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Title: Benchmarking Constraint Inference in Inverse Reinforcement Learning Abstract: When deploying Reinforcement Learning (RL) agents into a physical system, we must ensure that these agents are well aware of the underlying constraints. In many real-world problems, however, the constraints followed by expert agents (e... |
Title: What Can be Seen is What You Get: Structure Aware Point Cloud Augmentation Abstract: To train a well performing neural network for semantic segmentation, it is crucial to have a large dataset with available ground truth for the network to generalize on unseen data. In this paper we present novel point cloud augm... |
Title: Neural Activation Patterns (NAPs): Visual Explainability of Learned Concepts Abstract: A key to deciphering the inner workings of neural networks is understanding what a model has learned. Promising methods for discovering learned features are based on analyzing activation values, whereby current techniques focu... |
Title: Performance Prediction in Major League Baseball by Long Short-Term Memory Networks Abstract: Player performance prediction is a serious problem in every sport since it brings valuable future information for managers to make important decisions. In baseball industries, there already existed variable prediction sy... |
Title: A Machine Learning Data Fusion Model for Soil Moisture Retrieval Abstract: We develop a deep learning based convolutional-regression model that estimates the volumetric soil moisture content in the top ~5 cm of soil. Input predictors include Sentinel-1 (active radar), Sentinel-2 (optical imagery), and SMAP (pass... |
Title: Beyond IID: data-driven decision-making in heterogeneous environments Abstract: In this work, we study data-driven decision-making and depart from the classical identically and independently distributed (i.i.d.) assumption. We present a new framework in which historical samples are generated from unknown and dif... |
Title: Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability Abstract: Machine learning algorithms enable advanced decision making in contemporary intelligent systems. Research indicates that there is a tradeoff between their model performance... |
Title: Diversified Adversarial Attacks based on Conjugate Gradient Method Abstract: Deep learning models are vulnerable to adversarial examples, and adversarial attacks used to generate such examples have attracted considerable research interest. Although existing methods based on the steepest descent have achieved hig... |
Title: Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic Exploration Abstract: Massive practical works addressed by Deep Q-network (DQN) algorithm have indicated that stochastic policy, despite its simplicity, is the most frequently used exploration approach. However, most existing sto... |
Title: Autoencoder-based Attribute Noise Handling Method for Medical Data Abstract: Medical datasets are particularly subject to attribute noise, that is, missing and erroneous values. Attribute noise is known to be largely detrimental to learning performances. To maximize future learning performances it is primordial ... |
Title: Generating Diverse Indoor Furniture Arrangements Abstract: We present a method for generating arrangements of indoor furniture from human-designed furniture layout data. Our method creates arrangements that target specified diversity, such as the total price of all furniture in the room and the number of pieces ... |
Title: MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer Abstract: In this paper, we consider cooperative multi-agent reinforcement learning (MARL) with sparse reward. To tackle this problem, we propose a novel method named MASER: MARL with subgoals generated from experienc... |
Title: Analyzing Büchi Automata with Graph Neural Networks Abstract: B\"uchi Automata on infinite words present many interesting problems and are used frequently in program verification and model checking. A lot of these problems on B\"uchi automata are computationally hard, raising the question if a learning-based dat... |
Title: Revisiting lp-constrained Softmax Loss: A Comprehensive Study Abstract: Normalization is a vital process for any machine learning task as it controls the properties of data and affects model performance at large. The impact of particular forms of normalization, however, has so far been investigated in limited do... |
Title: S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning? Abstract: Collaborative multi-agent reinforcement learning (MARL) has been widely used in many practical applications, where each agent makes a decision based on its own observation. Most mainstream methods treat each loca... |
