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Title: Near-Optimal Sample Complexity Bounds for Constrained MDPs Abstract: In contrast to the advances in characterizing the sample complexity for solving Markov decision processes (MDPs), the optimal statistical complexity for solving constrained MDPs (CMDPs) remains unknown. We resolve this question by providing min... |
Title: Distributed Adversarial Training to Robustify Deep Neural Networks at Scale Abstract: Current deep neural networks (DNNs) are vulnerable to adversarial attacks, where adversarial perturbations to the inputs can change or manipulate classification. To defend against such attacks, an effective and popular approach... |
Title: On the impact of dataset size and class imbalance in evaluating machine-learning-based windows malware detection techniques Abstract: The purpose of this project was to collect and analyse data about the comparability and real-life applicability of published results focusing on Microsoft Windows malware, more sp... |
Title: RPLHR-CT Dataset and Transformer Baseline for Volumetric Super-Resolution from CT Scans Abstract: In clinical practice, anisotropic volumetric medical images with low through-plane resolution are commonly used due to short acquisition time and lower storage cost. Nevertheless, the coarse resolution may lead to d... |
Title: Contrastive Learning for Unsupervised Domain Adaptation of Time Series Abstract: Unsupervised domain adaptation (UDA) aims at learning a machine learning model using a labeled source domain that performs well on a similar yet different, unlabeled target domain. UDA is important in many applications such as medic... |
Title: Evaluating Graph Generative Models with Contrastively Learned Features Abstract: A wide range of models have been proposed for Graph Generative Models, necessitating effective methods to evaluate their quality. So far, most techniques use either traditional metrics based on subgraph counting, or the representati... |
Title: Towards Understanding Sharpness-Aware Minimization Abstract: Sharpness-Aware Minimization (SAM) is a recent training method that relies on worst-case weight perturbations which significantly improves generalization in various settings. We argue that the existing justifications for the success of SAM which are ba... |
Title: Transition-based Abstract Meaning Representation Parsing with Contextual Embeddings Abstract: The ability to understand and generate languages sets human cognition apart from other known life forms'. We study a way of combing two of the most successful routes to meaning of language--statistical language models a... |
Title: Convergence for score-based generative modeling with polynomial complexity Abstract: Score-based generative modeling (SGM) is a highly successful approach for learning a probability distribution from data and generating further samples. We prove the first polynomial convergence guarantees for the core mechanic b... |
Title: Acceleration of cerebral blood flow and arterial transit time maps estimation from multiple post-labeling delay arterial spin-labeled MRI via deep learning Abstract: Purpose: Arterial spin labeling (ASL) perfusion imaging indicates direct and absolute measurement of cerebral blood flow (CBF). Arterial transit ti... |
Title: Predicting conditional probability distributions of redshifts of Active Galactic Nuclei using Hierarchical Correlation Reconstruction Abstract: While there is a general focus on prediction of values, real data often only allows to predict conditional probability distributions, with capabilities bounded by condit... |
Title: AI-based Data Preparation and Data Analytics in Healthcare: The Case of Diabetes Abstract: The Associazione Medici Diabetologi (AMD) collects and manages one of the largest worldwide-available collections of diabetic patient records, also known as the AMD database. This paper presents the initial results of an o... |
Title: EGRU: Event-based GRU for activity-sparse inference and learning Abstract: The scalability of recurrent neural networks (RNNs) is hindered by the sequential dependence of each time step's computation on the previous time step's output. Therefore, one way to speed up and scale RNNs is to reduce the computation re... |
Title: iCITRIS: Causal Representation Learning for Instantaneous Temporal Effects Abstract: Causal representation learning is the task of identifying the underlying causal variables and their relations from high-dimensional observations, such as images. Recent work has shown that one can reconstruct the causal variable... |
Title: The Classification of Optical Galaxy Morphology Using Unsupervised Learning Techniques Abstract: The advent of large scale, data intensive astronomical surveys has caused the viability of human-based galaxy morphology classification methods to come into question. Put simply, too much astronomical data is being p... |
Title: SyntheX: Scaling Up Learning-based X-ray Image Analysis Through In Silico Experiments Abstract: Artificial intelligence (AI) now enables automated interpretation of medical images for clinical use. However, AI's potential use for interventional images (versus those involved in triage or diagnosis), such as for g... |
