Sattigeri, Prasanna

47 publications

ICLR 2026 Building a Foundational Guardrail for General Agentic Systems via Synthetic Data Yue Huang, Hang Hua, Yujun Zhou, Pengcheng Jing, Manish Nagireddy, Inkit Padhi, Greta Dolcetti, Zhangchen Xu, Subhajit Chaudhury, Ambrish Rawat, Liubov Nedoshivina, Pin-Yu Chen, Prasanna Sattigeri, Xiangliang Zhang

ICLR 2026 TrustGen: A Platform of Dynamic Benchmarking on the Trustworthiness of Generative Foundation Models Yue Huang, Chujie Gao, Siyuan Wu, Haoran Wang, Xiangqi Wang, Jiayi Ye, Yujun Zhou, Yanbo Wang, Jiawen Shi, Qihui Zhang, Han Bao, Zhaoyi Liu, Yuan Li, Tianrui Guan, Peiran Wang, Haomin Zhuang, Dongping Chen, Kehan Guo, Andy Zou, Bryan Hooi, Caiming Xiong, Elias Stengel-Eskin, Hongyang Zhang, Hongzhi Yin, Huan Zhang, Huaxiu Yao, Jieyu Zhang, Jaehong Yoon, Kai Shu, Ranjay Krishna, Swabha Swayamdipta, Weijia Shi, Xiang Li, Yuexing Hao, Zhihao Jia, Zhize Li, Xiuying Chen, Zhengzhong Tu, Xiyang Hu, Tianyi Zhou, Jieyu Zhao, Lichao Sun, Furong Huang, Or Cohen-Sasson, Prasanna Sattigeri, Anka Reuel, Max Lamparth, Yue Zhao, Nouha Dziri, Yu Su, Huan Sun, Heng Ji, Chaowei Xiao, Mohit Bansal, Nitesh V Chawla, Jian Pei, Jianfeng Gao, Michael Backes, Philip S. Yu, Neil Zhenqiang Gong, Pin-Yu Chen, Bo Li, Dawn Song, Xiangliang Zhang

TMLR 2025 Large Language Model Confidence Estimation via Black-Box Access Tejaswini Pedapati, Amit Dhurandhar, Soumya Ghosh, Soham Dan, Prasanna Sattigeri

TMLR 2025 The RealHumanEval: Evaluating Large Language Models’ Abilities to Support Programmers Hussein Mozannar, Valerie Chen, Mohammed Alsobay, Subhro Das, Sebastian Zhao, Dennis Wei, Manish Nagireddy, Prasanna Sattigeri, Ameet Talwalkar, David Sontag

NeurIPSW 2024 Adversarial Prompt Evaluation: Systematic Benchmarking of Guardrails Against Prompt Input Attacks on LLMs Giulio Zizzo, Giandomenico Cornacchia, Kieran Fraser, Muhammad Zaid Hameed, Ambrish Rawat, Beat Buesser, Mark Purcell, Pin-Yu Chen, Prasanna Sattigeri, Kush R. Varshney

NeurIPS 2024 Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage? Maohao Shen, J. Jon Ryu, Soumya Ghosh, Yuheng Bu, Prasanna Sattigeri, Subhro Das, Gregory W. Wornell

NeurIPSW 2024 Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI Ambrish Rawat, Stefan Schoepf, Giulio Zizzo, Giandomenico Cornacchia, Muhammad Zaid Hameed, Kieran Fraser, Erik Miehling, Beat Buesser, Elizabeth M. Daly, Mark Purcell, Prasanna Sattigeri, Pin-Yu Chen, Kush R. Varshney

TMLR 2024 Attribute Graphs Underlying Molecular Generative Models: Path to Learning with Limited Data Samuel C Hoffman, Payel Das, Karthikeyan Shanmugam, Kahini Wadhawan, Prasanna Sattigeri

AISTATS 2024 Causal Bandits with General Causal Models and Interventions Zirui Yan, Dennis Wei, Dmitriy A Katz, Prasanna Sattigeri, Ali Tajer

