Country
Implicit Regularization for Optimal Sparse Recovery
Tomas Vaskevicius, Varun Kanade, Patrick Rebeschini
Ifwecanset1 /w max ฮท 1 / (20 w max) thentheiterationcomplexityof Theorem 1 reduces to O ( ฮบeff( w ) logฮฑ 1). Themagnitudeofw max is, however, anunknownquantity.
Heterogeneous Multi-output Gaussian Process Prediction
Pablo Moreno-Muรฑoz, Antonio Artรฉs, Mauricio รlvarez
Neural Information Processing Systems http://nips.cc/
On the Target-kernel Alignment: a Unified Analysis with Kernel Complexity
Y et, under the strongly-aligned regime, KM suffers the saturation effect, while TKM can be continuously improved as the alignment becomes stronger. This further implies that TKM has a strong ability to capture the strong alignment and provide a theoretically guaranteed solution to eliminate the phenomena of saturation effect.
Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters
Pavel Dvurechenskii, Darina Dvinskikh, Alexander Gasnikov, Cesar Uribe, Angelia Nedich
Optimal transport (OT) [30, 25] has become increasingly popular in the machine learning and optimizationcommunity.
Schema-learning and rebinding as mechanisms of in-context learning and emergence
In-context learning (ICL) is one of the most powerful and most unexpected capabilities to emerge in recent transformer-based large language models (LLMs).
Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data
Xenia Miscouridou, Francois Caron, Yee Whye Teh
Neural Information Processing Systems http://nips.cc/