Acquiring and Classifying Signals from Nanopores and Ion-Channels. - Bharatan Konnanath; Prasanna Sattigeri; Trupthi Mathew; Andreas Spanias; Shalini Prasad; Michael Goryll; Trevor Thornton; Peter Knee (ICANN (2), 2009)

Analyte detection using an ion-channel sensor array. - Prasanna Sattigeri; Karthikeyan Natesan Ramamurthy; Jayaraman J. Thiagarajan; Michael Goryll; Andreas Spanias; Trevor Thornton (DSP, 2011)

Transform domain features for ion-channel signal classification. - Karthikeyan Natesan Ramamurthy; Jayaraman J. Thiagarajan; Prasanna Sattigeri; Michael Goryll; Andreas Spanias; Trevor Thornton; Stephen M. Phillips (Biomed. Signal Process. Control., 2011)

Learning dictionaries with graph embedding constraints. - Karthikeyan Natesan Ramamurthy; Jayaraman J. Thiagarajan; Prasanna Sattigeri; Andreas Spanias (ACSCC, 2012)

Implementation of a fast image coding and retrieval system using a GPU. - Prasanna Sattigeri; Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy; Andreas Spanias (ESPA, 2012)

Supervised local sparse coding of sub-image features for image retrieval. - Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy; Prasanna Sattigeri; Andreas Spanias (ICIP, 2012)

Boosted dictionaries for image restoration based on sparse representations. - Karthikeyan Natesan Ramamurthy; Jayaraman J. Thiagarajan; Andreas Spanias; Prasanna Sattigeri (ICASSP, 2013)

Ensemble Sparse Models for Image Analysis - Karthikeyan Natesan Ramamurthy; Jayaraman J. Thiagarajan; Prasanna Sattigeri; Andreas Spanias (CoRR, 2013)

A scalable feature learning and tag prediction framework for natural environment sounds. - Prasanna Sattigeri; Jayaraman J. Thiagarajan; Mohit Shah; Karthikeyan Natesan Ramamurthy; Andreas Spanias (ACSSC, 2014)

Automatic image annotation using inverse maps from semantic embeddings. - Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy; Prasanna Sattigeri; Peer-Timo Bremer; Andreas Spanias (ICIP, 2014)

Exploring Latent Structure in Data: Algorithms and Implementations. - Prasanna Sattigeri (2014)

Stable estimation of Granger-causal factors of country-level innovation. - Aurélie C. Lozano; Prasanna Sattigeri; Aleksandra Mojsilovic; Kush R. Varshney (GlobalSIP, 2016)

Robust Local Scaling Using Conditional Quantiles of Graph Similarities. - Jayaraman J. Thiagarajan; Prasanna Sattigeri; Karthikeyan Natesan Ramamurthy; Bhavya Kailkhura (ICDM Workshops, 2016)

Sparsifying Word Representations for Deep Unordered Sentence Modeling. - Prasanna Sattigeri; Jayaraman J. Thiagarajan (Rep4NLP@ACL, 2016)

Understanding Innovation to Drive Sustainable Development. - Prasanna Sattigeri; Aurélie C. Lozano; Aleksandra Mojsilovic; Kush R. Varshney; Mahmoud Naghshineh (CoRR, 2016)

TreeView: Peeking into Deep Neural Networks Via Feature-Space Partitioning. - Jayaraman J. Thiagarajan; Bhavya Kailkhura; Prasanna Sattigeri; Karthikeyan Natesan Ramamurthy (CoRR, 2016)

A deep learning approach to multiple kernel fusion. - Huan Song; Jayaraman J. Thiagarajan; Prasanna Sattigeri; Karthikeyan Natesan Ramamurthy; Andreas Spanias (ICASSP, 2017)

Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference. - Abhishek Kumar 001; Prasanna Sattigeri; Tom Fletcher (NIPS, 2017)

Improved Semi-supervised Learning with GANs using Manifold Invariances. - Abhishek Kumar 001; Prasanna Sattigeri; P. Thomas Fletcher (CoRR, 2017)