IJCAI 2024 ComVas: Contextual Moral Values Alignment System Inkit Padhi, Pierre L. Dognin, Jesus Rios, Ronny Luss, Swapnaja Achintalwar, Matthew Riemer, Miao Liu, Prasanna Sattigeri, Manish Nagireddy, Kush R. Varshney, Djallel Bouneffouf

JMLR 2024 Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling Qidong Yang, Alex Hernandez-Garcia, Paula Harder, Venkatesh Ramesh, Prasanna Sattigeri, Daniela Szwarcman, Campbell D. Watson, David Rolnick

NeurIPS 2024 Graph-Based Uncertainty Metrics for Long-Form Language Model Generations Mingjian Jiang, Yangjun Ruan, Prasanna Sattigeri, Salim Roukos, Tatsunori Hashimoto

NeurIPS 2024 Interventional Causal Discovery in a Mixture of DAGs Burak Varıcı, Dmitriy A. Katz, Dennis Wei, Prasanna Sattigeri, Ali Tajer

TMLR 2024 Separability Analysis for Causal Discovery in Mixture of DAGs Burak Varici, Dmitriy Katz, Dennis Wei, Prasanna Sattigeri, Ali Tajer

ICML 2024 Thermometer: Towards Universal Calibration for Large Language Models Maohao Shen, Subhro Das, Kristjan Greenewald, Prasanna Sattigeri, Gregory W. Wornell, Soumya Ghosh

NeurIPSW 2024 Value Alignment from Unstructured Text Inkit Padhi, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Manish Nagireddy, Pierre Dognin, Kush R. Varshney

NeurIPS 2024 WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia Yufang Hou, Alessandra Pascale, Javier Carnerero-Cano, Tigran Tchrakian, Radu Marinescu, Elizabeth Daly, Inkit Padhi, Prasanna Sattigeri

JMLR 2023 Causal Bandits for Linear Structural Equation Models Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer

NeurIPS 2023 Effective Human-AI Teams via Learned Natural Language Rules and Onboarding Hussein Mozannar, Jimin Lee, Dennis Wei, Prasanna Sattigeri, Subhro Das, David Sontag

NeurIPS 2023 Efficient Equivariant Transfer Learning from Pretrained Models Sourya Basu, Pulkit Katdare, Prasanna Sattigeri, Vijil Chenthamarakshan, Katherine Driggs-Campbell, Payel Das, Lav R. Varshney

AAAI 2023 Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models Sourya Basu, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Vijil Chenthamarakshan, Kush R. Varshney, Lav R. Varshney, Payel Das

NeurIPSW 2023 Influence Based Approaches to Algorithmic Fairness: A Closer Look Soumya Ghosh, Prasanna Sattigeri, Inkit Padhi, Manish Nagireddy, Jie Chen

AAAI 2023 Post-Hoc Uncertainty Learning Using a Dirichlet Meta-Model Maohao Shen, Yuheng Bu, Prasanna Sattigeri, Soumya Ghosh, Subhro Das, Gregory W. Wornell

NeurIPSW 2023 Simulating Iterative Human-AI Interaction in Programming with LLMs Hussein Mozannar, Valerie Chen, Dennis Wei, Prasanna Sattigeri, Manish Nagireddy, Subhro Das, Ameet Talwalkar, David Sontag

AISTATS 2023 Who Should Predict? Exact Algorithms for Learning to Defer to Humans Hussein Mozannar, Hunter Lang, Dennis Wei, Prasanna Sattigeri, Subhro Das, David Sontag

AAAI 2022 AI Explainability 360: Impact and Design Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilovic, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang

NeurIPS 2022 Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting Prasanna Sattigeri, Soumya Ghosh, Inkit Padhi, Pierre Dognin, Kush R Varshney

UAI 2022 Intervention Target Estimation in the Presence of Latent Variables Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer

ICML 2022 Selective Regression Under Fairness Criteria Abhin Shah, Yuheng Bu, Joshua K Lee, Subhro Das, Rameswar Panda, Prasanna Sattigeri, Gregory W Wornell

ICLR 2021 AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition Yue Meng, Rameswar Panda, Chung-Ching Lin, Prasanna Sattigeri, Leonid Karlinsky, Kate Saenko, Aude Oliva, Rogerio Feris

