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2020 – today
- 2024
[j13]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)- [c52]Zirui Yan, Dennis Wei, Dmitriy A. Katz, Prasanna Sattigeri, Ali Tajer:
Causal Bandits with General Causal Models and Interventions. AISTATS 2024: 4609-4617 - [c51]Maohao Shen, Subhro Das, Kristjan H. Greenewald, Prasanna Sattigeri, Gregory W. Wornell, Soumya Ghosh:
Thermometer: Towards Universal Calibration for Large Language Models. ICML 2024 - [i63]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 abs/2402.06160 (2024) - [i62]Zirui Yan, Dennis Wei, Dmitriy A. Katz-Rogozhnikov, Prasanna Sattigeri, Ali Tajer:
Causal Bandits with General Causal Models and Interventions. CoRR abs/2403.00233 (2024) - [i61]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 abs/2403.06009 (2024) - [i60]Maohao Shen, Subhro Das, Kristjan H. Greenewald, Prasanna Sattigeri, Gregory W. Wornell, Soumya Ghosh:
Thermometer: Towards Universal Calibration for Large Language Models. CoRR abs/2403.08819 (2024) - [i59]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 abs/2403.09704 (2024) - [i58]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 abs/2403.12805 (2024) - [i57]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 abs/2403.14459 (2024) - [i56]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 abs/2403.15115 (2024) - [i55]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 abs/2404.02806 (2024) - [i54]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 abs/2405.20434 (2024) - [i53]Tejaswini Pedapati, Amit Dhurandhar, Soumya Ghosh, Soham Dan, Prasanna Sattigeri:
Large Language Model Confidence Estimation via Black-Box Access. CoRR abs/2406.04370 (2024) - [i52]Burak Varici, Dmitriy A. Katz-Rogozhnikov, Dennis Wei, Prasanna Sattigeri, Ali Tajer:
Interventional Causal Discovery in a Mixture of DAGs. CoRR abs/2406.08666 (2024) - [i51]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 abs/2406.13805 (2024) - [i50]Manish Nagireddy, Inkit Padhi, Soumya Ghosh, Prasanna Sattigeri:
When in Doubt, Cascade: Towards Building Efficient and Capable Guardrails. CoRR abs/2407.06323 (2024) - 2023
- [j12]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): 565-567 (2023) - [c50]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: 6788-6796 - [c49]Maohao Shen, Yuheng Bu, Prasanna Sattigeri, Soumya Ghosh, Subhro Das, Gregory W. Wornell:
Post-hoc Uncertainty Learning Using a Dirichlet Meta-Model. AAAI 2023: 9772-9781 - [c48]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: 10520-10545 - [c47]Maohao Shen, Soumya Ghosh, Prasanna Sattigeri, Subhro Das, Yuheng Bu, Gregory W. Wornell:
Reliable Gradient-free and Likelihood-free Prompt Tuning. EACL (Findings) 2023: 2371-2384 - [c46]Brianna Richardson, Prasanna Sattigeri, Dennis Wei, Karthikeyan Natesan Ramamurthy, Kush R. Varshney, Amit Dhurandhar, Juan E. Gilbert:
Add-Remove-or-Relabel: Practitioner-Friendly Bias Mitigation via Influential Fairness. FAccT 2023: 736-752 - [c45]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 - [c44]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 - [i49]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 abs/2301.06197 (2023) - [i48]Abhin Shah, Maohao Shen, Jongha Jon Ryu, Subhro Das, Prasanna Sattigeri, Yuheng Bu, Gregory W. Wornell:
Group Fairness with Uncertainty in Sensitive Attributes. CoRR abs/2302.08077 (2023) - [i47]Maohao Shen, Soumya Ghosh, Prasanna Sattigeri, Subhro Das, Yuheng Bu, Gregory W. Wornell:
Reliable Gradient-free and Likelihood-free Prompt Tuning. CoRR abs/2305.00593 (2023) - [i46]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 abs/2305.09900 (2023) - [i45]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 abs/2305.14452 (2023) - [i44]Jirí Navrátil, Benjamin Elder, Matthew Arnold, Soumya Ghosh, Prasanna Sattigeri:
