Publications

Below the papers are arranged according to the different sub-topics I have worked on.

Robust Machine Learning

Robust Feature Learning for Multi-Index Models in High Dimensions
Alireza Mousavi-Hosseini, Adel Javanmard, Murat A. Erdogdu, 2024.

Learning Rate Schedules in the Presence of Distribution Shift
Matthew Fahrbach, Adel Javanmard, Vahab Mirrokni, Pratik Worah, ICML 2023

The curse of overparametrization in adversarial training: Precise analysis of robust generalization for random features regression
Hamed Hassani, Adel Javanmard, 2022
Accepted for publication in the Annals of Statistics

Precise Statistical Analysis of Classification Accuracies for Adversarial Training
Adel Javanmard and Mahdi Soltanolkotabi
Accepted for publication in the Annals of Statistics, 2022.

Adversarial robustness for latent models: Revisiting the robust-standard accuracies tradeoff
Adel Javanmard, Mohammad Mehrabi, 2021
Accepted for publication in Operations Research

Fundamental Tradeoffs in Distributionally Adversarial Training
Mohammad Mehrabi, Adel Javanmard, Ryan A. Rossi, Anup Rao, Tung Mai
ICML 2021 (Spotlight paper acceptance rate: \(3.5\%\)).

Precise Tradeoffs in Adversarial Training for Linear Regression
Adel Javanmard, Mahdi Soltanolkotabi, Hamed Hassani
Annual Conference on Learning Theory (COLT), 2020.

Statistical Inference/ Uncertainty-Aware Machine Learning

Causal Bootstrap for General Randomized Designs
Jennifer Brennan, Sébastien Lahaie, Adel Javanmard, Nick Doudchenko, Jean Pouget-Abadie, 2024.

Prediction Sets for High-Dimensional Mixture of Experts Models
Adel Javanmard, Simeng Shao, Jacob Bien, 2022
To appear in the Journal of Royal Statistical Society (Series B)

GRASP: A Goodness-of-Fit Test for Classification Learning
Adel Javanmard, Mohammad Mehrabi, 2022
Accepted for publication at Journal of Royal Statistical Society (Series B)

Pearson Chi-squared Conditional Randomization Test
Adel Javanmard, Mohammad Mehrabi, 2021
second round of revision, Journal of American Statistical Association

Controlling the False Split Rate in Tree-Based Aggregation [software]
Simeng Shao, Jacob Bien, Adel Javanmard, 2021
Accepted for publication in the Journal of American Statistical Association.

Online Debiasing for Adaptively Collected High-dimensional Data with Applications to Time Series Analysis [Website]
Yash Deshpande, Adel Javanmard, Mohammad Mehrabi
Accepted for publication in Journal of American Statistical Association (Theory and Methods), 2021.

A Flexible Framework for Hypothesis Testing in High-dimensions
Adel Javanmard and Jason D. Lee
Accepted for publication in Journal of Royal Statistical Society, Series B, 2019.

False Discovery Rate Control via Debiased Lasso
Adel Javanmard and Hamid Javadi
in Electronic Journal of Statistics (EJS), Volume 13, No .1, pages 1212-1253, 2019.

onlineFDR: an R package to control the false discovery rate for growing data repositories
David S Robertson, Jan Wildenhain, Adel Javanmard, and Natasha A Karp
in Bioinformatics Journal, Volume 35, Issue 20, Pages 4196–4199, 2019. [R package][Real data experiments]

Debiasing the Lasso: Optimal Sample Size for Gaussian Designs
Adel Javanmard and Andrea Montanari
in Annals of Statistics, Volume 46, No. 6A, pages 2593-2622, 2018.

Online Rules for Control of False Discovery Rate and False Discovery Exceedance
Adel Javanmard and Andrea Montanari
in Annals of Statistics, Vol. 46, No. 2, pages 526-554, 2018.

Confidence Intervals and Hypothesis Testing for High-Dimensional Regression [Website]
Adel Javanmard and Andrea Montanari
in Journal of Machine Learning Research, 15(1): 2869-2909, 2014.
(Short version appeared in Neural Information Processing Systems Foundation (NIPS), 2013)

Hypothesis Testing in High-Dimensional Regression under the Gaussian Random Design Model: Asymptotic Theory
Adel Javanmard and Andrea Montanari
Published in IEEE Transaction on Information Theory, 60(10):6522-6554, 2014.

Nearly Optimal Sample Size in Hypothesis Testing for High-Dimensional Regression
Adel Javanmard and Andrea Montanari
In Annual Allerton Conference on Communication, Control and Computing, 2013.

