
Globallyconvergent Iteratively Reweighted Least Squares for Robust Regression Problems
We provide the first global model recovery results for the IRLS (iterati...
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Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms
We study the problem of least squares linear regression where the datap...
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The Pitfalls of Simplicity Bias in Neural Networks
Several works have proposed Simplicity Bias (SB)—the tendency of standar...
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COVID19: Strategies for Allocation of Test Kits
With the increasing spread of COVID19, it is important to systematicall...
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DROCC: Deep Robust OneClass Classification
Classical approaches for oneclass problems such as oneclass SVM (Schol...
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RNNPool: Efficient Nonlinear Pooling for RAM Constrained Inference
Pooling operators are key components in most Convolutional Neural Networ...
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Soft Threshold Weight Reparameterization for Learnable Sparsity
Sparsity in Deep Neural Networks (DNNs) is studied extensively with the ...
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RichItem Recommendations for RichUsers via GCNN: Exploiting Dynamic and Static Side Information
We study the standard problem of recommending relevant items to users; a...
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OASIS: ILPGuided Synthesis of Loop Invariants
Finding appropriate inductive loop invariants for a program is a key cha...
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Learning Functions over Sets via Permutation Adversarial Networks
In this paper, we consider the problem of learning functions over sets, ...
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Efficient Algorithms for Smooth Minimax Optimization
This paper studies first order methods for solving smooth minimax optimi...
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Making the Last Iterate of SGD Information Theoretically Optimal
Stochastic gradient descent (SGD) is one of the most widely used algorit...
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Adaptive Hard Thresholding for Nearoptimal Consistent Robust Regression
We study the problem of robust linear regression with response variable ...
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SGD without Replacement: Sharper Rates for General Smooth Convex Functions
We study stochastic gradient descent without replacement () for smooth ...
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FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network
This paper develops the FastRNN and FastGRNN algorithms to address the t...
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Nonlinear Inductive Matrix Completion based on Onelayer Neural Networks
The goal of a recommendation system is to predict the interest of a user...
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NeuralGuided Deductive Search for RealTime Program Synthesis from Examples
Synthesizing userintended programs from a small number of inputoutput ...
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On the insufficiency of existing momentum schemes for Stochastic Optimization
Momentum based stochastic gradient methods such as heavy ball (HB) and N...
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Smoothed analysis for lowrank solutions to semidefinite programs in quadratic penalty form
Semidefinite programs (SDP) are important in learning and combinatorial ...
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Differentially Private Matrix Completion, Revisited
We study the problem of privacypreserving collaborative filtering where...
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Nonconvex Optimization for Machine Learning
A vast majority of machine learning algorithms train their models and pe...
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A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares)
This work provides a simplified proof of the statistical minimax optimal...
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Leveraging Distributional Semantics for MultiLabel Learning
We present a novel and scalable label embedding framework for largescal...
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Learning Mixture of Gaussians with Streaming Data
In this paper, we study the problem of learning a mixture of Gaussians w...
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Recovery Guarantees for Onehiddenlayer Neural Networks
In this paper, we consider regression problems with onehiddenlayer neu...
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Accelerating Stochastic Gradient Descent
There is widespread sentiment that it is not possible to effectively uti...
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Parallelizing Stochastic Approximation Through MiniBatching and TailAveraging
This work characterizes the benefits of averaging techniques widely used...
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Efficient and Consistent Robust Time Series Analysis
We study the problem of robust time series analysis under the standard a...
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Regret Bounds for Nondecomposable Metrics with Missing Labels
We consider the problem of recommending relevant labels (items) for a gi...
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Streaming PCA: Matching Matrix Bernstein and NearOptimal Finite Sample Guarantees for Oja's Algorithm
This work provides improved guarantees for streaming principle component...
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Structured Sparse Regression via Greedy HardThresholding
Several learning applications require solving highdimensional regressio...
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Tensor vs Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations
Robust tensor CP decomposition involves decomposing a tensor into low ra...
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Robust Regression via Hard Thresholding
We study the problem of Robust Least Squares Regression (RLSR) where sev...
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Surrogate Functions for Maximizing Precision at the Top
The problem of maximizing precision at the top of a ranked list, often d...
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Optimizing Nondecomposable Performance Measures: A Tale of Two Classes
Modern classification problems frequently present mild to severe label i...
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To Drop or Not to Drop: Robustness, Consistency and Differential Privacy Properties of Dropout
Training deep belief networks (DBNs) requires optimizing a nonconvex fu...
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Fast Exact Matrix Completion with Finite Samples
Matrix completion is the problem of recovering a low rank matrix by obse...
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Nonconvex Robust PCA
We propose a new method for robust PCA  the task of recovering a lowr...
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Online and Stochastic Gradient Methods for Nondecomposable Loss Functions
Modern applications in sensitive domains such as biometrics and medicine...
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On Iterative Hard Thresholding Methods for Highdimensional MEstimation
The use of Mestimators in generalized linear regression models in high ...
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Tighter Lowrank Approximation via Sampling the Leveraged Element
In this work, we propose a new randomized algorithm for computing a low...
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Universal Matrix Completion
The problem of lowrank matrix completion has recently generated a lot o...
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Learning Mixtures of Discrete Product Distributions using Spectral Decompositions
We study the problem of learning a distribution from samples, when the u...
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Memory Limited, Streaming PCA
We consider streaming, onepass principal component analysis (PCA), in t...
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Provable Inductive Matrix Completion
Consider a movie recommendation system where apart from the ratings info...
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Phase Retrieval using Alternating Minimization
Phase retrieval problems involve solving linear equations, but with miss...
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On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions
In this paper, we study the generalization properties of online learning...
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Lowrank Matrix Completion using Alternating Minimization
Alternating minimization represents a widely applicable and empirically ...
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The Interplay Between Stability and Regret in Online Learning
This paper considers the stability of online learning algorithms and its...
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Supervised Learning with Similarity Functions
We address the problem of general supervised learning when data can only...
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