Assessment of Prediction Intervals Using Uncertainty Characteristics Curves. CoRR abs/2310.03158 (2023) - [i43]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 abs/2311.01007 (2023) - 2022
- [j11]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): 461 (2022) - [j10]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): 1-17 (2022) - [c43]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: 12651-12657 - [c42]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: 333-335 - [c41]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: 3523-3527 - [c40]Abhin Shah, Yuheng Bu, Joshua K. Lee, Subhro Das, Rameswar Panda, Prasanna Sattigeri, Gregory W. Wornell:
Selective Regression under Fairness Criteria. ICML 2022: 19598-19615 - [c39]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 - [c38]Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri, Kush R. Varshney:
Causal Feature Selection for Algorithmic Fairness. SIGMOD Conference 2022: 276-285 - [c37]Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer:
Intervention target estimation in the presence of latent variables. UAI 2022: 2013-2023 - [i42]Samuel C. Hoffman, Kahini Wadhawan, Payel Das, Prasanna Sattigeri, Karthikeyan Shanmugam:
Causal Graphs Underlying Generative Models: Path to Learning with Limited Data. CoRR abs/2207.07174 (2022) - [i41]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 abs/2208.05424 (2022) - [i40]Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer:
Causal Bandits for Linear Structural Equation Models. CoRR abs/2208.12764 (2022) - [i39]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 abs/2210.06475 (2022) - [i38]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 abs/2212.06803 (2022) - [i37]Maohao Shen, Yuheng Bu, Prasanna Sattigeri, Soumya Ghosh, Subhro Das, Gregory W. Wornell:
Post-hoc Uncertainty Learning using a Dirichlet Meta-Model. CoRR abs/2212.07359 (2022) - 2021
- [j9]Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri, Kush R. Varshney:
Interventional Fairness with Indirect Knowledge of Unobserved Protected Attributes. Entropy 23(12): 1571 (2021) - [c36]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: 1743-1753 - [c35]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: 401-413 - [c34]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: 376-379 - [c33]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: 1781-1792 - [c32]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 - [c31]Joshua K. Lee, Yuheng Bu, Deepta Rajan, Prasanna Sattigeri, Rameswar Panda, Subhro Das, Gregory W. Wornell:
Fair Selective Classification Via Sufficiency. ICML 2021: 6076-6086 - [c30]Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri, Yunfeng Zhang, Karthikeyan Shanmugam, Chun-Chen Tu:
Leveraging Latent Features for Local Explanations. KDD 2021: 1139-1149 - [c29]Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer:
Scalable Intervention Target Estimation in Linear Models. NeurIPS 2021: 1494-1505 - [c28]Kartik Ahuja, Prasanna Sattigeri, Karthikeyan Shanmugam, Dennis Wei, Karthikeyan Natesan Ramamurthy, Murat Kocaoglu:
Conditionally independent data generation. UAI 2021: 2050-2060 - [i36]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 abs/2102.05775 (2021) - [i35]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 abs/2104.09829 (2021) - [i34]Jirí Navrátil, Benjamin Elder, Matthew Arnold, Soumya Ghosh, Prasanna Sattigeri:
Uncertainty Characteristics Curves: A Systematic Assessment of Prediction Intervals. CoRR abs/2106.00858 (2021) - [i33]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 abs/2106.01410 (2021) - [i32]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 abs/2109.12151 (2021) - [i31]Abhin Shah, Yuheng Bu, Joshua Ka-Wing Lee, Subhro Das, Rameswar Panda, Prasanna Sattigeri, Gregory W. Wornell:
Selective Regression Under Fairness Criteria. CoRR abs/2110.15403 (2021) - [i30]Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer:
Scalable Intervention Target Estimation in Linear Models. CoRR abs/2111.07512 (2021) - 2020
- [j8]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: 130:1-130:6 (2020) - [c27]Jayaraman J. Thiagarajan, Bindya Venkatesh, Prasanna Sattigeri, Peer-Timo Bremer:
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors. AAAI 2020: 6005-6012 - [c26]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: 13369-13381 - [c25]Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Jayaraman J. Thiagarajan, Bhavya Kailkhura:
Treeview and Disentangled Representations for Explaining Deep Neural Networks Decisions. ACSSC 2020: 284-288 - [c24]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: 86-104 - [c23]Zhiqiang Tang, Yunhe Gao, Leonid Karlinsky, Prasanna Sattigeri, Rogério Feris, Dimitris N. Metaxas:
OnlineAugment: Online Data Augmentation with Less Domain Knowledge. ECCV (7) 2020: 313-329 - [c22]Moshe Lichtenstein, Prasanna Sattigeri, Rogério Feris, Raja Giryes, Leonid Karlinsky:
TAFSSL: Task-Adaptive Feature Sub-Space Learning for Few-Shot Classification. ECCV (7) 2020: 522-539 - [c21]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: 696 - [c20]Jayaraman J. Thiagarajan, Bindya Venkatesh, Deepta Rajan, Prasanna Sattigeri:
Improving Reliability of Clinical Models Using Prediction Calibration. UNSURE/GRAIL@MICCAI 2020: 71-80 - [c19]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: 320-329 - [c18]N. Joseph Tatro, Pin-Yu Chen, Payel Das, Igor Melnyk, Prasanna Sattigeri, Rongjie Lai:
Optimizing Mode Connectivity via Neuron Alignment. NeurIPS 2020 - [i29]Bindya Venkatesh, Jayaraman J. Thiagarajan, Kowshik Thopalli, Prasanna Sattigeri:
Calibrate and Prune: Improving Reliability of Lottery Tickets Through Prediction Calibration. CoRR abs/2002.03875 (2020) - [i28]Moshe Lichtenstein, Prasanna Sattigeri, Rogério Schmidt Feris, Raja Giryes, Leonid Karlinsky:
TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classification. CoRR abs/2003.06670 (2020) - [i27]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 abs/2003.06798 (2020) - [i26]Jayaraman J. Thiagarajan, Prasanna Sattigeri, Deepta Rajan, Bindya Venkatesh:
Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models. CoRR abs/2004.14480 (2020) - [i25]Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri, Kush R. Varshney:
Fair Data Integration. CoRR abs/2006.06053 (2020) - [i24]Zhiqiang Tang, Yunhe Gao, Leonid Karlinsky, Prasanna Sattigeri, Rogério Feris, Dimitris N. Metaxas:
OnlineAugment: Online Data Augmentation with Less Domain Knowledge. CoRR abs/2007.09271 (2020) - [i23]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 abs/2007.15796 (2020) - [i22]N. Joseph Tatro, Pin-Yu Chen, Payel Das, Igor Melnyk, Prasanna Sattigeri, Rongjie Lai:
Optimizing Mode Connectivity via Neuron Alignment. CoRR abs/2009.02439 (2020) - [i21]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 abs/2009.04433 (2020) - [i20]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 abs/2010.13187 (2020) - [i19]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 abs/2011.07586 (2020) - [i18]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 abs/2012.15259 (2020)
2010 – 2019
- 2019
