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Title: Deep Embedded Clustering with Distribution Consistency Preservation for Attributed Networks Abstract: Many complex systems in the real world can be characterized by attributed networks. To mine the potential information in these networks, deep embedded clustering, which obtains node representations and clusters ... |
Title: Uncertainty quantification of two-phase flow in porous media via coupled-TgNN surrogate model Abstract: Uncertainty quantification (UQ) of subsurface two-phase flow usually requires numerous executions of forward simulations under varying conditions. In this work, a novel coupled theory-guided neural network (Tg... |
Title: A Quadrature Perspective on Frequency Bias in Neural Network Training with Nonuniform Data Abstract: Small generalization errors of over-parameterized neural networks (NNs) can be partially explained by the frequency biasing phenomenon, where gradient-based algorithms minimize the low-frequency misfit before red... |
Title: Deep Learning with Label Noise: A Hierarchical Approach Abstract: Deep neural networks are susceptible to label noise. Existing methods to improve robustness, such as meta-learning and regularization, usually require significant change to the network architecture or careful tuning of the optimization procedure. ... |
Title: MC-GEN:Multi-level Clustering for Private Synthetic Data Generation Abstract: Nowadays, machine learning is one of the most common technology to turn raw data into useful information in scientific and industrial processes. The performance of the machine learning model often depends on the size of dataset. Compan... |
Title: Fake It Till You Make It: Near-Distribution Novelty Detection by Score-Based Generative Models Abstract: We aim for image-based novelty detection. Despite considerable progress, existing models either fail or face a dramatic drop under the so-called ``near-distribution" setting, where the differences between nor... |
Title: Provably Auditing Ordinary Least Squares in Low Dimensions Abstract: Measuring the stability of conclusions derived from Ordinary Least Squares linear regression is critically important, but most metrics either only measure local stability (i.e. against infinitesimal changes in the data), or are only interpretab... |
Title: Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling Abstract: Sparse modeling for signal processing and machine learning has been at the focus of scientific research for over two decades. Among others, supervised sparsity-aware learning comprises two major pa... |
Title: Uniform Convergence and Generalization for Nonconvex Stochastic Minimax Problems Abstract: This paper studies the uniform convergence and generalization bounds for nonconvex-(strongly)-concave (NC-SC/NC-C) stochastic minimax optimization. We first establish the uniform convergence between the empirical minimax p... |
Title: So3krates -- Self-attention for higher-order geometric interactions on arbitrary length-scales Abstract: The application of machine learning methods in quantum chemistry has enabled the study of numerous chemical phenomena, which are computationally intractable with traditional ab-initio methods. However, some q... |
Title: Image Keypoint Matching using Graph Neural Networks Abstract: Image matching is a key component of many tasks in computer vision and its main objective is to find correspondences between features extracted from different natural images. When images are represented as graphs, image matching boils down to the prob... |
Title: Towards Communication-Learning Trade-off for Federated Learning at the Network Edge Abstract: In this letter, we study a wireless federated learning (FL) system where network pruning is applied to local users with limited resources. Although pruning is beneficial to reduce FL latency, it also deteriorates learni... |
Title: NeuPSL: Neural Probabilistic Soft Logic Abstract: We present Neural Probabilistic Soft Logic (NeuPSL), a novel neuro-symbolic (NeSy) framework that unites state-of-the-art symbolic reasoning with the low-level perception of deep neural networks. To explicitly model the boundary between neural and symbolic repres... |
Title: Personalized PageRank Graph Attention Networks Abstract: There has been a rising interest in graph neural networks (GNNs) for representation learning over the past few years. GNNs provide a general and efficient framework to learn from graph-structured data. However, GNNs typically only use the information of a ... |
Title: On the Symmetries of Deep Learning Models and their Internal Representations Abstract: Symmetry has been a fundamental tool in the exploration of a broad range of complex systems. In machine learning, symmetry has been explored in both models and data. In this paper we seek to connect the symmetries arising from... |
Title: Experience report of physics-informed neural networks in fluid simulations: pitfalls and frustration Abstract: The deep learning boom motivates researchers and practitioners of computational fluid dynamics eager to integrate the two areas.The PINN (physics-informed neural network) method is one such attempt. Whi... |
Title: Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference Abstract: We propose a general purpose Bayesian inference algorithm for expensive likelihoods, replacing the stochastic term in the Langevin equation with a deterministic density gradient term. The particle density is evaluated from... |
