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06/15/2023
Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization
We develop a re-weighted gradient descent technique for boosting the per...
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06/15/2023
Optimal Best-Arm Identification in Bandits with Access to Offline Data
Learning paradigms based purely on offline data as well as those based s...
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06/09/2023
End-to-End Neural Network Compression via ℓ_1/ℓ_2 Regularized Latency Surrogates
Neural network (NN) compression via techniques such as pruning, quantiza...
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01/30/2023
Near Optimal Private and Robust Linear Regression
We study the canonical statistical estimation problem of linear regressi...
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01/17/2023
Optimal Algorithms for Latent Bandits with Cluster Structure
We consider the problem of latent bandits with cluster structure where t...
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10/26/2021
Boosted CVaR Classification
Many modern machine learning tasks require models with high tail perform...
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06/19/2020
Learning Minimax Estimators via Online Learning
We consider the problem of designing minimax estimators for estimating t...
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06/13/2020
Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games
We consider the problem of online learning and its application to solvin...
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03/19/2019
Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression
We study the problem of robust linear regression with response variable ...
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03/19/2019
Online Non-Convex Learning: Following the Perturbed Leader is Optimal
We study the problem of online learning with non-convex losses, where th...
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01/27/2019
How Sensitive are Sensitivity-Based Explanations?
We propose a simple objective evaluation measure for explanations of a c...
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06/07/2018
On Adversarial Risk and Training
In this work we formally define the notions of adversarial perturbations...
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02/19/2018
Robust Estimation via Robust Gradient Estimation
We provide a new computationally-efficient class of estimators for risk ...
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05/19/2015
