Search Results for author: Varun Kanade

Found 32 papers, 5 papers with code

Towards optimally abstaining from prediction with OOD test examples

no code implementations NeurIPS 2021 Adam Tauman Kalai, Varun Kanade

Our work builds on a recent abstention algorithm of Goldwasser, Kalais, and Montasser (2020) for transductive binary classification.

Generalization Bounds

Efficient Learning with Arbitrary Covariate Shift

no code implementations15 Feb 2021 Adam Kalai, Varun Kanade

We give an efficient algorithm for learning a binary function in a given class C of bounded VC dimension, with training data distributed according to P and test data according to Q, where P and Q may be arbitrary distributions over X.

How Benign is Benign Overfitting ?

no code implementations ICLR 2021 Amartya Sanyal, Puneet K. Dokania, Varun Kanade, Philip Torr

We investigate two causes for adversarial vulnerability in deep neural networks: bad data and (poorly) trained models.

Adversarial Robustness Representation Learning

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

no code implementations16 Jul 2020 Bryn Elesedy, Varun Kanade, Yee Whye Teh

We analyse the pruning procedure behind the lottery ticket hypothesis arXiv:1803. 03635v5, iterative magnitude pruning (IMP), when applied to linear models trained by gradient flow.

How benign is benign overfitting?

no code implementations8 Jul 2020 Amartya Sanyal, Puneet K. Dokania, Varun Kanade, Philip H. S. Torr

We investigate two causes for adversarial vulnerability in deep neural networks: bad data and (poorly) trained models.

Adversarial Robustness Representation Learning

Differentiable Causal Backdoor Discovery

1 code implementation3 Mar 2020 Limor Gultchin, Matt J. Kusner, Varun Kanade, Ricardo Silva

Discovering the causal effect of a decision is critical to nearly all forms of decision-making.

Decision Making

The Statistical Complexity of Early-Stopped Mirror Descent

no code implementations NeurIPS 2020 Tomas Vaškevičius, Varun Kanade, Patrick Rebeschini

Recently there has been a surge of interest in understanding implicit regularization properties of iterative gradient-based optimization algorithms.

Online k-means Clustering

no code implementations15 Sep 2019 Vincent Cohen-Addad, Benjamin Guedj, Varun Kanade, Guy Rom

The specific formulation we use is the $k$-means objective: At each time step the algorithm has to maintain a set of k candidate centers and the loss incurred is the squared distance between the new point and the closest center.

Online Clustering

On the Hardness of Robust Classification

no code implementations NeurIPS 2019 Pascale Gourdeau, Varun Kanade, Marta Kwiatkowska, James Worrell

However if the adversary is restricted to perturbing $O(\log n)$ bits, then the class of monotone conjunctions can be robustly learned with respect to a general class of distributions (that includes the uniform distribution).

General Classification Learning Theory +1

Implicit Regularization for Optimal Sparse Recovery

1 code implementation NeurIPS 2019 Tomas Vaškevičius, Varun Kanade, Patrick Rebeschini

We investigate implicit regularization schemes for gradient descent methods applied to unpenalized least squares regression to solve the problem of reconstructing a sparse signal from an underdetermined system of linear measurements under the restricted isometry assumption.

Adaptive Reduced Rank Regression

1 code implementation NeurIPS 2020 Qiong Wu, Felix Ming Fai Wong, Zhenming Liu, Yanhua Li, Varun Kanade

We study the low rank regression problem $\my = M\mx + \epsilon$, where $\mx$ and $\my$ are $d_1$ and $d_2$ dimensional vectors respectively.

Clustering Redemption–Beyond the Impossibility of Kleinberg’s Axioms

no code implementations NeurIPS 2018 Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn

In this work, we take a different approach, based on the observation that the consistency axiom fails to be satisfied when the “correct” number of clusters changes.

Decentralized Cooperative Stochastic Bandits

1 code implementation NeurIPS 2019 David Martínez-Rubio, Varun Kanade, Patrick Rebeschini

We design a fully decentralized algorithm that uses an accelerated consensus procedure to compute (delayed) estimates of the average of rewards obtained by all the agents for each arm, and then uses an upper confidence bound (UCB) algorithm that accounts for the delay and error of the estimates.

Multi-Armed Bandits

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

no code implementations6 Aug 2018 Quentin Berthet, Varun Kanade

We study the problem of hypothesis testing between two discrete distributions, where we only have access to samples after the action of a known reversible Markov chain, playing the role of noise.

Two-sample testing

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

1 code implementation ICML 2018 Amartya Sanyal, Matt J. Kusner, Adrià Gascón, Varun Kanade

The main drawback of using fully homomorphic encryption is the amount of time required to evaluate large machine learning models on encrypted data.