Title: Provably Sample-Efficient RL with Side Information about Latent Dynamics Abstract: We study reinforcement learning (RL) in settings where observations are high-dimensional, but where an RL agent has access to abstract knowledge about the structure of the state space, as is the case, for example, when a robot is ... |
Title: FedControl: When Control Theory Meets Federated Learning Abstract: To date, the most popular federated learning algorithms use coordinate-wise averaging of the model parameters. We depart from this approach by differentiating client contributions according to the performance of local learning and its evolution. ... |
Title: Competitive Gradient Optimization Abstract: We study the problem of convergence to a stationary point in zero-sum games. We propose competitive gradient optimization (CGO ), a gradient-based method that incorporates the interactions between the two players in zero-sum games for optimization updates. We provide c... |
Title: Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction Abstract: Predicting the trajectories of surrounding objects is a critical task in self-driving and many other autonomous systems. Recent works demonstrate that adversarial attacks on trajectory prediction, where small crafted pertur... |
Title: Will Bilevel Optimizers Benefit from Loops Abstract: Bilevel optimization has arisen as a powerful tool for solving a variety of machine learning problems. Two current popular bilevel optimizers AID-BiO and ITD-BiO naturally involve solving one or two sub-problems, and consequently, whether we solve these proble... |
Title: Diffusion-LM Improves Controllable Text Generation Abstract: Controlling the behavior of language models (LMs) without re-training is a major open problem in natural language generation. While recent works have demonstrated successes on controlling simple sentence attributes (e.g., sentiment), there has been lit... |
Title: Calibrated Bagging Deep Learning for Image Semantic Segmentation: A Case Study on COVID-19 Chest X-ray Image Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19). Imaging tests such as chest X-ray (CXR) and computed tomography (CT) can provide useful i... |
Title: KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal Abstract: In this work, we consider and analyze the sample complexity of model-free reinforcement learning with a generative model. Particularly, we analyze mirror descent value iteration (MDVI) by Geist et al. (2019) and Vieillard et al. (2020... |
Title: MIP-GNN: A Data-Driven Framework for Guiding Combinatorial Solvers Abstract: Mixed-integer programming (MIP) technology offers a generic way of formulating and solving combinatorial optimization problems. While generally reliable, state-of-the-art MIP solvers base many crucial decisions on hand-crafted heuristic... |
Title: StarGraph: A Coarse-to-Fine Representation Method for Large-Scale Knowledge Graph Abstract: Conventional representation learning algorithms for knowledge graphs (KG) map each entity to a unique embedding vector, ignoring the rich information contained in neighbor entities. We propose a method named StarGraph, wh... |
Title: Targeted Adaptive Design Abstract: Modern advanced manufacturing and advanced materials design often require searches of relatively high-dimensional process control parameter spaces for settings that result in optimal structure, property, and performance parameters. The mapping from the former to the latter must... |
Title: ALMA: Hierarchical Learning for Composite Multi-Agent Tasks Abstract: Despite significant progress on multi-agent reinforcement learning (MARL) in recent years, coordination in complex domains remains a challenge. Work in MARL often focuses on solving tasks where agents interact with all other agents and entitie... |
Title: Robust Phi-Divergence MDPs Abstract: In recent years, robust Markov decision processes (MDPs) have emerged as a prominent modeling framework for dynamic decision problems affected by uncertainty. In contrast to classical MDPs, which only account for stochasticity by modeling the dynamics through a stochastic pro... |
Title: Generalized Reductions: Making any Hierarchical Clustering Fair and Balanced with Low Cost Abstract: Clustering is a fundamental building block of modern statistical analysis pipelines. Fair clustering has seen much attention from the machine learning community in recent years. We are some of the first to study ... |
Title: FadMan: Federated Anomaly Detection across Multiple Attributed Networks Abstract: Anomaly subgraph detection has been widely used in various applications, ranging from cyber attack in computer networks to malicious activities in social networks. Despite an increasing need for federated anomaly detection across m... |
Title: Constrained Langevin Algorithms with L-mixing External Random Variables Abstract: Langevin algorithms are gradient descent methods augmented with additive noise, and are widely used in Markov Chain Monte Carlo (MCMC) sampling, optimization, and learning. In recent years, the non-asymptotic analysis of Langevin a... |