Title: Robust Time Series Denoising with Learnable Wavelet Packet Transform Abstract: In many applications, signal denoising is often the first pre-processing step before any subsequent analysis or learning task. In this paper, we propose to apply a deep learning denoising model inspired by a signal processing, a learn... |
Title: Markov Decision Processes under Model Uncertainty Abstract: We introduce a general framework for Markov decision problems under model uncertainty in a discrete-time infinite horizon setting. By providing a dynamic programming principle we obtain a local-to-global paradigm, namely solving a local, i.e., a one tim... |
Title: AR-NeRF: Unsupervised Learning of Depth and Defocus Effects from Natural Images with Aperture Rendering Neural Radiance Fields Abstract: Fully unsupervised 3D representation learning has gained attention owing to its advantages in data collection. A successful approach involves a viewpoint-aware approach that le... |
Title: Towards Autonomous Grading In The Real World Abstract: In this work, we aim to tackle the problem of autonomous grading, where a dozer is required to flatten an uneven area. In addition, we explore methods for bridging the gap between a simulated environment and real scenarios. We design both a realistic physica... |
Title: Rank Diminishing in Deep Neural Networks Abstract: The rank of neural networks measures information flowing across layers. It is an instance of a key structural condition that applies across broad domains of machine learning. In particular, the assumption of low-rank feature representations leads to algorithmic ... |
Title: Energy-Efficient Wake-Up Signalling for Machine-Type Devices Based on Traffic-Aware Long-Short Term Memory Prediction Abstract: Reducing energy consumption is a pressing issue in low-power machine-type communication (MTC) networks. In this regard, the Wake-up Signal (WuS) technology, which aims to minimize the e... |
Title: Low-complexity deep learning frameworks for acoustic scene classification Abstract: In this report, we presents low-complexity deep learning frameworks for acoustic scene classification (ASC). The proposed frameworks can be separated into four main steps: Front-end spectrogram extraction, online data augmentatio... |
Title: Specifying and Testing $k$-Safety Properties for Machine-Learning Models Abstract: Machine-learning models are becoming increasingly prevalent in our lives, for instance assisting in image-classification or decision-making tasks. Consequently, the reliability of these models is of critical importance and has res... |
Title: Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inference Abstract: Neuromorphic computing is an emerging computing paradigm that moves away from batched processing towards the online, event-driven, processing of streaming data. Neuromorphic chips, when coupled with spike-based s... |
Title: Automatic Contact Tracing using Bluetooth Low Energy Signals and IMU Sensor Readings Abstract: In this report, we present our solution to the challenge provided by the SFI Centre for Machine Learning (ML-Labs) in which the distance between two phones needs to be estimated. It is a modified version of the NIST To... |
Title: A universal synthetic dataset for machine learning on spectroscopic data Abstract: To assist in the development of machine learning methods for automated classification of spectroscopic data, we have generated a universal synthetic dataset that can be used for model validation. This dataset contains artificial s... |
Title: Mediators: Conversational Agents Explaining NLP Model Behavior Abstract: The human-centric explainable artificial intelligence (HCXAI) community has raised the need for framing the explanation process as a conversation between human and machine. In this position paper, we establish desiderata for Mediators, text... |
Title: Machine Learning Training on a Real Processing-in-Memory System Abstract: Training machine learning algorithms is a computationally intensive process, which is frequently memory-bound due to repeatedly accessing large training datasets. As a result, processor-centric systems (e.g., CPU, GPU) suffer from costly d... |
Title: No-Regret Learning in Games with Noisy Feedback: Faster Rates and Adaptivity via Learning Rate Separation Abstract: We examine the problem of regret minimization when the learner is involved in a continuous game with other optimizing agents: in this case, if all players follow a no-regret algorithm, it is possib... |
Title: Exploring and Exploiting Hubness Priors for High-Quality GAN Latent Sampling Abstract: Despite the extensive studies on Generative Adversarial Networks (GANs), how to reliably sample high-quality images from their latent spaces remains an under-explored topic. In this paper, we propose a novel GAN latent samplin... |
Title: Reinforcement Learning-based Placement of Charging Stations in Urban Road Networks Abstract: The transition from conventional mobility to electromobility largely depends on charging infrastructure availability and optimal placement.This paper examines the optimal placement of charging stations in urban areas. We... |
Title: Relative Policy-Transition Optimization for Fast Policy Transfer Abstract: We consider the problem of policy transfer between two Markov Decision Processes (MDPs). We introduce a lemma based on existing theoretical results in reinforcement learning (RL) to measure the relativity between two arbitrary MDPs, that ... |
