Prasanna Sattigeri

ORCID

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 117

Top Venues

Arizona State University, Tempe, USA (PHD)

Education History

Arizona State University, Tempe, USA

PHD

Publications

Manish Nagireddy, Inkit Padhi, Soumya Ghosh, Prasanna Sattigeri
When in Doubt, Cascade: Towards Building Efficient and Capable Guardrails. CoRR (2024)
Jongha Jon Ryu, Maohao Shen, Soumya Ghosh, Yuheng Bu, Prasanna Sattigeri, Subhro Das, Gregory W. Wornell
Improved Evidential Deep Learning via a Mixture of Dirichlet Distributions. CoRR (2024)
Hyo Jin Do, Rachel Ostrand, Justin D. Weisz, Casey Dugan, Prasanna Sattigeri, Dennis Wei, Keerthiram Murugesan, Werner Geyer
Facilitating Human-LLM Collaboration through Factuality Scores and Source Attributions. CoRR (2024)
Zirui Yan, Dennis Wei, Dmitriy A. Katz-Rogozhnikov, Prasanna Sattigeri, Ali Tajer
Causal Bandits with General Causal Models and Interventions. CoRR (2024)
Erik Miehling, Manish Nagireddy, Prasanna Sattigeri, Elizabeth M. Daly, David Piorkowski, John T. Richards
Language Models in Dialogue: Conversational Maxims for Human-AI Interactions. CoRR (2024)
Burak Varici, Dmitriy Katz, Dennis Wei, Prasanna Sattigeri, Ali Tajer
Separability Analysis for Causal Discovery in Mixture of DAGs. Trans. Mach. Learn. Res. 2024 (2024)
Swapnaja Achintalwar, Ioana Baldini, Djallel Bouneffouf, Joan Byamugisha, Maria Chang, Pierre L. Dognin, Eitan Farchi, Ndivhuwo Makondo, Aleksandra Mojsilovic, Manish Nagireddy, Karthikeyan Natesan Ramamurthy, Inkit Padhi, Orna Raz, Jesus Rios, Prasanna Sattigeri, Moninder Singh, Siphiwe Thwala, Rosario A. Uceda-Sosa, Kush R. Varshney
Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations. CoRR (2024)
Pierre L. Dognin, Jesus Rios, Ronny Luss, Inkit Padhi, Matthew D. Riemer, Miao Liu, Prasanna Sattigeri, Manish Nagireddy, Kush R. Varshney, Djallel Bouneffouf
Contextual Moral Value Alignment Through Context-Based Aggregation. CoRR (2024)
Tejaswini Pedapati, Amit Dhurandhar, Soumya Ghosh, Soham Dan, Prasanna Sattigeri
Large Language Model Confidence Estimation via Black-Box Access. CoRR (2024)
Maohao Shen, Subhro Das, Kristjan H. Greenewald, Prasanna Sattigeri, Gregory W. Wornell, Soumya Ghosh
Thermometer: Towards Universal Calibration for Large Language Models. CoRR (2024)
Zirui Yan, Dennis Wei, Dmitriy A. Katz, Prasanna Sattigeri, Ali Tajer
Causal Bandits with General Causal Models and Interventions. AISTATS (2024)
Swapnaja Achintalwar, Adriana Alvarado Garcia, Ateret Anaby-Tavor, Ioana Baldini, Sara E. Berger, Bishwaranjan Bhattacharjee, Djallel Bouneffouf, Subhajit Chaudhury, Pin-Yu Chen, Lamogha Chiazor, Elizabeth M. Daly, Rogério Abreu de Paula, Pierre L. Dognin, Eitan Farchi, Soumya Ghosh, Michael Hind, Raya Horesh, George Kour, Ja Young Lee, Erik Miehling, Keerthiram Murugesan, Manish Nagireddy, Inkit Padhi, David Piorkowski, Ambrish Rawat, Orna Raz, Prasanna Sattigeri, Hendrik Strobelt, Sarathkrishna Swaminathan, Christoph Tillmann, Aashka Trivedi, Kush R. Varshney, Dennis Wei, Shalisha Witherspoon, Marcel Zalmanovici
Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations. CoRR (2024)
Lucas Monteiro Paes, Dennis Wei, Hyo Jin Do, Hendrik Strobelt, Ronny Luss, Amit Dhurandhar, Manish Nagireddy, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Werner Geyer, Soumya Ghosh
