Praneeth Netrapalli

According to our database1, Praneeth Netrapalli authored at least 91 papers between 2010 and 2024.

Collaborative distances:

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2024
HiRE: High Recall Approximate Top-k Estimation for Efficient LLM Inference.
CoRR, 2024

All Mistakes are not Equal: Comprehensive Hierarchy Aware Multilabel Predictions (CHAMP).
Proceedings of the Pattern Recognition - 27th International Conference, 2024

Tandem Transformers for Inference Efficient LLMs.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Second Order Methods for Bandit Optimization and Control.
Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

2023
Steering Deep Feature Learning with Backward Aligned Feature Updates.
CoRR, 2023

Optimistic MLE: A Generic Model-Based Algorithm for Partially Observable Sequential Decision Making.
Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 2023

Simplicity Bias in 1-Hidden Layer Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Multi-User Reinforcement Learning with Low Rank Rewards.
Proceedings of the International Conference on Machine Learning, 2023

Feature Reconstruction From Outputs Can Mitigate Simplicity Bias in Neural Networks.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Near Optimal Heteroscedastic Regression with Symbiotic Learning.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022
Consistent Multiclass Algorithms for Complex Metrics and Constraints.
CoRR, 2022

Learning an Invertible Output Mapping Can Mitigate Simplicity Bias in Neural Networks.
CoRR, 2022

DAFT: Distilling Adversarially Fine-tuned Models for Better OOD Generalization.
CoRR, 2022

All Mistakes Are Not Equal: Comprehensive Hierarchy Aware Multi-label Predictions (CHAMP).
CoRR, 2022

MET: Masked Encoding for Tabular Data.
CoRR, 2022

Reproducibility in Optimization: Theoretical Framework and Limits.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Focus on the Common Good: Group Distributional Robustness Follows.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Minimax Optimization with Smooth Algorithmic Adversaries.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
Making the Last Iterate of SGD Information Theoretically Optimal.
SIAM J. Optim., 2021

On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points.
J. ACM, 2021

Sample Efficient Linear Meta-Learning by Alternating Minimization.
CoRR, 2021

Statistically and Computationally Efficient Linear Meta-representation Learning.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Do Input Gradients Highlight Discriminative Features?
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Streaming Linear System Identification with Reverse Experience Replay.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Near-Optimal Lower Bounds For Convex Optimization For All Orders of Smoothness.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

No Quantum Speedup over Gradient Descent for Non-Smooth Convex Optimization.
Proceedings of the 12th Innovations in Theoretical Computer Science Conference, 2021

Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization.
Proceedings of the 38th International Conference on Machine Learning, 2021

Efficient Bandit Convex Optimization: Beyond Linear Losses.
Proceedings of the Conference on Learning Theory, 2021

2020
Learning Minimax Estimators via Online Learning.
CoRR, 2020

Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

The Pitfalls of Simplicity Bias in Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

MOReL: Model-Based Offline Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Efficient Domain Generalization via Common-Specific Low-Rank Decomposition.
Proceedings of the 37th International Conference on Machine Learning, 2020

What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?
Proceedings of the 37th International Conference on Machine Learning, 2020

Online Non-Convex Learning: Following the Perturbed Leader is Optimal.
Proceedings of the Algorithmic Learning Theory, 2020

Leverage Score Sampling for Faster Accelerated Regression and ERM.
Proceedings of the Algorithmic Learning Theory, 2020

P-SIF: Document Embeddings Using Partition Averaging.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Non-Gaussianity of Stochastic Gradient Noise.
CoRR, 2019

The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure.
CoRR, 2019

Stochastic Gradient Descent Escapes Saddle Points Efficiently.
CoRR, 2019

A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm.
CoRR, 2019

Minmax Optimization: Stable Limit Points of Gradient Descent Ascent are Locally Optimal.
CoRR, 2019

Efficient Algorithms for Smooth Minimax Optimization.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