Model Selection for High-Dimensional Regression under the Generalized Irrepresentability Condition
Adel Javanmard and Andrea Montanari
Advances in Neural Information Processing Systems Foundation (NIPS), 2013. [full version]

Private Learning

Retraining with Predicted Hard Labels Provably Increases Model Accuracy
Rudrajit Das, Inderjit S. Dhillon, Alessandro Epasto, Adel Javanmard, Jieming Mao, Vahab Mirrokni, Sujay Sanghavi, Peilin Zhong, 2024.

Optimistic Rates for Learning from Label Proportions
Gene Li, Lin Chen, Adel Javanmard, Vahab Mirrokni, COLT 2024.

PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses
Adel Javanmard, Matthew Fahrbach, Vahab Mirrokni, ICML 2024 (Spotlight paper acceptance rate: \(3.5\%\)).

Learning from Aggregate Responses: Instance Level versus Bag Level Loss Functions
Adel Javanmard, Lin Chen, Vahab Mirrokni, Ashwinkumar Badanidiyuru, Gang Fu, ICLR 2024.

Anonymous Learning via Look-Alike Clustering: A Precise Analysis of Model Generalization
Adel Javanmard, Vahab Mirrokni, NeurIPS 2023.

Causal Inference with Differentially Private (Clustered) Outcomes
Adel Javanmard, Vahab Mirrokni, Jean Pouget-Abadie, 2023
To appear in NeurIPS 2023.

Measuring Re-identification Risk
in collaboration with a great team at Google Research

ACM Journal on Management of Data (PACMMOD), 2023
ACM SIGMOD/PODS International Conference on Management of Data, 2023.
SecWeb workshop (Designing security for the Web), 2023

Near-Optimal Model Discrimination with Non-Disclosure
Dmitrii M. Ostrovskii, Mohamed Ndaoud, Adel Javanmard, Meisam Razaviyayn, 2020

Theoretical understanding of Optimization Landscape of Neural Networks

Analysis of a Two-Layer Neural Network via Displacement Convexity
Adel Javanmard, Marco Mondelli, Andrea Montanari
Accepted for publication in Annals of Statistics, 2019

Theoretical insights into the optimization landscape of over-parameterized shallow neural networks
Mahdi Soltanolkotabi, Adel Javanmard and Jason D. Lee
in IEEE Transaction on Information Theory, 65(2), pages 742-769, 2018.

Personalized Decision Making (with focus on Dynamic Pricing)

Multi-Task Dynamic Pricing in Credit Market with Contextual Information
Adel Javanmard, Jingwei Ji, Renyuan Xu, 2024.

Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model
Rashmi Ranjan Bhuyan, Adel Javanmard, Sungchul Kim, Gourab Mukherjee, Ryan A. Rossi, Tong Yu, Handong Zhao, 2023
Journal of Machine Learning Research 25.224 (2024): 1-46.

Multi-Product Dynamic Pricing in High-Dimensions with Heterogeneous Price Sensitivity
Adel Javanmard, Hamid Nazerzadeh, Simeng Shao
IEEE International Symposium on Information Theory (ISIT), 2020.

Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions
Negin Golrezaei, Adel Javanmard and Vahab Mirrokni
Accepted for publication in Operations Research, 2019.
(Preliminary version of this paper accepted to NeurIPS 2019.)

Dynamic Pricing in High-dimensions
Adel Javanmard and Hamid Nazerzadeh
in Journal of Machine Learning, 20, no 1 (2019): 315-363.
(Preliminary version of this paper presented at the Conference on Two-sided Marketplace Optimization: Search, Pricing, Matching & Growth (TSMO), 2018.)

Perishability of Data: Dynamic Pricing under Varying-Coefficient Models
Adel Javanmard
in Journal of Machine Learning Research, 18(53):1-31, 2017.

Iterative estimation methods (Approximate Message Passing)

State Evolution for General Approximate Message Passing Algorithms, with Applications to Spatial Coupling
Adel Javanmard and Andrea Montanari
Journal of Information and Inference, vol. 2, no. 2, pp 115-144, 2013.

Information-Theoretically Optimal Compressed Sensing via Spatial Coupling and Approximate Message Passing
David L. Donoho, Adel Javanmard, Andrea Montanari
IEEE Transaction on Information Theory, vol. 59, no. 11, pp 7434-7464, Nov 2013.
(Short version In Proc. of the IEEE International Symposium on Information Theory (ISIT), 2012.)

Subsampling at Information Theoretically Optimal Rates
Adel Javanmard, Andrea Montanari
In Proc. of the IEEE International Symposium on Information Theory (ISIT), 2012.

Network Analysis (Localization, Clustering, Community detection)

Perturbation Bounds for Procrustes, Classical Scaling, and Trilateration, with Applications to Manifold Learning
Ery Arias-Castro, Adel Javanmard, Bruno Pelletier
Accepted for publication in Journal of Machine Learning Research (JMLR), 2019.