Multi-Level Explanations for Generative Language Models. CoRR (2024)
Burak Varici, Dmitriy A. Katz-Rogozhnikov, Dennis Wei, Prasanna Sattigeri, Ali Tajer
Interventional Causal Discovery in a Mixture of DAGs. CoRR (2024)
Yufang Hou, Alessandra Pascale, Javier Carnerero-Cano, Tigran T. Tchrakian, Radu Marinescu, Elizabeth Daly, Inkit Padhi, Prasanna Sattigeri
WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia. CoRR (2024)
Hussein Mozannar, Valerie Chen, Mohammed Alsobay, Subhro Das, Sebastian Zhao, Dennis Wei, Manish Nagireddy, Prasanna Sattigeri, Ameet Talwalkar, David A. Sontag
The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers. CoRR (2024)
Sourya Basu, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Vijil Chenthamarakshan, Kush R. Varshney, Lav R. Varshney, Payel Das
Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models. AAAI (2023)
Qidong Yang, Alex Hernández-García, Paula Harder, Venkatesh Ramesh, Prasanna Sattigeri, Daniela Szwarcman, Campbell D. Watson, David Rolnick
Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling. CoRR (2023)
Abhin Shah, Maohao Shen, Jongha Jon Ryu, Subhro Das, Prasanna Sattigeri, Yuheng Bu, Gregory W. Wornell
Group Fairness with Uncertainty in Sensitive Attributes. CoRR (2023)
Maohao Shen, Soumya Ghosh, Prasanna Sattigeri, Subhro Das, Yuheng Bu, Gregory W. Wornell
Reliable Gradient-free and Likelihood-free Prompt Tuning. CoRR (2023)
Sourya Basu, Pulkit Katdare, Prasanna Sattigeri, Vijil Chenthamarakshan, Katherine Driggs Campbell, Payel Das, Lav R. Varshney
Efficient Equivariant Transfer Learning from Pretrained Models. NeurIPS (2023)
Jirí Navrátil, Benjamin Elder, Matthew Arnold, Soumya Ghosh, Prasanna Sattigeri
Assessment of Prediction Intervals Using Uncertainty Characteristics Curves. CoRR (2023)
Hussein Mozannar, Jimin J. Lee, Dennis Wei, Prasanna Sattigeri, Subhro Das, David A. Sontag
Effective Human-AI Teams via Learned Natural Language Rules and Onboarding. NeurIPS (2023)
Maohao Shen, Yuheng Bu, Prasanna Sattigeri, Soumya Ghosh, Subhro Das, Gregory W. Wornell
Post-hoc Uncertainty Learning Using a Dirichlet Meta-Model. AAAI (2023)
Sourya Basu, Pulkit Katdare, Prasanna Sattigeri, Vijil Chenthamarakshan, Katherine Rose Driggs-Campbell, Payel Das, Lav R. Varshney
Equivariant Few-Shot Learning from Pretrained Models. CoRR (2023)
Maohao Shen, Soumya Ghosh, Prasanna Sattigeri, Subhro Das, Yuheng Bu, Gregory W. Wornell
Reliable Gradient-free and Likelihood-free Prompt Tuning. EACL (Findings) (2023)
Hussein Mozannar, Hunter Lang, Dennis Wei, Prasanna Sattigeri, Subhro Das, David A. Sontag
Who Should Predict? Exact Algorithms For Learning to Defer to Humans. CoRR (2023)
Hussein Mozannar, Hunter Lang, Dennis Wei, Prasanna Sattigeri, Subhro Das, David A. Sontag
Who Should Predict? Exact Algorithms For Learning to Defer to Humans. AISTATS (2023)
Hussein Mozannar, Jimin J. Lee, Dennis Wei, Prasanna Sattigeri, Subhro Das, David A. Sontag
Effective Human-AI Teams via Learned Natural Language Rules and Onboarding. CoRR (2023)
Katy Ilonka Gero, Payel Das, Pierre L. Dognin, Inkit Padhi, Prasanna Sattigeri, Kush R. Varshney
The incentive gap in data work in the era of large models. Nat. Mac. Intell. 5 (6) (2023)
