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Title: Evaluating Short-Term Forecasting of Multiple Time Series in IoT Environments Abstract: Modern Internet of Things (IoT) environments are monitored via a large number of IoT enabled sensing devices, with the data acquisition and processing infrastructure setting restrictions in terms of computational power and en... |
Title: Participation and Data Valuation in IoT Data Markets through Distributed Coalitions Abstract: This paper considers a market for Internet of Things (IoT) data that is used to train machine learning models. The data is supplied to the market platform through a network and the price of the data is controlled based ... |
Title: Federated Data Analytics: A Study on Linear Models Abstract: As edge devices become increasingly powerful, data analytics are gradually moving from a centralized to a decentralized regime where edge compute resources are exploited to process more of the data locally. This regime of analytics is coined as federat... |
Title: On Calibrated Model Uncertainty in Deep Learning Abstract: Estimated uncertainty by approximate posteriors in Bayesian neural networks are prone to miscalibration, which leads to overconfident predictions in critical tasks that have a clear asymmetric cost or significant losses. Here, we extend the approximate i... |
Title: FixEval: Execution-based Evaluation of Program Fixes for Competitive Programming Problems Abstract: Source code repositories consist of large codebases, often containing error-prone programs. The increasing complexity of software has led to a drastic rise in time and costs for identifying and fixing these defect... |
Title: Gaussian Blue Noise Abstract: Among the various approaches for producing point distributions with blue noise spectrum, we argue for an optimization framework using Gaussian kernels. We show that with a wise selection of optimization parameters, this approach attains unprecedented quality, provably surpassing the... |
Title: Beyond Adult and COMPAS: Fairness in Multi-Class Prediction Abstract: We consider the problem of producing fair probabilistic classifiers for multi-class classification tasks. We formulate this problem in terms of "projecting" a pre-trained (and potentially unfair) classifier onto the set of models that satisfy ... |
Title: Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems Abstract: We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9.3B, their subsequent distillation into smaller ... |
Title: Search-Based Testing Approach for Deep Reinforcement Learning Agents Abstract: Deep Reinforcement Learning (DRL) algorithms have been increasingly employed during the last decade to solve various decision-making problems such as autonomous driving and robotics. However, these algorithms have faced great challeng... |
Title: Large-Scale Differentiable Causal Discovery of Factor Graphs Abstract: A common theme in causal inference is learning causal relationships between observed variables, also known as causal discovery. This is usually a daunting task, given the large number of candidate causal graphs and the combinatorial nature of... |
Title: Metric-Fair Classifier Derandomization Abstract: We study the problem of \emph{classifier derandomization} in machine learning: given a stochastic binary classifier $f: X \to [0,1]$, sample a deterministic classifier $\hat{f}: X \to \{0,1\}$ that approximates the output of $f$ in aggregate over any data distribu... |
Title: Adaptive Expert Models for Personalization in Federated Learning Abstract: Federated Learning (FL) is a promising framework for distributed learning when data is private and sensitive. However, the state-of-the-art solutions in this framework are not optimal when data is heterogeneous and non-Independent and Ide... |
Title: Efficient Approximation of Expected Hypervolume Improvement using Gauss-Hermite Quadrature Abstract: Many methods for performing multi-objective optimisation of computationally expensive problems have been proposed recently. Typically, a probabilistic surrogate for each objective is constructed from an initial d... |
Title: Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization Abstract: Real-world data collected from multiple domains can have multiple, distinct distribution shifts over multiple attributes. However, state-of-the art advances in domain generalization (DG) algorithms focus only on sp... |
Title: Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness Abstract: Certifiable robustness is a highly desirable property for adopting deep neural networks (DNNs) in safety-critical scenarios, but often demands tedious computations to establish. The main hurdle lies in the massive amount of non-lin... |
Title: Architectural Backdoors in Neural Networks Abstract: Machine learning is vulnerable to adversarial manipulation. Previous literature has demonstrated that at the training stage attackers can manipulate data and data sampling procedures to control model behaviour. A common attack goal is to plant backdoors i.e. f... |
Title: Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning Abstract: Class-incremental learning (CIL) suffers from the notorious dilemma between learning newly added classes and preserving previously learned class knowledge. That catastrophic forgetting issue could be mitigated by storing histori... |
