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ORCIDAdel Javanmard
Person information
- affiliation: University of Southern California, Department of Data Science and Operations Research, Los Angeles, CA USA
- affiliation (PhD 2014): Stanford University, CA, USA
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2020 – today
- 2026
[i56]Adel Javanmard, David P. Woodruff:
Progress on the Courtade-Kumar Conjecture: Optimal High-Noise Entropy Bounds and Generalized Coordinate-wise Mutual Information. CoRR abs/2601.09679 (2026)- [i55]David P. Woodruff, Vincent Cohen-Addad, Lalit Jain, Jieming Mao, Song Zuo, MohammadHossein Bateni, Simina Brânzei, Michael P. Brenner, Lin Chen, Ying Feng, Lance Fortnow, Gang Fu, Ziyi Guan, Zahra Hadizadeh, Mohammad T. Hajiaghayi, Mahdi JafariRaviz, Adel Javanmard, Karthik C. S., Ken-ichi Kawarabayashi, Ravi Kumar, Silvio Lattanzi, Euiwoong Lee, Yi Li, Ioannis Panageas, Dimitris Paparas, Benjamin Przybocki, Bernardo Subercaseaux, Ola Svensson, Shayan Taherijam, Xuan Wu, Eylon Yogev, Morteza Zadimoghaddam, Samson Zhou, Yossi Matias, James Manyika, Vahab Mirrokni:
Accelerating Scientific Research with Gemini: Case Studies and Common Techniques. CoRR abs/2602.03837 (2026) - [i54]Mahdi Sabbaghi, George J. Pappas, Adel Javanmard, Hamed Hassani:
Robust Policy Optimization to Prevent Catastrophic Forgetting. CoRR abs/2602.08813 (2026) - [i53]Tony Feng, Junehyuk Jung, Sang-Hyun Kim, Carlo Pagano, Sergei Gukov, Cheng-Chiang Tsai, David P. Woodruff, Adel Javanmard, Aryan Mokhtari, Dawsen Hwang, Yuri Chervonyi, Jonathan N. Lee, Garrett Bingham, Trieu H. Trinh, Vahab Mirrokni, Quoc V. Le, Thang Luong:
Aletheia tackles FirstProof autonomously. CoRR abs/2602.21201 (2026) - [i52]Adel Javanmard, Baharan Mirzasoleiman, Vahab Mirrokni:
Theoretical Perspectives on Data Quality and Synergistic Effects in Pre- and Post-Training Reasoning Models. CoRR abs/2603.01293 (2026) - 2025
- [j19]Adel Javanmard
, Mohammad Mehrabi:
Pearson Chi-Squared Conditional Randomization Test. IEEE Trans. Inf. Theory 71(12): 9617-9646 (2025) - [c31]Alireza Mousavi Hosseini, Adel Javanmard, Murat A. Erdogdu:
Robust Feature Learning for Multi-Index Models in High Dimensions. ICLR 2025 - [c30]Jennifer Rogers Brennan, Sébastien Lahaie, Adel Javanmard, Nick Doudchenko, Jean Pouget-Abadie:
Integer Programming for Generalized Causal Bootstrap Designs. ICML 2025 - [c29]Rudrajit Das, Inderjit S. Dhillon, Alessandro Epasto, Adel Javanmard, Jieming Mao, Vahab Mirrokni, Sujay Sanghavi, Peilin Zhong:
Retraining with Predicted Hard Labels Provably Increases Model Accuracy. ICML 2025 - [c28]Mike Heddes, Adel Javanmard, Kyriakos Axiotis, Gang Fu, MohammadHossein Bateni, Vahab Mirrokni:
DeepCrossAttention: Supercharging Transformer Residual Connections. ICML 2025 - [c27]Adel Javanmard, Vahab Mirrokni, Jean Pouget-Abadie:
Improving the Variance of Differentially Private Randomized Experiments through Clustering. ICML 2025 - [c26]Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Andrés Muñoz Medina, Vahab Mirrokni, Sergei Vassilvitskii, Peilin Zhong:
Differentially Private Synthetic Data Release for Topics API Outputs. KDD (2) 2025: 5379-5390 - [i51]Mike Heddes, Adel Javanmard, Kyriakos Axiotis, Gang Fu, MohammadHossein Bateni, Vahab Mirrokni:
DeepCrossAttention: Supercharging Transformer Residual Connections. CoRR abs/2502.06785 (2025) - [i50]Adel Javanmard, Rudrajit Das, Alessandro Epasto, Vahab Mirrokni:
Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing. CoRR abs/2505.15195 (2025) - [i49]Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Andrés Muñoz Medina, Vahab Mirrokni, Sergei Vassilvitskii, Peilin Zhong:
Differentially Private Synthetic Data Release for Topics API Outputs. CoRR abs/2506.23855 (2025) - [i48]Yuxuan Tao, Adel Javanmard:
Differentially Private Model-X Knockoffs via Johnson-Lindenstrauss Transform. CoRR abs/2508.04800 (2025) - [i47]Adel Javanmard, Baharan Mirzasoleiman, Vahab Mirrokni:
Understanding the Role of Training Data in Test-Time Scaling. CoRR abs/2510.03605 (2025) - 2024
- [j18]Adel Javanmard, Mohammad Mehrabi:
Adversarial Robustness for Latent Models: Revisiting the Robust-Standard Accuracies Tradeoff. Oper. Res. 72(3): 1016-1030 (2024) - [j17]Rashmi Ranjan Bhuyan, Adel Javanmard, Sungchul Kim, Gourab Mukherjee, Ryan A. Rossi, Tong Yu, Handong Zhao:
Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model. J. Mach. Learn. Res. 25: 224:1-224:46 (2024) - [c25]Gene Li, Lin Chen, Adel Javanmard, Vahab Mirrokni:
Optimistic Rates for Learning from Label Proportions. COLT 2024: 3437-3474 - [c24]Adel Javanmard, Lin Chen, Vahab Mirrokni, Ashwinkumar Badanidiyuru, Gang Fu:
Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions. ICLR 2024 - [c23]Adel Javanmard, Matthew Fahrbach, Vahab Mirrokni:
PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses. ICML 2024: 21410-21429 - [i46]Adel Javanmard, Lin Chen, Vahab Mirrokni, Ashwinkumar Badanidiyuru, Gang Fu:
Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions. CoRR abs/2401.11081 (2024) - [i45]Adel Javanmard, Matthew Fahrbach, Vahab Mirrokni:
PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses. CoRR abs/2402.04987 (2024) - [i44]Gene Li, Lin Chen, Adel Javanmard, Vahab Mirrokni:
Optimistic Rates for Learning from Label Proportions. CoRR abs/2406.00487 (2024) - [i43]Rudrajit Das, Inderjit S. Dhillon, Alessandro Epasto, Adel Javanmard, Jieming Mao, Vahab Mirrokni, Sujay Sanghavi, Peilin Zhong:
Retraining with Predicted Hard Labels Provably Increases Model Accuracy. CoRR abs/2406.11206 (2024) - [i42]Adel Javanmard, Jingwei Ji, Renyuan Xu:
Multi-Task Dynamic Pricing in Credit Market with Contextual Information. CoRR abs/2410.14839 (2024) - [i41]Alireza Mousavi Hosseini, Adel Javanmard, Murat A. Erdogdu:
Robust Feature Learning for Multi-Index Models in High Dimensions. CoRR abs/2410.16449 (2024) - 2023
- [j16]CJ Carey, Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Shankar Kumar, Andres Muñoz Medina, Vahab Mirrokni, Gabriel Henrique Nunes, Sergei Vassilvitskii, Peilin Zhong:
Measuring Re-identification Risk. Proc. ACM Manag. Data 1(2): 149:1-149:26 (2023) - [c22]Matthew Fahrbach, Adel Javanmard, Vahab Mirrokni, Pratik Worah:
Learning Rate Schedules in the Presence of Distribution Shift. ICML 2023: 9523-9546 - [c21]Adel Javanmard, Vahab Mirrokni:
Anonymous Learning via Look-Alike Clustering: A Precise Analysis of Model Generalization. NeurIPS 2023 - [i40]Matthew Fahrbach, Adel Javanmard, Vahab Mirrokni, Pratik Worah:
Learning Rate Schedules in the Presence of Distribution Shift. CoRR abs/2303.15634 (2023) - [i39]Rashmi Ranjan Bhuyan, Adel Javanmard, Sungchul Kim, Gourab Mukherjee, Ryan A. Rossi, Tong Yu, Handong Zhao:
Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model. CoRR abs/2303.15652 (2023) - [i38]CJ Carey, Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Shankar Kumar, Andrés Muñoz Medina, Vahab Mirrokni, Gabriel Henrique Nunes, Sergei Vassilvitskii, Peilin Zhong:
Measuring Re-identification Risk. CoRR abs/2304.07210 (2023) - [i37]Adel Javanmard, Vahab Mirrokni, Jean Pouget-Abadie:
Causal Inference with Differentially Private (Clustered) Outcomes. CoRR abs/2308.00957 (2023) - [i36]Adel Javanmard, Vahab Mirrokni:
Anonymous Learning via Look-Alike Clustering: A Precise Analysis of Model Generalization. CoRR abs/2310.04015 (2023) - 2022
- [i35]Hamed Hassani, Adel Javanmard:
The curse of overparametrization in adversarial training: Precise analysis of robust generalization for random features regression. CoRR abs/2201.05149 (2022) - [i34]Adel Javanmard, Mohammad Mehrabi:
GRASP: A Goodness-of-Fit Test for Classification Learning. CoRR abs/2209.02064 (2022) - 2021
- [j15]Negin Golrezaei, Adel Javanmard, Vahab S. Mirrokni:
Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions. Oper. Res. 69(1): 297-314 (2021) - [c20]Mohammad Mehrabi, Adel Javanmard, Ryan A. Rossi, Anup B. Rao, Tung Mai:
Fundamental Tradeoffs in Distributionally Adversarial Training. ICML 2021: 7544-7554 - [i33]Mohammad Mehrabi, Adel Javanmard, Ryan A. Rossi, Anup Rao, Tung Mai:
Fundamental Tradeoffs in Distributionally Adversarial Training. CoRR abs/2101.06309 (2021) - [i32]Simeng Shao, Jacob Bien, Adel Javanmard:
Controlling the False Split Rate in Tree-Based Aggregation. CoRR abs/2108.05350 (2021) - [i31]Adel Javanmard, Mohammad Mehrabi:
Adversarial robustness for latent models: Revisiting the robust-standard accuracies tradeoff. CoRR abs/2110.11950 (2021) - 2020
- [j14]Ery Arias-Castro, Adel Javanmard, Bruno Pelletier:
Perturbation Bounds for Procrustes, Classical Scaling, and Trilateration, with Applications to Manifold Learning. J. Mach. Learn. Res. 21: 15:1-15:37 (2020) - [c19]Adel Javanmard, Mahdi Soltanolkotabi, Hamed Hassani:
Precise Tradeoffs in Adversarial Training for Linear Regression. COLT 2020: 2034-2078 - [c18]Adel Javanmard, Hamid Nazerzadeh, Simeng Shao:
Multi-Product Dynamic Pricing in High-Dimensions with Heterogeneous Price Sensitivity. ISIT 2020: 2652-2657 - [i30]Adel Javanmard, Mahdi Soltanolkotabi, Hamed Hassani:
Precise Tradeoffs in Adversarial Training for Linear Regression. CoRR abs/2002.10477 (2020) - [i29]Negin Golrezaei, Adel Javanmard, Vahab S. Mirrokni:
Dynamic Incentive-aware Learning: Robust Pricing in Contextual Auctions. CoRR abs/2002.11137 (2020) - [i28]Adel Javanmard, Mahdi Soltanolkotabi:
Precise Statistical Analysis of Classification Accuracies for Adversarial Training. CoRR abs/2010.11213 (2020)
2010 – 2019
- 2019
- [j13]David S. Robertson, Jan Wildenhain, Adel Javanmard, Natasha A. Karp:
onlineFDR: an R package to control the false discovery rate for growing data repositories. Bioinform. 35(20): 4196-4199 (2019) - [j12]Adel Javanmard, Hamid Nazerzadeh:
Dynamic Pricing in High-dimensions. J. Mach. Learn. Res. 20: 9:1-9:49 (2019) - [j11]Mahdi Soltanolkotabi, Adel Javanmard, Jason D. Lee:
Theoretical Insights Into the Optimization Landscape of Over-Parameterized Shallow Neural Networks. IEEE Trans. Inf. Theory 65(2): 742-769 (2019) - [c17]Negin Golrezaei, Adel Javanmard, Vahab S. Mirrokni:
Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions. NeurIPS 2019: 9756-9766 - [i27]Adel Javanmard, Hamid Nazerzadeh, Simeng Shao:
Multi-Product Dynamic Pricing in High-Dimensions with Heterogenous Price Sensitivity. CoRR abs/1901.01030 (2019) - [i26]Adel Javanmard, Marco Mondelli, Andrea Montanari:
Analysis of a Two-Layer Neural Network via Displacement Convexity. CoRR abs/1901.01375 (2019) - [i25]Amin Jalali, Adel Javanmard, Maryam Fazel:
New Computational and Statistical Aspects of Regularized Regression with Application to Rare Feature Selection and Aggregation. CoRR abs/1904.05338 (2019) - [i24]Yash Deshpande, Adel Javanmard, Mohammad Mehrabi:
Online Debiasing for Adaptively Collected High-dimensional Data. CoRR abs/1911.01040 (2019) - 2018
- [i23]Adel Javanmard, Hamid Javadi:
False Discovery Rate Control via Debiased Lasso. CoRR abs/1803.04464 (2018) - [i22]Ery Arias-Castro, Adel Javanmard, Bruno Pelletier:
Perturbation Bounds for Procrustes, Classical Scaling, and Trilateration, with Applications to Manifold Learning. CoRR abs/1810.09569 (2018) - 2017
- [j10]Anand Bhaskar, Adel Javanmard, Thomas A. Courtade, David Tse:
Novel probabilistic models of spatial genetic ancestry with applications to stratification correction in genome-wide association studies. Bioinform. 33(6): 879-885 (2017) - [j9]Adel Javanmard:
Perishability of Data: Dynamic Pricing under Varying-Coefficient Models. J. Mach. Learn. Res. 18: 53:1-53:31 (2017) - [i21]Adel Javanmard:
Perishability of Data: Dynamic Pricing under Varying-Coefficient Models. CoRR abs/1701.03537 (2017) - [i20]Adel Javanmard, Jason D. Lee:
A Flexible Framework for Hypothesis Testing in High-dimensions. CoRR abs/1704.07971 (2017) - [i19]Mahdi Soltanolkotabi, Adel Javanmard, Jason D. Lee:
Theoretical insights into the optimization landscape of over-parameterized shallow neural networks. CoRR abs/1707.04926 (2017) - 2016
- [i18]Adel Javanmard, Andrea Montanari:
Online Rules for Control of False Discovery Rate and False Discovery Exceedance. CoRR abs/1603.09000 (2016) - [i17]Adel Javanmard, Andrea Montanari, Federico Ricci-Tersenghi:
Performance of a community detection algorithm based on semidefinite programming. CoRR abs/1603.09045 (2016) - [i16]Adel Javanmard, Hamid Nazerzadeh:
Dynamic Pricing in High-dimensions. CoRR abs/1609.07574 (2016) - 2015