Title: Interpretable machine learning optimization (InterOpt) for operational parameters: a case study of highly-efficient shale gas development Abstract: An algorithm named InterOpt for optimizing operational parameters is proposed based on interpretable machine learning, and is demonstrated via optimization of shale ... |
Title: Constrained Reinforcement Learning for Robotics via Scenario-Based Programming Abstract: Deep reinforcement learning (DRL) has achieved groundbreaking successes in a wide variety of robotic applications. A natural consequence is the adoption of this paradigm for safety-critical tasks, where human safety and expe... |
Title: FedSSO: A Federated Server-Side Second-Order Optimization Algorithm Abstract: In this work, we propose FedSSO, a server-side second-order optimization method for federated learning (FL). In contrast to previous works in this direction, we employ a server-side approximation for the Quasi-Newton method without req... |
Title: C-SENN: Contrastive Self-Explaining Neural Network Abstract: In this study, we use a self-explaining neural network (SENN), which learns unsupervised concepts, to acquire concepts that are easy for people to understand automatically. In concept learning, the hidden layer retains verbalizable features relevant to... |
Title: Deep Random Vortex Method for Simulation and Inference of Navier-Stokes Equations Abstract: Navier-Stokes equations are significant partial differential equations that describe the motion of fluids such as liquids and air. Due to the importance of Navier-Stokes equations, the development on efficient numerical s... |
Title: Shuffle Gaussian Mechanism for Differential Privacy Abstract: We study Gaussian mechanism in the shuffle model of differential privacy (DP). Particularly, we characterize the mechanism's R\'enyi differential privacy (RDP), showing that it is of the form: $$ \epsilon(\lambda) \leq \frac{1}{\lambda-1}\log\left(\fr... |
Title: Two-Dimensional Weisfeiler-Lehman Graph Neural Networks for Link Prediction Abstract: Link prediction is one important application of graph neural networks (GNNs). Most existing GNNs for link prediction are based on one-dimensional Weisfeiler-Lehman (1-WL) test. 1-WL-GNNs first compute node representations by it... |
Title: A Novel Long-term Iterative Mining Scheme for Video Salient Object Detection Abstract: The existing state-of-the-art (SOTA) video salient object detection (VSOD) models have widely followed short-term methodology, which dynamically determines the balance between spatial and temporal saliency fusion by solely con... |
Title: DASH: Distributed Adaptive Sequencing Heuristic for Submodular Maximization Abstract: The development of parallelizable algorithms for monotone, submodular maximization subject to cardinality constraint (SMCC) has resulted in two separate research directions: centralized algorithms with low adaptive complexity, ... |
Title: An Empirical Analysis on the Vulnerabilities of End-to-End Speech Segregation Models Abstract: End-to-end learning models have demonstrated a remarkable capability in performing speech segregation. Despite their wide-scope of real-world applications, little is known about the mechanisms they employ to group and ... |
Title: Good Time to Ask: A Learning Framework for Asking for Help in Embodied Visual Navigation Abstract: In reality, it is often more efficient to ask for help than to search the entire space to find an object with an unknown location. We present a learning framework that enables an agent to actively ask for help in s... |
Title: Eliminating The Impossible, Whatever Remains Must Be True Abstract: The rise of AI methods to make predictions and decisions has led to a pressing need for more explainable artificial intelligence (XAI) methods. One common approach for XAI is to produce a post-hoc explanation, explaining why a black box ML model... |
Title: Policy Optimization with Linear Temporal Logic Constraints Abstract: We study the problem of policy optimization (PO) with linear temporal logic (LTL) constraints. The language of LTL allows flexible description of tasks that may be unnatural to encode as a scalar cost function. We consider LTL-constrained PO as... |
Title: Meta-learning for Out-of-Distribution Detection via Density Estimation in Latent Space Abstract: Many neural network-based out-of-distribution (OoD) detection methods have been proposed. However, they require many training data for each target task. We propose a simple yet effective meta-learning method to detec... |