How to foster innovation: A data-driven approach to measuring economic competitiveness. - Caitlin Kuhlman; Karthikeyan Natesan Ramamurthy; Prasanna Sattigeri; Aurélie C. Lozano; Lei Cao 004; C. Reddy; Aleksandra Mojsilovic; Kush R. Varshney (IBM J. Res. Dev., 2017)

Real-time understanding of humanitarian crises via targeted information retrieval. - Kien Pham; Prasanna Sattigeri; Amit Dhurandhar; A. C. Jacob; M. Vukovic; P. Chataigner; Juliana Freire; Aleksandra Mojsilovic; Kush R. Varshney (IBM J. Res. Dev., 2017)

Data Driven Techniques for Organizing Scientific Articles Relevant to Biomimicry. - Yuanshuo Zhao; Ioana Baldini; Prasanna Sattigeri; Inkit Padhi; Yoong Keok Lee; Ethan Smith (AIES, 2018)

Variational Inference of Disentangled Latent Concepts from Unlabeled Observations. - Abhishek Kumar 001; Prasanna Sattigeri; Avinash Balakrishnan (ICLR (Poster), 2018)

Co-regularized Alignment for Unsupervised Domain Adaptation. - Abhishek Kumar 001; Prasanna Sattigeri; Kahini Wadhawan; Leonid Karlinsky; Rogério Schmidt Feris; Bill Freeman; Gregory W. Wornell (NeurIPS, 2018)

Fairness GAN. - Prasanna Sattigeri; Samuel C. Hoffman; Vijil Chenthamarakshan; Kush R. Varshney (CoRR, 2018)

Can Deep Clinical Models Handle Real-World Domain Shifts? - Jayaraman J. Thiagarajan; Deepta Rajan; Prasanna Sattigeri (CoRR, 2018)

AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias. - 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; et al. (CoRR, 2018)

Understanding Unequal Gender Classification Accuracy from Face Images. - Vidya Muthukumar; Tejaswini Pedapati; Nalini K. Ratha; Prasanna Sattigeri; Chai-Wah Wu; Brian Kingsbury; Abhishek Kumar 001; Samuel Thomas 001; Aleksandra Mojsilovic; Kush R. Varshney (CoRR, 2018)

Optimizing Kernel Machines Using Deep Learning. - Huan Song; Jayaraman J. Thiagarajan; Prasanna Sattigeri; Andreas Spanias (IEEE Trans. Neural Networks Learn. Syst., 2018)

Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks. - Joshua K. Lee; Prasanna Sattigeri; Gregory W. Wornell (NeurIPS, 2019)

Generating Contrastive Explanations with Monotonic Attribute Functions. - Ronny Luss; Pin-Yu Chen; Amit Dhurandhar; Prasanna Sattigeri; Karthikeyan Shanmugam; Chun-Chen Tu (CoRR, 2019)

One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques. - 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; et al. (CoRR, 2019)

Estimating Skin Tone and Effects on Classification Performance in Dermatology Datasets. - Newton M. Kinyanjui; Timothy Odonga; Celia Cintas; Noel C. F. Codella; Rameswar Panda; Prasanna Sattigeri; Kush R. Varshney (CoRR, 2019)

Drug Repurposing for Cancer: An NLP Approach to Identify Low-Cost Therapies. - 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 (CoRR, 2019)

AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias. - 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; et al. (IBM J. Res. Dev., 2019)

Fairness GAN: Generating datasets with fairness properties using a generative adversarial network. - Prasanna Sattigeri; Samuel C. Hoffman; Vijil Chenthamarakshan; Kush R. Varshney (IBM J. Res. Dev., 2019)

Think Your Artificial Intelligence Software Is Fair? Think Again. - 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; et al. (IEEE Softw., 2019)

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 (AAAI, 2020)

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

Treeview and Disentangled Representations for Explaining Deep Neural Networks Decisions. - Prasanna Sattigeri; Karthikeyan Natesan Ramamurthy; Jayaraman J. Thiagarajan; Bhavya Kailkhura (ACSSC, 2020)