Title: Optimizing Objective Functions from Trained ReLU Neural Networks via Sampling Abstract: This paper introduces scalable, sampling-based algorithms that optimize trained neural networks with ReLU activations. We first propose an iterative algorithm that takes advantage of the piecewise linear structure of ReLU neu... |
Title: FlowNet-PET: Unsupervised Learning to Perform Respiratory Motion Correction in PET Imaging Abstract: To correct for respiratory motion in PET imaging, an interpretable and unsupervised deep learning technique, FlowNet-PET, was constructed. The network was trained to predict the optical flow between two PET frame... |
Title: Multiscale Voxel Based Decoding For Enhanced Natural Image Reconstruction From Brain Activity Abstract: Reconstructing perceived images from human brain activity monitored by functional magnetic resonance imaging (fMRI) is hard, especially for natural images. Existing methods often result in blurry and unintelli... |
Title: TURJUMAN: A Public Toolkit for Neural Arabic Machine Translation Abstract: We present TURJUMAN, a neural toolkit for translating from 20 languages into Modern Standard Arabic (MSA). TURJUMAN exploits the recently-introduced text-to-text Transformer AraT5 model, endowing it with a powerful ability to decode into ... |
Title: Private and Byzantine-Proof Cooperative Decision-Making Abstract: The cooperative bandit problem is a multi-agent decision problem involving a group of agents that interact simultaneously with a multi-armed bandit, while communicating over a network with delays. The central idea in this problem is to design algo... |
Title: Momentum Stiefel Optimizer, with Applications to Suitably-Orthogonal Attention, and Optimal Transport Abstract: The problem of optimization on Stiefel manifold, i.e., minimizing functions of (not necessarily square) matrices that satisfy orthogonality constraints, has been extensively studied, partly due to rich... |
Title: Contrastive Learning Rivals Masked Image Modeling in Fine-tuning via Feature Distillation Abstract: Masked image modeling (MIM) learns representations with remarkably good fine-tuning performances, overshadowing previous prevalent pre-training approaches such as image classification, instance contrastive learnin... |
Title: PSL is Dead. Long Live PSL Abstract: Property Specification Language (PSL) is a form of temporal logic that has been mainly used in discrete domains (e.g. formal hardware verification). In this paper, we show that by merging machine learning techniques with PSL monitors, we can extend PSL to work on continuous d... |
Title: FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness Abstract: Transformers are slow and memory-hungry on long sequences, since the time and memory complexity of self-attention are quadratic in sequence length. Approximate attention methods have attempted to address this problem by trading... |
Title: Meta-Learning Adversarial Bandits Abstract: We study online learning with bandit feedback across multiple tasks, with the goal of improving average performance across tasks if they are similar according to some natural task-similarity measure. As the first to target the adversarial setting, we design a unified m... |
Title: Neural Basis Models for Interpretability Abstract: Due to the widespread use of complex machine learning models in real-world applications, it is becoming critical to explain model predictions. However, these models are typically black-box deep neural networks, explained post-hoc via methods with known faithfuln... |
Title: Robust Counterfactual Explanations for Random Forests Abstract: Counterfactual explanations describe how to modify a feature vector in order to flip the outcome of a trained classifier. Several heuristic and optimal methods have been proposed to generate these explanations. However, the robustness of counterfact... |
Title: Bayesian Robust Graph Contrastive Learning Abstract: Graph Neural Networks (GNNs) have been widely used to learn node representations and with outstanding performance on various tasks such as node classification. However, noise, which inevitably exists in real-world graph data, would considerably degrade the per... |
Title: Scalable Interpretability via Polynomials Abstract: Generalized Additive Models (GAMs) have quickly become the leading choice for fully-interpretable machine learning. However, unlike uninterpretable methods such as DNNs, they lack expressive power and easy scalability, and are hence not a feasible alternative f... |
Title: Spartan: Differentiable Sparsity via Regularized Transportation Abstract: We present Spartan, a method for training sparse neural network models with a predetermined level of sparsity. Spartan is based on a combination of two techniques: (1) soft top-k masking of low-magnitude parameters via a regularized optima... |
Title: Learning to Solve Combinatorial Graph Partitioning Problems via Efficient Exploration Abstract: From logistics to the natural sciences, combinatorial optimisation on graphs underpins numerous real-world applications. Reinforcement learning (RL) has shown particular promise in this setting as it can adapt to spec... |
Title: Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering Abstract: We propose a novel approach to the problem of clustering hierarchically aggregated time-series data, which has remained an understudied problem though it has several commercial applications. We first group time seri... |
