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Title: TempoRL: Temporal Priors for Exploration in Off-Policy Reinforcement Learning Abstract: Efficient exploration is a crucial challenge in deep reinforcement learning. Several methods, such as behavioral priors, are able to leverage offline data in order to efficiently accelerate reinforcement learning on complex t... |
Title: Subspace clustering in high-dimensions: Phase transitions \& Statistical-to-Computational gap Abstract: A simple model to study subspace clustering is the high-dimensional $k$-Gaussian mixture model where the cluster means are sparse vectors. Here we provide an exact asymptotic characterization of the statistica... |
Title: Kernel Ridgeless Regression is Inconsistent in Low Dimensions Abstract: We show that kernel interpolation for a large class of shift-invariant kernels is inconsistent in fixed dimension, even with bandwidth adaptive to the training set. |
Title: Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality Abstract: Finding different solutions to the same problem is a key aspect of intelligence associated with creativity and adaptation to novel situations. In reinforcement learning, a set of diverse policies can be useful for expl... |
Title: Transfer learning driven design optimization for inertial confinement fusion Abstract: Transfer learning is a promising approach to creating predictive models that incorporate simulation and experimental data into a common framework. In this technique, a neural network is first trained on a large database of sim... |
Title: Green Hierarchical Vision Transformer for Masked Image Modeling Abstract: We present an efficient approach for Masked Image Modeling (MIM) with hierarchical Vision Transformers (ViTs), e.g., Swin Transformer, allowing the hierarchical ViTs to discard masked patches and operate only on the visible ones. Our appro... |
Title: Pick up the PACE: Fast and Simple Domain Adaptation via Ensemble Pseudo-Labeling Abstract: Domain Adaptation (DA) has received widespread attention from deep learning researchers in recent years because of its potential to improve test accuracy with out-of-distribution labeled data. Most state-of-the-art DA algo... |
Title: A framework for overparameterized learning Abstract: An explanation for the success of deep neural networks is a central question in theoretical machine learning. According to classical statistical learning, the overparameterized nature of such models should imply a failure to generalize. Many argue that good em... |
Title: Are Transformers Effective for Time Series Forecasting? Abstract: Recently, there has been a surge of Transformer-based solutions for the time series forecasting (TSF) task, especially for the challenging long-term TSF problem. Transformer architecture relies on self-attention mechanisms to effectively extract t... |
Title: An Analytic Framework for Robust Training of Artificial Neural Networks Abstract: The reliability of a learning model is key to the successful deployment of machine learning in various industries. Creating a robust model, particularly one unaffected by adversarial attacks, requires a comprehensive understanding ... |
Title: Censored Quantile Regression Neural Networks Abstract: This paper considers doing quantile regression on censored data using neural networks (NNs). This adds to the survival analysis toolkit by allowing direct prediction of the target variable, along with a distribution-free characterisation of uncertainty, usin... |
Title: Mesoscopic modeling of hidden spiking neurons Abstract: Can we use spiking neural networks (SNN) as generative models of multi-neuronal recordings, while taking into account that most neurons are unobserved? Modeling the unobserved neurons with large pools of hidden spiking neurons leads to severely underconstra... |
Title: Sparse Graph Learning for Spatiotemporal Time Series Abstract: Outstanding achievements of graph neural networks for spatiotemporal time series prediction show that relational constraints introduce a positive inductive bias into neural forecasting architectures. Often, however, the relational information charact... |
Title: SemAffiNet: Semantic-Affine Transformation for Point Cloud Segmentation Abstract: Conventional point cloud semantic segmentation methods usually employ an encoder-decoder architecture, where mid-level features are locally aggregated to extract geometric information. However, the over-reliance on these class-agno... |