Title: Conformal prediction set for time-series Abstract: When building either prediction intervals for regression (with real-valued response) or prediction sets for classification (with categorical responses), uncertainty quantification is essential to studying complex machine learning methods. In this paper, we devel... |
Title: Performance analysis of coreset selection for quantum implementation of K-Means clustering algorithm Abstract: Quantum computing is anticipated to offer immense computational capabilities which could provide efficient solutions to many data science problems. However, the current generation of quantum devices are... |
Title: The Scattering Transform Network with Generalized Morse Wavelets and Its Application to Music Genre Classification Abstract: We propose to use the Generalized Morse Wavelets (GMWs) instead of commonly-used Morlet (or Gabor) wavelets in the Scattering Transform Network (STN), which we call the GMW-STN, for signal... |
Title: EPG2S: Speech Generation and Speech Enhancement based on Electropalatography and Audio Signals using Multimodal Learning Abstract: Speech generation and enhancement based on articulatory movements facilitate communication when the scope of verbal communication is absent, e.g., in patients who have lost the abili... |
Title: Let Invariant Rationale Discovery Inspire Graph Contrastive Learning Abstract: Leading graph contrastive learning (GCL) methods perform graph augmentations in two fashions: (1) randomly corrupting the anchor graph, which could cause the loss of semantic information, or (2) using domain knowledge to maintain sali... |
Title: Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training Abstract: Recently, Optimization-Derived Learning (ODL) has attracted attention from learning and vision areas, which designs learning models from the perspective of optimization. However, previous ODL approaches reg... |
Title: Domain Generalization via Selective Consistency Regularization for Time Series Classification Abstract: Domain generalization methods aim to learn models robust to domain shift with data from a limited number of source domains and without access to target domain samples during training. Popular domain alignment ... |
Title: Accelerating Inference and Language Model Fusion of Recurrent Neural Network Transducers via End-to-End 4-bit Quantization Abstract: We report on aggressive quantization strategies that greatly accelerate inference of Recurrent Neural Network Transducers (RNN-T). We use a 4 bit integer representation for both we... |
Title: Pure Exploration of Causal Bandits Abstract: Causal bandit problem integrates causal inference with multi-armed bandits. The pure exploration of causal bandits is the following online learning task: given a causal graph with unknown causal inference distributions, in each round we can choose to either intervene ... |
Title: Generalization Bounds for Data-Driven Numerical Linear Algebra Abstract: Data-driven algorithms can adapt their internal structure or parameters to inputs from unknown application-specific distributions, by learning from a training sample of inputs. Several recent works have applied this approach to problems in ... |
Title: Max-Margin Works while Large Margin Fails: Generalization without Uniform Convergence Abstract: A major challenge in modern machine learning is theoretically understanding the generalization properties of overparameterized models. Many existing tools rely on \em uniform convergence \em (UC), a property that, whe... |
Title: Multimodal Dialogue State Tracking Abstract: Designed for tracking user goals in dialogues, a dialogue state tracker is an essential component in a dialogue system. However, the research of dialogue state tracking has largely been limited to unimodality, in which slots and slot values are limited by knowledge do... |
Title: On Privacy and Personalization in Cross-Silo Federated Learning Abstract: While the application of differential privacy (DP) has been well-studied in cross-device federated learning (FL), there is a lack of work considering DP for cross-silo FL, a setting characterized by a limited number of clients each contain... |
Title: Explainable Models via Compression of Tree Ensembles Abstract: Ensemble models (bagging and gradient-boosting) of relational decision trees have proved to be one of the most effective learning methods in the area of probabilistic logic models (PLMs). While effective, they lose one of the most important aspect of... |
Title: Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback Abstract: The problem of online learning with graph feedback has been extensively studied in the literature due to its generality and potential to model various learning tasks. Existing works mainly study the adversarial and s... |
Title: Introducing the Huber mechanism for differentially private low-rank matrix completion Abstract: Performing low-rank matrix completion with sensitive user data calls for privacy-preserving approaches. In this work, we propose a novel noise addition mechanism for preserving differential privacy where the noise dis... |
