no code implementations • 4 Jun 2024 • Chiraag Kaushik, Justin Romberg, Vidya Muthukumar

The classical iteratively reweighted least-squares (IRLS) algorithm aims to recover an unknown signal from linear measurements by performing a sequence of weighted least squares problems, where the weights are recursively updated at each step.

no code implementations • 10 May 2024 • Kuo-Wei Lai, Vidya Muthukumar

Overparameterized models that achieve zero training error are observed to generalize well on average, but degrade in performance when faced with data that is under-represented in the training sample.

1 code implementation • 8 Apr 2024 • Ashwin Pananjady, Vidya Muthukumar, Andrew Thangaraj

Operating in the general setting in which the size of the state space may be much larger than the length $n$ of the trajectory, we develop a linear-runtime estimator called \emph{Windowed Good--Turing} (\textsc{WingIt}) and show that its risk decays as $\widetilde{\mathcal{O}}(\mathsf{T_{mix}}/n)$, where $\mathsf{T_{mix}}$ denotes the mixing time of the chain in total variation distance.

no code implementations • 18 Feb 2024 • Chiraag Kaushik, Ran Liu, Chi-Heng Lin, Amrit Khera, Matthew Y Jin, Wenrui Ma, Vidya Muthukumar, Eva L Dyer

Classification models are expected to perform equally well for different classes, yet in practice, there are often large gaps in their performance.

1 code implementation • NeurIPS 2023 • Tyler LaBonte, Vidya Muthukumar, Abhishek Kumar

In this work, we examine this impractical requirement and find that last-layer retraining can be surprisingly effective with no group annotations (other than for model selection) and only a handful of class annotations.

no code implementations • NeurIPS 2023 • Guanghui Wang, Zihao Hu, Claudio Gentile, Vidya Muthukumar, Jacob Abernethy

To address this limitation, we present a series of state-of-the-art implicit bias rates for mirror descent and steepest descent algorithms.

no code implementations • 3 May 2023 • Chiraag Kaushik, Andrew D. McRae, Mark A. Davenport, Vidya Muthukumar

The support vector machine (SVM) is a supervised learning algorithm that finds a maximum-margin linear classifier, often after mapping the data to a high-dimensional feature space via the kernel trick.

no code implementations • 13 Mar 2023 • Kuo-Wei Lai, Vidya Muthukumar

We provide a unified framework, applicable to a general family of convex losses and across binary and multiclass settings in the overparameterized regime, to approximately characterize the implicit bias of gradient descent in closed form.

no code implementations • 19 Feb 2023 • Jonathan N. Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar, Emma Brunskill

Whether this is possible for more realistic context distributions has remained an open and important question for tasks such as model selection.

no code implementations • 17 Oct 2022 • Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob Abernethy

The classical algorithms for online learning and decision-making have the benefit of achieving the optimal performance guarantees, but suffer from computational complexity limitations when implemented at scale.

no code implementations • 10 Oct 2022 • Chi-Heng Lin, Chiraag Kaushik, Eva L. Dyer, Vidya Muthukumar

Data augmentation (DA) is a powerful workhorse for bolstering performance in modern machine learning.

no code implementations • 9 Nov 2021 • Andrew D. McRae, Santhosh Karnik, Mark A. Davenport, Vidya Muthukumar

Our results recover prior independent-features results (with a much simpler analysis), but they furthermore show that harmless interpolation can occur in more general settings such as features that are a bounded orthonormal system.

no code implementations • 8 Nov 2021 • Vidya Muthukumar, Akshay Krishnamurthy

In this paper, we introduce new algorithms that a) explore in a data-adaptive manner, and b) provide model selection guarantees of the form $\mathcal{O}(d^{\alpha} T^{1- \alpha})$ with no feature diversity conditions whatsoever, where $d$ denotes the dimension of the linear model and $T$ denotes the total number of rounds.

no code implementations • 27 Sep 2021 • Adhyyan Narang, Vidya Muthukumar, Anant Sahai

We find that the learned model is susceptible to adversaries in an intermediate regime where classification generalizes but regression does not.

no code implementations • 6 Sep 2021 • Yehuda Dar, Vidya Muthukumar, Richard G. Baraniuk

The rapid recent progress in machine learning (ML) has raised a number of scientific questions that challenge the longstanding dogma of the field.

1 code implementation • 28 Jun 2021 • Wenshuo Guo, Kumar Krishna Agrawal, Aditya Grover, Vidya Muthukumar, Ashwin Pananjady

We introduce the "inverse bandit" problem of estimating the rewards of a multi-armed bandit instance from observing the learning process of a low-regret demonstrator.

no code implementations • NeurIPS 2021 • Ke Wang, Vidya Muthukumar, Christos Thrampoulidis

The literature on "benign overfitting" in overparameterized models has been mostly restricted to regression or binary classification; however, modern machine learning operates in the multiclass setting.

no code implementations • 3 Dec 2020 • Vidya Muthukumar, Soham Phade, Anant Sahai

We study the limiting behavior of the mixed strategies that result from optimal no-regret learning strategies in a repeated game setting where the stage game is any 2 by 2 competitive game.

no code implementations • 19 Nov 2020 • Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill

Towards this end, we consider the problem of model selection in RL with function approximation, given a set of candidate RL algorithms with known regret guarantees.

no code implementations • 22 Sep 2020 • Daniel Hsu, Vidya Muthukumar, Ji Xu

The support vector machine (SVM) is a well-established classification method whose name refers to the particular training examples, called support vectors, that determine the maximum margin separating hyperplane.

no code implementations • 16 May 2020 • Vidya Muthukumar, Adhyyan Narang, Vignesh Subramanian, Mikhail Belkin, Daniel Hsu, Anant Sahai

We compare classification and regression tasks in an overparameterized linear model with Gaussian features.

no code implementations • 24 May 2019 • Niladri S. Chatterji, Vidya Muthukumar, Peter L. Bartlett

We consider the stochastic linear (multi-armed) contextual bandit problem with the possibility of hidden simple multi-armed bandit structure in which the rewards are independent of the contextual information.

no code implementations • 21 Mar 2019 • Vidya Muthukumar, Kailas Vodrahalli, Vignesh Subramanian, Anant Sahai

A continuing mystery in understanding the empirical success of deep neural networks is their ability to achieve zero training error and generalize well, even when the training data is noisy and there are more parameters than data points.

no code implementations • 30 Nov 2018 • Vidya Muthukumar, Tejaswini Pedapati, Nalini Ratha, Prasanna Sattigeri, Chai-Wah Wu, Brian Kingsbury, Abhishek Kumar, Samuel Thomas, Aleksandra Mojsilovic, Kush R. Varshney

Recent work shows unequal performance of commercial face classification services in the gender classification task across intersectional groups defined by skin type and gender.

no code implementations • 22 May 2018 • Vidya Muthukumar, Mitas Ray, Anant Sahai, Peter L. Bartlett

We introduce algorithms for online, full-information prediction that are competitive with contextual tree experts of unknown complexity, in both probabilistic and adversarial settings.

no code implementations • 19 Jul 2017 • Ashwin Pananjady, Cheng Mao, Vidya Muthukumar, Martin J. Wainwright, Thomas A. Courtade

We show that when the assignment of items to the topology is arbitrary, these permutation-based models, unlike their parametric counterparts, do not admit consistent estimation for most comparison topologies used in practice.

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