Brianna Richardson, Prasanna Sattigeri, Dennis Wei, Karthikeyan Natesan Ramamurthy, Kush Varshney, Amit Dhurandhar, Juan E. Gilbert
Add-Remove-or-Relabel: Practitioner-Friendly Bias Mitigation via Influential Fairness. FAccT (2023)
Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer
Intervention target estimation in the presence of latent variables. UAI (2022)
Soumya Ghosh, Q. Vera Liao, Karthikeyan Natesan Ramamurthy, Jirí Navrátil, Prasanna Sattigeri, Kush R. Varshney, Yunfeng Zhang
Uncertainty Quantification 360: A Hands-on Tutorial. COMAD/CODS (2022)
Sourya Basu, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Vijil Chenthamarakshan, Kush R. Varshney, Lav R. Varshney, Payel Das
Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models. CoRR (2022)
Abhin Shah, Yuheng Bu, Joshua K. Lee, Subhro Das, Rameswar Panda, Prasanna Sattigeri, Gregory W. Wornell
Selective Regression under Fairness Criteria. ICML (2022)
Prasanna Sattigeri, Soumya Ghosh, Inkit Padhi, Pierre L. Dognin, Kush R. Varshney
Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting. CoRR (2022)
Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer
Causal Bandits for Linear Structural Equation Models. CoRR (2022)
Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri, Kush R. Varshney
Causal Feature Selection for Algorithmic Fairness. SIGMOD Conference (2022)
Joshua K. Lee, Yuheng Bu, Prasanna Sattigeri, Rameswar Panda, Gregory W. Wornell, Leonid Karlinsky, Rogério Feris
A Maximal Correlation Approach to Imposing Fairness in Machine Learning. ICASSP (2022)
Prasanna Sattigeri, Jayaraman J. Thiagarajan, Karthikeyan Ramamurthy, Andreas Spanias, Mahesh K. Banavar, Abhinav Dixit, Jie Fan, Mohit Malu, Kristen Jaskie, Sunil Rao, Uday Shankar Shanthamallu, Vivek Sivaraman Narayanaswamy, Sameeksha Katoch
Instruction Tools for Signal Processing and Machine Learning for Ion-Channel Sensors. Int. J. Virtual Pers. Learn. Environ. 12 (1) (2022)
Joshua K. Lee, Yuheng Bu, Prasanna Sattigeri, Rameswar Panda, Gregory W. Wornell, Leonid Karlinsky, Rogério Schmidt Feris
A Maximal Correlation Framework for Fair Machine Learning. Entropy 24 (4) (2022)
Paula Harder, Qidong Yang, Venkatesh Ramesh, Prasanna Sattigeri, Alex Hernández-García, Campbell D. Watson, Daniela Szwarcman, David S. Rolnick
Generating physically-consistent high-resolution climate data with hard-constrained neural networks. CoRR (2022)
Prasanna Sattigeri, Soumya Ghosh, Inkit Padhi, Pierre L. Dognin, Kush R. Varshney
Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting. NeurIPS (2022)
Maohao Shen, Yuheng Bu, Prasanna Sattigeri, Soumya Ghosh, Subhro Das, Gregory W. Wornell
Post-hoc Uncertainty Learning using a Dirichlet Meta-Model. CoRR (2022)
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
AI Explainability 360: Impact and Design. AAAI (2022)
Samuel C. Hoffman, Kahini Wadhawan, Payel Das, Prasanna Sattigeri, Karthikeyan Shanmugam
Causal Graphs Underlying Generative Models: Path to Learning with Limited Data. CoRR (2022)
Yue Meng, Rameswar Panda, Chung-Ching Lin, Prasanna Sattigeri, Leonid Karlinsky, Kate Saenko, Aude Oliva, Rogério Feris
AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition. CoRR (2021)
Abhin Shah, Yuheng Bu, Joshua Ka-Wing Lee, Subhro Das, Rameswar Panda, Prasanna Sattigeri, Gregory W. Wornell