OnlineAugment: Online Data Augmentation with Less Domain Knowledge. - Zhiqiang Tang 001; Yunhe Gao; Leonid Karlinsky; Prasanna Sattigeri; Rogério Feris; Dimitris N. Metaxas (ECCV (7), 2020)

TAFSSL: Task-Adaptive Feature Sub-Space Learning for Few-Shot Classification. - Moshe Lichtenstein; Prasanna Sattigeri; Rogério Feris; Raja Giryes; Leonid Karlinsky (ECCV (7), 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; Rogério Feris (ECCV (7), 2020)

AI explainability 360: hands-on tutorial. - 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; et al. (FAT*, 2020)

Fairness of Classifiers Across Skin Tones in Dermatology. - Newton M. Kinyanjui; Timothy Odonga; Celia Cintas; Noel C. F. Codella; Rameswar Panda; Prasanna Sattigeri; Kush R. Varshney (MICCAI (6), 2020)

Improving Reliability of Clinical Models Using Prediction Calibration. - Jayaraman J. Thiagarajan; Bindya Venkatesh; Deepta Rajan; Prasanna Sattigeri (UNSURE/GRAIL@MICCAI, 2020)

Optimizing Mode Connectivity via Neuron Alignment. - N. Joseph Tatro; Pin-Yu Chen; Payel Das; Igor Melnyk; Prasanna Sattigeri; Rongjie Lai (NeurIPS, 2020)

Calibrate and Prune: Improving Reliability of Lottery Tickets Through Prediction Calibration. - Bindya Venkatesh; Jayaraman J. Thiagarajan; Kowshik Thopalli; Prasanna Sattigeri (CoRR, 2020)

StarNet: towards weakly supervised few-shot detection and explainable few-shot classification. - Leonid Karlinsky; Joseph Shtok; Amit Alfassy; Moshe Lichtenstein; Sivan Harary; Eli Schwartz; Sivan Doveh; Prasanna Sattigeri; Rogério Schmidt Feris; Alexander M. Bronstein; Raja Giryes (CoRR, 2020)

Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models. - Jayaraman J. Thiagarajan; Prasanna Sattigeri; Deepta Rajan; Bindya Venkatesh (CoRR, 2020)

Fair Data Integration. - Sainyam Galhotra; Karthikeyan Shanmugam; Prasanna Sattigeri; Kush R. Varshney (CoRR, 2020)

not-so-BigGAN: Generating High-Fidelity Images on a Small Compute Budget. - Seungwook Han; Akash Srivastava; Cole L. Hurwitz; Prasanna Sattigeri; David D. Cox (CoRR, 2020)

Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling. - Akash Srivastava; Yamini Bansal; Yukun Ding; Cole L. Hurwitz; Kai Xu 016; Bernhard Egger 001; Prasanna Sattigeri; Josh Tenenbaum 001; David D. Cox; Dan Gutfreund (CoRR, 2020)

Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty. - 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; et al. (CoRR, 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 Mojsilovic; Sami Mourad; Pablo Pedemonte; et al. (J. Mach. Learn. Res., 2020)

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, 2021)

Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty. - 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; et al. (AIES, 2021)

AI Explainability 360 Toolkit. - 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; et al. (COMAD/CODS, 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 001; Alex M. Bronstein; et al. (ICCV, 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; Rogério Feris (ICLR, 2021)

Fair Selective Classification Via Sufficiency. - Joshua K. Lee; Yuheng Bu; Deepta Rajan; Prasanna Sattigeri; Rameswar Panda; Subhro Das; Gregory W. Wornell (ICML, 2021)

Leveraging Latent Features for Local Explanations. - Ronny Luss; Pin-Yu Chen; Amit Dhurandhar; Prasanna Sattigeri; Yunfeng Zhang; Karthikeyan Shanmugam; Chun-Chen Tu (KDD, 2021)

Scalable Intervention Target Estimation in Linear Models. - Burak Varici; Karthikeyan Shanmugam; Prasanna Sattigeri; Ali Tajer (NeurIPS, 2021)

Conditionally independent data generation. - Kartik Ahuja; Prasanna Sattigeri; Karthikeyan Shanmugam; Dennis Wei; Karthikeyan Natesan Ramamurthy; Murat Kocaoglu (UAI, 2021)

