Search Results for author: Arun Sai Suggala

Found 13 papers, 5 papers with code

Building Robust Ensembles via Margin Boosting

1 code implementation7 Jun 2022 Dinghuai Zhang, Hongyang Zhang, Aaron Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala

Consequently, an emerging line of work has focused on learning an ensemble of neural networks to defend against adversarial attacks.

Adversarial Robustness

Boosted CVaR Classification

1 code implementation NeurIPS 2021 Runtian Zhai, Chen Dan, Arun Sai Suggala, Zico Kolter, Pradeep Ravikumar

To learn such randomized classifiers, we propose the Boosted CVaR Classification framework which is motivated by a direct relationship between CVaR and a classical boosting algorithm called LPBoost.

Classification Decision Making +1

Learning Minimax Estimators via Online Learning

no code implementations19 Jun 2020 Kartik Gupta, Arun Sai Suggala, Adarsh Prasad, Praneeth Netrapalli, Pradeep Ravikumar

We view the problem of designing minimax estimators as finding a mixed strategy Nash equilibrium of a zero-sum game.

online learning

Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games

no code implementations NeurIPS 2020 Arun Sai Suggala, Praneeth Netrapalli

For Lipschitz and smooth nonconvex-nonconcave games, our algorithm requires access to an optimization oracle which computes the perturbed best response.

online learning

Online Non-Convex Learning: Following the Perturbed Leader is Optimal

no code implementations19 Mar 2019 Arun Sai Suggala, Praneeth Netrapalli

We show that the classical Follow the Perturbed Leader (FTPL) algorithm achieves optimal regret rate of $O(T^{-1/2})$ in this setting.

online learning

Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression

no code implementations19 Mar 2019 Arun Sai Suggala, Kush Bhatia, Pradeep Ravikumar, Prateek Jain

We provide a nearly linear time estimator which consistently estimates the true regression vector, even with $1-o(1)$ fraction of corruptions.

On the (In)fidelity and Sensitivity for Explanations

1 code implementation27 Jan 2019 Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Sai Suggala, David I. Inouye, Pradeep Ravikumar

We analyze optimal explanations with respect to both these measures, and while the optimal explanation for sensitivity is a vacuous constant explanation, the optimal explanation for infidelity is a novel combination of two popular explanation methods.

Revisiting Adversarial Risk

no code implementations7 Jun 2018 Arun Sai Suggala, Adarsh Prasad, Vaishnavh Nagarajan, Pradeep Ravikumar

Based on the modified definition, we show that there is no trade-off between adversarial and standard accuracies; there exist classifiers that are robust and achieve high standard accuracy.

Image Classification

Robust Estimation via Robust Gradient Estimation

no code implementations19 Feb 2018 Adarsh Prasad, Arun Sai Suggala, Sivaraman Balakrishnan, Pradeep Ravikumar

We provide a new computationally-efficient class of estimators for risk minimization.

Latent Feature Lasso

no code implementations ICML 2017 Ian En-Hsu Yen, Wei-Cheng Lee, Sung-En Chang, Arun Sai Suggala, Shou-De Lin, Pradeep Ravikumar

The latent feature model (LFM), proposed in (Griffiths \& Ghahramani, 2005), but possibly with earlier origins, is a generalization of a mixture model, where each instance is generated not from a single latent class but from a combination of latent features.

Ordinal Graphical Models: A Tale of Two Approaches

no code implementations ICML 2017 Arun Sai Suggala, Eunho Yang, Pradeep Ravikumar

While there have been some work on tractable approximations, these do not come with strong statistical guarantees, and moreover are relatively computationally expensive.

Vector-Space Markov Random Fields via Exponential Families

1 code implementation19 May 2015 Wesley Tansey, Oscar Hernan Madrid Padilla, Arun Sai Suggala, Pradeep Ravikumar

Specifically, VS-MRFs are the joint graphical model distributions where the node-conditional distributions belong to generic exponential families with general vector space domains.

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