Sivaraman Balakrishnan
36 papers · 2011–2023 · 7 conferences · across top CS/AI conferences
Achievements
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Academic Marathon
(12)
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(11)
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(16)
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(6)
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Trend Setter
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Century Club
(36)
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Keyword Collector
(67)
Conferences
NIPS (14)
AISTATS (7)
JMLR (7)
ICML (5)
ALT (1)
COLT (1)
ICLR (1)
Top co-authors
Research topics
Keywords
domain adaptation
(6)
pairwise comparison
(5)
distribution shift
(4)
semi-supervised learning
(4)
label shift
(4)
nonparametric regression
(3)
heavy-tailed distribution
(3)
robust estimation
(3)
sample complexity
(3)
two-sample test
(2)
covariate shift
(2)
unsupervised learning
(2)
hypothesis testing
(2)
curse of dimensionality
(2)
learning theory
(2)
convex optimization
(2)
quality estimation
(2)
density estimation
(2)
robust statistics
(2)
spectral clustering
(2)
Papers
Domain Adaptation under Missingness Shift
AISTATS 2023
RLSbench: Domain Adaptation Under Relaxed Label Shift
ICML 2023
Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms
NIPS 2023
Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift
NIPS 2023
Heavy-tailed Streaming Statistical Estimation
AISTATS 2022
Understanding Simultaneous Train and Test Robustness
ALT 2022
Two-Sample Testing on Ranked Preference Data and the Role of Modeling Assumptions
JMLR 2022
Leveraging unlabeled data to predict out-of-distribution performance
ICLR 2022
Domain Adaptation under Open Set Label Shift
NIPS 2022
Minimax Optimal Regression over Sobolev Spaces via Laplacian Regularization on Neighborhood Graphs
AISTATS 2021
Mixture Proportion Estimation and PU Learning:A Modern Approach
NIPS 2021
RATT: Leveraging Unlabeled Data to Guarantee Generalization
ICML 2021
On Proximal Policy Optimizationβs Heavy-tailed Gradients
ICML 2021
Path Length Bounds for Gradient Descent and Flow
JMLR 2021
Statistical Guarantees for Local Spectral Clustering on Random Neighborhood Graphs
JMLR 2021
Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information
JMLR 2020
A Robust Univariate Mean Estimator is All You Need
AISTATS 2020
On Learning Ising Models under Huber's Contamination Model
NIPS 2020
A Unified View of Label Shift Estimation
NIPS 2020
Low Permutation-rank Matrices: Structural Properties and Noisy Completion
JMLR 2019
How Many Samples are Needed to Estimate a Convolutional Neural Network?
NIPS 2018
Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information
ICML 2018
Stochastic Zeroth-order Optimization in High Dimensions
AISTATS 2018
Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates
NIPS 2018
Statistical and Computational Guarantees for the Baum-Welch Algorithm
JMLR 2017
Computationally Efficient Robust Sparse Estimation in High Dimensions
COLT 2017
Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues
ICML 2016
Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences
NIPS 2016
Statistical Inference for Cluster Trees
NIPS 2016
Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence
JMLR 2016
Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence
AISTATS 2015
Cluster Trees on Manifolds
NIPS 2013
Optimal kernel choice for large-scale two-sample tests
NIPS 2012
Minimax rates for homology inference
AISTATS 2012
Noise Thresholds for Spectral Clustering
NIPS 2011
Minimax Localization of Structural Information in Large Noisy Matrices
NIPS 2011