Statistical Learning
Resource-Aware Federated Self-Supervised Learning with Global Class Representations
As shown in Fig.1, client A,
Marginal Causal Flows for Validation and Inference
Investigating the marginal causal effect of an intervention on an outcome from complex data remains challenging due to the inflexibility of employed models and the lack of complexity in causal benchmark datasets, which often fail to reproduce intricate real-world data patterns. In this paper we introduce Frugal Flows, a novel likelihood-based machine learning model that uses normalising flows to flexibly learn the data-generating process, while also directly inferring the marginal causal quantities from observational data.
Text-Infused Attention and Foreground-Aware Modeling for Zero-Shot Temporal Action Detection
Ti-FAD outperforms the state-of-the-art methods on ZST AD benchmarks by a large margin: 41.2% (+ 11.0%) on THUMOS14 and 32.0% (+ 5.4%) on ActivityNet v1.3. Code is available at: https://github.com/Y
HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning
B matrices to avoid training interference.
GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts
Note that the shift components can be customized and expanded based on downstream tasks.
Stochastic Optimization Schemes for Performative Prediction with Nonconvex Loss
This paper studies a risk minimization problem with decision dependent data distribution.