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Title: When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction Abstract: The standard approach to personalization in machine learning consists of training a model with group attributes like sex, age group, and blood type. In this work, we show that this approach to personalization fails to i... |
Title: Your Neighbors Are Communicating: Towards Powerful and Scalable Graph Neural Networks Abstract: Message passing graph neural networks (GNNs) are known to have their expressiveness upper-bounded by 1-dimensional Weisfeiler-Lehman (1-WL) algorithm. To achieve more powerful GNNs, existing attempts either require ad... |
Title: Active Bayesian Causal Inference Abstract: Causal discovery and causal reasoning are classically treated as separate and consecutive tasks: one first infers the causal graph, and then uses it to estimate causal effects of interventions. However, such a two-stage approach is uneconomical, especially in terms of a... |
Title: Learning Robust Representations Of Generative Models Using Set-Based Artificial Fingerprints Abstract: With recent progress in deep generative models, the problem of identifying synthetic data and comparing their underlying generative processes has become an imperative task for various reasons, including fightin... |
Title: Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning Abstract: The quintessential model-based reinforcement-learning agent iteratively refines its estimates or prior beliefs about the true underlying model of the environment. Recent empirical successes in model-based reinforcement learnin... |
Title: Straggler-Resilient Personalized Federated Learning Abstract: Federated Learning is an emerging learning paradigm that allows training models from samples distributed across a large network of clients while respecting privacy and communication restrictions. Despite its success, federated learning faces several c... |
Title: Inference for Interpretable Machine Learning: Fast, Model-Agnostic Confidence Intervals for Feature Importance Abstract: In order to trust machine learning for high-stakes problems, we need models to be both reliable and interpretable. Recently, there has been a growing body of work on interpretable machine lear... |
Title: Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization Abstract: Directed Evolution (DE), a landmark wet-lab method originated in 1960s, enables discovery of novel protein designs via evolving a population of candidate sequences. Recent advances in biotechnology has made it possi... |
Title: Using Connectome Features to Constrain Echo State Networks Abstract: We report an improvement to the conventional Echo State Network (ESN), which already achieves competitive performance in one-dimensional time series prediction of dynamical systems. Our model -- a 20$\%$-dense ESN with reservoir weights derived... |
Title: ARC -- Actor Residual Critic for Adversarial Imitation Learning Abstract: Adversarial Imitation Learning (AIL) is a class of popular state-of-the-art Imitation Learning algorithms where an artificial adversary's misclassification is used as a reward signal and is optimized by any standard Reinforcement Learning ... |
Title: PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding Abstract: We are now witnessing significant progress of deep learning methods in a variety of tasks (or datasets) of proteins. However, there is a lack of a standard benchmark to evaluate the performance of different methods, which... |
Title: Search Space Adaptation for Differentiable Neural Architecture Search in Image Classification Abstract: As deep neural networks achieve unprecedented performance in various tasks, neural architecture search (NAS), a research field for designing neural network architectures with automated processes, is actively u... |
Title: AUTM Flow: Atomic Unrestricted Time Machine for Monotonic Normalizing Flows Abstract: Nonlinear monotone transformations are used extensively in normalizing flows to construct invertible triangular mappings from simple distributions to complex ones. In existing literature, monotonicity is usually enforced by res... |
Title: Interpretable Mixture of Experts for Structured Data Abstract: With the growth of machine learning for structured data, the need for reliable model explanations is essential, especially in high-stakes applications. We introduce a novel framework, Interpretable Mixture of Experts (IME), that provides interpretabi... |
Title: Learning Dynamics and Generalization in Reinforcement Learning Abstract: Solving a reinforcement learning (RL) problem poses two competing challenges: fitting a potentially discontinuous value function, and generalizing well to new observations. In this paper, we analyze the learning dynamics of temporal differe... |
Title: DeeprETA: An ETA Post-processing System at Scale Abstract: Estimated Time of Arrival (ETA) plays an important role in delivery and ride-hailing platforms. For example, Uber uses ETAs to calculate fares, estimate pickup times, match riders to drivers, plan deliveries, and more. Commonly used route planning algori... |
Title: Federated Adversarial Training with Transformers Abstract: Federated learning (FL) has emerged to enable global model training over distributed clients' data while preserving its privacy. However, the global trained model is vulnerable to the evasion attacks especially, the adversarial examples (AEs), carefully ... |