Robustness via Deep Low-Rank Representations

no code implementations ICLR 2019 Amartya Sanyal, Varun Kanade, Philip H. S. Torr, Puneet K. Dokania

To achieve low dimensionality of learned representations, we propose an easy-to-use, end-to-end trainable, low-rank regularizer (LR) that can be applied to any intermediate layer representation of a DNN.

General Classification Image Classification +1

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

no code implementations15 Feb 2018 Varun Kanade, Andrea Rocchetto, Simone Severini

We show that DNF formulae can be quantum PAC-learned in polynomial time under product distributions using a quantum example oracle.

Hierarchical Clustering Beyond the Worst-Case

no code implementations NeurIPS 2017 Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn

Hiererachical clustering, that is computing a recursive partitioning of a dataset to obtain clusters at increasingly finer granularity is a fundamental problem in data analysis.

General Classification Multi-class Classification

From which world is your graph

no code implementations NeurIPS 2017 Cheng Li, Felix Mf Wong, Zhenming Liu, Varun Kanade

This work focuses on unifying two of the most widely used link-formation models: the stochastic block model (SBM) and the small world (or latent space) model (SWM).

Dimensionality Reduction Stochastic Block Model

From which world is your graph?

no code implementations3 Nov 2017 Cheng Li, Felix Wong, Zhenming Liu, Varun Kanade

Discovering statistical structure from links is a fundamental problem in the analysis of social networks.

Dimensionality Reduction

Hierarchical Clustering: Objective Functions and Algorithms

no code implementations7 Apr 2017 Vincent Cohen-Addad, Varun Kanade, Frederik Mallmann-Trenn, Claire Mathieu

For similarity-based hierarchical clustering, Dasgupta showed that the divisive sparsest-cut approach achieves an $O(\log^{3/2} n)$-approximation.

Combinatorial Optimization Stochastic Block Model

Reliably Learning the ReLU in Polynomial Time

no code implementations30 Nov 2016 Surbhi Goel, Varun Kanade, Adam Klivans, Justin Thaler

These results are in contrast to known efficient algorithms for reliably learning linear threshold functions, where $\epsilon$ must be $\Omega(1)$ and strong assumptions are required on the marginal distribution.

Online Optimization of Smoothed Piecewise Constant Functions

no code implementations7 Apr 2016 Vincent Cohen-Addad, Varun Kanade

We study online optimization of smoothed piecewise constant functions over the domain [0, 1).

Learning with a Drifting Target Concept

no code implementations20 May 2015 Steve Hanneke, Varun Kanade, Liu Yang

Some of the results also describe an active learning variant of this setting, and provide bounds on the number of queries for the labels of points in the sequence sufficient to obtain the stated bounds on the error rates.

Active Learning

Distribution-Independent Reliable Learning

no code implementations20 Feb 2014 Varun Kanade, Justin Thaler

The goal in the positive reliable agnostic framework is to output a hypothesis with the following properties: (i) its false positive error rate is at most $\epsilon$, (ii) its false negative error rate is at most $\epsilon$ more than that of the best positive reliable classifier from the class.

Attribute-Efficient Evolvability of Linear Functions

no code implementations16 Sep 2013 Elaine Angelino, Varun Kanade

In a seminal paper, Valiant (2006) introduced a computational model for evolution to address the question of complexity that can arise through Darwinian mechanisms.

MCMC Learning

no code implementations13 Jul 2013 Varun Kanade, Elchanan Mossel

The theory of learning under the uniform distribution is rich and deep, with connections to cryptography, computational complexity, and the analysis of boolean functions to name a few areas.

Distributed Non-Stochastic Experts

no code implementations NeurIPS 2012 Varun Kanade, Zhenming Liu, Bozidar Radunovic

This paper shows the difficulty of simultaneously achieving regret asymptotically better than \sqrt{kT} and communication better than T. We give a novel algorithm that for an oblivious adversary achieves a non-trivial trade-off: regret O(\sqrt{k^{5(1+\epsilon)/6} T}) and communication O(T/k^\epsilon), for any value of \epsilon in (0, 1/5).

Learning using Local Membership Queries

no code implementations5 Nov 2012 Pranjal Awasthi, Vitaly Feldman, Varun Kanade

We introduce a new model of membership query (MQ) learning, where the learning algorithm is restricted to query points that are \emph{close} to random examples drawn from the underlying distribution.

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

no code implementations NeurIPS 2011 Sham M. Kakade, Varun Kanade, Ohad Shamir, Adam Kalai

In this paper, we provide algorithms for learning GLMs and SIMs, which are both computationally and statistically efficient.

Potential-Based Agnostic Boosting

no code implementations NeurIPS 2009 Varun Kanade, Adam Kalai

We prove strong noise-tolerance properties of a potential-based boosting algorithm, similar to MadaBoost (Domingo and Watanabe, 2000) and SmoothBoost (Servedio, 2003).

Learning Theory

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