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Found 12 papers, 5 papers with code
One-Way Matching of Datasets with Low Rank Signals
We study one-way matching of a pair of datasets with low rank signals.
Learning Torque Control for Quadrupedal Locomotion
Reinforcement learning (RL) has become a promising approach to developing controllers for quadrupedal robots.
Autonomous Navigation of Underactuated Bipedal Robots in Height-Constrained Environments
This demonstrates reliable autonomy to drive the robot to safely avoid obstacles while walking to the goal location in various kinds of height-constrained cluttered environments.
Real-time Geo-localization Using Satellite Imagery and Topography for Unmanned Aerial Vehicles
The capabilities of autonomous flight with unmanned aerial vehicles (UAVs) have significantly increased in recent times.
Weighted Training for Cross-Task Learning
In this paper, we introduce Target-Aware Weighted Training (TAWT), a weighted training algorithm for cross-task learning based on minimizing a representation-based task distance between the source and target tasks.
Estimating and Improving Dynamic Treatment Regimes With a Time-Varying Instrumental Variable
Estimating dynamic treatment regimes (DTRs) from retrospective observational data is challenging as some degree of unmeasured confounding is often expected.
A Theorem of the Alternative for Personalized Federated Learning
A widely recognized difficulty in federated learning arises from the statistical heterogeneity among clients: local datasets often come from different but not entirely unrelated distributions, and personalization is, therefore, necessary to achieve optimal results from each individual's perspective.
Federated $f$-Differential Privacy
Federated learning (FL) is a training paradigm where the clients collaboratively learn models by repeatedly sharing information without compromising much on the privacy of their local sensitive data.
Global and Individualized Community Detection in Inhomogeneous Multilayer Networks
Focusing on the symmetric two block case, we establish minimax rates for both global estimation of the common structure and individualized estimation of layer-wise community structures.
Label-Aware Neural Tangent Kernel: Toward Better Generalization and Local Elasticity
As a popular approach to modeling the dynamics of training overparametrized neural networks (NNs), the neural tangent kernels (NTK) are known to fall behind real-world NNs in generalization ability.
A Group-Theoretic Framework for Data Augmentation
Data augmentation is a widely used trick when training deep neural networks: in addition to the original data, properly transformed data are also added to the training set.
Valid Inference Corrected for Outlier Removal
Ordinary least square (OLS) estimation of a linear regression model is well-known to be highly sensitive to outliers.
