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Title: Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation Abstract: Bayesian Optimization (BO) has shown great promise for the global optimization of functions that are expensive to evaluate, but despite many successes, standard approaches can struggle in high dimensions. To improve the... |
Title: Interaction-Grounded Learning with Action-inclusive Feedback Abstract: Consider the problem setting of Interaction-Grounded Learning (IGL), in which a learner's goal is to optimally interact with the environment with no explicit reward to ground its policies. The agent observes a context vector, takes an action,... |
Title: Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability Abstract: Estimating personalized effects of treatments is a complex, yet pervasive problem. To tackle it, recent developments in the machine learning (ML) literature on heterogeneous treatment effect estimation gave rise to ... |
Title: MixGen: A New Multi-Modal Data Augmentation Abstract: Data augmentation is a necessity to enhance data efficiency in deep learning. For vision-language pre-training, data is only augmented either for images or for text in previous works. In this paper, we present MixGen: a joint data augmentation for vision-lang... |
Title: Spatially-Adaptive Multilayer Selection for GAN Inversion and Editing Abstract: Existing GAN inversion and editing methods work well for aligned objects with a clean background, such as portraits and animal faces, but often struggle for more difficult categories with complex scene layouts and object occlusions, ... |
Title: OmniMAE: Single Model Masked Pretraining on Images and Videos Abstract: Transformer-based architectures have become competitive across a variety of visual domains, most notably images and videos. While prior work has studied these modalities in isolation, having a common architecture suggests that one can train ... |
Title: Towards Understanding How Machines Can Learn Causal Overhypotheses Abstract: Recent work in machine learning and cognitive science has suggested that understanding causal information is essential to the development of intelligence. The extensive literature in cognitive science using the ``blicket detector'' envi... |
Title: Know your audience: specializing grounded language models with the game of Dixit Abstract: Effective communication requires adapting to the idiosyncratic common ground shared with each communicative partner. We study a particularly challenging instantiation of this problem: the popular game Dixit. We formulate a... |
Title: iBoot: Image-bootstrapped Self-Supervised Video Representation Learning Abstract: Learning visual representations through self-supervision is an extremely challenging task as the network needs to sieve relevant patterns from spurious distractors without the active guidance provided by supervision. This is achiev... |
Title: Constrained Submodular Optimization for Vaccine Design Abstract: Advances in machine learning have enabled the prediction of immune system responses to prophylactic and therapeutic vaccines. However, the engineering task of designing vaccines remains a challenge. In particular, the genetic variability of the hum... |
Title: BYOL-Explore: Exploration by Bootstrapped Prediction Abstract: We present BYOL-Explore, a conceptually simple yet general approach for curiosity-driven exploration in visually-complex environments. BYOL-Explore learns a world representation, the world dynamics, and an exploration policy all-together by optimizin... |
Title: Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data Abstract: We propose Compressed Vertical Federated Learning (C-VFL) for communication-efficient training on vertically partitioned data. In C-VFL, a server and multiple parties collaboratively train a model on their respective feat... |
Title: Boosting the Adversarial Transferability of Surrogate Model with Dark Knowledge Abstract: Deep neural networks (DNNs) for image classification are known to be vulnerable to adversarial examples. And, the adversarial examples have transferability, which means an adversarial example for a DNN model can fool anothe... |
Title: Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations Abstract: Estimating counterfactual outcomes over time has the potential to unlock personalized healthcare by assisting decision-makers to answer ''what-iF'' questions. Existing causal inference approaches typicall... |
Title: Pythae: Unifying Generative Autoencoders in Python -- A Benchmarking Use Case Abstract: In recent years, deep generative models have attracted increasing interest due to their capacity to model complex distributions. Among those models, variational autoencoders have gained popularity as they have proven both to ... |
Title: Deepfake histological images for enhancing digital pathology Abstract: An optical microscopic examination of thinly cut stained tissue on glass slides prepared from a FFPE tissue blocks is the gold standard for tissue diagnostics. In addition, the diagnostic abilities and expertise of any pathologist is dependen... |
Title: Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning Abstract: We study the asynchronous stochastic gradient descent algorithm for distributed training over $n$ workers which have varying computation and communication frequency over time. In this algorithm, workers compute s... |
Title: Adversarial Patch Attacks and Defences in Vision-Based Tasks: A Survey Abstract: Adversarial attacks in deep learning models, especially for safety-critical systems, are gaining more and more attention in recent years, due to the lack of trust in the security and robustness of AI models. Yet the more primitive a... |
Title: On Scaled Methods for Saddle Point Problems Abstract: Methods with adaptive scaling of different features play a key role in solving saddle point problems, primarily due to Adam's popularity for solving adversarial machine learning problems, including GANS training. This paper carries out a theoretical analysis ... |
Title: GoodBye WaveNet -- A Language Model for Raw Audio with Context of 1/2 Million Samples Abstract: Modeling long-term dependencies for audio signals is a particularly challenging problem, as even small-time scales yield on the order of a hundred thousand samples. With the recent advent of Transformers, neural archi... |
