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09/13/2023
Auto-Regressive Next-Token Predictors are Universal Learners
Large language models display remarkable capabilities in logical and mat...
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09/07/2023
Pareto Frontiers in Neural Feature Learning: Data, Compute, Width, and Luck
This work investigates the nuanced algorithm design choices for deep lea...
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09/04/2023
Corgi^2: A Hybrid Offline-Online Approach To Storage-Aware Data Shuffling For SGD
When using Stochastic Gradient Descent (SGD) for training machine learni...
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02/13/2023
SubTuning: Efficient Finetuning for Multi-Task Learning
Finetuning a pretrained model has become a standard approach for trainin...
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07/18/2022
Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit
There is mounting empirical evidence of emergent phenomena in the capabi...
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03/28/2022
Knowledge Distillation: Bad Models Can Be Good Role Models
Large neural networks trained in the overparameterized regime are able t...
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08/09/2021
On the Power of Differentiable Learning versus PAC and SQ Learning
We study the power of learning via mini-batch stochastic gradient descen...
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03/01/2021
Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels
We study the relative power of learning with gradient descent on differe...
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01/31/2021
The Connection Between Approximation, Depth Separation and Learnability in Neural Networks
Several recent works have shown separation results between deep neural n...
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10/03/2020
Computational Separation Between Convolutional and Fully-Connected Networks
Convolutional neural networks (CNN) exhibit unmatched performance in a m...
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08/18/2020
When Hardness of Approximation Meets Hardness of Learning
A supervised learning algorithm has access to a distribution of labeled ...
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02/18/2020
Learning Parities with Neural Networks
In recent years we see a rapidly growing line of research which shows le...
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02/03/2020
Proving the Lottery Ticket Hypothesis: Pruning is All You Need
The lottery ticket hypothesis (Frankle and Carbin, 2018), states that a ...
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10/25/2019
Learning Boolean Circuits with Neural Networks
Training neural-networks is computationally hard. However, in practice t...
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07/11/2019
On the Optimality of Trees Generated by ID3
Since its inception in the 1980s, ID3 has become one of the most success...
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06/20/2019
ID3 Learns Juntas for Smoothed Product Distributions
In recent years, there are many attempts to understand popular heuristic...
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06/12/2019
Decoupling Gating from Linearity
ReLU neural-networks have been in the focus of many recent theoretical w...
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03/08/2019
Is Deeper Better only when Shallow is Good?
Understanding the power of depth in feed-forward neural networks is an o...
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03/26/2018