Title: Robust One Round Federated Learning with Predictive Space Bayesian Inference Abstract: Making predictions robust is an important challenge. A separate challenge in federated learning (FL) is to reduce the number of communication rounds, particularly since doing so reduces performance in heterogeneous data settin... |
Title: Multiple Testing Framework for Out-of-Distribution Detection Abstract: We study the problem of Out-of-Distribution (OOD) detection, that is, detecting whether a learning algorithm's output can be trusted at inference time. While a number of tests for OOD detection have been proposed in prior work, a formal frame... |
Title: $C^*$-algebra Net: A New Approach Generalizing Neural Network Parameters to $C^*$-algebra Abstract: We propose a new framework that generalizes the parameters of neural network models to $C^*$-algebra-valued ones. $C^*$-algebra is a generalization of the space of complex numbers. A typical example is the space o... |
Title: The Fallacy of AI Functionality Abstract: Deployed AI systems often do not work. They can be constructed haphazardly, deployed indiscriminately, and promoted deceptively. However, despite this reality, scholars, the press, and policymakers pay too little attention to functionality. This leads to technical and po... |
Title: Resource-Efficient Separation Transformer Abstract: Transformers have recently achieved state-of-the-art performance in speech separation. These models, however, are computationally-demanding and require a lot of learnable parameters. This paper explores Transformer-based speech separation with a reduced computa... |
Title: Unbiased Teacher v2: Semi-supervised Object Detection for Anchor-free and Anchor-based Detectors Abstract: With the recent development of Semi-Supervised Object Detection (SS-OD) techniques, object detectors can be improved by using a limited amount of labeled data and abundant unlabeled data. However, there are... |
Title: Learning Multi-Task Transferable Rewards via Variational Inverse Reinforcement Learning Abstract: Many robotic tasks are composed of a lot of temporally correlated sub-tasks in a highly complex environment. It is important to discover situational intentions and proper actions by deliberating on temporal abstract... |
Title: Integrated Weak Learning Abstract: We introduce Integrated Weak Learning, a principled framework that integrates weak supervision into the training process of machine learning models. Our approach jointly trains the end-model and a label model that aggregates multiple sources of weak supervision. We introduce a ... |
Title: The Power of Regularization in Solving Extensive-Form Games Abstract: In this paper, we investigate the power of regularization, a common technique in reinforcement learning and optimization, in solving extensive-form games (EFGs). We propose a series of new algorithms based on regularizing the payoff functions ... |
Title: On the Limitations of Stochastic Pre-processing Defenses Abstract: Defending against adversarial examples remains an open problem. A common belief is that randomness at inference increases the cost of finding adversarial inputs. An example of such a defense is to apply a random transformation to inputs prior to ... |
Title: Artificial intelligence system based on multi-value classification of fully connected neural network for construction management Abstract: This study is devoted to solving the problem to determine the professional adaptive capabilities of construction management staff using artificial intelligence systems.It is ... |
Title: An Analysis of the Admissibility of the Objective Functions Applied in Evolutionary Multi-objective Clustering Abstract: A variety of clustering criteria has been applied as an objective function in Evolutionary Multi-Objective Clustering approaches (EMOCs). However, most EMOCs do not provide detailed analysis r... |
Title: Traffic-Twitter Transformer: A Nature Language Processing-joined Framework For Network-wide Traffic Forecasting Abstract: With accurate and timely traffic forecasting, the impacted traffic conditions can be predicted in advance to guide agencies and residents to respond to changes in traffic patterns appropriate... |
Title: Predicting Human Performance in Vertical Hierarchical Menu Selection in Immersive AR Using Hand-gesture and Head-gaze Abstract: There are currently limited guidelines on designing user interfaces (UI) for immersive augmented reality (AR) applications. Designers must reflect on their experience designing UI for d... |
