Rajiv Khanna
26 papers · 2014–2026 · 8 venues · across top CS/AI venues
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Machine Learning
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Optimization & Theory Β· Machine Learning
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Mathematics & Optimization
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Optimization
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Artificial Intelligence
(7)
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Keywords
greedy algorithm
(4)
submodular optimization
(4)
kernel methods
(3)
bayesian inference
(3)
feature selection
(3)
submodular function
(2)
column subset selection
(2)
information projection
(2)
nystrom method
(2)
spectral analysis
(2)
generalization bound
(2)
bayesian quadrature
(2)
sparse optimization
(2)
stochastic gradient descent
(2)
convergence analysis
(2)
low-rank approximation
(2)
approximate inference
(2)
maximum mean discrepancy
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variational inference
(2)
greedy optimization
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Papers
Align When They Want, Complement When They Need! Human-Centered Ensembles for Adaptive Human-AI Collaboration
AAAI 2026
Spectral Valleys and Sharp Failures in Greedy Determinant Maximization
COLT 2026
A Precise Characterization of SGD Stability Using Loss Surface Geometry
ICLR 2024
The Space Complexity of Approximating Logistic Loss
NeurIPS 2024
Fast Feature Selection with Fairness Constraints
AISTATS 2023
Generalization Guarantees via Algorithm-dependent Rademacher Complexity
COLT 2023
Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers
ICML 2022
Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification
ICLR 2021
Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective
AISTATS 2021
Geometric rates of convergence for kernel-based sampling algorithms
UAI 2021
Improved Guarantees and a Multiple-descent Curve for Column Subset Selection and the Nystrom Method (Extended Abstract)
IJCAI 2021
LocalNewton: Reducing communication rounds for distributed learning
UAI 2021
Boundary thickness and robustness in learning models
NeurIPS 2020
Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method
NeurIPS 2020
Interpreting Black Box Predictions using Fisher Kernels
AISTATS 2019
Learning Sparse Distributions using Iterative Hard Thresholding
NeurIPS 2019
Boosting Black Box Variational Inference
NeurIPS 2018
Boosting Variational Inference: an Optimization Perspective
AISTATS 2018
IHT dies hard: Provable accelerated Iterative Hard Thresholding
AISTATS 2018
A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe
AISTATS 2017
Information Projection and Approximate Inference for Structured Sparse Variables
AISTATS 2017
On Approximation Guarantees for Greedy Low Rank Optimization
ICML 2017
Scalable Greedy Feature Selection via Weak Submodularity
AISTATS 2017
Examples are not enough, learn to criticize! Criticism for Interpretability
NeurIPS 2016
Sparse Submodular Probabilistic PCA
AISTATS 2015
On Prior Distributions and Approximate Inference for Structured Variables
NeurIPS 2014