Phase Transitions in Semidefinite Relaxations [Website]
Adel Javanmard, Andrea Montanari and Federico Ricci-Tersenghi
In Proceedings of the National Academy of Sciences (PNAS), 113(16): E2218-E2223, 2016

Performance of a community detection algorithm based on semidefinite programming
Federico Ricci-Tersenghi, Adel Javanmard and Andrea Montanari
Proceedings for the International Meeting on High-dimensional Data Driven Science (HD3-2015), Kyoto, Dec 2015.

Localization from Incomplete Noisy Distance Measurements
Adel Javanmard, Andrea Montanari
Foundations of Computational Mathematics, vol. 13, no. 3, pp 297-345, June 2013.
(Short version in Proc. of the IEEE International Symposium on Information Theory (ISIT), 2011.
(Nominated for Best Student Paper Award))

Learning Linear Bayesian Networks with Latent Variables
Animashree Anandkumar, Daniel Hsu, Adel Javanmard, and Sham M. Kakade
In 30th International Conference on Machine Learning (ICML), 2013. [full version]

Miscelleneous

New Computational and Statistical Aspects of Regularized Regression with Application to Rare Feature Selection and Aggregation
Amin Jalali, Adel Javanmard, Maryam Fazel, 2019

Novel probabilistic models of spatial genetic ancestry with applications to stratification correction in genome-wide association studies [Software]
Anand Bhaskar, Adel Javanmard, Thomas Courtade and David Tse
in Bioinformatics Journal, March 2017, 33(6), pp. 879-885.

1-Bit Matrix Completion under Exact Low-Rank Constraint
Sonia Bhaskar and Adel Javanmard
In Conference on Information Sciences and Systems (CISS), 2015.

Nowhere-Zero Unoriented Flows in Hamiltonian Graphs
S. Akbari, A. Daemi, O. Hatami, A. Javanmard, A. Mehrabian
Ars Combinatoria Journal, Vol CXX,pp. 51-63, 2015

Efficient Reinforcement Learning for High Dimensional Linear Quadratic Systems
Morteza Ibrahimi, Adel Javanmard, Benjamin Van Roy
Advances in Neural Information Processing Systems Foundation (NIPS), 2012.

Minimax Risk of Truncated Series Estimators over Symmetric Convex Polytopes
Adel Javanmard, Li Zhang
In Proc. of the IEEE International Symposium on Information Theory (ISIT), 2012.
(Nominated for Best Student Paper Award)

Versatile Refresh: Low-Complexity Refresh Scheduling for High-throughput Multi-banked eDRAM
Mohammad Alizadeh, Adel Javanmard, Shang-Tse Chuang, Sundar Iyer, and Yi Lu
In Proc. of ACM SIGMETRICS 2012.

Multi-track Map Matching
Adel Javanmard, Maya Haridasan, Li Zhang
In Proc. of the 20th International Conference on Advances in Geographic Information Systems (GIS), 2012.
Extended Abstract in Proc. of the 10th international conference on Mobile systems, applications, and services (MobiSys), 2012.

Robust Max-Product Belief Propagation
Morteza Ibrahimi, Adel Javanmard, Yashodhan Kanoria, Andrea Montanari
Asilomar Conference on Signals, Systems and Computers, 2011 (Invited).

Analysis of DCTCP: Stability, Convergence, and Fairness
Mohammad Alizadeh, Adel Javanmard, Balaji Prabhakar
In Proc. of ACM SIGMETRICS 2011.

Zero-Sum Flows in Regular Graphs
S. Akbari, A. Daemi, O. Hatami, A. Javanmard, A. Mehrabian
Graphs and Combinatorics Journal, vol. 26, no. 5, pp. 603-615, 2010.

Analytical Evaluation of Average Delay and Maximum Stable Throughput along a Typical Two-Way Street for Vehicular Ad-Hoc Networks in Sparse Situation
Adel Javanmard, Farid Ashtiani
Elsevier Computer Communications, vol. 32, no. 16, pp. 1768–1780, Oct 2009.

Mobility Modeling, Spatial Traffic Distribution, and Probability of Connectivity for Sparse and Dense Vehicular Ad Hoc Networks
G. Hossein Mohimani, Farid Ashtiani, Adel Javanmard, Maziar Hamdi
IEEE Transaction on vehicular Technology, vol. 58, no. 4, pp. 1998–2007, May 2009.

Estimating the Mixing Matrix in Underdetermined Sparse Component Analysis (SCA) Using Consecutive Independent Component Analysis (ICA)
A. Javanmard, P. Pad, M. Babaie-Zadeh, C. Jutten
In Proc. of 15th European Signal Processing Conference (EUSIPCO), Lausanne, Switzerland, Aug 2008.