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Title: A Theory of Abstraction in Reinforcement Learning Abstract: Reinforcement learning defines the problem facing agents that learn to make good decisions through action and observation alone. To be effective problem solvers, such agents must efficiently explore vast worlds, assign credit from delayed feedback, and ... |
Title: Nonlinear Kernel Support Vector Machine with 0-1 Soft Margin Loss Abstract: Recent advance on linear support vector machine with the 0-1 soft margin loss ($L_{0/1}$-SVM) shows that the 0-1 loss problem can be solved directly. However, its theoretical and algorithmic requirements restrict us extending the linear ... |
Title: A Dynamical Estimation and Prediction for Covid19 on Romania using ensemble neural networks Abstract: In this paper, we propose an analysis of Covid19 evolution and prediction on Romania combined with the mathematical model of SIRD, an extension of the classical model SIR, which includes the deceased as a separa... |
Title: Disentangled Spatiotemporal Graph Generative Models Abstract: Spatiotemporal graph represents a crucial data structure where the nodes and edges are embedded in a geometric space and can evolve dynamically over time. Nowadays, spatiotemporal graph data is becoming increasingly popular and important, ranging from... |
Title: Interpretable Molecular Graph Generation via Monotonic Constraints Abstract: Designing molecules with specific properties is a long-lasting research problem and is central to advancing crucial domains such as drug discovery and material science. Recent advances in deep graph generative models treat molecule desi... |
Title: Short-term passenger flow prediction for multi-traffic modes: A Transformer and residual network based multi-task learning method Abstract: With the prevailing of mobility as a service (MaaS), it becomes increasingly important to manage multi-traffic modes simultaneously and cooperatively. As an important compon... |
Title: Identifying charge density and dielectric environment of graphene using Raman spectroscopy and deep learning Abstract: The impact of the environment on graphene's properties such as strain, charge density, and dielectric environment can be evaluated by Raman spectroscopy. These environmental interactions are not... |
Title: Towards Practices for Human-Centered Machine Learning Abstract: "Human-centered machine learning" (HCML) is a term that describes machine learning that applies to human-focused problems. Although this idea is noteworthy and generates scholarly excitement, scholars and practitioners have struggled to clearly defi... |
Title: An Analytical Approach to Compute the Exact Preimage of Feed-Forward Neural Networks Abstract: Neural networks are a convenient way to automatically fit functions that are too complex to be described by hand. The downside of this approach is that it leads to build a black-box without understanding what happened ... |
Title: Active learning with binary models for real time data labelling Abstract: Machine learning (ML) and Deep Learning (DL) tasks primarily depend on data. Most of the ML and DL applications involve supervised learning which requires labelled data. In the initial phases of ML realm lack of data used to be a problem, ... |
Title: Parameter-free Mirror Descent Abstract: We develop a modified online mirror descent framework that is suitable for building adaptive and parameter-free algorithms in unbounded domains. We leverage this technique to develop the first unconstrained online linear optimization algorithm achieving an optimal dynamic ... |
Title: Technological evaluation of two AFIS systems Abstract: This paper provides a technological evaluation of two Automatic Fingerprint Identification Systems (AFIS) used in forensic applications. Both of them are installed and working in Spanish police premises. The first one is a Printrak AFIS 2000 system with a da... |
Title: Memory Planning for Deep Neural Networks Abstract: We study memory allocation patterns in DNNs during inference, in the context of large-scale systems. We observe that such memory allocation patterns, in the context of multi-threading, are subject to high latencies, due to \texttt{mutex} contention in the system... |
Title: Physics-Informed Neural Networks for Quantum Eigenvalue Problems Abstract: Eigenvalue problems are critical to several fields of science and engineering. We expand on the method of using unsupervised neural networks for discovering eigenfunctions and eigenvalues for differential eigenvalue problems. The obtained... |
Title: Long-Tailed Classification with Gradual Balanced Loss and Adaptive Feature Generation Abstract: The real-world data distribution is essentially long-tailed, which poses great challenge to the deep model. In this work, we propose a new method, Gradual Balanced Loss and Adaptive Feature Generator (GLAG) to allevia... |
Title: On the sample complexity of stabilizing linear dynamical systems from data Abstract: Learning controllers from data for stabilizing dynamical systems typically follows a two step process of first identifying a model and then constructing a controller based on the identified model. However, learning models means ... |
Title: A Probabilistic Deep Image Prior for Computational Tomography Abstract: Existing deep-learning based tomographic image reconstruction methods do not provide accurate estimates of reconstruction uncertainty, hindering their real-world deployment. To address this limitation, we construct a Bayesian prior for tomog... |