Uncertainty Characteristics Curves: A Systematic Assessment of Prediction Intervals. - Jirí Navrátil 001; Benjamin Elder; Matthew Arnold; Soumya Ghosh; Prasanna Sattigeri (CoRR, 2021)

Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI. - Soumya Ghosh; Q. Vera Liao; Karthikeyan Natesan Ramamurthy; Jirí Navrátil 001; Prasanna Sattigeri; Kush R. Varshney; Yunfeng Zhang (CoRR, 2021)

Interventional Fairness with Indirect Knowledge of Unobserved Protected Attributes. - Sainyam Galhotra; Karthikeyan Shanmugam; Prasanna Sattigeri; Kush R. Varshney (Entropy, 2021)

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; et al. (AAAI, 2022)

Uncertainty Quantification 360: A Hands-on Tutorial. - Soumya Ghosh; Q. Vera Liao; Karthikeyan Natesan Ramamurthy; Jirí Navrátil 001; Prasanna Sattigeri; Kush R. Varshney; Yunfeng Zhang (COMAD/CODS, 2022)

A Maximal Correlation Approach to Imposing Fairness in Machine Learning. - Joshua K. Lee; Yuheng Bu; Prasanna Sattigeri; Rameswar Panda; Gregory W. Wornell; Leonid Karlinsky; Rogério Feris (ICASSP, 2022)

Selective Regression under Fairness Criteria. - Abhin Shah; Yuheng Bu; Joshua K. Lee; Subhro Das; Rameswar Panda; Prasanna Sattigeri; Gregory W. Wornell (ICML, 2022)

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

Causal Feature Selection for Algorithmic Fairness. - Sainyam Galhotra; Karthikeyan Shanmugam; Prasanna Sattigeri; Kush R. Varshney (SIGMOD Conference, 2022)

Intervention target estimation in the presence of latent variables. - Burak Varici; Karthikeyan Shanmugam; Prasanna Sattigeri; Ali Tajer (UAI, 2022)

Causal Graphs Underlying Generative Models: Path to Learning with Limited Data. - Samuel C. Hoffman; Kahini Wadhawan; Payel Das; Prasanna Sattigeri; Karthikeyan Shanmugam (CoRR, 2022)

Generating physically-consistent high-resolution climate data with hard-constrained neural networks. - Paula Harder; Qidong Yang; Venkatesh Ramesh; Prasanna Sattigeri; Alex Hernández-García; Campbell D. Watson; Daniela Szwarcman; David S. Rolnick (CoRR, 2022)

A Maximal Correlation Framework for Fair Machine Learning. - Joshua K. Lee; Yuheng Bu; Prasanna Sattigeri; Rameswar Panda; Gregory W. Wornell; Leonid Karlinsky; Rogério Schmidt Feris (Entropy, 2022)

Instruction Tools for Signal Processing and Machine Learning for Ion-Channel Sensors. - 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; et al. (Int. J. Virtual Pers. Learn. Environ., 2022)

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 (AAAI, 2023)

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

Who Should Predict? Exact Algorithms For Learning to Defer to Humans. - Hussein Mozannar; Hunter Lang; Dennis Wei; Prasanna Sattigeri; Subhro Das; David A. Sontag (AISTATS, 2023)

Reliable Gradient-free and Likelihood-free Prompt Tuning. - Maohao Shen; Soumya Ghosh; Prasanna Sattigeri; Subhro Das; Yuheng Bu; Gregory W. Wornell (EACL (Findings), 2023)

Add-Remove-or-Relabel: Practitioner-Friendly Bias Mitigation via Influential Fairness. - Brianna Richardson; Prasanna Sattigeri; Dennis Wei; Karthikeyan Natesan Ramamurthy; Kush R. Varshney; Amit Dhurandhar; Juan E. Gilbert (FAccT, 2023)

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

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

Group Fairness with Uncertainty in Sensitive Attributes. - Abhin Shah; Maohao Shen; Jongha Jon Ryu; Subhro Das; Prasanna Sattigeri; Yuheng Bu; Gregory W. Wornell (CoRR, 2023)