Title: Intrinsically motivated option learning: a comparative study of recent methods Abstract: Options represent a framework for reasoning across multiple time scales in reinforcement learning (RL). With the recent active interest in the unsupervised learning paradigm in the RL research community, the option framework... |
Title: A Novel Multi-Layer Modular Approach for Real-Time Gravitational-Wave Detection Abstract: Advanced LIGO and Advanced Virgo ground-based interferometers are poised to probe an unprecedentedly large volume of space, enhancing the discovery power of the observations to even new sources of gravitational wave emitter... |
Title: GPU-Accelerated Machine Learning in Non-Orthogonal Multiple Access Abstract: Non-orthogonal multiple access (NOMA) is an interesting technology that enables massive connectivity as required in future 5G and 6G networks. While purely linear processing already achieves good performance in NOMA systems, in certain ... |
Title: Analysis of function approximation and stability of general DNNs in directed acyclic graphs using un-rectifying analysis Abstract: A general lack of understanding pertaining to deep feedforward neural networks (DNNs) can be attributed partly to a lack of tools with which to analyze the composition of non-linear ... |
Title: Multi-Agent Neural Rewriter for Vehicle Routing with Limited Disclosure of Costs Abstract: We interpret solving the multi-vehicle routing problem as a team Markov game with partially observable costs. For a given set of customers to serve, the playing agents (vehicles) have the common goal to determine the team-... |
Title: High-Dimensional Bayesian Optimization with Constraints: Application to Powder Weighing Abstract: Bayesian optimization works effectively optimizing parameters in black-box problems. However, this method did not work for high-dimensional parameters in limited trials. Parameters can be efficiently explored by non... |
Title: Modeling the Machine Learning Multiverse Abstract: Amid mounting concern about the reliability and credibility of machine learning research, we present a principled framework for making robust and generalizable claims: the Multiverse Analysis. Our framework builds upon the Multiverse Analysis (Steegen et al., 20... |
Title: Flexible Raman Amplifier Optimization Based on Machine Learning-aided Physical Stimulated Raman Scattering Model Abstract: The problem of Raman amplifier optimization is studied. A differentiable interpolation function is obtained for the Raman gain coefficient using machine learning (ML), which allows for the g... |
Title: Efficient Human-in-the-loop System for Guiding DNNs Attention Abstract: Attention guidance is an approach to addressing dataset bias in deep learning, where the model relies on incorrect features to make decisions. Focusing on image classification tasks, we propose an efficient human-in-the-loop system to intera... |
Title: Top Two Algorithms Revisited Abstract: Top Two algorithms arose as an adaptation of Thompson sampling to best arm identification in multi-armed bandit models (Russo, 2016), for parametric families of arms. They select the next arm to sample from by randomizing among two candidate arms, a leader and a challenger.... |
Title: Recommender Transformers with Behavior Pathways Abstract: Sequential recommendation requires the recommender to capture the evolving behavior characteristics from logged user behavior data for accurate recommendations. However, user behavior sequences are viewed as a script with multiple ongoing threads intertwi... |
Title: Value Function Based Difference-of-Convex Algorithm for Bilevel Hyperparameter Selection Problems Abstract: Gradient-based optimization methods for hyperparameter tuning guarantee theoretical convergence to stationary solutions when for fixed upper-level variable values, the lower level of the bilevel program is... |
Title: Deep Neural Network Based Accelerated Failure Time Models using Rank Loss Abstract: An accelerated failure time (AFT) model assumes a log-linear relationship between failure times and a set of covariates. In contrast to other popular survival models that work on hazard functions, the effects of covariates are di... |
Title: EmProx: Neural Network Performance Estimation For Neural Architecture Search Abstract: Common Neural Architecture Search methods generate large amounts of candidate architectures that need training in order to assess their performance and find an optimal architecture. To minimize the search time we use different... |
Title: Biologically Inspired Neural Path Finding Abstract: The human brain can be considered to be a graphical structure comprising of tens of billions of biological neurons connected by synapses. It has the remarkable ability to automatically re-route information flow through alternate paths in case some neurons are d... |
Title: GoToNet: Fast Monocular Scene Exposure and Exploration Abstract: Autonomous scene exposure and exploration, especially in localization or communication-denied areas, useful for finding targets in unknown scenes, remains a challenging problem in computer navigation. In this work, we present a novel method for rea... |
Title: PRO-TIP: Phantom for RObust automatic ultrasound calibration by TIP detection Abstract: We propose a novel method to automatically calibrate tracked ultrasound probes. To this end we design a custom phantom consisting of nine cones with different heights. The tips are used as key points to be matched between mul... |
