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Found 12 papers, 4 papers with code
Causal Inference out of Control: Estimating the Steerability of Consumption
Regulators and academics are increasingly interested in the causal effect that algorithmic actions of a digital platform have on consumption.
Dual Multi-scale Mean Teacher Network for Semi-supervised Infection Segmentation in Chest CT Volume for COVID-19
Automated detecting lung infections from computed tomography (CT) data plays an important role for combating COVID-19.
Private optimization in the interpolation regime: faster rates and hardness results
In non-private stochastic convex optimization, stochastic gradient methods converge much faster on interpolation problems -- problems where there exists a solution that simultaneously minimizes all of the sample losses -- than on non-interpolating ones; we show that generally similar improvements are impossible in the private setting.
PV-RCNN++: Semantical Point-Voxel Feature Interaction for 3D Object Detection
Such a module will guide our PV-RCNN++ to integrate more object-related point-wise and voxel-wise features in the pivotal areas.
Semi-MoreGAN: A New Semi-supervised Generative Adversarial Network for Mixture of Rain Removal
Rain is one of the most common weather which can completely degrade the image quality and interfere with the performance of many computer vision tasks, especially under heavy rain conditions.
Approximate Function Evaluation via Multi-Armed Bandits
We design an instance-adaptive algorithm that learns to sample according to the importance of each coordinate, and with probability at least $1-\delta$ returns an $\epsilon$ accurate estimate of $f(\boldsymbol{\mu})$.
When A Conventional Filter Meets Deep Learning: Basis Composition Learning on Image Filters
Image filters are fast, lightweight and effective, which make these conventional wisdoms preferable as basic tools in vision tasks.
Federated Asymptotics: a model to compare federated learning algorithms
We propose an asymptotic framework to analyze the performance of (personalized) federated learning algorithms.
Accelerated, Optimal, and Parallel: Some Results on Model-Based Stochastic Optimization
We extend the Approximate-Proximal Point (aProx) family of model-based methods for solving stochastic convex optimization problems, including stochastic subgradient, proximal point, and bundle methods, to the minibatch and accelerated setting.
Minibatch Stochastic Approximate Proximal Point Methods
In contrast to standard stochastic gradient methods, these methods may have linear speedup in the minibatch setting even for non-smooth functions.
Handling Collocations in Hierarchical Latent Tree Analysis for Topic Modeling
Topic modeling has been one of the most active research areas in machine learning in recent years.
Greedy Frank-Wolfe Algorithm for Exemplar Selection
In this paper, we consider the problem of selecting representatives from a data set for arbitrary supervised/unsupervised learning tasks.
