Statistical Learning
Nonparametric Identifiability of Causal Representations from Unknown Interventions Julius von Kรผgelgen
We study causal representation learning, the task of inferring latent causal variables and their causal relations from high-dimensional functions ("mixtures") of the variables.
Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming
Classically, sketching has been applied to design low-memory algorithms in the streaming setting, when the input is presented to the algorithm as a sequence of updates.
Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming
Classically, sketching has been applied to design low-memory algorithms in the streaming setting, when the input is presented to the algorithm as a sequence of updates.
Semantic segmentation of sparse irregular point clouds for leaf/wood discrimination Yuchen Bai 1 Jean-Baptiste Durand
We also propose a loss function adapted to the severe class imbalance. We show that our model outperforms state-of-the-art alternatives on UA V point clouds.
Reliable learning in challenging environments
However, learning theoretic guarantees have only been considered in very specific settings.
Pareto Frontiers in Deep Feature Learning: Data, Compute, Width, and Luck Benjamin L. Edelman
This work investigates how these complexities necessarily arise for feature learning in the presence of computational-statistical gaps.