Title: SIXO: Smoothing Inference with Twisted Objectives Abstract: Sequential Monte Carlo (SMC) is an inference algorithm for state space models that approximates the posterior by sampling from a sequence of target distributions. The target distributions are often chosen to be the filtering distributions, but these ign... |
Title: Lazy and Fast Greedy MAP Inference for Determinantal Point Process Abstract: The maximum a posteriori (MAP) inference for determinantal point processes (DPPs) is crucial for selecting diverse items in many machine learning applications. Although DPP MAP inference is NP-hard, the greedy algorithm often finds high... |
Title: Private Synthetic Data with Hierarchical Structure Abstract: We study the problem of differentially private synthetic data generation for hierarchical datasets in which individual data points are grouped together (e.g., people within households). In particular, to measure the similarity between the synthetic dat... |
Title: Towards Universal Sequence Representation Learning for Recommender Systems Abstract: In order to develop effective sequential recommenders, a series of sequence representation learning (SRL) methods are proposed to model historical user behaviors. Most existing SRL methods rely on explicit item IDs for developin... |
Title: Faster Optimization-Based Meta-Learning Adaptation Phase Abstract: Neural networks require a large amount of annotated data to learn. Meta-learning algorithms propose a way to decrease the number of training samples to only a few. One of the most prominent optimization-based meta-learning algorithms is Model-Agn... |
Title: Compressive Clustering with an Optical Processing Unit Abstract: We explore the use of Optical Processing Units (OPU) to compute random Fourier features for sketching, and adapt the overall compressive clustering pipeline to this setting. We also propose some tools to help tuning a critical hyper-parameter of co... |
Title: Why Quantization Improves Generalization: NTK of Binary Weight Neural Networks Abstract: Quantized neural networks have drawn a lot of attention as they reduce the space and computational complexity during the inference. Moreover, there has been folklore that quantization acts as an implicit regularizer and thus... |
Title: Safe-FinRL: A Low Bias and Variance Deep Reinforcement Learning Implementation for High-Freq Stock Trading Abstract: In recent years, many practitioners in quantitative finance have attempted to use Deep Reinforcement Learning (DRL) to build better quantitative trading (QT) strategies. Nevertheless, many existin... |
Title: Local distance preserving auto-encoders using Continuous k-Nearest Neighbours graphs Abstract: Auto-encoder models that preserve similarities in the data are a popular tool in representation learning. In this paper we introduce several auto-encoder models that preserve local distances when mapping from the data ... |
Title: Superiority of GNN over NN in generalizing bandlimited functions Abstract: We constructively show, via rigorous mathematical arguments, that GNN architectures outperform those of NN in approximating bandlimited functions on compact $d$-dimensional Euclidean grids. We show that the former only need $\mathcal{M}$ ... |
Title: Geometrically Guided Integrated Gradients Abstract: Interpretability methods for deep neural networks mainly focus on the sensitivity of the class score with respect to the original or perturbed input, usually measured using actual or modified gradients. Some methods also use a model-agnostic approach to underst... |
Title: Provable Benefit of Multitask Representation Learning in Reinforcement Learning Abstract: As representation learning becomes a powerful technique to reduce sample complexity in reinforcement learning (RL) in practice, theoretical understanding of its advantage is still limited. In this paper, we theoretically ch... |
Title: Pixel to Binary Embedding Towards Robustness for CNNs Abstract: There are several problems with the robustness of Convolutional Neural Networks (CNNs). For example, the prediction of CNNs can be changed by adding a small magnitude of noise to an input, and the performances of CNNs are degraded when the distribut... |
Title: Latent Diffusion Energy-Based Model for Interpretable Text Modeling Abstract: Latent space Energy-Based Models (EBMs), also known as energy-based priors, have drawn growing interests in generative modeling. Fueled by its flexibility in the formulation and strong modeling power of the latent space, recent works b... |
Title: Content Popularity Prediction in Fog-RANs: A Clustered Federated Learning Based Approach Abstract: In this paper, the content popularity prediction problem in fog radio access networks (F-RANs) is investigated. Based on clustered federated learning, we propose a novel mobility-aware popularity prediction policy,... |
Title: Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations Abstract: Due to the computational cost of running inference for a neural network, the need to deploy the inferential steps on a third party's compute environment or hardware is common. If the third party is not ... |