Title: Double Sampling Randomized Smoothing Abstract: Neural networks (NNs) are known to be vulnerable against adversarial perturbations, and thus there is a line of work aiming to provide robustness certification for NNs, such as randomized smoothing, which samples smoothing noises from a certain distribution to certi... |
Title: Barrier Certified Safety Learning Control: When Sum-of-Square Programming Meets Reinforcement Learning Abstract: Safety guarantee is essential in many engineering implementations. Reinforcement learning provides a useful way to strengthen safety. However, reinforcement learning algorithms cannot completely guara... |
Title: "Understanding Robustness Lottery": A Comparative Visual Analysis of Neural Network Pruning Approaches Abstract: Deep learning approaches have provided state-of-the-art performance in many applications by relying on extremely large and heavily overparameterized neural networks. However, such networks have been s... |
Title: Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications Abstract: Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields, including pattern recognition, robotics, recommendation-systems, and... |
Title: Lifelong Wandering: A realistic few-shot online continual learning setting Abstract: Online few-shot learning describes a setting where models are trained and evaluated on a stream of data while learning emerging classes. While prior work in this setting has achieved very promising performance on instance classi... |
Title: PROFHIT: Probabilistic Robust Forecasting for Hierarchical Time-series Abstract: Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting, where the goal is to model and forecast multivariate time-series that have underlying hierarchical relations. Most methods focus ... |
Title: Distributed Online Learning Algorithm With Differential Privacy Strategy for Convex Nondecomposable Global Objectives Abstract: In this paper, we deal with a general distributed constrained online learning problem with privacy over time-varying networks, where a class of nondecomposable objective functions are c... |
Title: Forming Effective Human-AI Teams: Building Machine Learning Models that Complement the Capabilities of Multiple Experts Abstract: Machine learning (ML) models are increasingly being used in application domains that often involve working together with human experts. In this context, it can be advantageous to defe... |
Title: Analysis and Extensions of Adversarial Training for Video Classification Abstract: Adversarial training (AT) is a simple yet effective defense against adversarial attacks to image classification systems, which is based on augmenting the training set with attacks that maximize the loss. However, the effectiveness... |
Title: BlindFL: Vertical Federated Machine Learning without Peeking into Your Data Abstract: Due to the rising concerns on privacy protection, how to build machine learning (ML) models over different data sources with security guarantees is gaining more popularity. Vertical federated learning (VFL) describes such a cas... |
Title: Cyclocopula Technique to Study the Relationship Between Two Cyclostationary Time Series with Fractional Brownian Motion Errors Abstract: Detection of the relationship between two time series is so important in environmental and hydrological studies. Several parametric and non-parametric approaches can be applied... |
Title: Personalized Federated Learning via Variational Bayesian Inference Abstract: Federated learning faces huge challenges from model overfitting due to the lack of data and statistical diversity among clients. To address these challenges, this paper proposes a novel personalized federated learning method via Bayesia... |
Title: Research Topic Flows in Co-Authorship Networks Abstract: In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part... |
Title: Double Check Your State Before Trusting It: Confidence-Aware Bidirectional Offline Model-Based Imagination Abstract: The learned policy of model-free offline reinforcement learning (RL) methods is often constrained to stay within the support of datasets to avoid possible dangerous out-of-distribution actions or ... |
Title: Patch-level Representation Learning for Self-supervised Vision Transformers Abstract: Recent self-supervised learning (SSL) methods have shown impressive results in learning visual representations from unlabeled images. This paper aims to improve their performance further by utilizing the architectural advantage... |
Title: Continual Learning with Guarantees via Weight Interval Constraints Abstract: We introduce a new training paradigm that enforces interval constraints on neural network parameter space to control forgetting. Contemporary Continual Learning (CL) methods focus on training neural networks efficiently from a stream of... |
Title: Differentially Private Multi-Party Data Release for Linear Regression Abstract: Differentially Private (DP) data release is a promising technique to disseminate data without compromising the privacy of data subjects. However the majority of prior work has focused on scenarios where a single party owns all the da... |