Title: DeepJoint: Robust Survival Modelling Under Clinical Presence Shift Abstract: Observational data in medicine arise as a result of the complex interaction between patients and the healthcare system. The sampling process is often highly irregular and itself constitutes an informative process. When using such data t... |
Title: Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations Abstract: Modeling multivariate time series as temporal signals over a (possibly dynamic) graph is an effective representational framework that allows for developing models for time series analysis. In fact, discrete sequenc... |
Title: Embed to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency Abstract: Reinforcement learning in partially observed Markov decision processes (POMDPs) faces two challenges. (i) It often takes the full history to predict the future, which induces a sample complexity that sc... |
Title: FedAug: Reducing the Local Learning Bias Improves Federated Learning on Heterogeneous Data Abstract: Federated Learning (FL) is a machine learning paradigm that learns from data kept locally to safeguard the privacy of clients, whereas local SGD is typically employed on the clients' devices to improve communicat... |
Title: SigMaNet: One Laplacian to Rule Them All Abstract: This paper introduces SigMaNet, a generalized Graph Convolutional Network (GCN) capable of handling both undirected and directed graphs with weights not restricted in sign and magnitude. The cornerstone of SigMaNet is the introduction of a generalized Laplacian ... |
Title: AutoTSG: Learning and Synthesis for Incident Troubleshooting Abstract: Incident management is a key aspect of operating large-scale cloud services. To aid with faster and efficient resolution of incidents, engineering teams document frequent troubleshooting steps in the form of Troubleshooting Guides (TSGs), to ... |
Title: Continual evaluation for lifelong learning: Identifying the stability gap Abstract: Introducing a time dependency on the data generating distribution has proven to be difficult for gradient-based training of neural networks, as the greedy updates result in catastrophic forgetting of previous timesteps. Continual... |
Title: Follow-the-Perturbed-Leader for Adversarial Markov Decision Processes with Bandit Feedback Abstract: We consider regret minimization for Adversarial Markov Decision Processes (AMDPs), where the loss functions are changing over time and adversarially chosen, and the learner only observes the losses for the visite... |
Title: Variance-Aware Sparse Linear Bandits Abstract: It is well-known that the worst-case minimax regret for sparse linear bandits is $\widetilde{\Theta}\left(\sqrt{dT}\right)$ where $d$ is the ambient dimension and $T$ is the number of time steps (ignoring the dependency on sparsity). On the other hand, in the benign... |
Title: Opinion Spam Detection: A New Approach Using Machine Learning and Network-Based Algorithms Abstract: E-commerce is the fastest-growing segment of the economy. Online reviews play a crucial role in helping consumers evaluate and compare products and services. As a result, fake reviews (opinion spam) are becoming ... |
Title: Machine Learning Models Are Not Necessarily Biased When Constructed Properly: Evidence from Neuroimaging Studies Abstract: Despite the great promise that machine learning has offered in many fields of medicine, it has also raised concerns about potential biases and poor generalization across genders, age distrib... |
Title: Avoiding Barren Plateaus with Classical Deep Neural Networks Abstract: Variational quantum algorithms (VQAs) are among the most promising algorithms in the era of Noisy Intermediate Scale Quantum Devices. The VQAs are applied to a variety of tasks, such as in chemistry simulations, optimization problems, and qua... |
Title: A Fair Federated Learning Framework With Reinforcement Learning Abstract: Federated learning (FL) is a paradigm where many clients collaboratively train a model under the coordination of a central server, while keeping the training data locally stored. However, heterogeneous data distributions over different cli... |
Title: Your Transformer May Not be as Powerful as You Expect Abstract: Relative Positional Encoding (RPE), which encodes the relative distance between any pair of tokens, is one of the most successful modifications to the original Transformer. As far as we know, theoretical understanding of the RPE-based Transformers i... |
Title: Looking for Out-of-Distribution Environments in Critical Care: A case study with the eICU Database Abstract: Generalizing to new populations and domains in machine learning is still an open problem which has seen increased interest recently. In particular, clinical models show a significant performance drop when... |