Selective Regression Under Fairness Criteria. CoRR (2021)
Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri, Kush R. Varshney
Interventional Fairness with Indirect Knowledge of Unobserved Protected Attributes. Entropy 23 (12) (2021)
Yue Meng, Rameswar Panda, Chung-Ching Lin, Prasanna Sattigeri, Leonid Karlinsky, Kate Saenko, Aude Oliva, Rogério Feris
AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition. ICLR (2021)
Leonid Karlinsky, Joseph Shtok, Amit Alfassy, Moshe Lichtenstein, Sivan Harary, Eli Schwartz, Sivan Doveh, Prasanna Sattigeri, Rogério Feris, Alex M. Bronstein, Raja Giryes
StarNet: towards Weakly Supervised Few-Shot Object Detection. AAAI (2021)
Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer
Scalable Intervention Target Estimation in Linear Models. NeurIPS (2021)
Soumya Ghosh, Q. Vera Liao, Karthikeyan Natesan Ramamurthy, Jirí Navrátil, Prasanna Sattigeri, Kush R. Varshney, Yunfeng Zhang
Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI. CoRR (2021)
Kartik Ahuja, Prasanna Sattigeri, Karthikeyan Shanmugam, Dennis Wei, Karthikeyan Natesan Ramamurthy, Murat Kocaoglu
Conditionally independent data generation. UAI (2021)
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 M. Bronstein, Kate Saenko, Shimon Ullman, Raja Giryes, Rogério Feris, Leonid Karlinsky
Detector-Free Weakly Supervised Grounding by Separation. ICCV (2021)
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
AI Explainability 360 Toolkit. COMAD/CODS (2021)
Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri, Yunfeng Zhang, Karthikeyan Shanmugam, Chun-Chen Tu
Leveraging Latent Features for Local Explanations. KDD (2021)
Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer
Scalable Intervention Target Estimation in Linear Models. CoRR (2021)
Joshua K. Lee, Yuheng Bu, Deepta Rajan, Prasanna Sattigeri, Rameswar Panda, Subhro Das, Gregory W. Wornell
Fair Selective Classification Via Sufficiency. ICML (2021)
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
AI Explainability 360: Impact and Design. CoRR (2021)
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 M. Bronstein, Kate Saenko, Shimon Ullman, Raja Giryes, Rogério Feris, Leonid Karlinsky
Detector-Free Weakly Supervised Grounding by Separation. CoRR (2021)
Umang Bhatt, Javier Antorán, Yunfeng Zhang, Q. Vera Liao, Prasanna Sattigeri, Riccardo Fogliato, Gabrielle Gauthier Melançon, Ranganath Krishnan, Jason Stanley, Omesh Tickoo, Lama Nachman, Rumi Chunara, Madhulika Srikumar, Adrian Weller, Alice Xiang
Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty. AIES (2021)
Jirí Navrátil, Benjamin Elder, Matthew Arnold, Soumya Ghosh, Prasanna Sattigeri
Uncertainty Characteristics Curves: A Systematic Assessment of Prediction Intervals. CoRR (2021)
Umang Bhatt, Yunfeng Zhang, Javier Antorán, Q. Vera Liao, Prasanna Sattigeri, Riccardo Fogliato, Gabrielle Gauthier Melançon, Ranganath Krishnan, Jason Stanley, Omesh Tickoo, Lama Nachman, Rumi Chunara, Adrian Weller, Alice Xiang
Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty. CoRR (2020)
Yue Meng, Chung-Ching Lin, Rameswar Panda, Prasanna Sattigeri, Leonid Karlinsky, Aude Oliva, Kate Saenko, Rogério Feris
AR-Net: Adaptive Frame Resolution for Efficient Action Recognition. CoRR (2020)
