Statistical Learning
In-Context Learning with Representations: Contextual Generalization of Trained Transformers Tong Y ang CMU Y u Huang
This paper investigates the training dynamics of transformers by gradient descent through the lens of non-linear regression tasks.
On the Necessity of Collaboration for Online Model Selection with Decentralized Data Junfan Li1 Zheshun Wu
We consider model selection for online supervised learning.
Identification of Analytic Nonlinear Dynamical Systems with Non-asymptotic Guarantees
We provide non-asymptotic convergence rates for LSE and SME under i.i.d.
Protected Test-Time Adaptation via Online Entropy Matching: A Betting Approach Y arin Bar
We present a novel approach for test-time adaptation via online self-training, consisting of two components.
Federated Learning over Connected Modes Dennis Grinwald
In each communication round, each client uniformly samples points in the neighborhood of their projected point (top-right plot), and jointly train the solution simplex.
Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Models
Accurately modeling weather variation pattern from large amount of meteorological variables sequences is increasingly vital for providing efficient weather analysis support for disaster warning.
Deep Learning for Computing Convergence Rates of Markov Chains
Markovian models, estimating the rate of convergence to equilibrium is critical.
Hybrid Generative AI for De Novo Design of Co-Crystals with Enhanced Tabletability
Furthermore, we explore the potential of language models in generating co-crystals. Finally, we present numerous previously unknown co-crystals predicted by GEM-CODE and discuss its potential in accelerating drug development.