Title: The convergent Indian buffet process Abstract: We propose a new Bayesian nonparametric prior for latent feature models, which we call the convergent Indian buffet process (CIBP). We show that under the CIBP, the number of latent features is distributed as a Poisson distribution with the mean monotonically increa... |
Title: When a RF Beats a CNN and GRU, Together -- A Comparison of Deep Learning and Classical Machine Learning Approaches for Encrypted Malware Traffic Classification Abstract: Internet traffic classification is widely used to facilitate network management. It plays a crucial role in Quality of Services (QoS), Quality ... |
Title: Evaluating Self-Supervised Learning for Molecular Graph Embeddings Abstract: Graph Self-Supervised Learning (GSSL) paves the way for learning graph embeddings without expert annotation, which is particularly impactful for molecular graphs since the number of possible molecules is enormous and labels are expensiv... |
Title: DCASE 2022: Comparative Analysis Of CNNs For Acoustic Scene Classification Under Low-Complexity Considerations Abstract: Acoustic scene classification is an automatic listening problem that aims to assign an audio recording to a pre-defined scene based on its audio data. Over the years (and in past editions of t... |
Title: Balancing Discriminability and Transferability for Source-Free Domain Adaptation Abstract: Conventional domain adaptation (DA) techniques aim to improve domain transferability by learning domain-invariant representations; while concurrently preserving the task-discriminability knowledge gathered from the labeled... |
Title: MoDi: Unconditional Motion Synthesis from Diverse Data Abstract: The emergence of neural networks has revolutionized the field of motion synthesis. Yet, learning to unconditionally synthesize motions from a given distribution remains a challenging task, especially when the motions are highly diverse. We present ... |
Title: Hardness prediction of age-hardening aluminum alloy based on ensemble learning Abstract: With the rapid development of artificial intelligence, the combination of material database and machine learning has driven the progress of material informatics. Because aluminum alloy is widely used in many fields, so it is... |
Title: On Error and Compression Rates for Prototype Rules Abstract: We study the close interplay between error and compression in the non-parametric multiclass classification setting in terms of prototype learning rules. We focus in particular on a close variant of a recently proposed compression-based learning rule te... |
Title: Partial Identifiability for Nonnegative Matrix Factorization Abstract: Given a nonnegative matrix factorization, $R$, and a factorization rank, $r$, Exact nonnegative matrix factorization (Exact NMF) decomposes $R$ as the product of two nonnegative matrices, $C$ and $S$ with $r$ columns, such as $R = CS^\top$. A... |
Title: AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation Abstract: Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a large-sca... |
Title: Acoustic Modeling for End-to-End Empathetic Dialogue Speech Synthesis Using Linguistic and Prosodic Contexts of Dialogue History Abstract: We propose an end-to-end empathetic dialogue speech synthesis (DSS) model that considers both the linguistic and prosodic contexts of dialogue history. Empathy is the active ... |
Title: Automated analysis of continuum fields from atomistic simulations using statistical machine learning Abstract: Atomistic simulations of the molecular dynamics/statics kind are regularly used to study small scale plasticity. Contemporary simulations are performed with tens to hundreds of millions of atoms, with s... |
Title: Time Interval-enhanced Graph Neural Network for Shared-account Cross-domain Sequential Recommendation Abstract: Shared-account Cross-domain Sequential Recommendation (SCSR) task aims to recommend the next item via leveraging the mixed user behaviors in multiple domains. It is gaining immense research attention a... |
Title: Generalized Leverage Scores: Geometric Interpretation and Applications Abstract: In problems involving matrix computations, the concept of leverage has found a large number of applications. In particular, leverage scores, which relate the columns of a matrix to the subspaces spanned by its leading singular vecto... |
Title: Active Nearest Neighbor Regression Through Delaunay Refinement Abstract: We introduce an algorithm for active function approximation based on nearest neighbor regression. Our Active Nearest Neighbor Regressor (ANNR) relies on the Voronoi-Delaunay framework from computational geometry to subdivide the space into ... |
Title: Neural tangent kernel analysis of shallow $\alpha$-Stable ReLU neural networks Abstract: There is a recent literature on large-width properties of Gaussian neural networks (NNs), i.e. NNs whose weights are distributed according to Gaussian distributions. Two popular problems are: i) the study of the large-width ... |
Title: Neural Scene Representation for Locomotion on Structured Terrain Abstract: We propose a learning-based method to reconstruct the local terrain for locomotion with a mobile robot traversing urban environments. Using a stream of depth measurements from the onboard cameras and the robot's trajectory, the algorithm ... |