Title: Beyond Gradients: Exploiting Adversarial Priors in Model Inversion Attacks Abstract: Collaborative machine learning settings like federated learning can be susceptible to adversarial interference and attacks. One class of such attacks is termed model inversion attacks, characterised by the adversary reverse-engi... |
Title: Attention-based Contextual Multi-View Graph Convolutional Networks for Short-term Population Prediction Abstract: Short-term future population prediction is a crucial problem in urban computing. Accurate future population prediction can provide rich insights for urban planners or developers. However, predicting ... |
Title: DreamingV2: Reinforcement Learning with Discrete World Models without Reconstruction Abstract: The present paper proposes a novel reinforcement learning method with world models, DreamingV2, a collaborative extension of DreamerV2 and Dreaming. DreamerV2 is a cutting-edge model-based reinforcement learning from p... |
Title: A predictive analytics approach for stroke prediction using machine learning and neural networks Abstract: The negative impact of stroke in society has led to concerted efforts to improve the management and diagnosis of stroke. With an increased synergy between technology and medical diagnosis, caregivers create... |
Title: Gait Events Prediction using Hybrid CNN-RNN-based Deep Learning models through a Single Waist-worn Wearable Sensor Abstract: Elderly gait is a source of rich information about their physical and mental health condition. As an alternative to the multiple sensors on the lower body parts, a single sensor on the pel... |
Title: Analysis of Digitalized ECG Signals Based on Artificial Intelligence and Spectral Analysis Methods Specialized in ARVC Abstract: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited heart muscle disease that appears between the second and forth decade of a patient's life, being responsible for ... |
Title: Wavelet-Based Multi-Class Seizure Type Classification System Abstract: Epilepsy is one of the most common brain diseases that affect more than 1\% of the world's population. It is characterized by recurrent seizures, which come in different types and are treated differently. Electroencephalography (EEG) is commo... |
Title: A Deep Bayesian Neural Network for Cardiac Arrhythmia Classification with Rejection from ECG Recordings Abstract: With the development of deep learning-based methods, automated classification of electrocardiograms (ECGs) has recently gained much attention. Although the effectiveness of deep neural networks has b... |
Title: A comparative study of several parameterizations for speaker recognition Abstract: This paper presents an exhaustive study about the robustness of several parameterizations, in speaker verification and identification tasks. We have studied several mismatch conditions: different recording sessions, microphones, a... |
Title: Mental State Classification Using Multi-graph Features Abstract: We consider the problem of extracting features from passive, multi-channel electroencephalogram (EEG) devices for downstream inference tasks related to high-level mental states such as stress and cognitive load. Our proposed method leverages recent... |
Title: Multi-task Learning Approach for Modulation and Wireless Signal Classification for 5G and Beyond: Edge Deployment via Model Compression Abstract: Future communication networks must address the scarce spectrum to accommodate extensive growth of heterogeneous wireless devices. Wireless signal recognition is becomi... |
Title: Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders Abstract: We study how to use Deep Variational Autoencoders for a fast simulation of jets of particles at the LHC. We represent jets as a list of constituents, characterized by their momenta. Starting from a simulation of the jet before ... |
Title: Causal Structure Learning with Greedy Unconditional Equivalence Search Abstract: We consider the problem of characterizing directed acyclic graph (DAG) models up to unconditional equivalence, i.e., when two DAGs have the same set of unconditional d-separation statements. Each unconditional equivalence class (UEC... |
Title: E-LMC: Extended Linear Model of Coregionalization for Predictions of Spatial Fields Abstract: Physical simulations based on partial differential equations typically generate spatial fields results, which are utilized to calculate specific properties of a system for engineering design and optimization. Due to the... |
Title: Multi-Objective Latent Space Optimization of Generative Molecular Design Models Abstract: Molecular design based on generative models, such as variational autoencoders (VAEs), has become increasingly popular in recent years due to its efficiency for exploring high-dimensional molecular space to identify molecule... |
Title: On genetic programming representations and fitness functions for interpretable dimensionality reduction Abstract: Dimensionality reduction (DR) is an important technique for data exploration and knowledge discovery. However, most of the main DR methods are either linear (e.g., PCA), do not provide an explicit ma... |