Title: BppAttack: Stealthy and Efficient Trojan Attacks against Deep Neural Networks via Image Quantization and Contrastive Adversarial Learning Abstract: Deep neural networks are vulnerable to Trojan attacks. Existing attacks use visible patterns (e.g., a patch or image transformations) as triggers, which are vulnerab... |
Title: Multi-fidelity power flow solver Abstract: We propose a multi-fidelity neural network (MFNN) tailored for rapid high-dimensional grid power flow simulations and contingency analysis with scarce high-fidelity contingency data. The proposed model comprises two networks -- the first one trained on DC approximation ... |
Title: Feature Forgetting in Continual Representation Learning Abstract: In continual and lifelong learning, good representation learning can help increase performance and reduce sample complexity when learning new tasks. There is evidence that representations do not suffer from "catastrophic forgetting" even in plain ... |
Title: Transfer and Share: Semi-Supervised Learning from Long-Tailed Data Abstract: Long-Tailed Semi-Supervised Learning (LTSSL) aims to learn from class-imbalanced data where only a few samples are annotated. Existing solutions typically require substantial cost to solve complex optimization problems, or class-balance... |
Title: QUIC-FL: Quick Unbiased Compression for Federated Learning Abstract: Distributed Mean Estimation (DME) is a fundamental building block in communication efficient federated learning. In DME, clients communicate their lossily compressed gradients to the parameter server, which estimates the average and updates the... |
Title: Deep Active Learning with Noise Stability Abstract: Uncertainty estimation for unlabeled data is crucial to active learning. With a deep neural network employed as the backbone model, the data selection process is highly challenging due to the potential over-confidence of the model inference. Existing methods re... |
Title: TransBoost: Improving the Best ImageNet Performance using Deep Transduction Abstract: This paper deals with deep transductive learning, and proposes TransBoost as a procedure for fine-tuning any deep neural model to improve its performance on any (unlabeled) test set provided at training time. TransBoost is insp... |
Title: How Powerful are K-hop Message Passing Graph Neural Networks Abstract: The most popular design paradigm for Graph Neural Networks (GNNs) is 1-hop message passing -- aggregating features from 1-hop neighbors repeatedly. However, the expressive power of 1-hop message passing is bounded by the Weisfeiler-Lehman (1-... |
Title: Learning the spatio-temporal relationship between wind and significant wave height using deep learning Abstract: Ocean wave climate has a significant impact on near-shore and off-shore human activities, and its characterisation can help in the design of ocean structures such as wave energy converters and sea dik... |
Title: The Effect of Task Ordering in Continual Learning Abstract: We investigate the effect of task ordering on continual learning performance. We conduct an extensive series of empirical experiments on synthetic and naturalistic datasets and show that reordering tasks significantly affects the amount of catastrophic ... |
Title: Towards Learning Universal Hyperparameter Optimizers with Transformers Abstract: Meta-learning hyperparameter optimization (HPO) algorithms from prior experiments is a promising approach to improve optimization efficiency over objective functions from a similar distribution. However, existing methods are restric... |
Title: Fair Representation Learning through Implicit Path Alignment Abstract: We consider a fair representation learning perspective, where optimal predictors, on top of the data representation, are ensured to be invariant with respect to different sub-groups. Specifically, we formulate this intuition as a bi-level opt... |
Title: SARS-CoV-2 Result Interpretation based on Image Analysis of Lateral Flow Devices Abstract: The widely used gene quantisation technique, Lateral Flow Device (LFD), is now commonly used to detect the presence of SARS-CoV-2. It is enabling the control and prevention of the spread of the virus. Depending on the vira... |
Title: Gaussian Universality of Linear Classifiers with Random Labels in High-Dimension Abstract: While classical in many theoretical settings, the assumption of Gaussian i.i.d. inputs is often perceived as a strong limitation in the analysis of high-dimensional learning. In this study, we redeem this line of work in t... |