Title: Switchable Representation Learning Framework with Self-compatibility Abstract: Real-world visual search systems involve deployments on multiple platforms with different computing and storage resources. Deploying a unified model that suits the minimal-constrain platforms leads to limited accuracy. It is expected ... |
Title: A machine-generated catalogue of Charon's craters and implications for the Kuiper belt Abstract: In this paper we investigate Charon's craters size distribution using a deep learning model. This is motivated by the recent results of Singer et al. (2019) who, using manual cataloging, found a change in the size di... |
Title: Rank the triplets: A ranking-based multiple instance learning framework for detecting HPV infection in head and neck cancers using routine H&E images Abstract: The aetiology of head and neck squamous cell carcinoma (HNSCC) involves multiple carcinogens such as alcohol, tobacco and infection with human papillomav... |
Title: Concentration of Data Encoding in Parameterized Quantum Circuits Abstract: Variational quantum algorithms have been acknowledged as a leading strategy to realize near-term quantum advantages in meaningful tasks, including machine learning and combinatorial optimization. When applied to tasks involving classical ... |
Title: Learning with little mixing Abstract: We study square loss in a realizable time-series framework with martingale difference noise. Our main result is a fast rate excess risk bound which shows that whenever a trajectory hypercontractivity condition holds, the risk of the least-squares estimator on dependent data ... |
Title: Maximum Likelihood Training for Score-Based Diffusion ODEs by High-Order Denoising Score Matching Abstract: Score-based generative models have excellent performance in terms of generation quality and likelihood. They model the data distribution by matching a parameterized score network with first-order data scor... |
Title: ProGNNosis: A Data-driven Model to Predict GNN Computation Time Using Graph Metrics Abstract: Graph Neural Networks (GNN) show great promise in problems dealing with graph-structured data. One of the unique points of GNNs is their flexibility to adapt to multiple problems, which not only leads to wide applicabil... |
Title: Gradient Descent for Low-Rank Functions Abstract: Several recent empirical studies demonstrate that important machine learning tasks, e.g., training deep neural networks, exhibit low-rank structure, where the loss function varies significantly in only a few directions of the input space. In this paper, we levera... |
Title: Gradient-Based Adversarial and Out-of-Distribution Detection Abstract: We propose to utilize gradients for detecting adversarial and out-of-distribution samples. We introduce confounding labels -- labels that differ from normal labels seen during training -- in gradient generation to probe the effective expressi... |
Title: On the Surprising Behaviour of node2vec Abstract: Graph embedding techniques are a staple of modern graph learning research. When using embeddings for downstream tasks such as classification, information about their stability and robustness, i.e., their susceptibility to sources of noise, stochastic effects, or ... |
Title: Catastrophic overfitting is a bug but also a feature Abstract: Despite clear computational advantages in building robust neural networks, adversarial training (AT) using single-step methods is unstable as it suffers from catastrophic overfitting (CO): Networks gain non-trivial robustness during the first stages ... |
Title: Noisy Learning for Neural ODEs Acts as a Robustness Locus Widening Abstract: We investigate the problems and challenges of evaluating the robustness of Differential Equation-based (DE) networks against synthetic distribution shifts. We propose a novel and simple accuracy metric which can be used to evaluate intr... |
Title: Simple and Efficient Architectures for Semantic Segmentation Abstract: Though the state-of-the architectures for semantic segmentation, such as HRNet, demonstrate impressive accuracy, the complexity arising from their salient design choices hinders a range of model acceleration tools, and further they make use o... |
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... |
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: Functional Output Regression with Infimal Convolution: Exploring the Huber and $ε$-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 $\epsilon$-in... |
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: 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: 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: 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: 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: User Engagement 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 behaviors ... |
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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: Reinforcement Learning in Macroeconomic Policy Design: A New Frontier? Abstract: Agent-based computational macroeconomics is a field with a rich academic history, yet one which has struggled to enter mainstream policy design toolboxes, plagued by the challenges associated with representing a complex and dynamic ... |
Title: Neural tangent kernel analysis of shallow $α$-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 behav... |
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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: "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: Identifying Electrocardiogram Abnormalities Using a Handcrafted-Rule-Enhanced Neural Network Abstract: A large number of people suffer from life-threatening cardiac abnormalities, and electrocardiogram (ECG) analysis is beneficial to determining whether an individual is at risk of such abnormalities. Automatic E... |
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: 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: 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... |
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