Title: On the Generalization of Representations in Reinforcement Learning Abstract: In reinforcement learning, state representations are used to tractably deal with large problem spaces. State representations serve both to approximate the value function with few parameters, but also to generalize to newly encountered s... |
Title: DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition Abstract: The MultiCoNER shared task aims at detecting semantically ambiguous and complex named entities in short and low-context settings for multiple languages. The lack of contexts makes the recognition of amb... |
Title: Optimal quantum dataset for learning a unitary transformation Abstract: Unitary transformations formulate the time evolution of quantum states. How to learn a unitary transformation efficiently is a fundamental problem in quantum machine learning. The most natural and leading strategy is to train a quantum machi... |
Title: Bayesian Optimisation for Robust Model Predictive Control under Model Parameter Uncertainty Abstract: We propose an adaptive optimisation approach for tuning stochastic model predictive control (MPC) hyper-parameters while jointly estimating probability distributions of the transition model parameters based on p... |
Title: Global-Local Regularization Via Distributional Robustness Abstract: Despite superior performance in many situations, deep neural networks are often vulnerable to adversarial examples and distribution shifts, limiting model generalization ability in real-world applications. To alleviate these problems, recent app... |
Title: Neural Score Matching for High-Dimensional Causal Inference Abstract: Traditional methods for matching in causal inference are impractical for high-dimensional datasets. They suffer from the curse of dimensionality: exact matching and coarsened exact matching find exponentially fewer matches as the input dimensi... |
Title: DeepNet: Scaling Transformers to 1,000 Layers Abstract: In this paper, we propose a simple yet effective method to stabilize extremely deep Transformers. Specifically, we introduce a new normalization function (DeepNorm) to modify the residual connection in Transformer, accompanying with theoretically derived in... |
Title: Contrasting random and learned features in deep Bayesian linear regression Abstract: Understanding how feature learning affects generalization is among the foremost goals of modern deep learning theory. Here, we study how the ability to learn representations affects the generalization performance of a simple cla... |
Title: Parameter estimation for WMTI-Watson model of white matter using encoder-decoder recurrent neural network Abstract: Biophysical modelling of the diffusion MRI signal provides estimates of specific microstructural tissue properties. Although nonlinear optimization such as non-linear least squares (NLLS) is the mo... |
Title: Path sampling of recurrent neural networks by incorporating known physics Abstract: Recurrent neural networks have seen widespread use in modeling dynamical systems in varied domains such as weather prediction, text prediction and several others. Often one wishes to supplement the experimentally observed dynamic... |
Title: Dual Embodied-Symbolic Concept Representations for Deep Learning Abstract: Motivated by recent findings from cognitive neural science, we advocate the use of a dual-level model for concept representations: the embodied level consists of concept-oriented feature representations, and the symbolic level consists of... |
Title: Beyond Ans\"atze: Learning Quantum Circuits as Unitary Operators Abstract: This paper explores the advantages of optimizing quantum circuits on $N$ wires as operators in the unitary group $U(2^N)$. We run gradient-based optimization in the Lie algebra $\mathfrak u(2^N)$ and use the exponential map to parametrize... |
Title: Learning Intermediate Representations using Graph Neural Networks for NUMA and Prefetchers Optimization Abstract: There is a large space of NUMA and hardware prefetcher configurations that can significantly impact the performance of an application. Previous studies have demonstrated how a model can automatically... |
Title: Towards a Common Speech Analysis Engine Abstract: Recent innovations in self-supervised representation learning have led to remarkable advances in natural language processing. That said, in the speech processing domain, self-supervised representation learning-based systems are not yet considered state-of-the-art... |
Title: Side-effects of Learning from Low Dimensional Data Embedded in an Euclidean Space Abstract: The low dimensional manifold hypothesis posits that the data found in many applications, such as those involving natural images, lie (approximately) on low dimensional manifolds embedded in a high dimensional Euclidean sp... |
Title: A Neural Ordinary Differential Equation Model for Visualizing Deep Neural Network Behaviors in Multi-Parametric MRI based Glioma Segmentation Abstract: Purpose: To develop a neural ordinary differential equation (ODE) model for visualizing deep neural network (DNN) behavior during multi-parametric MRI (mp-MRI) b... |
Title: Distributional Reinforcement Learning for Scheduling of Chemical Production Processes Abstract: Reinforcement Learning (RL) has recently received significant attention from the process systems engineering and control communities. Recent works have investigated the application of RL to identify optimal scheduling... |