Title: Accelerating Federated Learning via Sampling Anchor Clients with Large Batches Abstract: Using large batches in recent federated learning studies has improved convergence rates, but it requires additional computation overhead compared to using small batches. To overcome this limitation, we propose a unified fram... |
Title: Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach Abstract: The fog radio access network (F-RAN) is a promising technology in which the user mobile devices (MDs) can offload computation tasks to the nearby fog access points (F-APs). Due to the limited reso... |
Title: Confident Sinkhorn Allocation for Pseudo-Labeling Abstract: Semi-supervised learning is a critical tool in reducing machine learning's dependence on labeled data. It has, however, been applied primarily to image and language data, by exploiting the inherent spatial and semantic structure therein. These methods d... |
Title: Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques Abstract: We present the task description of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2022 Challenge Task 2: "... |
Title: On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms Abstract: Stochastic gradient descent (SGD) algorithm is the method of choice in many machine learning tasks thanks to its scalability and efficiency in dealing with large-scale problems. In this paper, we focus on the shuffling versio... |
Title: X-Risk Analysis for AI Research Abstract: Artificial intelligence (AI) has the potential to greatly improve society, but as with any powerful technology, it comes with heightened risks and responsibilities. Current AI research lacks a systematic discussion of how to manage long-tail risks from AI systems, includ... |
Title: IGN : Implicit Generative Networks Abstract: In this work, we build recent advances in distributional reinforcement learning to give a state-of-art distributional variant of the model based on the IQN. We achieve this by using the GAN model's generator and discriminator function with the quantile regression to a... |
Title: A Directed-Evolution Method for Sparsification and Compression of Neural Networks with Application to Object Identification and Segmentation and considerations of optimal quantization using small number of bits Abstract: This work introduces Directed-Evolution (DE) method for sparsification of neural networks, w... |
Title: ChordMixer: A Scalable Neural Attention Model for Sequences with Different Lengths Abstract: Sequential data naturally have different lengths in many domains, with some very long sequences. As an important modeling tool, neural attention should capture long-range interaction in such sequences. However, most exis... |
Title: Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Conservative Natural Policy Gradient Primal-Dual Algorithm Abstract: We consider the problem of constrained Markov decision process (CMDP) in continuous state-actions spaces where the goal is to maximize the expected cumulative reward... |
Title: InBiaseD: Inductive Bias Distillation to Improve Generalization and Robustness through Shape-awareness Abstract: Humans rely less on spurious correlations and trivial cues, such as texture, compared to deep neural networks which lead to better generalization and robustness. It can be attributed to the prior know... |
Title: GAN based Data Augmentation to Resolve Class Imbalance Abstract: The number of credit card fraud has been growing as technology grows and people can take advantage of it. Therefore, it is very important to implement a robust and effective method to detect such frauds. The machine learning algorithms are appropri... |
Title: GLIPv2: Unifying Localization and Vision-Language Understanding Abstract: We present GLIPv2, a grounded VL understanding model, that serves both localization tasks (e.g., object detection, instance segmentation) and Vision-Language (VL) understanding tasks (e.g., VQA, image captioning). GLIPv2 elegantly unifies ... |
Title: Deep Reinforcement Learning for Optimal Investment and Saving Strategy Selection in Heterogeneous Profiles: Intelligent Agents working towards retirement Abstract: The transition from defined benefit to defined contribution pension plans shifts the responsibility for saving toward retirement from governments and... |
Title: Stochastic Gradient Descent without Full Data Shuffle Abstract: Stochastic gradient descent (SGD) is the cornerstone of modern machine learning (ML) systems. Despite its computational efficiency, SGD requires random data access that is inherently inefficient when implemented in systems that rely on block-address... |
Title: Bounding and Approximating Intersectional Fairness through Marginal Fairness Abstract: Discrimination in machine learning often arises along multiple dimensions (a.k.a. protected attributes); it is then desirable to ensure \emph{intersectional fairness} -- i.e., that no subgroup is discriminated against. It is k... |
Title: Case-Based Inverse Reinforcement Learning Using Temporal Coherence Abstract: Providing expert trajectories in the context of Imitation Learning is often expensive and time-consuming. The goal must therefore be to create algorithms which require as little expert data as possible. In this paper we present an algor... |
Title: A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games Abstract: Algorithms designed for single-agent reinforcement learning (RL) generally fail to converge to equilibria in two-player zero-sum (2p0s) games. Conversely, game-theoretic algorithms for approximating... |