Equivariant Few-Shot Learning from Pretrained Models. - Sourya Basu; Pulkit Katdare; Prasanna Sattigeri; Vijil Chenthamarakshan; Katherine Rose Driggs-Campbell; Payel Das; Lav R. Varshney (CoRR, 2023)

Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling. - Qidong Yang; Alex Hernández-García; Paula Harder; Venkatesh Ramesh; Prasanna Sattigeri; Daniela Szwarcman; Campbell D. Watson; David Rolnick (CoRR, 2023)

Assessment of Prediction Intervals Using Uncertainty Characteristics Curves. - Jirí Navrátil 001; Benjamin Elder; Matthew Arnold; Soumya Ghosh; Prasanna Sattigeri (CoRR, 2023)

Causal Bandits for Linear Structural Equation Models. - Burak Varici; Karthikeyan Shanmugam; Prasanna Sattigeri; Ali Tajer (J. Mach. Learn. Res., 2023)

The incentive gap in data work in the era of large models. - Katy Ilonka Gero; Payel Das; Pierre L. Dognin; Inkit Padhi; Prasanna Sattigeri; Kush R. Varshney (Nat. Mac. Intell., 2023)

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

Language Models in Dialogue: Conversational Maxims for Human-AI Interactions. - Erik Miehling; Manish Nagireddy; Prasanna Sattigeri; Elizabeth Daly; David Piorkowski; John T. Richards (EMNLP (Findings), 2024)

Value Alignment from Unstructured Text. - Inkit Padhi; Karthikeyan Natesan Ramamurthy; Prasanna Sattigeri; Manish Nagireddy; Pierre L. Dognin; Kush R. Varshney (EMNLP (Industry Track), 2024)

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

ComVas: Contextual Moral Values Alignment System. - Inkit Padhi; Pierre L. Dognin; Jesus Rios; Ronny Luss; Swapnaja Achintalwar; Matthew Riemer; Miao Liu 001; Prasanna Sattigeri; Manish Nagireddy; Kush R. Varshney; Djallel Bouneffouf 001 (IJCAI, 2024)

Group Fairness with Uncertain Sensitive Attributes. - Abhin Shah; Maohao Shen; Jongha Jon Ryu; Subhro Das; Prasanna Sattigeri; Yuheng Bu; Gregory W. Wornell (ISIT, 2024)

WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia. - Yufang Hou 001; Alessandra Pascale; Javier Carnerero-Cano; Tigran T. Tchrakian; Radu Marinescu 002; Elizabeth Daly; Inkit Padhi; Prasanna Sattigeri (NeurIPS, 2024)

Graph-based Uncertainty Metrics for Long-form Language Model Generations. - Mingjian Jiang; Yangjun Ruan; Prasanna Sattigeri; Salim Roukos; Tatsunori B. Hashimoto (NeurIPS, 2024)

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

Interventional Causal Discovery in a Mixture of DAGs. - Burak Varici; Dmitriy Katz; Dennis Wei; Prasanna Sattigeri; Ali Tajer (NeurIPS, 2024)

Improved Evidential Deep Learning via a Mixture of Dirichlet Distributions. - Jongha Jon Ryu; Maohao Shen; Soumya Ghosh; Yuheng Bu; Prasanna Sattigeri; Subhro Das; Gregory W. Wornell (CoRR, 2024)

Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations. - Swapnaja Achintalwar; Adriana Alvarado Garcia; Ateret Anaby-Tavor; Ioana Baldini; Sara E. Berger; Bishwaranjan Bhattacharjee; Djallel Bouneffouf 001; Subhajit Chaudhury; Pin-Yu Chen; Lamogha Chiazor; Elizabeth M. Daly; Rogério Abreu de Paula; et al. (CoRR, 2024)

Contextual Moral Value Alignment Through Context-Based Aggregation. - Pierre L. Dognin; Jesus Rios; Ronny Luss; Inkit Padhi; Matthew D. Riemer; Miao Liu 001; Prasanna Sattigeri; Manish Nagireddy; Kush R. Varshney; Djallel Bouneffouf 001 (CoRR, 2024)

