy consumption and carbon footprint of deep learning (DL) due to growing compute requirements has become a cause of concern. In this work, we focus on the carbon footprint of developing DL mode... |
Title: Sharper Bounds for Proximal Gradient Algorithms with Errors Abstract: We analyse the convergence of the proximal gradient algorithm for convex composite problems in the presence of gradient and proximal computational inaccuracies. We derive new tighter deterministic and probabilistic bounds that we use to verify... |
Title: Safety-aware metrics for object detectors in autonomous driving Abstract: We argue that object detectors in the safety critical domain should prioritize detection of objects that are most likely to interfere with the actions of the autonomous actor. Especially, this applies to objects that can impact the actor's... |
Title: Plan Your Target and Learn Your Skills: Transferable State-Only Imitation Learning via Decoupled Policy Optimization Abstract: Recent progress in state-only imitation learning extends the scope of applicability of imitation learning to real-world settings by relieving the need for observing expert actions. Howev... |
Title: Cloud-Edge Training Architecture for Sim-to-Real Deep Reinforcement Learning Abstract: Deep reinforcement learning (DRL) is a promising approach to solve complex control tasks by learning policies through interactions with the environment. However, the training of DRL policies requires large amounts of training ... |
Title: Do Explanations Explain? Model Knows Best Abstract: It is a mystery which input features contribute to a neural network's output. Various explanation (feature attribution) methods are proposed in the literature to shed light on the problem. One peculiar observation is that these explanations (attributions) point... |
Title: Speech watermarking: an approach for the forensic analysis of digital telephonic recordings Abstract: In this article, the authors discuss the problem of forensic authentication of digital audio recordings. Although forensic audio has been addressed in several articles, the existing approaches are focused on ana... |
Title: A photonic chip-based machine learning approach for the prediction of molecular properties Abstract: Machine learning methods have revolutionized the discovery process of new molecules and materials. However, the intensive training process of neural networks for molecules with ever increasing complexity has resu... |
Title: Integrating Statistical Uncertainty into Neural Network-Based Speech Enhancement Abstract: Speech enhancement in the time-frequency domain is often performed by estimating a multiplicative mask to extract clean speech. However, most neural network-based methods perform point estimation, i.e., their output consis... |
Title: Evaluating Local Model-Agnostic Explanations of Learning to Rank Models with Decision Paths Abstract: Local explanations of learning-to-rank (LTR) models are thought to extract the most important features that contribute to the ranking predicted by the LTR model for a single data point. Evaluating the accuracy o... |
Title: Intrinsically-Motivated Reinforcement Learning: A Brief Introduction Abstract: Reinforcement learning (RL) is one of the three basic paradigms of machine learning. It has demonstrated impressive performance in many complex tasks like Go and StarCraft, which is increasingly involved in smart manufacturing and aut... |
Title: Mixed Reality Depth Contour Occlusion Using Binocular Similarity Matching and Three-dimensional Contour Optimisation Abstract: Mixed reality applications often require virtual objects that are partly occluded by real objects. However, previous research and commercial products have limitations in terms of perform... |
Title: Adaptive Discounting of Implicit Language Models in RNN-Transducers Abstract: RNN-Transducer (RNN-T) models have become synonymous with streaming end-to-end ASR systems. While they perform competitively on a number of evaluation categories, rare words pose a serious challenge to RNN-T models. One main reason for... |
Title: Differentiable Causal Discovery Under Latent Interventions Abstract: Recent work has shown promising results in causal discovery by leveraging interventional data with gradient-based methods, even when the intervened variables are unknown. However, previous work assumes that the correspondence between samples an... |
Title: Uncertainty Estimation for Heatmap-based Landmark Localization Abstract: Automatic anatomical landmark localization has made great strides by leveraging deep learning methods in recent years. The ability to quantify the uncertainty of these predictions is a vital ingredient needed to see these methods adopted in... |
Title: Boosting the Performance of Quantum Annealers using Machine Learning Abstract: Noisy intermediate-scale quantum (NISQ) devices are spearheading the second quantum revolution. Of these, quantum annealers are the only ones currently offering real world, commercial applications on as many as 5000 qubits. The size o... |
Title: Exploring Scalable, Distributed Real-Time Anomaly Detection for Bridge Health Monitoring Abstract: Modern real-time Structural Health Monitoring systems can generate a considerable amount of information that must be processed and evaluated for detecting early anomalies and generating prompt warnings and alarms a... |
Title: Distributed Methods with Absolute Compression and Error Compensation Abstract: Distributed optimization methods are often applied to solving huge-scale problems like training neural networks with millions and even billions of parameters. In such applications, communicating full vectors, e.g., (stochastic) gradie... |