Title: Science through Machine Learning: Quantification of Poststorm Thermospheric Cooling Abstract: Machine learning (ML) is often viewed as a black-box regression technique that is unable to provide considerable scientific insight. ML models are universal function approximators and - if used correctly - can provide s... |
Title: An Industry 4.0 example: real-time quality control for steel-based mass production using Machine Learning on non-invasive sensor data Abstract: Insufficient steel quality in mass production can cause extremely costly damage to tooling, production downtimes and low quality products. Automatic, fast and cheap stra... |
Title: Analysis of Branch Specialization and its Application in Image Decomposition Abstract: Branched neural networks have been used extensively for a variety of tasks. Branches are sub-parts of the model that perform independent processing followed by aggregation. It is known that this setting induces a phenomenon ca... |
Title: Geometric Policy Iteration for Markov Decision Processes Abstract: Recently discovered polyhedral structures of the value function for finite state-action discounted Markov decision processes (MDP) shed light on understanding the success of reinforcement learning. We investigate the value function polytope in gr... |
Title: Self-critiquing models for assisting human evaluators Abstract: We fine-tune large language models to write natural language critiques (natural language critical comments) using behavioral cloning. On a topic-based summarization task, critiques written by our models help humans find flaws in summaries that they ... |
Title: SGD Noise and Implicit Low-Rank Bias in Deep Neural Networks Abstract: We analyze deep ReLU neural networks trained with mini-batch Stochastic Gradient Descent (SGD) and weight decay. We study the source of SGD noise and prove that when training with weight decay, the only solutions of SGD at convergence are zer... |
Title: Neurotoxin: Durable Backdoors in Federated Learning Abstract: Due to their decentralized nature, federated learning (FL) systems have an inherent vulnerability during their training to adversarial backdoor attacks. In this type of attack, the goal of the attacker is to use poisoned updates to implant so-called b... |
Title: Dual-Stream Transformer with Cross-Attention on Whole-Slide Image Pyramids for Cancer Prognosis Abstract: The cancer prognosis on gigapixel Whole-Slide Images (WSIs) has always been a challenging task. Most existing approaches focus solely on single-resolution images. The multi-resolution schemes, utilizing imag... |
Title: Learning-Based Data Storage [Vision] (Technical Report) Abstract: Deep neural network (DNN) and its variants have been extensively used for a wide spectrum of real applications such as image classification, face/speech recognition, fraud detection, and so on. In addition to many important machine learning tasks,... |
Title: The Rough Topology for Numerical Data Abstract: In this paper, we give a generalization of the rough topology and the core to numerical data by classifying objects in terms of the attribute values. New approach to find the core for numerical data is discussed. Then a measurement to find whether an attribute is i... |
Title: Distributed Differential Privacy in Multi-Armed Bandits Abstract: We consider the standard $K$-armed bandit problem under a distributed trust model of differential privacy (DP), which enables to guarantee privacy without a trustworthy server. Under this trust model, previous work largely focus on achieving priva... |
Title: Mining Multi-Label Samples from Single Positive Labels Abstract: Conditional generative adversarial networks (cGANs) have shown superior results in class-conditional generation tasks. In order to simultaneously control multiple conditions, cGANs require multi-label training datasets, where multiple labels can be... |
Title: Consistent Attack: Universal Adversarial Perturbation on Embodied Vision Navigation Abstract: Embodied agents in vision navigation coupled with deep neural networks have attracted increasing attention. However, deep neural networks are vulnerable to malicious adversarial noises, which may potentially cause catas... |
Title: Matching options to tasks using Option-Indexed Hierarchical Reinforcement Learning Abstract: The options framework in Hierarchical Reinforcement Learning breaks down overall goals into a combination of options or simpler tasks and associated policies, allowing for abstraction in the action space. Ideally, these ... |
Title: Regularization Penalty Optimization for Addressing Data Quality Variance in OoD Algorithms Abstract: Due to the poor generalization performance of traditional empirical risk minimization (ERM) in the case of distributional shift, Out-of-Distribution (OoD) generalization algorithms receive increasing attention. H... |
Title: Learning to Detect with Constant False Alarm Rate Abstract: We consider the use of machine learning for hypothesis testing with an emphasis on target detection. Classical model-based solutions rely on comparing likelihoods. These are sensitive to imperfect models and are often computationally expensive. In contr... |
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