SGD without Replacement: Sharper Rates for General Smooth Convex Functions.
Proceedings of the 36th International Conference on Machine Learning, 2019

Open Problem: Do Good Algorithms Necessarily Query Bad Points?
Proceedings of the Conference on Learning Theory, 2019

2018
Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness.
Proceedings of the 9th Innovations in Theoretical Computer Science Conference, 2018

On the insufficiency of existing momentum schemes for Stochastic Optimization.
Proceedings of the 6th International Conference on Learning Representations, 2018

Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent.
Proceedings of the Conference On Learning Theory, 2018

Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form.
Proceedings of the Conference On Learning Theory, 2018

Accelerating Stochastic Gradient Descent for Least Squares Regression.
Proceedings of the Conference On Learning Theory, 2018

2017
A Clustering Approach to Learning Sparsely Used Overcomplete Dictionaries.
IEEE Trans. Inf. Theory, 2017

Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification.
J. Mach. Learn. Res., 2017

Thresholding based Efficient Outlier Robust PCA.
CoRR, 2017

Accelerating Stochastic Gradient Descent.
CoRR, 2017

How to Escape Saddle Points Efficiently.
Proceedings of the 34th International Conference on Machine Learning, 2017

A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares).
Proceedings of the 37th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science, 2017

Thresholding Based Outlier Robust PCA.
Proceedings of the 30th Conference on Learning Theory, 2017

Global Convergence of Non-Convex Gradient Descent for Computing Matrix Squareroot.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016
Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization.
SIAM J. Optim., 2016

Learning Planar Ising Models.
J. Mach. Learn. Res., 2016

Parallelizing Stochastic Approximation Through Mini-Batching and Tail-Averaging.
CoRR, 2016

Matching Matrix Bernstein with Little Memory: Near-Optimal Finite Sample Guarantees for Oja's Algorithm.
CoRR, 2016

Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Faster Eigenvector Computation via Shift-and-Invert Preconditioning.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm.
Proceedings of the 29th Conference on Learning Theory, 2016

Information-theoretic thresholds for community detection in sparse networks.
Proceedings of the 29th Conference on Learning Theory, 2016

2015
Phase Retrieval Using Alternating Minimization.
IEEE Trans. Signal Process., 2015

Robust Shift-and-Invert Preconditioning: Faster and More Sample Efficient Algorithms for Eigenvector Computation.
CoRR, 2015

Computing Matrix Squareroot via Non Convex Local Search.
CoRR, 2015

Convergence Rates of Active Learning for Maximum Likelihood Estimation.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Fast Exact Matrix Completion with Finite Samples.
Proceedings of The 28th Conference on Learning Theory, 2015

2014
Non-Reconstructability in the Stochastic Block Model.
CoRR, 2014

Non-convex Robust PCA.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Learning structure of power-law Markov networks.
Proceedings of the 2014 IEEE International Symposium on Information Theory, Honolulu, HI, USA, June 29, 2014

Learning Sparsely Used Overcomplete Dictionaries.
Proceedings of The 27th Conference on Learning Theory, 2014

2013
Exact Recovery of Sparsely Used Overcomplete Dictionaries.
CoRR, 2013

Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization.
CoRR, 2013

Low-rank matrix completion using alternating minimization.
Proceedings of the Symposium on Theory of Computing Conference, 2013

One-Bit Compressed Sensing: Provable Support and Vector Recovery.
Proceedings of the 30th International Conference on Machine Learning, 2013

2012
Finding the Graph of Epidemic Cascades
CoRR, 2012

Learning the graph of epidemic cascades.
Proceedings of the ACM SIGMETRICS/PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems, 2012

Learning Markov graphs up to edit distance.
Proceedings of the 2012 IEEE International Symposium on Information Theory, 2012

2010
Learning Planar Ising Models
CoRR, 2010

Greedy learning of Markov network structure.
Proceedings of the 48th Annual Allerton Conference on Communication, 2010


  Loading...