Title: Federated Non-negative Matrix Factorization for Short Texts Topic Modeling with Mutual Information Abstract: Non-negative matrix factorization (NMF) based topic modeling is widely used in natural language processing (NLP) to uncover hidden topics of short text documents. Usually, training a high-quality topic mo... |
Title: Federated Split BERT for Heterogeneous Text Classification Abstract: Pre-trained BERT models have achieved impressive performance in many natural language processing (NLP) tasks. However, in many real-world situations, textual data are usually decentralized over many clients and unable to be uploaded to a centra... |
Title: DeepTechnome: Mitigating Unknown Bias in Deep Learning Based Assessment of CT Images Abstract: Reliably detecting diseases using relevant biological information is crucial for real-world applicability of deep learning techniques in medical imaging. We debias deep learning models during training against unknown b... |
Title: Embedding Principle in Depth for the Loss Landscape Analysis of Deep Neural Networks Abstract: Unraveling the general structure underlying the loss landscapes of deep neural networks (DNNs) is important for the theoretical study of deep learning. Inspired by the embedding principle of DNN loss landscape, we prov... |
Title: On the Eigenvalues of Global Covariance Pooling for Fine-grained Visual Recognition Abstract: The Fine-Grained Visual Categorization (FGVC) is challenging because the subtle inter-class variations are difficult to be captured. One notable research line uses the Global Covariance Pooling (GCP) layer to learn powe... |
Title: Triangular Contrastive Learning on Molecular Graphs Abstract: Recent contrastive learning methods have shown to be effective in various tasks, learning generalizable representations invariant to data augmentation thereby leading to state of the art performances. Regarding the multifaceted nature of large unlabel... |
Title: Evaluating Multimodal Interactive Agents Abstract: Creating agents that can interact naturally with humans is a common goal in artificial intelligence (AI) research. However, evaluating these interactions is challenging: collecting online human-agent interactions is slow and expensive, yet faster proxy metrics o... |
Title: Acute Lymphoblastic Leukemia Detection Using Hypercomplex-Valued Convolutional Neural Networks Abstract: This paper features convolutional neural networks defined on hypercomplex algebras applied to classify lymphocytes in blood smear digital microscopic images. Such classification is helpful for the diagnosis o... |
Title: Privacy-Preserving Wavelet Neural Network with Fully Homomorphic Encryption Abstract: The main aim of Privacy-Preserving Machine Learning (PPML) is to protect the privacy and provide security to the data used in building Machine Learning models. There are various techniques in PPML such as Secure Multi-Party Com... |
Title: Active Labeling: Streaming Stochastic Gradients Abstract: The workhorse of machine learning is stochastic gradient descent. To access stochastic gradients, it is common to consider iteratively input/output pairs of a training dataset. Interestingly, it appears that one does not need full supervision to access st... |
Title: Denial-of-Service Attacks on Learned Image Compression Abstract: Deep learning techniques have shown promising results in image compression, with competitive bitrate and image reconstruction quality from compressed latent. However, while image compression has progressed towards higher peak signal-to-noise ratio ... |
Title: DT-SV: A Transformer-based Time-domain Approach for Speaker Verification Abstract: Speaker verification (SV) aims to determine whether the speaker's identity of a test utterance is the same as the reference speech. In the past few years, extracting speaker embeddings using deep neural networks for SV systems has... |
Title: Constrained Reinforcement Learning for Short Video Recommendation Abstract: The wide popularity of short videos on social media poses new opportunities and challenges to optimize recommender systems on the video-sharing platforms. Users provide complex and multi-faceted responses towards recommendations, includi... |
Title: DT+GNN: A Fully Explainable Graph Neural Network using Decision Trees Abstract: We propose the fully explainable Decision Tree Graph Neural Network (DT+GNN) architecture. In contrast to existing black-box GNNs and post-hoc explanation methods, the reasoning of DT+GNN can be inspected at every step. To achieve th... |