Title: AutoMO-Mixer: An automated multi-objective Mixer model for balanced, safe and robust prediction in medicine Abstract: Accurately identifying patient's status through medical images plays an important role in diagnosis and treatment. Artificial intelligence (AI), especially the deep learning, has achieved great s... |
Title: iSTFTNet: Fast and Lightweight Mel-Spectrogram Vocoder Incorporating Inverse Short-Time Fourier Transform Abstract: In recent text-to-speech synthesis and voice conversion systems, a mel-spectrogram is commonly applied as an intermediate representation, and the necessity for a mel-spectrogram vocoder is increasi... |
Title: Benchmark Evaluation of Counterfactual Algorithms for XAI: From a White Box to a Black Box Abstract: Counterfactual explanations have recently been brought to light as a potentially crucial response to obtaining human-understandable explanations from predictive models in Explainable Artificial Intelligence (XAI)... |
Title: Quantum Approximate Optimization Algorithm for Bayesian network structure learning Abstract: Bayesian network structure learning is an NP-hard problem that has been faced by a number of traditional approaches in recent decades. Currently, quantum technologies offer a wide range of advantages that can be exploite... |
Title: Differentiable Control Barrier Functions for Vision-based End-to-End Autonomous Driving Abstract: Guaranteeing safety of perception-based learning systems is challenging due to the absence of ground-truth state information unlike in state-aware control scenarios. In this paper, we introduce a safety guaranteed l... |
Title: R-GCN: The R Could Stand for Random Abstract: The inception of the Relational Graph Convolutional Network (R-GCN) marked a milestone in the Semantic Web domain as a widely cited method that generalises end-to-end hierarchical representation learning to Knowledge Graphs (KGs). R-GCNs generate representations for ... |
Title: Rethinking Efficient Lane Detection via Curve Modeling Abstract: This paper presents a novel parametric curve-based method for lane detection in RGB images. Unlike state-of-the-art segmentation-based and point detection-based methods that typically require heuristics to either decode predictions or formulate a l... |
Title: Improving \textit{Tug-of-War} sketch using Control-Variates method Abstract: Computing space-efficient summary, or \textit{a.k.a. sketches}, of large data, is a central problem in the streaming algorithm. Such sketches are used to answer \textit{post-hoc} queries in several data analytics tasks. The algorithm fo... |
Title: The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights Abstract: Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused on solving problem instances in isolation, ignoring that they often s... |
Title: Contextformer: A Transformer with Spatio-Channel Attention for Context Modeling in Learned Image Compression Abstract: Entropy modeling is a key component for high-performance image compression algorithms. Recent developments in autoregressive context modeling helped learning-based methods to surpass their class... |
Title: Graph clustering with Boltzmann machines Abstract: Graph clustering is the process of grouping vertices into densely connected sets called clusters. We tailor two mathematical programming formulations from the literature, to this problem. In doing so, we obtain a heuristic approximation to the intra-cluster dens... |
Title: Interpretable Off-Policy Learning via Hyperbox Search Abstract: Personalized treatment decisions have become an integral part of modern medicine. Thereby, the aim is to make treatment decisions based on individual patient characteristics. Numerous methods have been developed for learning such policies from obser... |
Title: Rate-Distortion Theoretic Generalization Bounds for Stochastic Learning Algorithms Abstract: Understanding generalization in modern machine learning settings has been one of the major challenges in statistical learning theory. In this context, recent years have witnessed the development of various generalization... |
Title: AutoDIME: Automatic Design of Interesting Multi-Agent Environments Abstract: Designing a distribution of environments in which RL agents can learn interesting and useful skills is a challenging and poorly understood task, for multi-agent environments the difficulties are only exacerbated. One approach is to trai... |
Title: Ontological Learning from Weak Labels Abstract: Ontologies encompass a formal representation of knowledge through the definition of concepts or properties of a domain, and the relationships between those concepts. In this work, we seek to investigate whether using this ontological information will improve learni... |
Title: Better Supervisory Signals by Observing Learning Paths Abstract: Better-supervised models might have better performance. In this paper, we first clarify what makes for good supervision for a classification problem, and then explain two existing label refining methods, label smoothing and knowledge distillation, ... |
Title: The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods Abstract: In many object recognition applications, the set of possible categories is an open set, and the deployed recognition system will encounter novel objects belonging to categories unseen during training. Detecting such "novel cat... |
