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Shouvanik Chakrabarti

6 accepted papers

2025

A Unified Framework for Provably Efficient Algorithms to Estimate Shapley Values

NeurIPS 2025poster

Shapley values have emerged as a critical tool for explaining which features impact the decisions made by machine learning models. However, computing exact Shapley values is difficult, generally requiring an exponential (in the feature dimension) number of model evaluations. To address this, many mo…

Cited by 0SourceScholar
2025

Fast Zeroth-Order Convex Optimization with Quantum Gradient Methods

NeurIPS 2025poster

We study quantum algorithms based on quantum (sub)gradient estimation using noisy function evaluation oracles, and demonstrate the first dimension-independent query complexities (up to poly-logarithmic factors) for zeroth-order convex optimization in both smooth and nonsmooth settings. Interestingly…

Cited by 2SourceScholar
2023

Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels

ICML 2023poster

A quantum neural network (QNN) is a parameterized mapping efficiently implementable on near-term Noisy Intermediate-Scale Quantum (NISQ) computers. It can be used for supervised learning when combined with classical gradient-based optimizers. Despite the existing empirical and theoretical investigat…

Cited by 15SourcePDFScholar
2021

Sublinear Classical and Quantum Algorithms for General Matrix Games

AAAI 2021technical

We investigate sublinear classical and quantum algorithms for matrix games, a fundamental problem in optimization and machine learning, with provable guarantees. Given a matrix, sublinear algorithms for the matrix game were previously known only for two special cases: (1) the maximizing vectors live…

Cited by 22SourcePDFScholar
2019

Quantum Wasserstein Generative Adversarial Networks

NeurIPS 2019poster

The study of quantum generative models is well-motivated, not only because of its importance in quantum machine learning and quantum chemistry but also because of the perspective of its implementation on near-term quantum machines. Inspired by previous studies on the adversarial training of classica…

2019

Sublinear quantum algorithms for training linear and kernel-based classifiers

ICML 2019oral

We investigate quantum algorithms for classification, a fundamental problem in machine learning, with provable guarantees. Given $n$ $d$-dimensional data points, the state-of-the-art (and optimal) classical algorithm for training classifiers with constant margin by Clarkson et al. runs in $\tilde{O}…

Cited by 86SourcePDFScholar