Title: PaSca: a Graph Neural Architecture Search System under the Scalable Paradigm Abstract: Graph neural networks (GNNs) have achieved state-of-the-art performance in various graph-based tasks. However, as mainstream GNNs are designed based on the neural message passing mechanism, they do not scale well to data size ... |
Title: Multi-Task Multi-Scale Learning For Outcome Prediction in 3D PET Images Abstract: Background and Objectives: Predicting patient response to treatment and survival in oncology is a prominent way towards precision medicine. To that end, radiomics was proposed as a field of study where images are used instead of in... |
Title: Variational Autoencoders Without the Variation Abstract: Variational autoencdoers (VAE) are a popular approach to generative modelling. However, exploiting the capabilities of VAEs in practice can be difficult. Recent work on regularised and entropic autoencoders have begun to explore the potential, for generati... |
Title: ONBRA: Rigorous Estimation of the Temporal Betweenness Centrality in Temporal Networks Abstract: In network analysis, the betweenness centrality of a node informally captures the fraction of shortest paths visiting that node. The computation of the betweenness centrality measure is a fundamental task in the anal... |
Title: Generative Adversarial Networks Abstract: Generative Adversarial Networks (GANs) are very popular frameworks for generating high-quality data, and are immensely used in both the academia and industry in many domains. Arguably, their most substantial impact has been in the area of computer vision, where they achi... |
Title: Learning Robust Real-Time Cultural Transmission without Human Data Abstract: Cultural transmission is the domain-general social skill that allows agents to acquire and use information from each other in real-time with high fidelity and recall. In humans, it is the inheritance process that powers cumulative cultu... |
Title: Runtime Detection of Executional Errors in Robot-Assisted Surgery Abstract: Despite significant developments in the design of surgical robots and automated techniques for objective evaluation of surgical skills, there are still challenges in ensuring safety in robot-assisted minimally-invasive surgery (RMIS). Th... |
Title: E-LANG: Energy-Based Joint Inferencing of Super and Swift Language Models Abstract: Building huge and highly capable language models has been a trend in the past years. Despite their great performance, they incur high computational cost. A common solution is to apply model compression or choose light-weight arch... |
Title: HyperPrompt: Prompt-based Task-Conditioning of Transformers Abstract: Prompt-Tuning is a new paradigm for finetuning pre-trained language models in a parameter-efficient way. Here, we explore the use of HyperNetworks to generate hyper-prompts: we propose HyperPrompt, a novel architecture for prompt-based task-co... |
Title: Tricks and Plugins to GBM on Images and Sequences Abstract: Convolutional neural networks (CNNs) and transformers, which are composed of multiple processing layers and blocks to learn the representations of data with multiple abstract levels, are the most successful machine learning models in recent years. Howev... |
Title: Topic Analysis for Text with Side Data Abstract: Although latent factor models (e.g., matrix factorization) obtain good performance in predictions, they suffer from several problems including cold-start, non-transparency, and suboptimal recommendations. In this paper, we employ text with side data to tackle thes... |
Title: Multi-Layer Perceptron Neural Network for Improving Detection Performance of Malicious Phishing URLs Without Affecting Other Attack Types Classification Abstract: The hypothesis here states that neural network algorithms such as Multi-layer Perceptron (MLP) have higher accuracy in differentiating malicious and s... |
Title: Enhanced Nearest Neighbor Classification for Crowdsourcing Abstract: In machine learning, crowdsourcing is an economical way to label a large amount of data. However, the noise in the produced labels may deteriorate the accuracy of any classification method applied to the labelled data. We propose an enhanced ne... |
Title: TANDEM: Learning Joint Exploration and Decision Making with Tactile Sensors Abstract: Inspired by the human ability to perform complex manipulation in the complete absence of vision (like retrieving an object from a pocket), the robotic manipulation field is motivated to develop new methods for tactile-based obj... |
Title: Code Smells in Machine Learning Systems Abstract: As Deep learning (DL) systems continuously evolve and grow, assuring their quality becomes an important yet challenging task. Compared to non-DL systems, DL systems have more complex team compositions and heavier data dependency. These inherent characteristics wo... |
Title: Stable, accurate and efficient deep neural networks for inverse problems with analysis-sparse models Abstract: Solving inverse problems is a fundamental component of science, engineering and mathematics. With the advent of deep learning, deep neural networks have significant potential to outperform existing stat... |
Title: The quantum low-rank approximation problem Abstract: We consider a quantum version of the famous low-rank approximation problem. Specifically, we consider the distance $D(\rho,\sigma)$ between two normalized quantum states, $\rho$ and $\sigma$, where the rank of $\sigma$ is constrained to be at most $R$. For bot... |