Title: U-PET: MRI-based Dementia Detection with Joint Generation of Synthetic FDG-PET Images Abstract: Alzheimer's disease (AD) is the most common cause of dementia. An early detection is crucial for slowing down the disease and mitigating risks related to the progression. While the combination of MRI and FDG-PET is th... |
Title: A Machine Learning-based Digital Twin for Electric Vehicle Battery Modeling Abstract: The widespread adoption of Electric Vehicles (EVs) is limited by their reliance on batteries with presently low energy and power densities compared to liquid fuels and are subject to aging and performance deterioration over tim... |
Title: TransDrift: Modeling Word-Embedding Drift using Transformer Abstract: In modern NLP applications, word embeddings are a crucial backbone that can be readily shared across a number of tasks. However as the text distributions change and word semantics evolve over time, the downstream applications using the embeddi... |
Title: CARLANE: A Lane Detection Benchmark for Unsupervised Domain Adaptation from Simulation to multiple Real-World Domains Abstract: Unsupervised Domain Adaptation demonstrates great potential to mitigate domain shifts by transferring models from labeled source domains to unlabeled target domains. While Unsupervised ... |
Title: Reinforcement Learning-enhanced Shared-account Cross-domain Sequential Recommendation Abstract: Shared-account Cross-domain Sequential Recommendation (SCSR) is an emerging yet challenging task that simultaneously considers the shared-account and cross-domain characteristics in the sequential recommendation. Exis... |
Title: Unsupervised Space Partitioning for Nearest Neighbor Search Abstract: Approximate Nearest Neighbor Search (ANNS) in high dimensional spaces is crucial for many real-life applications (e.g., e-commerce, web, multimedia, etc.) dealing with an abundance of data. In this paper, we propose an end-to-end learning fram... |
Title: On the well-spread property and its relation to linear regression Abstract: We consider the robust linear regression model $\boldsymbol{y} = X\beta^* + \boldsymbol{\eta}$, where an adversary oblivious to the design $X \in \mathbb{R}^{n \times d}$ may choose $\boldsymbol{\eta}$ to corrupt all but a (possibly vani... |
Title: Applications of Machine Learning to the Identification of Anomalous ER Claims Abstract: Improper health insurance payments resulting from fraud and upcoding result in tens of billions of dollars in excess health care costs annually in the United States, motivating machine learning researchers to build anomaly de... |
Title: Deep Neural Imputation: A Framework for Recovering Incomplete Brain Recordings Abstract: Neuroscientists and neuroengineers have long relied on multielectrode neural recordings to study the brain. However, in a typical experiment, many factors corrupt neural recordings from individual electrodes, including elect... |
Title: DeepJSCC-Q: Constellation Constrained Deep Joint Source-Channel Coding Abstract: Recent works have shown that modern machine learning techniques can provide an alternative approach to the long-standing joint source-channel coding (JSCC) problem. Very promising initial results, superior to popular digital schemes... |
Title: Is Continual Learning Truly Learning Representations Continually? Abstract: Continual learning (CL) aims to learn from sequentially arriving tasks without forgetting previous tasks. Whereas CL algorithms have tried to achieve higher average test accuracy across all the tasks learned so far, learning continuously... |
Title: Closed-Form Diffeomorphic Transformations for Time Series Alignment Abstract: Time series alignment methods call for highly expressive, differentiable and invertible warping functions which preserve temporal topology, i.e diffeomorphisms. Diffeomorphic warping functions can be generated from the integration of v... |
Title: On Private Online Convex Optimization: Optimal Algorithms in $\ell_p$-Geometry and High Dimensional Contextual Bandits Abstract: Differentially private (DP) stochastic convex optimization (SCO) is ubiquitous in trustworthy machine learning algorithm design. This paper studies the DP-SCO problem with streaming da... |
Title: Learning to Infer Structures of Network Games Abstract: Strategic interactions between a group of individuals or organisations can be modelled as games played on networks, where a player's payoff depends not only on their actions but also on those of their neighbours. Inferring the network structure from observe... |
Title: Using adversarial images to improve outcomes of federated learning for non-IID data Abstract: One of the important problems in federated learning is how to deal with unbalanced data. This contribution introduces a novel technique designed to deal with label skewed non-IID data, using adversarial inputs, created ... |
Title: Large-scale, multi-centre, multi-disease validation of an AI clinical tool for cine CMR analysis Abstract: INTRODUCTION: Artificial intelligence (AI) has the potential to facilitate the automation of CMR analysis for biomarker extraction. However, most AI algorithms are trained on a specific input domain (e.g., ... |
Title: Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification Abstract: Although deep neural networks are capable of achieving performance superior to humans on various tasks, they are notorious for requiring large... |