Title: Behavioural Curves Analysis Using Near-Infrared-Iris Image Sequences Abstract: This paper proposes a new method to estimate behavioural curves from a stream of Near-Infra-Red (NIR) iris video frames. This method can be used in a Fitness For Duty system (FFD). The research focuses on determining the effect of ext... |
Title: Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion Abstract: Forecasting pedestrians' future motions is essential for autonomous driving systems to safely navigate in urban areas. However, existing prediction algorithms often overly rely on past observed trajectories and tend to fail around abrupt dyn... |
Title: Learning from Label Proportions by Learning with Label Noise Abstract: Learning from label proportions (LLP) is a weakly supervised classification problem where data points are grouped into bags, and the label proportions within each bag are observed instead of the instance-level labels. The task is to learn a c... |
Title: Computational Fluid Dynamics and Machine Learning as tools for Optimization of Micromixers geometry Abstract: This work explores a new approach for optimization in the field of microfluidics, using the combination of CFD (Computational Fluid Dynamics), and Machine Learning techniques. The objective of this combi... |
Title: No More Than 6ft Apart: Robust K-Means via Radius Upper Bounds Abstract: Centroid based clustering methods such as k-means, k-medoids and k-centers are heavily applied as a go-to tool in exploratory data analysis. In many cases, those methods are used to obtain representative centroids of the data manifold for v... |
Title: ARM 4-BIT PQ: SIMD-based Acceleration for Approximate Nearest Neighbor Search on ARM Abstract: We accelerate the 4-bit product quantization (PQ) on the ARM architecture. Notably, the drastic performance of the conventional 4-bit PQ strongly relies on x64-specific SIMD register, such as AVX2; hence, we cannot yet... |
Title: Non-linear predictive vector quantization of speech Abstract: In this paper we propose a Non-Linear Predictive Vector quantizer (PVQ) for speech coding, based on Multi-Layer Perceptrons. We also propose a method to evaluate if a quantizer is well designed, and if it exploits the correlation between consecutive o... |
Title: Cellular Segmentation and Composition in Routine Histology Images using Deep Learning Abstract: Identification and quantification of nuclei in colorectal cancer haematoxylin \& eosin (H\&E) stained histology images is crucial to prognosis and patient management. In computational pathology these tasks are referre... |
Title: Machine Learning for CUDA+MPI Design Rules Abstract: We present a new strategy for automatically exploring the design space of key CUDA+MPI programs and providing design rules that discriminate slow from fast implementations. In such programs, the order of operations (e.g., GPU kernels, MPI communication) and as... |
Title: BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active Annotation Abstract: In this paper, we propose a novel semi-supervised learning (SSL) framework named BoostMIS that combines adaptive pseudo labeling and informative active annotation to unleash the pot... |
Title: Evolving symbolic density functionals Abstract: Systematic development of accurate density functionals has been a decades-long challenge for scientists. Despite the emerging application of machine learning (ML) in approximating functionals, the resulting ML functionals usually contain more than tens of thousands... |
Title: A streamable large-scale clinical EEG dataset for Deep Learning Abstract: Deep Learning has revolutionized various fields, including Computer Vision, Natural Language Processing, as well as Biomedical research. Within the field of neuroscience, specifically in electrophysiological neuroimaging, researchers are s... |
Title: Building 3D Generative Models from Minimal Data Abstract: We propose a method for constructing generative models of 3D objects from a single 3D mesh and improving them through unsupervised low-shot learning from 2D images. Our method produces a 3D morphable model that represents shape and albedo in terms of Gaus... |
Title: Show Me What and Tell Me How: Video Synthesis via Multimodal Conditioning Abstract: Most methods for conditional video synthesis use a single modality as the condition. This comes with major limitations. For example, it is problematic for a model conditioned on an image to generate a specific motion trajectory d... |
Title: Style-ERD: Responsive and Coherent Online Motion Style Transfer Abstract: Motion style transfer is a common method for enriching character animation. Motion style transfer algorithms are often designed for offline settings where motions are processed in segments. However, for online animation applications, such ... |
Title: Machine Learning Simulates Agent-Based Model Towards Policy Abstract: Public Policies are not intrinsically positive or negative. Rather, policies provide varying levels of effects across different recipients. Methodologically, computational modeling enables the application of a combination of multiple influence... |
Title: Concept-based Explanations for Out-Of-Distribution Detectors Abstract: Out-of-distribution (OOD) detection plays a crucial role in ensuring the safe deployment of deep neural network (DNN) classifiers. While a myriad of methods have focused on improving the performance of OOD detectors, a critical gap remains in... |
Title: A Small Gain Analysis of Single Timescale Actor Critic Abstract: We consider a version of actor-critic which uses proportional step-sizes and only one critic update with a single sample from the stationary distribution per actor step. We provide an analysis of this method using the small-gain theorem. Specifical... |