Title: Partial Likelihood Thompson Sampling Abstract: We consider the problem of deciding how best to target and prioritize existing vaccines that may offer protection against new variants of an infectious disease. Sequential experiments are a promising approach; however, challenges due to delayed feedback and the over... |
Title: Keeping Minimal Experience to Achieve Efficient Interpretable Policy Distillation Abstract: Although deep reinforcement learning has become a universal solution for complex control tasks, its real-world applicability is still limited because lacking security guarantees for policies. To address this problem, we p... |
Title: Personalized Federated Learning With Graph Abstract: Knowledge sharing and model personalization are two key components in the conceptual framework of personalized federated learning (PFL). Existing PFL methods focus on proposing new model personalization mechanisms while simply implementing knowledge sharing by... |
Title: GSC Loss: A Gaussian Score Calibrating Loss for Deep Learning Abstract: Cross entropy (CE) loss integrated with softmax is an orthodox component in most classification-based frameworks, but it fails to obtain an accurate probability distribution of predicted scores that is critical for further decision-making of... |
Title: CandidateDrug4Cancer: An Open Molecular Graph Learning Benchmark on Drug Discovery for Cancer Abstract: Anti-cancer drug discoveries have been serendipitous, we sought to present the Open Molecular Graph Learning Benchmark, named CandidateDrug4Cancer, a challenging and realistic benchmark dataset to facilitate s... |
Title: PUMA: Performance Unchanged Model Augmentation for Training Data Removal Abstract: Preserving the performance of a trained model while removing unique characteristics of marked training data points is challenging. Recent research usually suggests retraining a model from scratch with remaining training data or re... |
Title: Adversarially Robust Learning with Tolerance Abstract: We study the problem of tolerant adversarial PAC learning with respect to metric perturbation sets. In adversarial PAC learning, an adversary is allowed to replace a test point $x$ with an arbitrary point in a closed ball of radius $r$ centered at $x$. In th... |
Title: Transfer Learning of High-Fidelity Opacity Spectra in Autoencoders and Surrogate Models Abstract: Simulations of high energy density physics are expensive, largely in part for the need to produce non-local thermodynamic equilibrium opacities. High-fidelity spectra may reveal new physics in the simulations not se... |
Title: FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours Abstract: Protein structure prediction is an important method for understanding gene translation and protein function in the domain of structural biology. AlphaFold introduced the Transformer model to the field of protein structure prediction wi... |
Title: Faith-Shap: The Faithful Shapley Interaction Index Abstract: Shapley values, which were originally designed to assign attributions to individual players in coalition games, have become a commonly used approach in explainable machine learning to provide attributions to input features for black-box machine learnin... |
Title: Follow your Nose: Using General Value Functions for Directed Exploration in Reinforcement Learning Abstract: Exploration versus exploitation dilemma is a significant problem in reinforcement learning (RL), particularly in complex environments with large state space and sparse rewards. When optimizing for a parti... |
Title: A Learning Based Framework for Handling Uncertain Lead Times in Multi-Product Inventory Management Abstract: Most existing literature on supply chain and inventory management consider stochastic demand processes with zero or constant lead times. While it is true that in certain niche scenarios, uncertainty in le... |
Title: Sampling Random Group Fair Rankings Abstract: In this paper, we consider the problem of randomized group fair ranking that merges given ranked list of items from different sensitive demographic groups while satisfying given lower and upper bounds on the representation of each group in the top ranks. Our randomiz... |
Title: Combining Reinforcement Learning and Optimal Transport for the Traveling Salesman Problem Abstract: The traveling salesman problem is a fundamental combinatorial optimization problem with strong exact algorithms. However, as problems scale up, these exact algorithms fail to provide a solution in a reasonable tim... |
Title: Weakly Supervised Correspondence Learning Abstract: Correspondence learning is a fundamental problem in robotics, which aims to learn a mapping between state, action pairs of agents of different dynamics or embodiments. However, current correspondence learning methods either leverage strictly paired data -- whic... |
Title: MIAShield: Defending Membership Inference Attacks via Preemptive Exclusion of Members Abstract: In membership inference attacks (MIAs), an adversary observes the predictions of a model to determine whether a sample is part of the model's training data. Existing MIA defenses conceal the presence of a target sampl... |
