Saeid Tizpaz-Niari

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 33

Top Topics

Quantitative And Qualitative

Top Venues

Publications

Saeid Tizpaz-Niari, Sriram Sankaranarayanan
Worst-Case Convergence Time of ML Algorithms via Extreme Value Theory. CoRR (2024)
Saeid Tizpaz-Niari, Sriram Sankaranarayanan
Worst-Case Convergence Time of ML Algorithms via Extreme Value Theory. CAIN (2024)
Salvador Robles Herrera, Verya Monjezi, Vladik Kreinovich, Ashutosh Trivedi, Saeid Tizpaz-Niari
Predicting Fairness of ML Software Configuration. CoRR (2024)
Salvador Robles Herrera, Verya Monjezi, Vladik Kreinovich, Ashutosh Trivedi, Saeid Tizpaz-Niari
Predicting Fairness of ML Software Configurations. PROMISE (2024)
Verya Monjezi, Ashish Kumar, Gang Tan, Ashutosh Trivedi, Saeid Tizpaz-Niari
Causal Graph Fuzzing for Fair ML Sofware Development. ICSE Companion (2024)
Reyhaneh Jabbarvand, Saeid Tizpaz-Niari, Earl T. Barr, Satish Chandra
Summary of the 1st Interpretability and Robustness in Neural Software Engineering (InteNSE 2023). ACM SIGSOFT Softw. Eng. Notes 49 (1) (2024)
Vishnu Asutosh Dasu, Ashish Kumar, Saeid Tizpaz-Niari, Gang Tan
NeuFair: Neural Network Fairness Repair with Dropout. CoRR (2024)
Normen Yu, Luciana Carreon, Gang Tan, Saeid Tizpaz-Niari
FairLay-ML: Intuitive Debugging of Fairness in Data-Driven Social-Critical Software. CoRR (2024)
Saeid Tizpaz-Niari, Luc Longpré, Olga Kosheleva, Vladik Kreinovich
Fast - Asymptotically Optimal - Methods for Determining the Optimal Number of Features. IUKM (1) (2023)
Mohammad J. Hashemi, Eric Keller, Saeid Tizpaz-Niari
Detecting Unseen Anomalies in Network Systems by Leveraging Neural Networks. IEEE Trans. Netw. Serv. Manag. 20 (3) (2023)
Dananjay Srinivas, Rohan Das, Saeid Tizpaz-Niari, Ashutosh Trivedi, Maria Leonor Pacheco
On the Potential and Limitations of Few-Shot In-Context Learning to Generate Metamorphic Specifications for Tax Preparation Software. CoRR (2023)
Normen Yu, Gang Tan, Saeid Tizpaz-Niari
FairLay-ML: Intuitive Remedies for Unfairness in Data-Driven Social-Critical Algorithms. CoRR (2023)
Verya Monjezi, Ashutosh Trivedi, Gang Tan, Saeid Tizpaz-Niari
Information-Theoretic Testing and Debugging of Fairness Defects in Deep Neural Networks. ICSE (2023)
Verya Monjezi, Ashutosh Trivedi, Gang Tan, Saeid Tizpaz-Niari
Information-Theoretic Testing and Debugging of Fairness Defects in Deep Neural Networks. CoRR (2023)
Saeid Tizpaz-Niari, Verya Monjezi, Morgan Wagner, Shiva Darian, Krystia Reed, Ashutosh Trivedi
Metamorphic Testing and Debugging of Tax Preparation Software. ICSE (SEIS) (2023)
Saeid Tizpaz-Niari, Ashish Kumar, Gang Tan, Ashutosh Trivedi
Fairness-aware Configuration of Machine Learning Libraries. ICSE (2022)
Saeid Tizpaz-Niari, Morgan Wagner, Shiva Darian, Krystia Reed, Ashutosh Trivedi
Metamorphic Testing and Debugging of Tax Preparation Software. CoRR (2022)
Saeid Tizpaz-Niari, Ashish Kumar, Gang Tan, Ashutosh Trivedi
Fairness-aware Configuration of Machine Learning Libraries. CoRR (2022)
Yannic Noller, Saeid Tizpaz-Niari
QFuzz: quantitative fuzzing for side channels. ISSTA (2021)
Yannic Noller, Saeid Tizpaz-Niari
QFuzz: Quantitative Fuzzing for Side Channels. CoRR (2021)
Saeid Tizpaz-Niari, Pavol Cerný, Sriram Sankaranarayanan, Ashutosh Trivedi
Quantitative estimation of side-channel leaks with neural networks. Int. J. Softw. Tools Technol. Transf. 23 (4) (2021)
Saeid Tizpaz-Niari, Pavol Cerný, Ashutosh Trivedi
Detecting and Understanding Real-World Differential Performance Bugs in Machine Learning Libraries. CoRR (2020)
Saeid Tizpaz-Niari, Pavol Cerný, Ashutosh Trivedi
Data-Driven Debugging for Functional Side Channels. NDSS (2020)
Saeid Tizpaz-Niari, Pavol Cerný, Ashutosh Trivedi
Detecting and understanding real-world differential performance bugs in machine learning libraries. ISSTA (2020)
Saeid Tizpaz-Niari, Pavol Cerný, Sriram Sankaranarayanan, Ashutosh Trivedi
Efficient Detection and Quantification of Timing Leaks with Neural Networks. RV (2019)
Saeid Tizpaz-Niari, Pavol Cerný, Sriram Sankaranarayanan, Ashutosh Trivedi
Efficient Detection and Quantification of Timing Leaks with Neural Networks. CoRR (2019)
Saeid Tizpaz-Niari, Pavol Cerný, Ashutosh Trivedi
Quantitative Mitigation of Timing Side Channels. CAV (1) (2019)
Saeid Tizpaz-Niari, Pavol Cerný, Ashutosh Trivedi
Quantitative Mitigation of Timing Side Channels. CoRR (2019)
Saeid Tizpaz-Niari, Pavol Cerný, Ashutosh Trivedi
Data-Driven Debugging for Functional Side Channels. CoRR (2018)
Saeid Tizpaz-Niari, Pavol Cerný, Bor-Yuh Evan Chang, Ashutosh Trivedi
Differential Performance Debugging With Discriminant Regression Trees. AAAI (2018)
Saeid Tizpaz-Niari, Pavol Cerný, Bor-Yuh Evan Chang, Sriram Sankaranarayanan, Ashutosh Trivedi
Discriminating Traces with Time. CoRR (2017)
Saeid Tizpaz-Niari, Pavol Cerný, Bor-Yuh Evan Chang, Ashutosh Trivedi
Differential Performance Debugging with Discriminant Regression Trees. CoRR (2017)
Saeid Tizpaz-Niari, Pavol Cerný, Bor-Yuh Evan Chang, Sriram Sankaranarayanan, Ashutosh Trivedi
Discriminating Traces with Time. TACAS (2) (2017)
Saeid Tizpaz-Niari, Amir Hossein Jahangir
Verification of OSPF vulnerabilities by colored Petri net. SIN (2013)