Mehrdad Mahdavi

According to our database1, Mehrdad Mahdavi authored at least 62 papers between 2008 and 2021.

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Bibliography

2021
Pareto Efficient Fairness in Supervised Learning: From Extraction to Tracing.
CoRR, 2021

On the Importance of Sampling in Learning Graph Convolutional Networks.
CoRR, 2021

Federated Learning with Compression: Unified Analysis and Sharp Guarantees.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Local Stochastic Gradient Descent Ascent: Convergence Analysis and Communication Efficiency.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Guiding Conventional Protein-Ligand Docking Software with Convolutional Neural Networks.
J. Chem. Inf. Model., 2020

Adaptive Personalized Federated Learning.
CoRR, 2020

Online Structured Meta-learning.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

GCN meets GPU: Decoupling "When to Sample" from "How to Sample".
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Distributionally Robust Federated Averaging.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Targeted Data-driven Regularization for Out-of-Distribution Generalization.
Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networks.
Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Communication-efficient k-Means for Edge-based Machine Learning.
Proceedings of the 40th IEEE International Conference on Distributed Computing Systems, 2020

Learning to Quantize Deep Neural Networks: A Competitive-Collaborative Approach.
Proceedings of the 57th ACM/IEEE Design Automation Conference, 2020

2019
Efficient Fair Principal Component Analysis.
CoRR, 2019

On the Convergence of Local Descent Methods in Federated Learning.
CoRR, 2019

Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Learning Feature Nonlinearities with Regularized Binned Regression.
Proceedings of the IEEE International Symposium on Information Theory, 2019

Mixed Precision Quantization Scheme for Re-configurable ReRAM Crossbars Targeting Different Energy Harvesting Scenarios.
Proceedings of the Internet of Things. A Confluence of Many Disciplines, 2019

Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex Optimization.
Proceedings of the 36th International Conference on Machine Learning, 2019

2017
Learning Feature Nonlinearities with Non-Convex Regularized Binned Regression.
CoRR, 2017

Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016
Train and Test Tightness of LP Relaxations in Structured Prediction.
Proceedings of the 33nd International Conference on Machine Learning, 2016

2015
An efficient primal dual prox method for non-smooth optimization.
Mach. Learn., 2015

On the Tightness of LP Relaxations for Structured Prediction.
CoRR, 2015

Smooth and Strong: MAP Inference with Linear Convergence.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Lower and Upper Bounds on the Generalization of Stochastic Exponentially Concave Optimization.
Proceedings of The 28th Conference on Learning Theory, 2015

2014
Matrix Factorization with Explicit Trust and Distrust Side Information for Improved Social Recommendation.
ACM Trans. Inf. Syst., 2014

Random Projections for Classification: A Recovery Approach.
IEEE Trans. Inf. Theory, 2014

Regret bounded by gradual variation for online convex optimization.
Mach. Learn., 2014

Excess Risk Bounds for Exponentially Concave Losses.
CoRR, 2014

Exploiting Smoothness in Statistical Learning, Sequential Prediction, and Stochastic Optimization.
CoRR, 2014

Binary Excess Risk for Smooth Convex Surrogates.
CoRR, 2014

Matrix Factorization with Explicit Trust and Distrust Relationships.
CoRR, 2014

2013
Improved Bounds for the Nyström Method With Application to Kernel Classification.
IEEE Trans. Inf. Theory, 2013

Efficient stochastic algorithms for document clustering.
Inf. Sci., 2013

Sparse Multiple Kernel Learning with Geometric Convergence Rate
CoRR, 2013

MixedGrad: An O(1/T) Convergence Rate Algorithm for Stochastic Smooth Optimization.
CoRR, 2013

Improving the Minimax Rate of Active Learning.
CoRR, 2013

Stochastic Convex Optimization with Multiple Objectives.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Mixed Optimization for Smooth Functions.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Linear Convergence with Condition Number Independent Access of Full Gradients.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Recovering the Optimal Solution by Dual Random Projection.
Proceedings of the COLT 2013, 2013

Passive Learning with Target Risk.
Proceedings of the COLT 2013, 2013

2012
Trading regret for efficiency: online convex optimization with long term constraints.
J. Mach. Learn. Res., 2012

Online Optimization with Gradual Variations.
Proceedings of the COLT 2012, 2012

Online Stochastic Optimization with Multiple Objectives
CoRR, 2012

Recovering Optimal Solution by Dual Random Projection
CoRR, 2012

An Improved Bound for the Nystrom Method for Large Eigengap
CoRR, 2012

Efficient Constrained Regret Minimization
CoRR, 2012

Nyström Method vs Random Fourier Features: A Theoretical and Empirical Comparison.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Stochastic Gradient Descent with Only One Projection.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Multiple Kernel Learning from Noisy Labels by Stochastic Programming.
Proceedings of the 29th International Conference on Machine Learning, 2012

Robust Ensemble Clustering by Matrix Completion.
Proceedings of the 12th IEEE International Conference on Data Mining, 2012

Online Kernel Selection: Algorithms and Evaluations.
Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012

2011
Regret Bound by Variation for Online Convex Optimization
CoRR, 2011

Improved Bound for the Nystrom's Method and its Application to Kernel Classification
CoRR, 2011

2010
Web Text Mining Using Harmony Search.
Proceedings of the Recent Advances In Harmony Search Algorithm, 2010

2009
Harmony <i>K</i>-means algorithm for document clustering.
Data Min. Knowl. Discov., 2009

2008
Harmony search based algorithms for bandwidth-delay-constrained least-cost multicast routing.
Comput. Commun., 2008

Global-best harmony search.
Appl. Math. Comput., 2008

Novel meta-heuristic algorithms for clustering web documents.
Appl. Math. Comput., 2008

Hybridization of K-Means and Harmony Search Methods for Web Page Clustering.
Proceedings of the 2008 IEEE / WIC / ACM International Conference on Web Intelligence, 2008


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