- [j8]Saieed Akbari, Akbar Daemi, Omid Hatami, Adel Javanmard, Abbas Mehrabian:
Nowhere-zero Unoriented Flows in Hamiltonian Graphs. Ars Comb. 120: 51-63 (2015) - [c16]Sonia A. Bhaskar, Adel Javanmard:
1-bit matrix completion under exact low-rank constraint. CISS 2015: 1-6 - [i15]Adel Javanmard, Andrea Montanari:
On Online Control of False Discovery Rate. CoRR abs/1502.06197 (2015) - [i14]Adel Javanmard, Andrea Montanari, Federico Ricci-Tersenghi:
Phase Transitions in Semidefinite Relaxations. CoRR abs/1511.08769 (2015) - 2014
- [j7]Adel Javanmard, Andrea Montanari:
Confidence intervals and hypothesis testing for high-dimensional regression. J. Mach. Learn. Res. 15(1): 2869-2909 (2014) - [j6]Adel Javanmard, Andrea Montanari:
Hypothesis Testing in High-Dimensional Regression Under the Gaussian Random Design Model: Asymptotic Theory. IEEE Trans. Inf. Theory 60(10): 6522-6554 (2014) - 2013
- [j5]Adel Javanmard, Andrea Montanari:
Localization from Incomplete Noisy Distance Measurements. Found. Comput. Math. 13(3): 297-345 (2013) - [j4]David L. Donoho, Adel Javanmard, Andrea Montanari:
Information-Theoretically Optimal Compressed Sensing via Spatial Coupling and Approximate Message Passing. IEEE Trans. Inf. Theory 59(11): 7434-7464 (2013) - [c15]Adel Javanmard, Andrea Montanari:
Nearly optimal sample size in hypothesis testing for high-dimensional regression. Allerton 2013: 1427-1434 - [c14]Animashree Anandkumar, Daniel J. Hsu, Adel Javanmard, Sham M. Kakade:
Learning Linear Bayesian Networks with Latent Variables. ICML (1) 2013: 249-257 - [c13]Adel Javanmard, Andrea Montanari:
Confidence Intervals and Hypothesis Testing for High-Dimensional Statistical Models. NIPS 2013: 1187-1195 - [c12]Adel Javanmard, Andrea Montanari:
Model Selection for High-Dimensional Regression under the Generalized Irrepresentability Condition. NIPS 2013: 3012-3020 - [i13]Adel Javanmard, Andrea Montanari:
Hypothesis Testing in High-Dimensional Regression under the Gaussian Random Design Model: Asymptotic Theory. CoRR abs/1301.4240 (2013) - [i12]Morteza Ibrahimi, Adel Javanmard, Benjamin Van Roy:
Efficient Reinforcement Learning for High Dimensional Linear Quadratic Systems. CoRR abs/1303.5984 (2013) - [i11]Adel Javanmard, Andrea Montanari:
Model Selection for High-Dimensional Regression under the Generalized Irrepresentability Condition. CoRR abs/1305.0355 (2013) - [i10]Adel Javanmard, Andrea Montanari:
Confidence Intervals and Hypothesis Testing for High-Dimensional Regression. CoRR abs/1306.3171 (2013) - [i9]Adel Javanmard, Andrea Montanari:
Nearly Optimal Sample Size in Hypothesis Testing for High-Dimensional Regression. CoRR abs/1311.0274 (2013) - 2012
- [c11]Adel Javanmard, Maya Haridasan, Li Zhang:
Multi-track map matching. SIGSPATIAL/GIS 2012: 394-397 - [c10]David L. Donoho, Adel Javanmard, Andrea Montanari:
Information-theoretically optimal compressed sensing via spatial coupling and approximate message passing. ISIT 2012: 1231-1235 - [c9]Adel Javanmard, Li Zhang:
The minimax risk of truncated series estimators for symmetric convex polytopes. ISIT 2012: 1633-1637 - [c8]Adel Javanmard, Andrea Montanari:
