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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
- 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 - [i44]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) - [i43]Adel Javanmard, Matthew Fahrbach, Vahab Mirrokni:
PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses. CoRR abs/2402.04987 (2024) - [i42]Gene Li, Lin Chen, Adel Javanmard, Vahab Mirrokni:
Optimistic Rates for Learning from Label Proportions. CoRR abs/2406.00487 (2024) - [i41]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) - 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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