Title: Early Stage Convergence and Global Convergence of Training Mildly Parameterized Neural Networks Abstract: The convergence of GD and SGD when training mildly parameterized neural networks starting from random initialization is studied. For a broad range of models and loss functions, including the most commonly us... |
Title: Which models are innately best at uncertainty estimation? Abstract: Deep neural networks must be equipped with an uncertainty estimation mechanism when deployed for risk-sensitive tasks. This paper studies the relationship between deep architectures and their training regimes with their corresponding selective p... |
Title: HPGNN: Using Hierarchical Graph Neural Networks for Outdoor Point Cloud Processing Abstract: Inspired by recent improvements in point cloud processing for autonomous navigation, we focus on using hierarchical graph neural networks for processing and feature learning over large-scale outdoor LiDAR point clouds. W... |
Title: Perspectives of Non-Expert Users on Cyber Security and Privacy: An Analysis of Online Discussions on Twitter Abstract: Current research on users` perspectives of cyber security and privacy related to traditional and smart devices at home is very active, but the focus is often more on specific modern devices such... |
Title: Never mind the metrics -- what about the uncertainty? Visualising confusion matrix metric distributions Abstract: There are strong incentives to build models that demonstrate outstanding predictive performance on various datasets and benchmarks. We believe these incentives risk a narrow focus on models and on th... |
Title: Vanilla Feature Distillation for Improving the Accuracy-Robustness Trade-Off in Adversarial Training Abstract: Adversarial training has been widely explored for mitigating attacks against deep models. However, most existing works are still trapped in the dilemma between higher accuracy and stronger robustness si... |
Title: Estimating and Mitigating the Congestion Effect of Curbside Pick-ups and Drop-offs: A Causal Inference Approach Abstract: Curb space is one of the busiest areas in urban road networks. Especially in recent years, the rapid increase of ride-hailing trips and commercial deliveries has induced massive pick-ups/drop... |
Title: A Survey on Deep Learning based Channel Estimation in Doubly Dispersive Environments Abstract: Wireless communications systems are impacted by multi-path fading and Doppler shift in dynamic environments, where the channel becomes doubly-dispersive and its estimation becomes an arduous task. Only a few pilots are... |
Title: Factored Conditional Filtering: Tracking States and Estimating Parameters in High-Dimensional Spaces Abstract: This paper introduces the factored conditional filter, a new filtering algorithm for simultaneously tracking states and estimating parameters in high-dimensional state spaces. The conditional nature of ... |
Title: Functional Ensemble Distillation Abstract: Bayesian models have many desirable properties, most notable is their ability to generalize from limited data and to properly estimate the uncertainty in their predictions. However, these benefits come at a steep computational cost as Bayesian inference, in most cases, ... |
Title: Machine learning applications for electricity market agent-based models: A systematic literature review Abstract: The electricity market has a vital role to play in the decarbonisation of the energy system. However, the electricity market is made up of many different variables and data inputs. These variables an... |
Title: GridShift: A Faster Mode-seeking Algorithm for Image Segmentation and Object Tracking Abstract: In machine learning and computer vision, mean shift (MS) qualifies as one of the most popular mode-seeking algorithms used for clustering and image segmentation. It iteratively moves each data point to the weighted me... |
Title: Performance Comparison of Simple Transformer and Res-CNN-BiLSTM for Cyberbullying Classification Abstract: The task of text classification using Bidirectional based LSTM architectures is computationally expensive and time consuming to train. For this, transformers were discovered which effectively give good perf... |
Title: Variable-rate hierarchical CPC leads to acoustic unit discovery in speech Abstract: The success of deep learning comes from its ability to capture the hierarchical structure of data by learning high-level representations defined in terms of low-level ones. In this paper we explore self-supervised learning of hie... |
Title: Statistical Deep Learning for Spatial and Spatio-Temporal Data Abstract: Deep neural network models have become ubiquitous in recent years, and have been applied to nearly all areas of science, engineering, and industry. These models are particularly useful for data that have strong dependencies in space (e.g., ... |
Title: U(1) Symmetry-breaking Observed in Generic CNN Bottleneck Layers Abstract: We report on a significant discovery linking deep convolutional neural networks (CNN) to biological vision and fundamental particle physics. A model of information propagation in a CNN is proposed via an analogy to an optical system, wher... |