Title: Canonical foliations of neural networks: application to robustness Abstract: Adversarial attack is an emerging threat to the trustability of machine learning. Understanding these attacks is becoming a crucial task. We propose a new vision on neural network robustness using Riemannian geometry and foliation theor... |
Title: Continual Learning of Multi-modal Dynamics with External Memory Abstract: We study the problem of fitting a model to a dynamical environment when new modes of behavior emerge sequentially. The learning model is aware when a new mode appears, but it does not have access to the true modes of individual training se... |
Title: ES-dRNN with Dynamic Attention for Short-Term Load Forecasting Abstract: Short-term load forecasting (STLF) is a challenging problem due to the complex nature of the time series expressing multiple seasonality and varying variance. This paper proposes an extension of a hybrid forecasting model combining exponent... |
Title: Neuro-Symbolic Verification of Deep Neural Networks Abstract: Formal verification has emerged as a powerful approach to ensure the safety and reliability of deep neural networks. However, current verification tools are limited to only a handful of properties that can be expressed as first-order constraints over ... |
Title: CD-GAN: a robust fusion-based generative adversarial network for unsupervised change detection between heterogeneous images Abstract: In the context of Earth observation, the detection of changes is performed from multitemporal images acquired by sensors with possibly different characteristics and modalities. Ev... |
Title: GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation Abstract: Graph Neural Networks (GNNs) are powerful models designed for graph data that learn node representation by recursively aggregating information from each node's local neighborhood. However, despite their state-of-the-art per... |
Title: Parallel Spatio-Temporal Attention-Based TCN for Multivariate Time Series Prediction Abstract: As industrial systems become more complex and monitoring sensors for everything from surveillance to our health become more ubiquitous, multivariate time series prediction is taking an important place in the smooth-run... |
Title: A density peaks clustering algorithm with sparse search and K-d tree Abstract: Density peaks clustering has become a nova of clustering algorithm because of its simplicity and practicality. However, there is one main drawback: it is time-consuming due to its high computational complexity. Herein, a density peaks... |
Title: Predicting the temporal dynamics of turbulent channels through deep learning Abstract: The success of recurrent neural networks (RNNs) has been demonstrated in many applications related to turbulence, including flow control, optimization, turbulent features reproduction as well as turbulence prediction and model... |
Title: L4KDE: Learning for KinoDynamic Tree Expansion Abstract: We present the Learning for KinoDynamic Tree Expansion (L4KDE) method for kinodynamic planning. Tree-based planning approaches, such as rapidly exploring random tree (RRT), are the dominant approach to finding globally optimal plans in continuous state-spa... |
Title: Chained Generalisation Bounds Abstract: This work discusses how to derive upper bounds for the expected generalisation error of supervised learning algorithms by means of the chaining technique. By developing a general theoretical framework, we establish a duality between generalisation bounds based on the regul... |
Title: Boosted Ensemble Learning based on Randomized NNs for Time Series Forecasting Abstract: Time series forecasting is a challenging problem particularly when a time series expresses multiple seasonality, nonlinear trend and varying variance. In this work, to forecast complex time series, we propose ensemble learnin... |
Title: Beyond GAP screening for Lasso by exploiting new dual cutting half-spaces with supplementary material Abstract: In this paper, we propose a novel safe screening test for Lasso. Our procedure is based on a safe region with a dome geometry and exploits a canonical representation of the set of half-spaces (referred... |
Title: Learning Efficiently Function Approximation for Contextual MDP Abstract: We study learning contextual MDPs using a function approximation for both the rewards and the dynamics. We consider both the case where the dynamics is known and unknown, and the case that the dynamics dependent or independent of the contex... |
Title: Improving the Diversity of Bootstrapped DQN via Noisy Priors Abstract: Q-learning is one of the most well-known Reinforcement Learning algorithms. There have been tremendous efforts to develop this algorithm using neural networks. Bootstrapped Deep Q-Learning Network is amongst one of them. It utilizes multiple ... |
Title: UAV-Aided Decentralized Learning over Mesh Networks Abstract: Decentralized learning empowers wireless network devices to collaboratively train a machine learning (ML) model relying solely on device-to-device (D2D) communication. It is known that the convergence speed of decentralized optimization algorithms sev... |
Title: Continual Feature Selection: Spurious Features in Continual Learning Abstract: Continual Learning (CL) is the research field addressing learning without forgetting when the data distribution is not static. This paper studies spurious features' influence on continual learning algorithms. We show that continual le... |
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