Title: Collaborative Distillation Meta Learning for Simulation Intensive Hardware Design Abstract: This paper proposes a novel collaborative distillation meta learning (CDML) framework for simulation intensive hardware design problems. Deep reinforcement learning (DRL) has shown promising performance in various hardwar... |
Title: Friends to Help: Saving Federated Learning from Client Dropout Abstract: Federated learning (FL) is an outstanding distributed machine learning framework due to its benefits on data privacy and communication efficiency. Since full client participation in many cases is infeasible due to constrained resources, par... |
Title: QSpeech: Low-Qubit Quantum Speech Application Toolkit Abstract: Quantum devices with low qubits are common in the Noisy Intermediate-Scale Quantum (NISQ) era. However, Quantum Neural Network (QNN) running on low-qubit quantum devices would be difficult since it is based on Variational Quantum Circuit (VQC), whic... |
Title: Penalizing Proposals using Classifiers for Semi-Supervised Object Detection Abstract: Obtaining gold standard annotated data for object detection is often costly, involving human-level effort. Semi-supervised object detection algorithms solve the problem with a small amount of gold-standard labels and a large un... |
Title: A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning Abstract: Real-world applications require the classification model to adapt to new classes without forgetting old ones. Correspondingly, Class-Incremental Learning (CIL) aims to train a model with limited memory size to meet this requi... |
Title: Aggregating Gradients in Encoded Domain for Federated Learning Abstract: Malicious attackers and an honest-but-curious server can steal private client data from uploaded gradients in federated learning. Although current protection methods (e.g., additive homomorphic cryptosystem) can guarantee the security of th... |
Title: SymNMF-Net for The Symmetric NMF Problem Abstract: Recently, many works have demonstrated that Symmetric Non-negative Matrix Factorization~(SymNMF) enjoys a great superiority for various clustering tasks. Although the state-of-the-art algorithms for SymNMF perform well on synthetic data, they cannot consistently... |
Title: Fast Vision Transformers with HiLo Attention Abstract: Vision Transformers (ViTs) have triggered the most recent and significant breakthroughs in computer vision. Their efficient designs are mostly guided by the indirect metric of computational complexity, i.e., FLOPs, which however has a clear gap with the dire... |
Title: Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization Abstract: Deep reinforcement learning (DRL)-based combinatorial optimization (CO) methods (i.e., DRL-NCO) have shown significant merit over the conventional CO solvers as DRL-NCO is capable of learning CO solvers without supervised labels att... |
Title: $O(N^2)$ Universal Antisymmetry in Fermionic Neural Networks Abstract: Fermionic neural network (FermiNet) is a recently proposed wavefunction Ansatz, which is used in variational Monte Carlo (VMC) methods to solve the many-electron Schr\"{o}dinger equation. FermiNet proposes permutation-equivariant architecture... |
Title: More Recent Advances in (Hyper)Graph Partitioning Abstract: In recent years, significant advances have been made in the design and evaluation of balanced (hyper)graph partitioning algorithms. We survey trends of the last decade in practical algorithms for balanced (hyper)graph partitioning together with future r... |
Title: Orthogonal Stochastic Configuration Networks with Adaptive Construction Parameter for Data Analytics Abstract: As a randomized learner model, SCNs are remarkable that the random weights and biases are assigned employing a supervisory mechanism to ensure universal approximation and fast learning. However, the ran... |
Title: AI for Porosity and Permeability Prediction from Geologic Core X-Ray Micro-Tomography Abstract: Geologic cores are rock samples that are extracted from deep under the ground during the well drilling process. They are used for petroleum reservoirs' performance characterization. Traditionally, physical studies of ... |
Title: Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost Abstract: We study distributed contextual linear bandits with stochastic contexts, where $N$ agents act cooperatively to solve a linear bandit-optimization problem with $d$-dimensional features. For this problem, we propose a distribut... |
Title: On Learning Mixture of Linear Regressions in the Non-Realizable Setting Abstract: While mixture of linear regressions (MLR) is a well-studied topic, prior works usually do not analyze such models for prediction error. In fact, {\em prediction} and {\em loss} are not well-defined in the context of mixtures. In th... |
