989 citations · 6.1k across the 184 of their papers we have counts for
13 papers · 1 filter
A Study on the Calibration of In-context Learning
Hanlin Zhang, Yi-Fan Zhang, Yaodong Yu +5
Accurate uncertainty quantification is crucial for the safe deployment of machine learning models, and prior research has demonstrated improvements in the calibration of modern lan…
Feature emergence via margin maximization: case studies in algebraic tasks
Depen Morwani, Benjamin L. Edelman, Costin-Andrei Oncescu +2
Understanding the internal representations learned by neural networks is a cornerstone challenge in the science of machine learning. While there have been significant recent stride…
Pareto Frontiers in Neural Feature Learning: Data, Compute, Width, and Luck
Benjamin L. Edelman, Surbhi Goel, Sham Kakade +2
In modern deep learning, algorithmic choices (such as width, depth, and learning rate) are known to modulate nuanced resource tradeoffs. This work investigates how these complexiti…
Learning an Inventory Control Policy with General Inventory Arrival Dynamics
Sohrab Andaz, Carson Eisenach, Dhruv Madeka +4
In this paper we address the problem of learning and backtesting inventory control policies in the presence of general arrival dynamics -- which we term as a quantity-over-time arr…
MatFormer: Nested Transformer for Elastic Inference
Devvrit, Sneha Kudugunta, Aditya Kusupati +8
Foundation models are applied in a broad spectrum of settings with different inference constraints, from massive multi-accelerator clusters to resource-constrained standalone mobil…
Scaling Laws for Imitation Learning in Single-Agent Games
Jens Tuyls, Dhruv Madeka, Kari Torkkola +3
Imitation Learning (IL) is one of the most widely used methods in machine learning. Yet, many works find it is often unable to fully recover the underlying expert behavior, even in…