Varun Kanade | DeepAI

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Philip H. S. Torr
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∙ 11/22/2022

Simplicity Bias in Transformers and their Ability to Learn Sparse Boolean Functions

Despite the widespread success of Transformers on NLP tasks, recent work...

0 Satwik Bhattamishra, et al. ∙

∙ 10/12/2022

When are Local Queries Useful for Robust Learning?

Distributional assumptions have been shown to be necessary for the robus...

0 Pascale Gourdeau, et al. ∙

∙ 08/25/2022

Partial Matrix Completion

In the matrix completion problem, one wishes to reconstruct a low-rank m...

0 Varun Kanade, et al. ∙

∙ 05/24/2022

Beyond Impossibility: Balancing Sufficiency, Separation and Accuracy

Among the various aspects of algorithmic fairness studied in recent year...

0 Limor Gultchin, et al. ∙

∙ 05/12/2022

Sample Complexity Bounds for Robustly Learning Decision Lists against Evasion Attacks

A fundamental problem in adversarial machine learning is to quantify how...

0 Pascale Gourdeau, et al. ∙

∙ 02/23/2022

Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition

The local Rademacher complexity framework is one of the most successful ...

0 Varun Kanade, et al. ∙

∙ 05/28/2021

Towards optimally abstaining from prediction

A common challenge across all areas of machine learning is that training...

0 Adam Tauman Kalai, et al. ∙

∙ 02/15/2021

Efficient Learning with Arbitrary Covariate Shift

We give an efficient algorithm for learning a binary function in a given...

0 Adam Kalai, et al. ∙

∙ 07/16/2020

Lottery Tickets in Linear Models: An Analysis of Iterative Magnitude Pruning

We analyse the pruning procedure behind the lottery ticket hypothesis ar...

10 Bryn Elesedy, et al. ∙

∙ 07/08/2020

How benign is benign overfitting?

We investigate two causes for adversarial vulnerability in deep neural n...

7 Amartya Sanyal, et al. ∙

∙ 03/03/2020

Differentiable Causal Backdoor Discovery

Discovering the causal effect of a decision is critical to nearly all fo...

0 Limor Gultchin, et al. ∙

∙ 02/01/2020

The Statistical Complexity of Early Stopped Mirror Descent

Recently there has been a surge of interest in understanding implicit re...

0 Tomas Vaškevičius, et al. ∙

∙ 09/15/2019

Online k-means Clustering

We study the problem of online clustering where a clustering algorithm h...

6 Vincent Cohen-Addad, et al. ∙

∙ 09/12/2019

On the Hardness of Robust Classification

It is becoming increasingly important to understand the vulnerability of...

0 Pascale Gourdeau, et al. ∙

∙ 09/11/2019

Implicit Regularization for Optimal Sparse Recovery

We investigate implicit regularization schemes for gradient descent meth...

0 Tomas Vaškevičius, et al. ∙

∙ 05/28/2019

Adaptive Reduced Rank Regression

Low rank regression has proven to be useful in a wide range of forecasti...

0 Qiong Wu, et al. ∙

∙ 10/10/2018

Decentralized Cooperative Stochastic Multi-armed Bandits

We study a decentralized cooperative stochastic multi-armed bandit probl...

0 David Martinez Rubio, et al. ∙

∙ 08/06/2018

Statistical Windows in Testing for the Initial Distribution of a Reversible Markov Chain

We study the problem of hypothesis testing between two discrete distribu...

0 Quentin Berthet, et al. ∙

∙ 06/09/2018

TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service

Machine learning methods are widely used for a variety of prediction pro...

0 Amartya Sanyal, et al. ∙

∙ 04/19/2018

Low Rank Structure of Learned Representations

A key feature of neural networks, particularly deep convolutional neural...

0 Amartya Sanyal, et al. ∙

∙ 02/15/2018

Learning DNFs under product distributions via μ-biased quantum Fourier sampling

We show that DNF formulae can be quantum PAC-learned in polynomial time ...

0 Varun Kanade, et al. ∙

∙ 11/03/2017

From which world is your graph?

Discovering statistical structure from links is a fundamental problem in...

0 Cheng Li, et al. ∙

∙ 04/07/2016

Online Optimization of Smoothed Piecewise Constant Functions

We study online optimization of smoothed piecewise constant functions ov...

0 Vincent Cohen-Addad, et al. ∙

∙ 07/13/2013

MCMC Learning

The theory of learning under the uniform distribution is rich and deep, ...

0 Varun Kanade, et al. ∙

∙ 11/14/2012

Distributed Non-Stochastic Experts

We consider the online distributed non-stochastic experts problem, where...

0 Varun Kanade, et al. ∙

∙ 11/05/2012

Learning using Local Membership Queries

We introduce a new model of membership query (MQ) learning, where the le...

0 Pranjal Awasthi, et al. ∙

∙ 04/11/2011

Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression

Generalized Linear Models (GLMs) and Single Index Models (SIMs) provide ...

0 Sham Kakade, et al. ∙

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