UAI 2021 Conditionally Independent Data Generation Kartik Ahuja, Prasanna Sattigeri, Karthikeyan Shanmugam, Dennis Wei, Karthikeyan Natesan Ramamurthy, Murat Kocaoglu

ICCV 2021 Detector-Free Weakly Supervised Grounding by Separation Assaf Arbelle, Sivan Doveh, Amit Alfassy, Joseph Shtok, Guy Lev, Eli Schwartz, Hilde Kuehne, Hila Barak Levi, Prasanna Sattigeri, Rameswar Panda, Chun-Fu Chen, Alex Bronstein, Kate Saenko, Shimon Ullman, Raja Giryes, Rogerio Feris, Leonid Karlinsky

ICML 2021 Fair Selective Classification via Sufficiency Joshua K Lee, Yuheng Bu, Deepta Rajan, Prasanna Sattigeri, Rameswar Panda, Subhro Das, Gregory W Wornell

NeurIPS 2021 Scalable Intervention Target Estimation in Linear Models Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer

AAAI 2021 StarNet: Towards Weakly Supervised Few-Shot Object Detection Leonid Karlinsky, Joseph Shtok, Amit Alfassy, Moshe Lichtenstein, Sivan Harary, Eli Schwartz, Sivan Doveh, Prasanna Sattigeri, Rogério Feris, Alex M. Bronstein, Raja Giryes

AAAI 2020 A Natural Language Processing System for Extracting Evidence of Drug Repurposing from Scientific Publications Shivashankar Subramanian, Ioana Baldini, Sushma Ravichandran, Dmitriy A. Katz-Rogozhnikov, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Kush R. Varshney, Annmarie Wang, Pradeep Mangalath, Laura B. Kleiman

MLOSS 2020 AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilović, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang

ECCV 2020 AR-Net: Adaptive Frame Resolution for Efficient Action Recognition Yue Meng, Chung-Ching Lin, Rameswar Panda, Prasanna Sattigeri, Leonid Karlinsky, Aude Oliva, Kate Saenko, Rogerio Feris

AAAI 2020 Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors Jayaraman J. Thiagarajan, Bindya Venkatesh, Prasanna Sattigeri, Peer-Timo Bremer

ICLR 2020 Cz-Gem: A Framework for Disentangled Representation Learning Akash Srivastava, Yamini Bansal, Yukun Ding, Bernhard Egger, Prasanna Sattigeri, Josh Tenenbaum, David D. Cox, Dan Gutfreund

ECCV 2020 OnlineAugment: Online Data Augmentation with Less Domain Knowledge Zhiqiang Tang, Yunhe Gao, Leonid Karlinsky, Prasanna Sattigeri, Rogerio Feris, Dimitris Metaxas

NeurIPS 2020 Optimizing Mode Connectivity via Neuron Alignment Norman Tatro, Pin-Yu Chen, Payel Das, Igor Melnyk, Prasanna Sattigeri, Rongjie Lai

ECCV 2020 TAFSSL: Task-Adaptive Feature Sub-Space Learning for Few-Shot Classification Moshe Lichtenstein, Prasanna Sattigeri, Rogerio Feris, Raja Giryes, Leonid Karlinsky

NeurIPS 2019 Learning New Tricks from Old Dogs: Multi-Source Transfer Learning from Pre-Trained Networks Joshua Lee, Prasanna Sattigeri, Gregory Wornell

NeurIPS 2018 Co-Regularized Alignment for Unsupervised Domain Adaptation Abhishek Kumar, Prasanna Sattigeri, Kahini Wadhawan, Leonid Karlinsky, Rogerio Feris, Bill Freeman, Gregory Wornell

ICLR 2018 Variational Inference of Disentangled Latent Concepts from Unlabeled Observations Abhishek Kumar, Prasanna Sattigeri, Avinash Balakrishnan

NeurIPS 2017 Semi-Supervised Learning with GANs: Manifold Invariance with Improved Inference Abhishek Kumar, Prasanna Sattigeri, Tom Fletcher