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
A Natural Language Processing System for Extracting Evidence of Drug Repurposing from Scientific Publications. AAAI (2020)
Leonid Karlinsky, Joseph Shtok, Amit Alfassy, Moshe Lichtenstein, Sivan Harary, Eli Schwartz, Sivan Doveh, Prasanna Sattigeri, Rogério Schmidt Feris, Alexander M. Bronstein, Raja Giryes
StarNet: towards weakly supervised few-shot detection and explainable few-shot classification. CoRR (2020)
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
AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models. J. Mach. Learn. Res. 21 (2020)
Yue Meng, Chung-Ching Lin, Rameswar Panda, Prasanna Sattigeri, Leonid Karlinsky, Aude Oliva, Kate Saenko, Rogério Feris
AR-Net: Adaptive Frame Resolution for Efficient Action Recognition. ECCV (7) (2020)
Newton M. Kinyanjui, Timothy Odonga, Celia Cintas, Noel C. F. Codella, Rameswar Panda, Prasanna Sattigeri, Kush R. Varshney
Fairness of Classifiers Across Skin Tones in Dermatology. MICCAI (6) (2020)
Jayaraman J. Thiagarajan, Bindya Venkatesh, Deepta Rajan, Prasanna Sattigeri
Improving Reliability of Clinical Models Using Prediction Calibration. UNSURE/GRAIL@MICCAI (2020)
Akash Srivastava, Yamini Bansal, Yukun Ding, Cole L. Hurwitz, Kai Xu, Bernhard Egger, Prasanna Sattigeri, Josh Tenenbaum, David D. Cox, Dan Gutfreund
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling. CoRR (2020)
N. Joseph Tatro, Pin-Yu Chen, Payel Das, Igor Melnyk, Prasanna Sattigeri, Rongjie Lai
Optimizing Mode Connectivity via Neuron Alignment. CoRR (2020)
N. Joseph Tatro, Pin-Yu Chen, Payel Das, Igor Melnyk, Prasanna Sattigeri, Rongjie Lai
Optimizing Mode Connectivity via Neuron Alignment. NeurIPS (2020)
Bindya Venkatesh, Jayaraman J. Thiagarajan, Kowshik Thopalli, Prasanna Sattigeri
Calibrate and Prune: Improving Reliability of Lottery Tickets Through Prediction Calibration. CoRR (2020)
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
AI explainability 360: hands-on tutorial. FAT* (2020)
Moshe Lichtenstein, Prasanna Sattigeri, Rogério Schmidt Feris, Raja Giryes, Leonid Karlinsky
TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classification. CoRR (2020)
Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri, Kush R. Varshney
Fair Data Integration. CoRR (2020)
Joshua K. Lee, Yuheng Bu, Prasanna Sattigeri, Rameswar Panda, Gregory W. Wornell, Leonid Karlinsky, Rogério Feris
A Maximal Correlation Approach to Imposing Fairness in Machine Learning. CoRR (2020)
Zhiqiang Tang, Yunhe Gao, Leonid Karlinsky, Prasanna Sattigeri, Rogério Feris, Dimitris N. Metaxas
OnlineAugment: Online Data Augmentation with Less Domain Knowledge. CoRR (2020)
Jayaraman J. Thiagarajan, Prasanna Sattigeri, Deepta Rajan, Bindya Venkatesh
Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models. CoRR (2020)
Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Jayaraman J. Thiagarajan, Bhavya Kailkhura
Treeview and Disentangled Representations for Explaining Deep Neural Networks Decisions. ACSSC (2020)
Moshe Lichtenstein, Prasanna Sattigeri, Rogério Feris, Raja Giryes, Leonid Karlinsky
TAFSSL: Task-Adaptive Feature Sub-Space Learning for Few-Shot Classification. ECCV (7) (2020)