Title: Models of human preference for learning reward functions Abstract: The utility of reinforcement learning is limited by the alignment of reward functions with the interests of human stakeholders. One promising method for alignment is to learn the reward function from human-generated preferences between pairs of t... |
Title: Enforcing Group Fairness in Algorithmic Decision Making: Utility Maximization Under Sufficiency Abstract: Binary decision making classifiers are not fair by default. Fairness requirements are an additional element to the decision making rationale, which is typically driven by maximizing some utility function. In... |
Title: OntoMerger: An Ontology Integration Library for Deduplicating and Connecting Knowledge Graph Nodes Abstract: Duplication of nodes is a common problem encountered when building knowledge graphs (KGs) from heterogeneous datasets, where it is crucial to be able to merge nodes having the same meaning. OntoMerger is ... |
Title: Zero-Shot Voice Conditioning for Denoising Diffusion TTS Models Abstract: We present a novel way of conditioning a pretrained denoising diffusion speech model to produce speech in the voice of a novel person unseen during training. The method requires a short (~3 seconds) sample from the target person, and gener... |
Title: Augmenting Netflix Search with In-Session Adapted Recommendations Abstract: We motivate the need for recommendation systems that can cater to the members in-the-moment intent by leveraging their interactions from the current session. We provide an overview of an end-to-end in-session adaptive recommendations sys... |
Title: Use-Case-Grounded Simulations for Explanation Evaluation Abstract: A growing body of research runs human subject evaluations to study whether providing users with explanations of machine learning models can help them with practical real-world use cases. However, running user studies is challenging and costly, an... |
Title: Towards Individual Grevy's Zebra Identification via Deep 3D Fitting and Metric Learning Abstract: This paper combines deep learning techniques for species detection, 3D model fitting, and metric learning in one pipeline to perform individual animal identification from photographs by exploiting unique coat patter... |
Title: Diffusion-GAN: Training GANs with Diffusion Abstract: For stable training of generative adversarial networks (GANs), injecting instance noise into the input of the discriminator is considered as a theoretically sound solution, which, however, has not yet delivered on its promise in practice. This paper introduce... |
Title: Information Threshold, Bayesian Inference and Decision-Making Abstract: We define the information threshold as the point of maximum curvature in the prior vs. posterior Bayesian curve, both of which are described as a function of the true positive and negative rates of the classification system in question. The ... |
Title: Sharper Rates and Flexible Framework for Nonconvex SGD with Client and Data Sampling Abstract: We revisit the classical problem of finding an approximately stationary point of the average of $n$ smooth and possibly nonconvex functions. The optimal complexity of stochastic first-order methods in terms of the numb... |
Title: AugLoss: A Learning Methodology for Real-World Dataset Corruption Abstract: Deep Learning (DL) models achieve great successes in many domains. However, DL models increasingly face safety and robustness concerns, including noisy labeling in the training stage and feature distribution shifts in the testing stage. ... |
Title: Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillation Abstract: Contrastive learning has shown great promise over annotation scarcity problems in the context of medical image segmentation. Existing approaches typically assume a balanced class distribution for bot... |
Title: Asymptotic Instance-Optimal Algorithms for Interactive Decision Making Abstract: Past research on interactive decision making problems (bandits, reinforcement learning, etc.) mostly focuses on the minimax regret that measures the algorithm's performance on the hardest instance. However, an ideal algorithm should... |
Title: JigsawHSI: a network for Hyperspectral Image classification Abstract: This article describes Jigsaw, a convolutional neural network (CNN) used in geosciences and based on Inception but tailored for geoscientific analyses. Introduces JigsawHSI (based on Jigsaw) and uses it on the land-use land-cover (LULC) classi... |
Title: Hashing Learning with Hyper-Class Representation Abstract: Existing unsupervised hash learning is a kind of attribute-centered calculation. It may not accurately preserve the similarity between data. This leads to low down the performance of hash function learning. In this paper, a hash algorithm is proposed wit... |
Title: Complex Locomotion Skill Learning via Differentiable Physics Abstract: Differentiable physics enables efficient gradient-based optimizations of neural network (NN) controllers. However, existing work typically only delivers NN controllers with limited capability and generalizability. We present a practical learn... |
Title: Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets Abstract: We study the problem of online learning in competitive settings in the context of two-sided matching markets. In particular, one side of the market, the agents, must learn about their preferences over the other ... |
