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08/01/2022
What Can Transformers Learn In-Context? A Case Study of Simple Function Classes
In-context learning refers to the ability of a model to condition on a p...
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01/12/2022
On the Statistical Complexity of Sample Amplification
Given n i.i.d. samples drawn from an unknown distribution P, when is it ...
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12/22/2021
An Alternate Policy Gradient Estimator for Softmax Policies
Policy gradient (PG) estimators for softmax policies are ineffective wit...
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11/17/2021
How and When Random Feedback Works: A Case Study of Low-Rank Matrix Factorization
The success of gradient descent in ML and especially for learning neural...
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11/30/2020
A Model for Ant Trail Formation and its Convergence Properties
We introduce a model for ant trail formation, building upon previous wor...
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07/01/2020
Gradient Temporal-Difference Learning with Regularized Corrections
It is still common to use Q-learning and temporal difference (TD) learni...
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02/12/2020
CROFT: A scalable three-dimensional parallel Fast Fourier Transform (FFT) implementation for High Performance Clusters
The FFT of three-dimensional (3D) input data is an important computation...
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04/26/2019
Sample Amplification: Increasing Dataset Size even when Learning is Impossible
Given data drawn from an unknown distribution, D, to what extent is it p...
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11/15/2018