- [j7]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): 3:1-3:9 (2019) - [j6]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): 4:1-4:15 (2019) - [j5]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): 76-80 (2019) - [c17]Joshua K. Lee, Prasanna Sattigeri, Gregory W. Wornell:
Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks. NeurIPS 2019: 4372-4382 - [i17]Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri, Karthikeyan Shanmugam, Chun-Chen Tu:
Generating Contrastive Explanations with Monotonic Attribute Functions. CoRR abs/1905.12698 (2019) - [i16]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 abs/1909.03012 (2019) - [i15]Jayaraman J. Thiagarajan, Bindya Venkatesh, Prasanna Sattigeri, Peer-Timo Bremer:
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors. CoRR abs/1909.04079 (2019) - [i14]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 abs/1910.13268 (2019) - [i13]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 abs/1911.07819 (2019) - 2018
- [j4]Huan Song, Jayaraman J. Thiagarajan, Prasanna Sattigeri, Andreas Spanias:
Optimizing Kernel Machines Using Deep Learning. IEEE Trans. Neural Networks Learn. Syst. 29(11): 5528-5540 (2018) - [c16]Yuanshuo Zhao, Ioana Baldini, Prasanna Sattigeri, Inkit Padhi, Yoong Keok Lee, Ethan Smith:
Data Driven Techniques for Organizing Scientific Articles Relevant to Biomimicry. AIES 2018: 347-353 - [c15]Abhishek Kumar, Prasanna Sattigeri, Avinash Balakrishnan:
Variational Inference of Disentangled Latent Concepts from Unlabeled Observations. ICLR (Poster) 2018 - [c14]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: 9367-9378 - [i12]Prasanna Sattigeri, Samuel C. Hoffman, Vijil Chenthamarakshan, Kush R. Varshney:
Fairness GAN. CoRR abs/1805.09910 (2018) - [i11]Jayaraman J. Thiagarajan, Deepta Rajan, Prasanna Sattigeri:
Can Deep Clinical Models Handle Real-World Domain Shifts? CoRR abs/1809.07806 (2018) - [i10]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, Understanding, and Mitigating Unwanted Algorithmic Bias. CoRR abs/1810.01943 (2018) - [i9]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 abs/1811.05443 (2018) - [i8]Vidya Muthukumar, Tejaswini Pedapati, Nalini K. Ratha, Prasanna Sattigeri, Chai-Wah Wu, Brian Kingsbury, Abhishek Kumar, Samuel Thomas, Aleksandra Mojsilovic, Kush R. Varshney:
Understanding Unequal Gender Classification Accuracy from Face Images. CoRR abs/1812.00099 (2018) - 2017
- [j3]Kien Pham, Prasanna Sattigeri, Amit Dhurandhar, A. C. Jacob, M. Vukovic, P. Chataigner, Juliana Freire, Aleksandra Mojsilovic, Kush R. Varshney:
Real-time understanding of humanitarian crises via targeted information retrieval. IBM J. Res. Dev. 61(6): 7:1-7:12 (2017) - [j2]Caitlin Kuhlman, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Aurélie C. Lozano, Lei Cao, C. Reddy, Aleksandra Mojsilovic, Kush R. Varshney:
How to foster innovation: A data-driven approach to measuring economic competitiveness. IBM J. Res. Dev. 61(6): 11:1-11:12 (2017) - [c13]Huan Song, Jayaraman J. Thiagarajan, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Andreas Spanias:
A deep learning approach to multiple kernel fusion. ICASSP 2017: 2292-2296 - [c12]Abhishek Kumar, Prasanna Sattigeri, Tom Fletcher:
Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference. NIPS 2017: 5534-5544 - [i7]Abhishek Kumar, Prasanna Sattigeri, P. Thomas Fletcher:
Improved Semi-supervised Learning with GANs using Manifold Invariances. CoRR abs/1705.08850 (2017) - [i6]Abhishek Kumar, Prasanna Sattigeri, Avinash Balakrishnan:
Variational Inference of Disentangled Latent Concepts from Unlabeled Observations. CoRR abs/1711.00848 (2017) - [i5]Huan Song, Jayaraman J. Thiagarajan, Prasanna Sattigeri, Andreas Spanias:
Optimizing Kernel Machines using Deep Learning. CoRR abs/1711.05374 (2017) - 2016
- [c11]