97 citations · 369 across the 20 of their papers we have counts for
5 papers · 1 filter
Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models
Steve Yadlowsky, Lyric Doshi, Nilesh Tripuraneni
Transformer models, notably large language models (LLMs), have the remarkable ability to perform in-context learning (ICL) -- to perform new tasks when prompted with unseen input-o…
On the Theory of Transfer Learning: The Importance of Task Diversity
Nilesh Tripuraneni, Michael I. Jordan, Chi Jin
We provide new statistical guarantees for transfer learning via representation learning--when transfer is achieved by learning a feature representation shared across different task…
Provable Meta-Learning of Linear Representations
Nilesh Tripuraneni, Chi Jin, Michael I. Jordan
Meta-learning, or learning-to-learn, seeks to design algorithms that can utilize previous experience to rapidly learn new skills or adapt to new environments. Representation learni…
Averaging Stochastic Gradient Descent on Riemannian Manifolds
Nilesh Tripuraneni, Nicolas Flammarion, Francis Bach +1
We consider the minimization of a function defined on a Riemannian manifold accessible only through unbiased estimates of its gradients. We develop a geometric framew…
Stochastic Cubic Regularization for Fast Nonconvex Optimization
Nilesh Tripuraneni, Mitchell Stern, Chi Jin +2
This paper proposes a stochastic variant of a classic algorithm---the cubic-regularized Newton method [Nesterov and Polyak 2006]. The proposed algorithm efficiently escapes saddle…