Title: A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification Abstract: Bottleneck identification is a challenging task in network analysis, especially when the network is not fully specified. To address this task, we develop a unified online learning framework based on combinatorial semi-... |
Title: A Truthful Owner-Assisted Scoring Mechanism Abstract: Alice (owner) has knowledge of the underlying quality of her items measured in grades. Given the noisy grades provided by an independent party, can Bob (appraiser) obtain accurate estimates of the ground-truth grades of the items by asking Alice a question ab... |
Title: Fault-Tolerant Collaborative Inference through the Edge-PRUNE Framework Abstract: Collaborative inference has received significant research interest in machine learning as a vehicle for distributing computation load, reducing latency, as well as addressing privacy preservation in communications. Recent collabora... |
Title: Zero-Shot Video Question Answering via Frozen Bidirectional Language Models Abstract: Video question answering (VideoQA) is a complex task that requires diverse multi-modal data for training. Manual annotation of question and answers for videos, however, is tedious and prohibits scalability. To tackle this probl... |
Title: Long Range Graph Benchmark Abstract: Graph Neural Networks (GNNs) that are based on the message passing (MP) paradigm exchange information between 1-hop neighbors to build node representations at each layer. In principle, such networks are not able to capture long-range interactions (LRI) that may be desired or ... |
Title: Adversarial Privacy Protection on Speech Enhancement Abstract: Speech is easily leaked imperceptibly, such as being recorded by mobile phones in different situations. Private content in speech may be maliciously extracted through speech enhancement technology. Speech enhancement technology has developed rapidly ... |
Title: Not All Lotteries Are Made Equal Abstract: The Lottery Ticket Hypothesis (LTH) states that for a reasonably sized neural network, a sub-network within the same network yields no less performance than the dense counterpart when trained from the same initialization. This work investigates the relation between mode... |
Title: User Engagement and Churn in Mobile Health Applications Abstract: Mobile health apps are revolutionizing the healthcare ecosystem by improving communication, efficiency, and quality of service. In low- and middle-income countries, they also play a unique role as a source of information about health outcomes and ... |
Title: ResNorm: Tackling Long-tailed Degree Distribution Issue in Graph Neural Networks via Normalization Abstract: Graph Neural Networks (GNNs) have attracted much attention due to their ability in learning representations from graph-structured data. Despite the successful applications of GNNs in many domains, the opt... |
Title: MAGIC: Microlensing Analysis Guided by Intelligent Computation Abstract: The modeling of binary microlensing light curves via the standard sampling-based method can be challenging, because of the time-consuming light curve computation and the pathological likelihood landscape in the high-dimensional parameter sp... |
Title: Learning Physics between Digital Twins with Low-Fidelity Models and Physics-Informed Gaussian Processes Abstract: A digital twin is a computer model that represents an individual, for example, a component, a patient or a process. In many situations, we want to gain knowledge about an individual from its data whi... |
Title: Inherent Inconsistencies of Feature Importance Abstract: The black-box nature of modern machine learning techniques invokes a practical and ethical need for explainability. Feature importance aims to meet this need by assigning scores to features, so humans can understand their influence on predictions. Feature ... |
Title: A Closer Look at Smoothness in Domain Adversarial Training Abstract: Domain adversarial training has been ubiquitous for achieving invariant representations and is used widely for various domain adaptation tasks. In recent times, methods converging to smooth optima have shown improved generalization for supervis... |
Title: Functional Output Regression with Infimal Convolution: Exploring the Huber and $\epsilon$-insensitive Losses Abstract: The focus of the paper is functional output regression (FOR) with convoluted losses. While most existing work consider the square loss setting, we leverage extensions of the Huber and the $\epsi... |
Title: Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking Consistency Abstract: Visual domain adaptation (DA) seeks to transfer trained models to unseen, unlabeled domains across distribution shift, but approaches typically focus on adapting convolutional neural network architectures ... |
Title: All the World's a (Hyper)Graph: A Data Drama Abstract: We introduce Hyperbard, a dataset of diverse relational data representations derived from Shakespeare's plays. Our representations range from simple graphs capturing character co-occurrence in single scenes to hypergraphs encoding complex communication setti... |
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