Title: Convergence and sample complexity of natural policy gradient primal-dual methods for constrained MDPs Abstract: We study sequential decision making problems aimed at maximizing the expected total reward while satisfying a constraint on the expected total utility. We employ the natural policy gradient method to s... |
Title: Finite-Sample Maximum Likelihood Estimation of Location Abstract: We consider 1-dimensional location estimation, where we estimate a parameter $\lambda$ from $n$ samples $\lambda + \eta_i$, with each $\eta_i$ drawn i.i.d. from a known distribution $f$. For fixed $f$ the maximum-likelihood estimate (MLE) is well-... |
Title: Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data Abstract: Recently, Self-Supervised Representation Learning (SSRL) has attracted much attention in the field of computer vision, speech, natural language processing (NLP), and recently, with other types of mod... |
Title: Bi-SimCut: A Simple Strategy for Boosting Neural Machine Translation Abstract: We introduce Bi-SimCut: a simple but effective training strategy to boost neural machine translation (NMT) performance. It consists of two procedures: bidirectional pretraining and unidirectional finetuning. Both procedures utilize Si... |
Title: Markovian Interference in Experiments Abstract: We consider experiments in dynamical systems where interventions on some experimental units impact other units through a limiting constraint (such as a limited inventory). Despite outsize practical importance, the best estimators for this `Markovian' interference p... |
Title: Adaptive Rollout Length for Model-Based RL Using Model-Free Deep RL Abstract: Model-based reinforcement learning promises to learn an optimal policy from fewer interactions with the environment compared to model-free reinforcement learning by learning an intermediate model of the environment in order to predict ... |
Title: Efficient Minimax Optimal Global Optimization of Lipschitz Continuous Multivariate Functions Abstract: In this work, we propose an efficient minimax optimal global optimization algorithm for multivariate Lipschitz continuous functions. To evaluate the performance of our approach, we utilize the average regret in... |
Title: Restructuring Graph for Higher Homophily via Learnable Spectral Clustering Abstract: While a growing body of literature has been studying new Graph Neural Networks (GNNs) that work on both homophilic and heterophilic graphs, little work has been done on adapting classical GNNs to less-homophilic graphs. Although... |
Title: Automated Circuit Sizing with Multi-objective Optimization based on Differential Evolution and Bayesian Inference Abstract: With the ever increasing complexity of specifications, manual sizing for analog circuits recently became very challenging. Especially for innovative, large-scale circuits designs, with tens... |
Title: Semi-Supervised Segmentation of Mitochondria from Electron Microscopy Images Using Spatial Continuity Abstract: Morphology of mitochondria plays critical roles in mediating their physiological functions. Accurate segmentation of mitochondria from 3D electron microscopy (EM) images is essential to quantitative ch... |
Title: Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks Abstract: Federated learning (FL) over resource-constrained wireless networks has recently attracted much attention. However, most existing studies consider one FL task in single-cell wireless networks and ignore the impa... |
Title: A Simple yet Effective Method for Graph Classification Abstract: In deep neural networks, better results can often be obtained by increasing the complexity of previously developed basic models. However, it is unclear whether there is a way to boost performance by decreasing the complexity of such models. Intuiti... |
Title: Is More Data All You Need? A Causal Exploration Abstract: Curating a large scale medical imaging dataset for machine learning applications is both time consuming and expensive. Balancing the workload between model development, data collection and annotations is difficult for machine learning practitioners, espec... |
Title: Embrace the Gap: VAEs Perform Independent Mechanism Analysis Abstract: Variational autoencoders (VAEs) are a popular framework for modeling complex data distributions; they can be efficiently trained via variational inference by maximizing the evidence lower bound (ELBO), at the expense of a gap to the exact (lo... |
Title: Fast Adversarial Training with Adaptive Step Size Abstract: While adversarial training and its variants have shown to be the most effective algorithms to defend against adversarial attacks, their extremely slow training process makes it hard to scale to large datasets like ImageNet. The key idea of recent works ... |
Title: Towards Responsible AI for Financial Transactions Abstract: The application of AI in finance is increasingly dependent on the principles of responsible AI. These principles - explainability, fairness, privacy, accountability, transparency and soundness form the basis for trust in future AI systems. In this study... |
Title: Evaluating the Predictive Performance of Positive-Unlabelled Classifiers: a brief critical review and practical recommendations for improvement Abstract: Positive-Unlabelled (PU) learning is a growing area of machine learning that aims to learn classifiers from data consisting of labelled positive and unlabelled... |