Title: StudioGAN: A Taxonomy and Benchmark of GANs for Image Synthesis Abstract: Generative Adversarial Network (GAN) is one of the state-of-the-art generative models for realistic image synthesis. While training and evaluating GAN becomes increasingly important, the current GAN research ecosystem does not provide reli... |
Title: Geometric Matrix Completion via Sylvester Multi-Graph Neural Network Abstract: Despite the success of the Sylvester equation empowered methods on various graph mining applications, such as semi-supervised label learning and network alignment, there also exists several limitations. The Sylvester equation's inabil... |
Title: A Universal Adversarial Policy for Text Classifiers Abstract: Discovering the existence of universal adversarial perturbations had large theoretical and practical impacts on the field of adversarial learning. In the text domain, most universal studies focused on adversarial prefixes which are added to all texts.... |
Title: All you need is feedback: Communication with block attention feedback codes Abstract: Deep learning based channel code designs have recently gained interest as an alternative to conventional coding algorithms, particularly for channels for which existing codes do not provide effective solutions. Communication ov... |
Title: Bounding Evidence and Estimating Log-Likelihood in VAE Abstract: Many crucial problems in deep learning and statistics are caused by a variational gap, i.e., a difference between evidence and evidence lower bound (ELBO). As a consequence, in the classical VAE model, we obtain only the lower bound on the log-like... |
Title: Data Augmentation vs. Equivariant Networks: A Theory of Generalization on Dynamics Forecasting Abstract: Exploiting symmetry in dynamical systems is a powerful way to improve the generalization of deep learning. The model learns to be invariant to transformation and hence is more robust to distribution shift. Da... |
Title: SNN2ANN: A Fast and Memory-Efficient Training Framework for Spiking Neural Networks Abstract: Spiking neural networks are efficient computation models for low-power environments. Spike-based BP algorithms and ANN-to-SNN (ANN2SNN) conversions are successful techniques for SNN training. Nevertheless, the spike-bas... |
Title: Compression and Data Similarity: Combination of Two Techniques for Communication-Efficient Solving of Distributed Variational Inequalities Abstract: Variational inequalities are an important tool, which includes minimization, saddles, games, fixed-point problems. Modern large-scale and computationally expensive ... |
Title: Prevent Car Accidents by Using AI Abstract: Transportation facilities are becoming more developed as society develops, and people's travel demand is increasing, but so are the traffic safety issues that arise as a result. And car accidents are a major issue all over the world. The cost of traffic fatalities and ... |
Title: A generalized regionalization framework for geographical modelling and its application in spatial regression Abstract: In presence of spatial heterogeneity, models applied to geographic data face a trade-off between producing general results and capturing local variations. Modelling at a regional scale may allow... |
Title: ADBench: Anomaly Detection Benchmark Abstract: Given a long list of anomaly detection algorithms developed in the last few decades, how do they perform with regard to (i) varying levels of supervision, (ii) different types of anomalies, and (iii) noisy and corrupted data? In this work, we answer these key questi... |
Title: Efficient End-to-End AutoML via Scalable Search Space Decomposition Abstract: End-to-end AutoML has attracted intensive interests from both academia and industry which automatically searches for ML pipelines in a space induced by feature engineering, algorithm/model selection, and hyper-parameter tuning. Existin... |
Title: Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation Abstract: Myopic exploration policies such as epsilon-greedy, softmax, or Gaussian noise fail to explore efficiently in some reinforcement learning tasks and yet, they perform well in many others. In fact, in practice, they are ofte... |
Title: Agricultural Plantation Classification using Transfer Learning Approach based on CNN Abstract: Hyper-spectral images are images captured from a satellite that gives spatial and spectral information of specific region.A Hyper-spectral image contains much more number of channels as compared to a RGB image, hence c... |