Title: Generalizing Brain Decoding Across Subjects with Deep Learning Abstract: Decoding experimental variables from brain imaging data is gaining popularity, with applications in brain-computer interfaces and the study of neural representations. Decoding is typically subject-specific and does not generalise well over ... |
Title: Solving infinite-horizon POMDPs with memoryless stochastic policies in state-action space Abstract: Reward optimization in fully observable Markov decision processes is equivalent to a linear program over the polytope of state-action frequencies. Taking a similar perspective in the case of partially observable M... |
Title: Capturing Graphs with Hypo-Elliptic Diffusions Abstract: Convolutional layers within graph neural networks operate by aggregating information about local neighbourhood structures; one common way to encode such substructures is through random walks. The distribution of these random walks evolves according to a di... |
Title: Surrogate modeling for Bayesian optimization beyond a single Gaussian process Abstract: Bayesian optimization (BO) has well-documented merits for optimizing black-box functions with an expensive evaluation cost. Such functions emerge in applications as diverse as hyperparameter tuning, drug discovery, and roboti... |
Title: Sharpness-Aware Training for Free Abstract: Modern deep neural networks (DNNs) have achieved state-of-the-art performances but are typically over-parameterized. The over-parameterization may result in undesirably large generalization error in the absence of other customized training strategies. Recently, a line ... |
Title: AANG: Automating Auxiliary Learning Abstract: When faced with data-starved or highly complex end-tasks, it is commonplace for machine learning practitioners to introduce auxiliary objectives as supplementary learning signals. Whilst much work has been done to formulate useful auxiliary objectives, their construc... |
Title: Deep Coding Patterns Design for Compressive Near-Infrared Spectral Classification Abstract: Compressive spectral imaging (CSI) has emerged as an attractive compression and sensing technique, primarily to sense spectral regions where traditional systems result in highly costly such as in the near-infrared spectru... |
Title: Finite mixture of skewed sub-Gaussian stable distributions Abstract: We propose the finite mixture of skewed sub-Gaussian stable distributions. The maximum likelihood estimator for the parameters of proposed finite mixture model is computed through the expectation-maximization algorithm. The proposed model conta... |
Title: Simple Unsupervised Object-Centric Learning for Complex and Naturalistic Videos Abstract: Unsupervised object-centric learning aims to represent the modular, compositional, and causal structure of a scene as a set of object representations and thereby promises to resolve many critical limitations of traditional ... |
Title: Dual Convexified Convolutional Neural Networks Abstract: We propose the framework of dual convexified convolutional neural networks (DCCNNs). In this framework, we first introduce a primal learning problem motivated from convexified convolutional neural networks (CCNNs), and then construct the dual convex traini... |
Title: Benign Overparameterization in Membership Inference with Early Stopping Abstract: Does a neural network's privacy have to be at odds with its accuracy? In this work, we study the effects the number of training epochs and parameters have on a neural network's vulnerability to membership inference (MI) attacks, wh... |
Title: Contrastive Siamese Network for Semi-supervised Speech Recognition Abstract: This paper introduces contrastive siamese (c-siam) network, an architecture for leveraging unlabeled acoustic data in speech recognition. c-siam is the first network that extracts high-level linguistic information from speech by matchin... |
Title: Average Adjusted Association: Efficient Estimation with High Dimensional Confounders Abstract: The log odds ratio is a common parameter to measure association between (binary) outcome and exposure variables. Much attention has been paid to its parametric but robust estimation, or its nonparametric estimation as ... |
Title: Double Deep Q Networks for Sensor Management in Space Situational Awareness Abstract: We present a novel Double Deep Q Network (DDQN) application to a sensor management problem in space situational awareness (SSA). Frequent launches of satellites into Earth orbit pose a significant sensor management challenge, w... |
Title: Group-invariant max filtering Abstract: Given a real inner product space $V$ and a group $G$ of linear isometries, we construct a family of $G$-invariant real-valued functions on $V$ that we call max filters. In the case where $V=\mathbb{R}^d$ and $G$ is finite, a suitable max filter bank separates orbits, and i... |
Title: Learning to Control Linear Systems can be Hard Abstract: In this paper, we study the statistical difficulty of learning to control linear systems. We focus on two standard benchmarks, the sample complexity of stabilization, and the regret of the online learning of the Linear Quadratic Regulator (LQR). Prior resu... |
Title: Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces Abstract: We study a class of dynamical systems modelled as Markov chains that admit an invariant distribution via the corresponding transfer, or Koopman, operator. While data-driven algorithms to reconstruct such ope... |