Title: Continuous and Distribution-free Probabilistic Wind Power Forecasting: A Conditional Normalizing Flow Approach Abstract: We present a data-driven approach for probabilistic wind power forecasting based on conditional normalizing flow (CNF). In contrast with the existing, this approach is distribution-free (as fo... |
Title: Tackling covariate shift with node-based Bayesian neural networks Abstract: Bayesian neural networks (BNNs) promise improved generalization under covariate shift by providing principled probabilistic representations of epistemic uncertainty. However, weight-based BNNs often struggle with high computational compl... |
Title: Detecting Interlocutor Confusion in Situated Human-Avatar Dialogue: A Pilot Study Abstract: In order to enhance levels of engagement with conversational systems, our long term research goal seeks to monitor the confusion state of a user and adapt dialogue policies in response to such user confusion states. To th... |
Title: Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation Abstract: Sparse Gaussian Processes are a key component of high-throughput Bayesian optimisation (BO) loops -- an increasingly common setting where evaluation budgets are large and highly parallelised. By using representativ... |
Title: Spam Detection Using BERT Abstract: Emails and SMSs are the most popular tools in today communications, and as the increase of emails and SMSs users are increase, the number of spams is also increases. Spam is any kind of unwanted, unsolicited digital communication that gets sent out in bulk, spam emails and SMS... |
Title: Class Prior Estimation under Covariate Shift -- no Problem? Abstract: We show that in the context of classification the property of source and target distributions to be related by covariate shift may break down when the information content captured in the covariates is reduced, for instance by discretization of... |
Title: Optimization-based Block Coordinate Gradient Coding for Mitigating Partial Stragglers in Distributed Learning Abstract: Gradient coding schemes effectively mitigate full stragglers in distributed learning by introducing identical redundancy in coded local partial derivatives corresponding to all model parameters... |
Title: Mean Estimation in High-Dimensional Binary Markov Gaussian Mixture Models Abstract: We consider a high-dimensional mean estimation problem over a binary hidden Markov model, which illuminates the interplay between memory in data, sample size, dimension, and signal strength in statistical inference. In this model... |
Title: Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning Abstract: In federated learning (FL), model performance typically suffers from client drift induced by data heterogeneity, and mainstream works focus on correcting client drift. We propose a different approach named virtual ... |
Title: An Optimal Transport Approach to Personalized Federated Learning Abstract: Federated learning is a distributed machine learning paradigm, which aims to train a model using the local data of many distributed clients. A key challenge in federated learning is that the data samples across the clients may not be iden... |
Title: Learning to Control under Time-Varying Environment Abstract: This paper investigates the problem of regret minimization in linear time-varying (LTV) dynamical systems. Due to the simultaneous presence of uncertainty and non-stationarity, designing online control algorithms for unknown LTV systems remains a chall... |
Title: Transfer Learning based Search Space Design for Hyperparameter Tuning Abstract: The tuning of hyperparameters becomes increasingly important as machine learning (ML) models have been extensively applied in data mining applications. Among various approaches, Bayesian optimization (BO) is a successful methodology ... |
Title: UTTS: Unsupervised TTS with Conditional Disentangled Sequential Variational Auto-encoder Abstract: In this paper, we propose a novel unsupervised text-to-speech (UTTS) framework which does not require text-audio pairs for the TTS acoustic modeling (AM). UTTS is a multi-speaker speech synthesizer developed from t... |
Title: Sparse Bayesian Learning for Complex-Valued Rational Approximations Abstract: Surrogate models are used to alleviate the computational burden in engineering tasks, which require the repeated evaluation of computationally demanding models of physical systems, such as the efficient propagation of uncertainties. Fo... |
Title: Persistent Homology of Coarse Grained State Space Networks Abstract: This work is dedicated to the topological analysis of complex transitional networks for dynamic state detection. Transitional networks are formed from time series data and they leverage graph theory tools to reveal information about the underly... |
Title: Certified Robustness in Federated Learning Abstract: Federated learning has recently gained significant attention and popularity due to its effectiveness in training machine learning models on distributed data privately. However, as in the single-node supervised learning setup, models trained in federated learni... |