Title: LordNet: Learning to Solve Parametric Partial Differential Equations without Simulated Data Abstract: Neural operators, as a powerful approximation to the non-linear operators between infinite-dimensional function spaces, have proved to be promising in accelerating the solution of partial differential equations ... |
Title: Terrain Classification using Transfer Learning on Hyperspectral Images: A Comparative study Abstract: A Hyperspectral image contains much more number of channels as compared to a RGB image, hence containing more information about entities within the image. The convolutional neural network (CNN) and the Multi-Lay... |
Title: Towards Adversarial Attack on Vision-Language Pre-training Models Abstract: While vision-language pre-training model (VLP) has shown revolutionary improvements on various vision-language (V+L) tasks, the studies regarding its adversarial robustness remain largely unexplored. This paper studied the adversarial at... |
Title: Label and Distribution-discriminative Dual Representation Learning for Out-of-Distribution Detection Abstract: To classify in-distribution samples, deep neural networks learn label-discriminative representations, which, however, are not necessarily distribution-discriminative according to the information bottlen... |
Title: Scalable Neural Data Server: A Data Recommender for Transfer Learning Abstract: Absence of large-scale labeled data in the practitioner's target domain can be a bottleneck to applying machine learning algorithms in practice. Transfer learning is a popular strategy for leveraging additional data to improve the do... |
Title: Out-of-distribution Detection by Cross-class Vicinity Distribution of In-distribution Data Abstract: Deep neural networks only learn to map in-distribution inputs to their corresponding ground truth labels in the training phase without differentiating out-of-distribution samples from in-distribution ones. This r... |
Title: Faster Sampling from Log-Concave Distributions over Polytopes via a Soft-Threshold Dikin Walk Abstract: We consider the problem of sampling from a $d$-dimensional log-concave distribution $\pi(\theta) \propto e^{-f(\theta)}$ constrained to a polytope $K$ defined by $m$ inequalities. Our main result is a "soft-th... |
Title: Supervision Adaptation Balances In-Distribution Generalization and Out-of-Distribution Detection Abstract: When there is a discrepancy between in-distribution (ID) samples and out-of-distribution (OOD) samples, deep neural networks trained on ID samples suffer from high-confidence prediction on OOD samples. This... |
Title: 0/1 Deep Neural Networks via Block Coordinate Descent Abstract: The step function is one of the simplest and most natural activation functions for deep neural networks (DNNs). As it counts 1 for positive variables and 0 for others, its intrinsic characteristics (e.g., discontinuity and no viable information of s... |
Title: Gray Learning from Non-IID Data with Out-of-distribution Samples Abstract: The quality of the training data annotated by experts cannot be guaranteed, even more so for non-IID data consisting of both in- and out-of-distribution samples (i.e., in-distribution and out-of-distribution samples hold different distrib... |
Title: Frank-Wolfe-based Algorithms for Approximating Tyler's M-estimator Abstract: Tyler's M-estimator is a well known procedure for robust and heavy-tailed covariance estimation. Tyler himself suggested an iterative fixed-point algorithm for computing his estimator however, it requires super-linear (in the size of th... |
Title: Productive Reproducible Workflows for DNNs: A Case Study for Industrial Defect Detection Abstract: As Deep Neural Networks (DNNs) have become an increasingly ubiquitous workload, the range of libraries and tooling available to aid in their development and deployment has grown significantly. Scalable, production ... |
Title: A Unified Understanding of Deep NLP Models for Text Classification Abstract: The rapid development of deep natural language processing (NLP) models for text classification has led to an urgent need for a unified understanding of these models proposed individually. Existing methods cannot meet the need for unders... |
Title: Quantifying Uncertainty In Traffic State Estimation Using Generative Adversarial Networks Abstract: This paper aims to quantify uncertainty in traffic state estimation (TSE) using the generative adversarial network based physics-informed deep learning (PIDL). The uncertainty of the focus arises from fundamental ... |
Title: Nested bandits Abstract: In many online decision processes, the optimizing agent is called to choose between large numbers of alternatives with many inherent similarities; in turn, these similarities imply closely correlated losses that may confound standard discrete choice models and bandit algorithms. We study... |
