Statistical Learning
Convergence Analysis of Split Federated Learning on Heterogeneous Data
However, FL is usually computationally intensive.
Schrรถdinger Bridge Flow for Unpaired Data Translation
Mass transport problems arise in many areas of machine learning whereby one wants to compute a map transporting one distribution to another.
CSPG: Crossing Sparse Proximity Graphs for Approximate Nearest Neighbor Search
An efficient two-staged approach is designed for exploring CSPG, with fast approaching and cross-partition expansion.
Conditional Outcome Equivalence: A Quantile Alternative to CATE
The conditional quantile treatment effect (CQTE) can provide insight into the effect of a treatment beyond the conditional average treatment effect (CA TE). This ability to provide information over multiple quantiles of the response makes the CQTE especially valuable in cases where the effect of a treatment is not well-modelled by a location shift, even conditionally on the covariates. Nevertheless, the estimation of the CQTE is challenging and often depends upon the smoothness of the individual quantiles as a function of the covariates rather than smoothness of the CQTE itself. This is in stark contrast to the CA TE where it is possible to obtain high-quality estimates which have less dependency upon the smoothness of the nuisance parameters when the CA TE itself is smooth. Moreover, relative smoothness of the CQTE lacks the interpretability of smoothness of the CA TE making it less clear whether it is a reasonable assumption to make.
Multi-Scale Representation Learning for Protein Fitness Prediction
Given the limited availability of functional annotations from wet-lab experiments, previous methods have primarily relied on self-supervised models trained on vast, unlabeled protein sequence or structure datasets.
Unveiling the Hidden Structure of Self-Attention via Kernel Principal Component Analysis
The remarkable success of transformers in sequence modeling tasks, spanning various applications in natural language processing and computer vision, is attributed to the critical role of self-attention.
From Causal to Concept-Based Representation Learning
A key question in this growing field is to provide rigorous conditions under which latent factors can be identified and thus, potentially learned.
A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening Guy Bar-Shalom
This paper introduces a new Subgraph GNN framework to address these issues.
MUVERA: Multi-Vector Retrieval via Fixed Dimensional Encodings Laxman Dhulipala Google Research and UMD Majid Hadian Google DeepMind Rajesh Jayaram
Neural embedding models have become a fundamental component of modern information retrieval (IR) pipelines.
An Information Theoretic Perspective on Conformal Prediction
More precisely, we prove three different ways to upper bound the intrinsic uncertainty, as described by the conditional entropy of the target variable given the inputs, by combining CP with information theoretical inequalities.