Title: Robustness Evaluation and Adversarial Training of an Instance Segmentation Model Abstract: To evaluate the robustness of non-classifier models, we propose probabilistic local equivalence, based on the notion of randomized smoothing, as a way to quantitatively evaluate the robustness of an arbitrary function. In ... |
Title: RLSS: A Deep Reinforcement Learning Algorithm for Sequential Scene Generation Abstract: We present RLSS: a reinforcement learning algorithm for sequential scene generation. This is based on employing the proximal policy optimization (PPO) algorithm for generative problems. In particular, we consider how to effec... |
Title: Dynaformer: A Deep Learning Model for Ageing-aware Battery Discharge Prediction Abstract: Electrochemical batteries are ubiquitous devices in our society. When they are employed in mission-critical applications, the ability to precisely predict the end of discharge under highly variable environmental and operati... |
Title: Binding Dancers Into Attractors Abstract: To effectively perceive and process observations in our environment, feature binding and perspective taking are crucial cognitive abilities. Feature binding combines observed features into one entity, called a Gestalt. Perspective taking transfers the percept into a cano... |
Title: Conversation Group Detection With Spatio-Temporal Context Abstract: In this work, we propose an approach for detecting conversation groups in social scenarios like cocktail parties and networking events, from overhead camera recordings. We posit the detection of conversation groups as a learning problem that cou... |
Title: Learning "best" kernels from data in Gaussian process regression. With application to aerodynamics Abstract: This paper introduces algorithms to select/design kernels in Gaussian process regression/kriging surrogate modeling techniques. We adopt the setting of kernel method solutions in ad hoc functional spaces,... |
Title: Towards Group Learning: Distributed Weighting of Experts Abstract: Aggregating signals from a collection of noisy sources is a fundamental problem in many domains including crowd-sourcing, multi-agent planning, sensor networks, signal processing, voting, ensemble learning, and federated learning. The core questi... |
Title: A Deep Reinforcement Learning Framework For Column Generation Abstract: Column Generation (CG) is an iterative algorithm for solving linear programs (LPs) with an extremely large number of variables (columns). CG is the workhorse for tackling large-scale integer linear programs, which rely on CG to solve LP rela... |
Title: Automated visual inspection of silicon detectors in CMS experiment Abstract: In the CMS experiment at CERN, Geneva, a large number of HGCAL sensor modules are fabricated in advanced laboratories around the world. Each sensor module contains about 700 checkpoints for visual inspection thus making it almost imposs... |
Title: On the duality between contrastive and non-contrastive self-supervised learning Abstract: Recent approaches in self-supervised learning of image representations can be categorized into different families of methods and, in particular, can be divided into contrastive and non-contrastive approaches. While differen... |
Title: Constraints on parameter choices for successful reservoir computing Abstract: Echo-state networks are simple models of discrete dynamical systems driven by a time series. By selecting network parameters such that the dynamics of the network is contractive, characterized by a negative maximal Lyapunov exponent, t... |
Title: Effects of Auxiliary Knowledge on Continual Learning Abstract: In Continual Learning (CL), a neural network is trained on a stream of data whose distribution changes over time. In this context, the main problem is how to learn new information without forgetting old knowledge (i.e., Catastrophic Forgetting). Most... |
Title: Pessimistic Off-Policy Optimization for Learning to Rank Abstract: Off-policy learning is a framework for optimizing policies without deploying them, using data collected by another policy. In recommender systems, this is especially challenging due to the imbalance in logged data: some items are recommended and ... |
Title: [Reproducibility Report] Explainable Deep One-Class Classification Abstract: Fully Convolutional Data Description (FCDD), an explainable version of the Hypersphere Classifier (HSC), directly addresses image anomaly detection (AD) and pixel-wise AD without any post-hoc explainer methods. The authors claim that FC... |
Title: Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning Abstract: In this paper, we use tools from rate-distortion theory to establish new upper bounds on the generalization error of statistical distributed learning algorithms. Specifically, there are $K$ clients whose individually chos... |
Title: CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations Abstract: The excessive runtime of high-fidelity partial differential equation (PDE) solvers makes them unsuitable for time-critical applications. We propose to accelerate PDE solvers using reduced-order modeling (ROM). Whereas... |
Title: Real-World Image Super-Resolution by Exclusionary Dual-Learning Abstract: Real-world image super-resolution is a practical image restoration problem that aims to obtain high-quality images from in-the-wild input, has recently received considerable attention with regard to its tremendous application potentials. A... |
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