Title: Inference and Sampling for Archimax Copulas Abstract: Understanding multivariate dependencies in both the bulk and the tails of a distribution is an important problem for many applications, such as ensuring algorithms are robust to observations that are infrequent but have devastating effects. Archimax copulas a... |
Title: What Dense Graph Do You Need for Self-Attention? Abstract: Transformers have made progress in miscellaneous tasks, but suffer from quadratic computational and memory complexities. Recent works propose sparse Transformers with attention on sparse graphs to reduce complexity and remain strong performance. While ef... |
Title: Prototype Based Classification from Hierarchy to Fairness Abstract: Artificial neural nets can represent and classify many types of data but are often tailored to particular applications -- e.g., for "fair" or "hierarchical" classification. Once an architecture has been selected, it is often difficult for humans... |
Title: Deep Ensembles for Graphs with Higher-order Dependencies Abstract: Graph neural networks (GNNs) continue to achieve state-of-the-art performance on many graph learning tasks, but rely on the assumption that a given graph is a sufficient approximation of the true neighborhood structure. In the presence of higher-... |
Title: Counterfactual Fairness with Partially Known Causal Graph Abstract: Fair machine learning aims to avoid treating individuals or sub-populations unfavourably based on \textit{sensitive attributes}, such as gender and race. Those methods in fair machine learning that are built on causal inference ascertain discrim... |
Title: Exploring Techniques for the Analysis of Spontaneous Asynchronicity in MPI-Parallel Applications Abstract: This paper studies the utility of using data analytics and machine learning techniques for identifying, classifying, and characterizing the dynamics of large-scale parallel (MPI) programs. To this end, we r... |
Title: Guided Exploration of Data Summaries Abstract: Data summarization is the process of producing interpretable and representative subsets of an input dataset. It is usually performed following a one-shot process with the purpose of finding the best summary. A useful summary contains k individually uniform sets that... |
Title: Non-Markovian policies occupancy measures Abstract: A central object of study in Reinforcement Learning (RL) is the Markovian policy, in which an agent's actions are chosen from a memoryless probability distribution, conditioned only on its current state. The family of Markovian policies is broad enough to be in... |
Title: Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer Abstract: Spatio-temporal graph learning is a key method for urban computing tasks, such as traffic flow, taxi demand and air quality forecasting. Due to the high cost of data collection, some developing cities have few available data, wh... |
Title: Deep Reinforcement Learning for Distributed and Uncoordinated Cognitive Radios Resource Allocation Abstract: This paper presents a novel deep reinforcement learning-based resource allocation technique for the multi-agent environment presented by a cognitive radio network where the interactions of the agents duri... |
Title: Auditing Differential Privacy in High Dimensions with the Kernel Quantum Rényi Divergence Abstract: Differential privacy (DP) is the de facto standard for private data release and private machine learning. Auditing black-box DP algorithms and mechanisms to certify whether they satisfy a certain DP guarantee is c... |
Title: Combining observational datasets from multiple environments to detect hidden confounding Abstract: A common assumption in causal inference from observational data is the assumption of no hidden confounding. Yet it is, in general, impossible to verify the presence of hidden confounding factors from a single datas... |
Title: Standalone Neural ODEs with Sensitivity Analysis Abstract: This paper presents the Standalone Neural ODE (sNODE), a continuous-depth neural ODE model capable of describing a full deep neural network. This uses a novel nonlinear conjugate gradient (NCG) descent optimization scheme for training, where the Sobolev ... |
Title: Fairness and Welfare Quantification for Regret in Multi-Armed Bandits Abstract: We extend the notion of regret with a welfarist perspective. Focussing on the classic multi-armed bandit (MAB) framework, the current work quantifies the performance of bandit algorithms by applying a fundamental welfare function, na... |
Title: Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Design Abstract: Our world is ambiguous and this is reflected in the data we use to train our algorithms. This is especially true when we try to model natural processes where collected data is affected by noisy measur... |
Title: Client Selection in Nonconvex Federated Learning: Improved Convergence Analysis for Optimal Unbiased Sampling Strategy Abstract: Federated learning (FL) is a distributed machine learning paradigm that selects a subset of clients to participate in training to reduce communication burdens. However, partial client ... |