Title: Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck Abstract: The information bottleneck framework provides a systematic approach to learn representations that compress nuisance information in inputs and extract semantically meaningful information about the predictions. However,... |
Title: Geodesic Gramian Denoising Applied to the Images Contaminated With Noise Sampled From Diverse Probability Distributions Abstract: As quotidian use of sophisticated cameras surges, people in modern society are more interested in capturing fine-quality images. However, the quality of the images might be inferior t... |
Title: Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions Abstract: Reinforcement learning (RL) is acquiring a key role in the space of adaptive interventions (AIs), attracting a substantial interest within methodological and theoretical literature and becoming increasingly popu... |
Title: Plant Species Recognition with Optimized 3D Polynomial Neural Networks and Variably Overlapping Time-Coherent Sliding Window Abstract: Recently, the EAGL-I system was developed to rapidly create massive labeled datasets of plants intended to be commonly used by farmers and researchers to create AI-driven solutio... |
Title: How to Train Unstable Looped Tensor Network Abstract: A rising problem in the compression of Deep Neural Networks is how to reduce the number of parameters in convolutional kernels and the complexity of these layers by low-rank tensor approximation. Canonical polyadic tensor decomposition (CPD) and Tucker tensor... |
Title: Low-cost prediction of molecular and transition state partition functions via machine learning Abstract: We have generated an open-source dataset of over 30000 organic chemistry gas phase partition functions. With this data, a machine learning deep neural network estimator was trained to predict partition functi... |
Title: Scaling R-GCN Training with Graph Summarization Abstract: Training of Relational Graph Convolutional Networks (R-GCN) is a memory intense task. The amount of gradient information that needs to be stored during training for real-world graphs is often too large for the amount of memory available on most GPUs. In t... |
Title: Target Network and Truncation Overcome The Deadly Triad in $Q$-Learning Abstract: $Q$-learning with function approximation is one of the most empirically successful while theoretically mysterious reinforcement learning (RL) algorithms, and was identified in Sutton (1999) as one of the most important theoretical ... |
Title: Important Object Identification with Semi-Supervised Learning for Autonomous Driving Abstract: Accurate identification of important objects in the scene is a prerequisite for safe and high-quality decision making and motion planning of intelligent agents (e.g., autonomous vehicles) that navigate in complex and d... |
Title: Training privacy-preserving video analytics pipelines by suppressing features that reveal information about private attributes Abstract: Deep neural networks are increasingly deployed for scene analytics, including to evaluate the attention and reaction of people exposed to out-of-home advertisements. However, t... |
Title: Safe Reinforcement Learning for Legged Locomotion Abstract: Designing control policies for legged locomotion is complex due to the under-actuated and non-continuous robot dynamics. Model-free reinforcement learning provides promising tools to tackle this challenge. However, a major bottleneck of applying model-f... |
Title: Acceleration of Federated Learning with Alleviated Forgetting in Local Training Abstract: Federated learning (FL) enables distributed optimization of machine learning models while protecting privacy by independently training local models on each client and then aggregating parameters on a central server, thereby... |
Title: Ensemble Knowledge Guided Sub-network Search and Fine-tuning for Filter Pruning Abstract: Conventional NAS-based pruning algorithms aim to find the sub-network with the best validation performance. However, validation performance does not successfully represent test performance, i.e., potential performance. Also... |
Title: Unfreeze with Care: Space-Efficient Fine-Tuning of Semantic Parsing Models Abstract: Semantic parsing is a key NLP task that maps natural language to structured meaning representations. As in many other NLP tasks, SOTA performance in semantic parsing is now attained by fine-tuning a large pretrained language mod... |
Title: Audio-visual speech separation based on joint feature representation with cross-modal attention Abstract: Multi-modal based speech separation has exhibited a specific advantage on isolating the target character in multi-talker noisy environments. Unfortunately, most of current separation strategies prefer a stra... |
Title: Deep Partial Multiplex Network Embedding Abstract: Network embedding is an effective technique to learn the low-dimensional representations of nodes in networks. Real-world networks are usually with multiplex or having multi-view representations from different relations. Recently, there has been increasing inter... |
Title: Koopman operator for time-dependent reliability analysis Abstract: Time-dependent structural reliability analysis of nonlinear dynamical systems is non-trivial; subsequently, scope of most of the structural reliability analysis methods is limited to time-independent reliability analysis only. In this work, we pr... |
