Amit Daniely | DeepAI

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∙ 07/02/2023

Multiclass Boosting: Simple and Intuitive Weak Learning Criteria

We study a generalization of boosting to the multiclass setting. We intr...

0 Nataly Brukhim, et al. ∙

∙ 05/25/2023

Most Neural Networks Are Almost Learnable

We present a PTAS for learning random constant-depth networks. We show t...

0 Amit Daniely, et al. ∙

∙ 02/15/2023

Efficiently Learning Neural Networks: What Assumptions May Suffice?

Understanding when neural networks can be learned efficiently is a funda...

0 Amit Daniely, et al. ∙

∙ 11/17/2022

On the Sample Complexity of Two-Layer Networks: Lipschitz vs. Element-Wise Lipschitz Activation

We investigate the sample complexity of bounded two-layer neural network...

0 Amit Daniely, et al. ∙

∙ 09/26/2022

Approximate Description Length, Covering Numbers, and VC Dimension

Recently, Daniely and Granot [arXiv:1910.05697] introduced a new notion ...

0 Amit Daniely, et al. ∙

∙ 02/10/2022

Monotone Learning

The amount of training-data is one of the key factors which determines t...

0 Olivier Bousquet, et al. ∙

∙ 06/22/2021

Asynchronous Stochastic Optimization Robust to Arbitrary Delays

We consider stochastic optimization with delayed gradients where, at eac...

10 Alon Cohen, et al. ∙

∙ 05/20/2021

An Exact Poly-Time Membership-Queries Algorithm for Extraction a three-Layer ReLU Network

As machine learning increasingly becomes more prevalent in our everyday ...

0 Amit Daniely, et al. ∙

∙ 01/20/2021

From Local Pseudorandom Generators to Hardness of Learning

We prove hardness-of-learning results under a well-studied assumption on...

0 Amit Daniely, et al. ∙

∙ 10/28/2020

Most ReLU Networks Suffer from ℓ^2 Adversarial Perturbations

We consider ReLU networks with random weights, in which the dimension de...

0 Amit Daniely, et al. ∙

∙ 06/05/2020

Hardness of Learning Neural Networks with Natural Weights

Neural networks are nowadays highly successful despite strong hardness r...

0 Amit Daniely, et al. ∙

∙ 03/28/2020

Memorizing Gaussians with no over-parameterizaion via gradient decent on neural networks

We prove that a single step of gradient decent over depth two network, w...

0 Amit Daniely, et al. ∙

∙ 02/18/2020

Learning Parities with Neural Networks

In recent years we see a rapidly growing line of research which shows le...

2 Amit Daniely, et al. ∙

∙ 02/09/2020

On the Complexity of Minimizing Convex Finite Sums Without Using the Indices of the Individual Functions

Recent advances in randomized incremental methods for minimizing L-smoot...

5 Yossi Arjevani, et al. ∙

∙ 11/22/2019

Neural Networks Learning and Memorization with (almost) no Over-Parameterization

Many results in recent years established polynomial time learnability of...

0 Amit Daniely, et al. ∙

∙ 10/13/2019

Generalization Bounds for Neural Networks via Approximate Description Length

We investigate the sample complexity of networks with bounds on the magn...

0 Amit Daniely, et al. ∙

∙ 09/26/2019

The Implicit Bias of Depth: How Incremental Learning Drives Generalization

A leading hypothesis for the surprising generalization of neural network...

0 Daniel Gissin, et al. ∙

∙ 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...

0 Alon Brutzkus, et al. ∙

∙ 06/20/2019

ID3 Learns Juntas for Smoothed Product Distributions

In recent years, there are many attempts to understand popular heuristic...

0 Alon Brutzkus, et al. ∙

∙ 04/07/2019

Competitive ratio versus regret minimization: achieving the best of both worlds

We consider online algorithms under both the competitive ratio criteria ...

0 Amit Daniely, et al. ∙

∙ 09/24/2018

Learning without Interaction Requires Separation

One of the key resources in large-scale learning systems is the number o...

2 Amit Daniely, et al. ∙

∙ 05/07/2018

Planning and Learning with Stochastic Action Sets

In many practical uses of reinforcement learning (RL) the set of actions...

0 Craig Boutilier, et al. ∙

∙ 03/08/2018

Learning with Rules

Complex classifiers may exhibit "embarassing" failures in cases that wou...

0 Deborah Cohen, et al. ∙

∙ 02/27/2017

SGD Learns the Conjugate Kernel Class of the Network

We show that the standard stochastic gradient decent (SGD) algorithm is ...

0 Amit Daniely, et al. ∙

∙ 02/27/2017

Depth Separation for Neural Networks

Let f:S^d-1×S^d-1→S be a function of the form f(x,x') = g(〈x,x'〉) for g:...

0 Amit Daniely, et al. ∙

∙ 02/18/2016

Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity

We develop a general duality between neural networks and compositional k...

0 Amit Daniely, et al. ∙

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