Title: Leveraging Dependency Grammar for Fine-Grained Offensive Language Detection using Graph Convolutional Networks Abstract: The last few years have witnessed an exponential rise in the propagation of offensive text on social media. Identification of this text with high precision is crucial for the well-being of soc... |
Title: Cost-efficient Gaussian Tensor Network Embeddings for Tensor-structured Inputs Abstract: This work discusses tensor network embeddings, which are random matrices ($S$) with tensor network structure. These embeddings have been used to perform dimensionality reduction of tensor network structured inputs $x$ and ac... |
Title: Transferable Adversarial Attack based on Integrated Gradients Abstract: The vulnerability of deep neural networks to adversarial examples has drawn tremendous attention from the community. Three approaches, optimizing standard objective functions, exploiting attention maps, and smoothing decision surfaces, are c... |
Title: Grammar Detection for Sentiment Analysis through Improved Viterbi Algorithm Abstract: Grammar Detection, also referred to as Parts of Speech Tagging of raw text, is considered an underlying building block of the various Natural Language Processing pipelines like named entity recognition, question answering, and ... |
Title: Matryoshka Representations for Adaptive Deployment Abstract: Learned representations are a central component in modern ML systems, serving a multitude of downstream tasks. When training such representations, it is often the case that computational and statistical constraints for each downstream task are unknown.... |
Title: Unsupervised Reinforcement Adaptation for Class-Imbalanced Text Classification Abstract: Class imbalance naturally exists when train and test models in different domains. Unsupervised domain adaptation (UDA) augments model performance with only accessible annotations from the source domain and unlabeled data fro... |
Title: Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search Abstract: Nonlinear dynamics is ubiquitous in nature and commonly seen in various science and engineering disciplines. Distilling analytical expressions that govern nonlinear dynamics from limited data remains vital but challen... |
Title: On the Evolution of A.I. and Machine Learning: Towards Measuring and Understanding Impact, Influence, and Leadership at Premier A.I. Conferences Abstract: Artificial Intelligence is now recognized as a general-purpose technology with ample impact on human life. In this work, we aim to understand the evolution of... |
Title: RACE: A Reinforcement Learning Framework for Improved Adaptive Control of NoC Channel Buffers Abstract: Network-on-chip (NoC) architectures rely on buffers to store flits to cope with contention for router resources during packet switching. Recently, reversible multi-function channel (RMC) buffers have been prop... |
Title: Cali3F: Calibrated Fast Fair Federated Recommendation System Abstract: The increasingly stringent regulations on privacy protection have sparked interest in federated learning. As a distributed machine learning framework, it bridges isolated data islands by training a global model over devices while keeping data... |
Title: Understanding Metrics for Paraphrasing Abstract: Paraphrase generation is a difficult problem. This is not only because of the limitations in text generation capabilities but also due that to the lack of a proper definition of what qualifies as a paraphrase and corresponding metrics to measure how good it is. Me... |
Title: Learning black- and gray-box chemotactic PDEs/closures from agent based Monte Carlo simulation data Abstract: We propose a machine learning framework for the data-driven discovery of macroscopic chemotactic Partial Differential Equations (PDEs) -- and the closures that lead to them -- from high-fidelity, individ... |
Title: GraphPMU: Event Clustering via Graph Representation Learning Using Locationally-Scarce Distribution-Level Fundamental and Harmonic PMU Measurements Abstract: This paper is concerned with the complex task of identifying the type and cause of the events that are captured by distribution-level phasor measurement un... |
Title: Contextual Pandora's Box Abstract: Pandora's Box is a fundamental stochastic optimization problem, where the decision-maker must find a good alternative while minimizing the search cost of exploring the value of each alternative. In the original formulation, it is assumed that accurate priors are given for the v... |