Title: Fairness-aware Model-agnostic Positive and Unlabeled Learning Abstract: With the increasing application of machine learning in high-stake decision-making problems, potential algorithmic bias towards people from certain social groups poses negative impacts on individuals and our society at large. In the real-worl... |
Title: Finding Diverse and Predictable Subgraphs for Graph Domain Generalization Abstract: This paper focuses on out-of-distribution generalization on graphs where performance drops due to the unseen distribution shift. Previous graph domain generalization works always resort to learning an invariant predictor among di... |
Title: Bayesian Optimization under Stochastic Delayed Feedback Abstract: Bayesian optimization (BO) is a widely-used sequential method for zeroth-order optimization of complex and expensive-to-compute black-box functions. The existing BO methods assume that the function evaluation (feedback) is available to the learner... |
Title: An Embedded Feature Selection Framework for Control Abstract: Reducing sensor requirements while keeping optimal control performance is crucial to many industrial control applications to achieve robust, low-cost, and computation-efficient controllers. However, existing feature selection solutions for the typical... |
Title: Generational Differences in Automobility: Comparing America's Millennials and Gen Xers Using Gradient Boosting Decision Trees Abstract: Whether the Millennials are less auto-centric than the previous generations has been widely discussed in the literature. Most existing studies use regression models and assume t... |
Title: LogGENE: A smooth alternative to check loss for Deep Healthcare Inference Tasks Abstract: High-throughput Genomics is ushering a new era in personalized health care, and targeted drug design and delivery. Mining these large datasets, and obtaining calibrated predictions is of immediate relevance and utility. In ... |
Title: A Survey on Model-based Reinforcement Learning Abstract: Reinforcement learning (RL) solves sequential decision-making problems via a trial-and-error process interacting with the environment. While RL achieves outstanding success in playing complex video games that allow huge trial-and-error, making errors is al... |
Title: Adversarially trained neural representations may already be as robust as corresponding biological neural representations Abstract: Visual systems of primates are the gold standard of robust perception. There is thus a general belief that mimicking the neural representations that underlie those systems will yield... |
Title: Mitigating Learning Complexity in Physics and Equality Constrained Artificial Neural Networks Abstract: Physics-informed neural networks (PINNs) have been proposed to learn the solution of partial differential equations (PDE). In PINNs, the residual form of the PDE of interest and its boundary conditions are lum... |
Title: TrafficFlowGAN: Physics-informed Flow based Generative Adversarial Network for Uncertainty Quantification Abstract: This paper proposes the TrafficFlowGAN, a physics-informed flow based generative adversarial network (GAN), for uncertainty quantification (UQ) of dynamical systems. TrafficFlowGAN adopts a normali... |
Title: FRAPPE: $\underline{\text{F}}$ast $\underline{\text{Ra}}$nk $\underline{\text{App}}$roximation with $\underline{\text{E}}$xplainable Features for Tensors Abstract: Tensor decompositions have proven to be effective in analyzing the structure of multidimensional data. However, most of these methods require a key p... |
Title: Knowledge Learning with Crowdsourcing: A Brief Review and Systematic Perspective Abstract: Big data have the characteristics of enormous volume, high velocity, diversity, value-sparsity, and uncertainty, which lead the knowledge learning from them full of challenges. With the emergence of crowdsourcing, versatil... |
Title: Robust Imitation Learning against Variations in Environment Dynamics Abstract: In this paper, we propose a robust imitation learning (IL) framework that improves the robustness of IL when environment dynamics are perturbed. The existing IL framework trained in a single environment can catastrophically fail with ... |
Title: Laziness, Barren Plateau, and Noise in Machine Learning Abstract: We define \emph{laziness} to describe a large suppression of variational parameter updates for neural networks, classical or quantum. In the quantum case, the suppression is exponential in the number of qubits for randomized variational quantum ci... |
