Praneeth Netrapalli

A model of errors in transformers
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Spark Transformer: Reactivating Sparsity in FFN and Attention
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Faster Approx. Top-K: Harnessing the Full Power of Two Stages
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Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
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Compressing Many-Shots in In-Context Learning
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All Mistakes are not Equal: Comprehensive Hierarchy Aware Multilabel Predictions (CHAMP)
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HiRE: High Recall Approximate Top-k Estimation for Efficient LLM Inference
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Tandem Transformers for Inference Efficient LLMs
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Second Order Methods for Bandit Optimization and Control
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Simplicity Bias in 1-Hidden Layer Neural Networks
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Optimistic MLE: A Generic Model-Based Algorithm for Partially Observable Sequential Decision Making
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Feature Reconstruction From Outputs Can Mitigate Simplicity Bias in Neural Networks
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The Feature Speed Formula: a flexible approach to scale hyper-parameters of deep neural networks
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Near Optimal Heteroscedastic Regression with Symbiotic Learning
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All Mistakes Are Not Equal: Comprehensive Hierarchy Aware Multi-label Predictions (CHAMP)
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MET: Masked Encoding for Tabular Data
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Reproducibility in Optimization: Theoretical Framework and Limits
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Learning an Invertible Output Mapping Can Mitigate Simplicity Bias in Neural Networks
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DAFT: Distilling Adversarially Fine-tuned Models for Better OOD Generalization
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Multi-User Reinforcement Learning with Low Rank Rewards
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Consistent Multiclass Algorithms for Complex Metrics and Constraints
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MET: Masked Encoding for Tabular Data
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Sample Efficient Linear Meta-Learning by Alternating Minimization
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Do Input Gradients Highlight Discriminative Features?
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Streaming Linear System Identification with Reverse Experience Replay
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Optimal Regret Algorithm for Pseudo-1d Bandit Convex Optimization
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Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems
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Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs
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Statistically and Computationally Efficient Linear Meta-representation Learning
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Efficient Bandit Convex Optimization: Beyond Linear Losses
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Focus on the Common Good: Group Distributional Robustness Follows
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Near-Optimal Lower Bounds For Convex Optimization For All Orders of Smoothness
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Minimax Optimization with Smooth Algorithmic Adversaries
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MOReL : Model-Based Offline Reinforcement Learning
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Efficient Domain Generalization via Common-Specific Low-Rank Decomposition
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Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games
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P-SIF: Document Embeddings Using Partition Averaging
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Learning Minimax Estimators via Online Learning
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Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method
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Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms
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No quantum speedup over gradient descent for non-smooth convex optimization
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The Pitfalls of Simplicity Bias in Neural Networks
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Stochastic Gradient Descent Escapes Saddle Points Efficiently
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What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?
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Online Non-Convex Learning: Following the Perturbed Leader is Optimal
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Making the Last Iterate of SGD Information Theoretically Optimal
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SGD without Replacement: Sharper Rates for General Smooth Convex Functions
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Open Problem: Do Good Algorithms Necessarily Query Bad Points?
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Efficient Algorithms for Smooth Minimax Optimization
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On Nonconvex Optimization for Machine Learning
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Minmax Optimization: Stable Limit Points of Gradient Descent Ascent are Locally Optimal
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A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm
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Non-Gaussianity of Stochastic Gradient Noise
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Universality Patterns in the Training of Neural Networks
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Making the Last Iterate of SGD Information Theoretically Optimal
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Stochastic Gradient Descent and Its Variants in Machine Learning
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The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure
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M AKING THE L AST I TERATE OF SGD I NFORMATION T HEORETICALLY O PTIMAL
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Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds
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Rethinking learning rate schedules for stochastic optimization
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On the Insufficiency of Existing Momentum Schemes for Stochastic Optimization
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37th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science, FSTTCS 2017, Kanpur, India, December 11-15, 2017
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Learnability Of Learned Neural Networks
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Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form
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Unsupervised Document Representation using Partition Word-Vectors Averaging
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Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent
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A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares)
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Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness
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Thresholding based Efficient Outlier Robust PCA
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Leverage Score Sampling for Faster Accelerated Regression and ERM
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Thresholding Based Outlier Robust PCA
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How to Escape Saddle Points Efficiently
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Accelerating Stochastic Gradient Descent
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Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis
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Faster Eigenvector Computation via Shift-and-Invert Preconditioning
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Matching Matrix Bernstein with Little Memory: Near-Optimal Finite Sample Guarantees for Oja's Algorithm
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Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification
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Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent
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Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm
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Information-theoretic thresholds for community detection in sparse networks
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Parallelizing Stochastic Approximation Through Mini-Batching and Tail-Averaging
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Global Convergence of Non-Convex Gradient Descent for Computing Matrix Squareroot
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Convergence Rates of Active Learning for Maximum Likelihood Estimation
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Robust Shift-and-Invert Preconditioning: Faster and More Sample Efficient Algorithms for Eigenvector Computation
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Computing Matrix Squareroot via Non Convex Local Search
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J ul 2 01 4 A Clustering Approach to Learn Sparsely-Used Overcomplete Dictionaries
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Learning Sparsely Used Overcomplete Dictionaries
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Provable non-convex robust PCA
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Fast Exact Matrix Completion with Finite Samples
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Provable alternating minimization for non-convex learning problems
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Non-Reconstructability in the Stochastic Block Model
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Learning structure of power-law Markov networks
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Non-convex Robust PCA
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Phase Retrieval Using Alternating Minimization
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Exact Recovery of Sparsely Used Overcomplete Dictionaries
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A Clustering Approach to Learning Sparsely Used Overcomplete Dictionaries
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Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization
2013177 citationsauthor #4
One-Bit Compressed Sensing: Provable Support and Vector Recovery
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Theory of Computing
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Learning Markov graphs up to edit distance
201213 citationsauthor #2

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