Title: Learning to segment with limited annotations: Self-supervised pretraining with regression and contrastive loss in MRI Abstract: Obtaining manual annotations for large datasets for supervised training of deep learning (DL) models is challenging. The availability of large unlabeled datasets compared to labeled one... |
Title: Trainable Weight Averaging for Fast Convergence and Better Generalization Abstract: Stochastic gradient descent (SGD) and its variants are commonly considered as the de-facto methods to train deep neural networks (DNNs). While recent improvements to SGD mainly focus on the descent algorithm itself, few works pay... |
Title: Deep-XFCT: Deep learning 3D-mineral liberation analysis with micro X-ray fluorescence and computed tomography Abstract: The rapid development of X-ray micro-computed tomography (micro-CT) opens new opportunities for 3D analysis of particle and grain-size characterisation, determination of particle densities and ... |
Title: Optimal Neural Network Approximation of Wasserstein Gradient Direction via Convex Optimization Abstract: The computation of Wasserstein gradient direction is essential for posterior sampling problems and scientific computing. The approximation of the Wasserstein gradient with finite samples requires solving a va... |
Title: Undersampling is a Minimax Optimal Robustness Intervention in Nonparametric Classification Abstract: While a broad range of techniques have been proposed to tackle distribution shift, the simple baseline of training on an $\textit{undersampled}$ dataset often achieves close to state-of-the-art-accuracy across se... |
Title: Identifying Patient-Specific Root Causes with the Heteroscedastic Noise Model Abstract: Complex diseases are caused by a multitude of factors that may differ between patients even within the same diagnostic category. A few underlying root causes may nevertheless initiate the development of disease within each pa... |
Title: BRIGHT -- Graph Neural Networks in Real-Time Fraud Detection Abstract: Detecting fraudulent transactions is an essential component to control risk in e-commerce marketplaces. Apart from rule-based and machine learning filters that are already deployed in production, we want to enable efficient real-time inferenc... |
Title: Factorized Structured Regression for Large-Scale Varying Coefficient Models Abstract: Recommender Systems (RS) pervade many aspects of our everyday digital life. Proposed to work at scale, state-of-the-art RS allow the modeling of thousands of interactions and facilitate highly individualized recommendations. Co... |
Title: Learning to Query Internet Text for Informing Reinforcement Learning Agents Abstract: Generalization to out of distribution tasks in reinforcement learning is a challenging problem. One successful approach improves generalization by conditioning policies on task or environment descriptions that provide informati... |
Title: Entropy Maximization with Depth: A Variational Principle for Random Neural Networks Abstract: To understand the essential role of depth in neural networks, we investigate a variational principle for depth: Does increasing depth perform an implicit optimization for the representations in neural networks? We prove... |
Title: Tight Lower Bounds on Worst-Case Guarantees for Zero-Shot Learning with Attributes Abstract: We develop a rigorous mathematical analysis of zero-shot learning with attributes. In this setting, the goal is to label novel classes with no training data, only detectors for attributes and a description of how those a... |
Title: Forecasting Patient Demand at Urgent Care Clinics using Machine Learning Abstract: Urgent care clinics and emergency departments around the world periodically suffer from extended wait times beyond patient expectations due to inadequate staffing levels. These delays have been linked with adverse clinical outcome... |
Title: Semi-supervised Drifted Stream Learning with Short Lookback Abstract: In many scenarios, 1) data streams are generated in real time; 2) labeled data are expensive and only limited labels are available in the beginning; 3) real-world data is not always i.i.d. and data drift over time gradually; 4) the storage of ... |
Title: Urban Rhapsody: Large-scale exploration of urban soundscapes Abstract: Noise is one of the primary quality-of-life issues in urban environments. In addition to annoyance, noise negatively impacts public health and educational performance. While low-cost sensors can be deployed to monitor ambient noise levels at ... |
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