Title: Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits Abstract: We study the Bayesian regret of the renowned Thompson Sampling algorithm in contextual bandits with binary losses and adversarially-selected contexts. We adapt the information-theoretic perspect... |
Title: Federated Semi-Supervised Learning with Prototypical Networks Abstract: With the increasing computing power of edge devices, Federated Learning (FL) emerges to enable model training without privacy concerns. The majority of existing studies assume the data are fully labeled on the client side. In practice, howev... |
Title: Fast Causal Orientation Learning in Directed Acyclic Graphs Abstract: Causal relationships among a set of variables are commonly represented by a directed acyclic graph. The orientations of some edges in the causal DAG can be discovered from observational/interventional data. Further edges can be oriented by ite... |
Title: Dynamic Domain Generalization Abstract: Domain generalization (DG) is a fundamental yet very challenging research topic in machine learning. The existing arts mainly focus on learning domain-invariant features with limited source domains in a static model. Unfortunately, there is a lack of training-free mechanis... |
Title: (De-)Randomized Smoothing for Decision Stump Ensembles Abstract: Tree-based models are used in many high-stakes application domains such as finance and medicine, where robustness and interpretability are of utmost importance. Yet, methods for improving and certifying their robustness are severely under-explored,... |
Title: Sample-Efficient Optimisation with Probabilistic Transformer Surrogates Abstract: Faced with problems of increasing complexity, recent research in Bayesian Optimisation (BO) has focused on adapting deep probabilistic models as flexible alternatives to Gaussian Processes (GPs). In a similar vein, this paper inves... |
Title: Bias Reduction via Cooperative Bargaining in Synthetic Graph Dataset Generation Abstract: In general, to draw robust conclusions from a dataset, all the analyzed population must be represented on said dataset. Having a dataset that does not fulfill this condition normally leads to selection bias. Additionally, g... |
Title: How Tempering Fixes Data Augmentation in Bayesian Neural Networks Abstract: While Bayesian neural networks (BNNs) provide a sound and principled alternative to standard neural networks, an artificial sharpening of the posterior usually needs to be applied to reach comparable performance. This is in stark contras... |
Title: EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks Abstract: Graph Neural Networks (GNNs) have received extensive research attention for their promising performance in graph machine learning. Despite their extraordinary predictive accuracy, existing approaches, such as GCN and GPRGN... |
Title: Transformers from an Optimization Perspective Abstract: Deep learning models such as the Transformer are often constructed by heuristics and experience. To provide a complementary foundation, in this work we study the following problem: Is it possible to find an energy function underlying the Transformer model, ... |
Title: A Combination of Deep Neural Networks and K-Nearest Neighbors for Credit Card Fraud Detection Abstract: Detection of a Fraud transaction on credit cards became one of the major problems for financial institutions, organizations and companies. As the global financial system is highly connected to non-cash transac... |
Title: Automated Dynamic Algorithm Configuration Abstract: The performance of an algorithm often critically depends on its parameter configuration. While a variety of automated algorithm configuration methods have been proposed to relieve users from the tedious and error-prone task of manually tuning parameters, there ... |
Title: TraClets: Harnessing the power of computer vision for trajectory classification Abstract: Due to the advent of new mobile devices and tracking sensors in recent years, huge amounts of data are being produced every day. Therefore, novel methodologies need to emerge that dive through this vast sea of information a... |
Title: MIMII DG: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection for Domain Generalization Task Abstract: We present a machine sound dataset to benchmark domain generalization techniques for anomalous sound detection (ASD). To handle performance degradation caused by domain shifts that ... |
Title: Comparison of Deep Learning Segmentation and Multigrader-annotated Mandibular Canals of Multicenter CBCT scans Abstract: Deep learning approach has been demonstrated to automatically segment the bilateral mandibular canals from CBCT scans, yet systematic studies of its clinical and technical validation are scarc... |
Title: Probabilistic Systems with Hidden State and Unobservable Transitions Abstract: We consider probabilistic systems with hidden state and unobservable transitions, an extension of Hidden Markov Models (HMMs) that in particular admits unobservable {\epsilon}-transitions (also called null transitions), allowing state... |
Title: MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models Abstract: State-of-the-art causal discovery methods usually assume that the observational data is complete. However, the missing data problem is pervasive in many practical scenarios such as clinical trials, economics... |
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