Title: Primal Estimated Subgradient Solver for SVM for Imbalanced Classification Abstract: We aim to demonstrate in experiments that our cost sensitive PEGASOS SVM balances achieve good performance on imbalanced data sets with a Majority to Minority Ratio ranging from 8.6 to one through 130 to one. We evaluate the perf... |
Title: Adversarial Scrutiny of Evidentiary Statistical Software Abstract: The U.S. criminal legal system increasingly relies on software output to convict and incarcerate people. In a large number of cases each year, the government makes these consequential decisions based on evidence from statistical software -- such ... |
Title: Enforcing Continuous Physical Symmetries in Deep Learning Network for Solving Partial Differential Equations Abstract: As a typical {application} of deep learning, physics-informed neural network (PINN) {has been} successfully used to find numerical solutions of partial differential equations (PDEs), but how to ... |
Title: AutoGML: Fast Automatic Model Selection for Graph Machine Learning Abstract: Given a graph learning task, such as link prediction, on a new graph dataset, how can we automatically select the best method as well as its hyperparameters (collectively called a model)? Model selection for graph learning has been larg... |
Title: Scalable Classifier-Agnostic Channel Selection for MTSC Abstract: Accuracy is a key focus of current work in time series classification. However, speed and data reduction in many applications is equally important, especially when the data scale and storage requirements increase rapidly. Current MTSC algorithms n... |
Title: DECK: Model Hardening for Defending Pervasive Backdoors Abstract: Pervasive backdoors are triggered by dynamic and pervasive input perturbations. They can be intentionally injected by attackers or naturally exist in normally trained models. They have a different nature from the traditional static and localized b... |
Title: Pisces: Efficient Federated Learning via Guided Asynchronous Training Abstract: Federated learning (FL) is typically performed in a synchronous parallel manner, where the involvement of a slow client delays a training iteration. Current FL systems employ a participant selection strategy to select fast clients wi... |
Title: Motley: Benchmarking Heterogeneity and Personalization in Federated Learning Abstract: Personalized federated learning considers learning models unique to each client in a heterogeneous network. The resulting client-specific models have been purported to improve metrics such as accuracy, fairness, and robustness... |
Title: Machine Learning in Sports: A Case Study on Using Explainable Models for Predicting Outcomes of Volleyball Matches Abstract: Machine Learning has become an integral part of engineering design and decision making in several domains, including sports. Deep Neural Networks (DNNs) have been the state-of-the-art meth... |
Title: Optimal Dynamic Regret in LQR Control Abstract: We consider the problem of nonstochastic control with a sequence of quadratic losses, i.e., LQR control. We provide an efficient online algorithm that achieves an optimal dynamic (policy) regret of $\tilde{O}(\text{max}\{n^{1/3} \mathcal{TV}(M_{1:n})^{2/3}, 1\})$, ... |
Title: Multistream Gaze Estimation with Anatomical Eye Region Isolation by Synthetic to Real Transfer Learning Abstract: We propose a novel neural pipeline, MSGazeNet, that learns gaze representations by taking advantage of the eye anatomy information through a multistream framework. Our proposed solution comprises two... |
Title: Mutation-Driven Follow the Regularized Leader for Last-Iterate Convergence in Zero-Sum Games Abstract: In this study, we consider a variant of the Follow the Regularized Leader (FTRL) dynamics in two-player zero-sum games. FTRL is guaranteed to converge to a Nash equilibrium when time-averaging the strategies, w... |
Title: Reduced Robust Random Cut Forest for Out-Of-Distribution detection in machine learning models Abstract: Most machine learning-based regressors extract information from data collected via past observations of limited length to make predictions in the future. Consequently, when input to these trained models is dat... |
Title: GaLeNet: Multimodal Learning for Disaster Prediction, Management and Relief Abstract: After a natural disaster, such as a hurricane, millions are left in need of emergency assistance. To allocate resources optimally, human planners need to accurately analyze data that can flow in large volumes from several sourc... |
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