Jayaraman J. Thiagarajan, Bindya Venkatesh, Prasanna Sattigeri, Peer-Timo Bremer
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors. AAAI (2020)
Seungwook Han, Akash Srivastava, Cole L. Hurwitz, Prasanna Sattigeri, David D. Cox
not-so-BigGAN: Generating High-Fidelity Images on a Small Compute Budget. CoRR (2020)
Zhiqiang Tang, Yunhe Gao, Leonid Karlinsky, Prasanna Sattigeri, Rogério Feris, Dimitris N. Metaxas
OnlineAugment: Online Data Augmentation with Less Domain Knowledge. ECCV (7) (2020)
Newton M. Kinyanjui, Timothy Odonga, Celia Cintas, Noel C. F. Codella, Rameswar Panda, Prasanna Sattigeri, Kush R. Varshney
Estimating Skin Tone and Effects on Classification Performance in Dermatology Datasets. CoRR (2019)
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
Drug Repurposing for Cancer: An NLP Approach to Identify Low-Cost Therapies. CoRR (2019)
Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri, Karthikeyan Shanmugam, Chun-Chen Tu
Generating Contrastive Explanations with Monotonic Attribute Functions. CoRR (2019)
Prasanna Sattigeri, Samuel C. Hoffman, Vijil Chenthamarakshan, Kush R. Varshney
Fairness GAN: Generating datasets with fairness properties using a generative adversarial network. IBM J. Res. Dev. 63 (4/5) (2019)
Rachel K. E. Bellamy, Kuntal Dey, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Sameep Mehta, Aleksandra Mojsilovic, Seema Nagar, Karthikeyan Natesan Ramamurthy, John T. Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, Yunfeng Zhang
Think Your Artificial Intelligence Software Is Fair? Think Again. IEEE Softw. 36 (4) (2019)
Jayaraman J. Thiagarajan, Bindya Venkatesh, Prasanna Sattigeri, Peer-Timo Bremer
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors. CoRR (2019)
Rachel K. E. Bellamy, Kuntal Dey, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, Seema Nagar, Karthikeyan Natesan Ramamurthy, John T. Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, Yunfeng Zhang
AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias. IBM J. Res. Dev. 63 (4/5) (2019)
Joshua K. Lee, Prasanna Sattigeri, Gregory W. Wornell
Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks. NeurIPS (2019)
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
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques. CoRR (2019)
Abhishek Kumar, Prasanna Sattigeri, Kahini Wadhawan, Leonid Karlinsky, Rogério Schmidt Feris, William T. Freeman, Gregory W. Wornell
Co-regularized Alignment for Unsupervised Domain Adaptation. CoRR (2018)
Prasanna Sattigeri, Samuel C. Hoffman, Vijil Chenthamarakshan, Kush R. Varshney
Fairness GAN. CoRR (2018)
Huan Song, Jayaraman J. Thiagarajan, Prasanna Sattigeri, Andreas Spanias
Optimizing Kernel Machines Using Deep Learning. IEEE Trans. Neural Networks Learn. Syst. 29 (11) (2018)
Abhishek Kumar, Prasanna Sattigeri, Kahini Wadhawan, Leonid Karlinsky, Rogério Schmidt Feris, Bill Freeman, Gregory W. Wornell
Co-regularized Alignment for Unsupervised Domain Adaptation. NeurIPS (2018)
Abhishek Kumar, Prasanna Sattigeri, Avinash Balakrishnan
Variational Inference of Disentangled Latent Concepts from Unlabeled Observations. ICLR (Poster) (2018)

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Publication Activity (10 Years)

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Prasanna Sattigeri