Title: A Similarity-based Framework for Classification Task Abstract: Similarity-based method gives rise to a new class of methods for multi-label learning and also achieves promising performance. In this paper, we generalize this method, resulting in a new framework for classification task. Specifically, we unite simi... |
Title: ECMG: Exemplar-based Commit Message Generation Abstract: Commit messages concisely describe the content of code diffs (i.e., code changes) and the intent behind them. Recently, many approaches have been proposed to generate commit messages automatically. The information retrieval-based methods reuse the commit m... |
Title: Meta Mirror Descent: Optimiser Learning for Fast Convergence Abstract: Optimisers are an essential component for training machine learning models, and their design influences learning speed and generalisation. Several studies have attempted to learn more effective gradient-descent optimisers via solving a bi-lev... |
Title: Towards Efficient and Scalable Sharpness-Aware Minimization Abstract: Recently, Sharpness-Aware Minimization (SAM), which connects the geometry of the loss landscape and generalization, has demonstrated significant performance boosts on training large-scale models such as vision transformers. However, the update... |
Title: Bayesian Learning Approach to Model Predictive Control Abstract: This study presents a Bayesian learning perspective towards model predictive control algorithms. High-level frameworks have been developed separately in the earlier studies on Bayesian learning and sampling-based model predictive control. On one ha... |
Title: A Novel Dual Dense Connection Network for Video Super-resolution Abstract: Video super-resolution (VSR) refers to the reconstruction of high-resolution (HR) video from the corresponding low-resolution (LR) video. Recently, VSR has received increasing attention. In this paper, we propose a novel dual dense connec... |
Title: MaxDropoutV2: An Improved Method to Drop out Neurons in Convolutional Neural Networks Abstract: In the last decade, exponential data growth supplied the machine learning-based algorithms' capacity and enabled their usage in daily life activities. Additionally, such an improvement is partially explained due to th... |
Title: Flurry: a Fast Framework for Reproducible Multi-layered Provenance Graph Representation Learning Abstract: Complex heterogeneous dynamic networks like knowledge graphs are powerful constructs that can be used in modeling data provenance from computer systems. From a security perspective, these attributed graphs ... |
Title: The Impact of Differential Privacy on Group Disparity Mitigation Abstract: The performance cost of differential privacy has, for some applications, been shown to be higher for minority groups; fairness, conversely, has been shown to disproportionally compromise the privacy of members of such groups. Most work in... |
Title: Don't Be So Dense: Sparse-to-Sparse GAN Training Without Sacrificing Performance Abstract: Generative adversarial networks (GANs) have received an upsurging interest since being proposed due to the high quality of the generated data. While achieving increasingly impressive results, the resource demands associate... |
Title: A Robust Spectral Algorithm for Overcomplete Tensor Decomposition Abstract: We give a spectral algorithm for decomposing overcomplete order-4 tensors, so long as their components satisfy an algebraic non-degeneracy condition that holds for nearly all (all but an algebraic set of measure $0$) tensors over $(\math... |
Title: Machine Learning Applications in Lung Cancer Diagnosis, Treatment and Prognosis Abstract: The recent development of imaging and sequencing technologies enables systematic advances in the clinical study of lung cancer. Meanwhile, the human mind is limited in effectively handling and fully utilizing the accumulati... |
Title: Object-centric Process Predictive Analytics Abstract: Object-centric processes (a.k.a. Artifact-centric processes) are implementations of a paradigm where an instance of one process is not executed in isolation but interacts with other instances of the same or other processes. Interactions take place through bri... |
Title: Off-Policy Evaluation in Embedded Spaces Abstract: Off-policy evaluation methods are important in recommendation systems and search engines, whereby data collected under an old logging policy is used to predict the performance of a new target policy. However, in practice most systems are not observed to recommen... |
Title: Fuzzy Forests For Feature Selection in High-Dimensional Survey Data: An Application to the 2020 U.S. Presidential Election Abstract: An increasingly common methodological issue in the field of social science is high-dimensional and highly correlated datasets that are unamenable to the traditional deductive frame... |
Title: Bathymetry Inversion using a Deep-Learning-Based Surrogate for Shallow Water Equations Solvers Abstract: River bathymetry is critical for many aspects of water resources management. We propose and demonstrate a bathymetry inversion method using a deep-learning-based surrogate for shallow water equations solvers.... |
Title: Distributional Hardness Against Preconditioned Lasso via Erasure-Robust Designs Abstract: Sparse linear regression with ill-conditioned Gaussian random designs is widely believed to exhibit a statistical/computational gap, but there is surprisingly little formal evidence for this belief, even in the form of exam... |
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