Subsampling at information theoretically optimal rates. ISIT 2012: 2431-2435 - [c7]Adel Javanmard, Maya Haridasan, Li Zhang:
Poster: multi-track map matching. MobiSys 2012: 503-504 - [c6]Morteza Ibrahimi, Adel Javanmard, Benjamin Van Roy:
Efficient Reinforcement Learning for High Dimensional Linear Quadratic Systems. NIPS 2012: 2645-2653 - [c5]Mohammad Alizadeh, Adel Javanmard, Shang-Tse Chuang, Sundar Iyer, Yi Lu:
Versatile refresh: low complexity refresh scheduling for high-throughput multi-banked eDRAM. SIGMETRICS 2012: 247-258 - [i8]Adel Javanmard, Li Zhang:
The minimax risk of truncated series estimators for symmetric convex polytopes. CoRR abs/1201.2462 (2012) - [i7]Adel Javanmard, Andrea Montanari:
Subsampling at Information Theoretically Optimal Rates. CoRR abs/1202.2525 (2012) - [i6]Adel Javanmard, Maya Haridasan, Li Zhang:
Multi-track Map Matching. CoRR abs/1209.2759 (2012) - [i5]Animashree Anandkumar, Daniel J. Hsu, Adel Javanmard, Sham M. Kakade:
Learning Linear Bayesian Networks with Latent Variables. CoRR abs/1209.5350 (2012) - [i4]Adel Javanmard, Andrea Montanari:
State Evolution for General Approximate Message Passing Algorithms, with Applications to Spatial Coupling. CoRR abs/1211.5164 (2012) - 2011
- [c4]Morteza Ibrahimi, Adel Javanmard, Yashodhan Kanoria, Andrea Montanari:
Robust max-product belief propagation. ACSCC 2011: 43-49 - [c3]Adel Javanmard, Andrea Montanari:
Localization from incomplete noisy distance measurements. ISIT 2011: 1584-1588 - [c2]Mohammad Alizadeh, Adel Javanmard, Balaji Prabhakar:
Analysis of DCTCP: stability, convergence, and fairness. SIGMETRICS 2011: 73-84 - [i3]Adel Javanmard, Andrea Montanari:
Localization from Incomplete Noisy Distance Measurements. CoRR abs/1103.1417 (2011) - [i2]Morteza Ibrahimi, Adel Javanmard, Yashodhan Kanoria, Andrea Montanari:
Robust Max-Product Belief Propagation. CoRR abs/1111.6214 (2011) - [i1]David L. Donoho, Adel Javanmard, Andrea Montanari:
Information-Theoretically Optimal Compressed Sensing via Spatial Coupling and Approximate Message Passing. CoRR abs/1112.0708 (2011) - 2010
- [j3]Saieed Akbari, Akbar Daemi, Omid Hatami, Adel Javanmard, Abbas Mehrabian:
Zero-Sum Flows in Regular Graphs. Graphs Comb. 26(5): 603-615 (2010)
2000 – 2009
- 2009
- [j2]Adel Javanmard, Farid Ashtiani:
Analytical evaluation of average delay and maximum stable throughput along a typical two-way street for vehicular ad hoc networks in sparse situations. Comput. Commun. 32(16): 1768-1780 (2009) - [j1]G. Hosein Mohimani, Farid Ashtiani, Adel Javanmard, Maziyar Hamdi:
Mobility Modeling, Spatial Traffic Distribution, and Probability of Connectivity for Sparse and Dense Vehicular Ad Hoc Networks. IEEE Trans. Veh. Technol. 58(4): 1998-2007 (2009) - 2008
- [c1]Adel Javanmard, P. Pad, Massoud Babaie-Zadeh, Christian Jutten:
Estimating the mixing matrix in underdetermined Sparse Component Analysis (SCA) using consecutive independent component analysis (ICA). EUSIPCO 2008: 1-5
Coauthor Index
Vahab S. Mirrokni
aka: Vahab Mirrokni
aka: Vahab Mirrokni
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