Facilitating Human-LLM Collaboration through Factuality Scores and Source Attributions. - Hyo Jin Do; Rachel Ostrand; Justin D. Weisz; Casey Dugan; Prasanna Sattigeri; Dennis Wei; Keerthiram Murugesan; Werner Geyer (CoRR, 2024)

When in Doubt, Cascade: Towards Building Efficient and Capable Guardrails. - Manish Nagireddy; Inkit Padhi; Soumya Ghosh; Prasanna Sattigeri (CoRR, 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; et al. (CoRR, 2024)

Graph-based Uncertainty Metrics for Long-form Language Model Outputs. - Mingjian Jiang; Yangjun Ruan; Prasanna Sattigeri; Salim Roukos; Tatsunori Hashimoto (CoRR, 2024)

Granite Guardian. - Inkit Padhi; Manish Nagireddy; Giandomenico Cornacchia; Subhajit Chaudhury; Tejaswini Pedapati; Pierre L. Dognin; Keerthiram Murugesan; Erik Miehling; Martin Santillan Cooper; Kieran Fraser; Giulio Zizzo; Muhammad Zaid Hameed; et al. (CoRR, 2024)

Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations. - Swapnaja Achintalwar; Ioana Baldini; Djallel Bouneffouf 001; Joan Byamugisha; Maria Chang 001; Pierre L. Dognin; Eitan Farchi; Ndivhuwo Makondo; Aleksandra Mojsilovic; Manish Nagireddy; Karthikeyan Natesan Ramamurthy; Inkit Padhi; et al. (IEEE Internet Comput., 2024)

Attribute Graphs Underlying Molecular Generative Models: Path to Learning with Limited Data. - Samuel C. Hoffman; Payel Das; Karthikeyan Shanmugam; Kahini Wadhawan; Prasanna Sattigeri (Trans. Mach. Learn. Res., 2024)

Separability Analysis for Causal Discovery in Mixture of DAGs. - Burak Varici; Dmitriy Katz; Dennis Wei; Prasanna Sattigeri; Ali Tajer (Trans. Mach. Learn. Res., 2024)

Multi-Level Explanations for Generative Language Models. - 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 (ACL (1), 2025)

Contextual Value Alignment. - Pierre Dognin; Jesus Rios; Ronny Luss; Prasanna Sattigeri; Miao Liu 001; Inkit Padhi; Matthew Riemer; Manish Nagireddy; Kush R. Varshney; Djallel Bouneffouf 001 (ICASSP, 2025)

Evaluating the Prompt Steerability of Large Language Models. - Erik Miehling; Michael Desmond; Karthikeyan Natesan Ramamurthy; Elizabeth M. Daly; Kush R. Varshney; Eitan Farchi; Pierre Dognin; Jesus Rios; Djallel Bouneffouf 001; Miao Liu 001; Prasanna Sattigeri (NAACL (Long Papers), 2025)

Granite Guardian: Comprehensive LLM Safeguarding. - Inkit Padhi; Manish Nagireddy; Giandomenico Cornacchia; Subhajit Chaudhury; Tejaswini Pedapati; Pierre L. Dognin; Keerthiram Murugesan; Erik Miehling; Martín Santillán Cooper; Kieran Fraser; Giulio Zizzo; Muhammad Zaid Hameed; et al. (NAACL (Industry Track), 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 A. Sontag (Trans. Mach. Learn. Res., 2025)

Large Language Model Confidence Estimation via Black-Box Access. - Tejaswini Pedapati; Amit Dhurandhar; Soumya Ghosh; Soham Dan; Prasanna Sattigeri (Trans. Mach. Learn. Res., 2025)

On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective. - Yue Huang 001; Chujie Gao; Siyuan Wu 001; Haoran Wang 005; Xiangqi Wang; Yujun Zhou 010; Yanbo Wang 005; Jiayi Ye; Jiawen Shi; Qihui Zhang; Yuan Li 032; Han Bao 003; et al. (CoRR, 2025)

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 (CoRR, 2025)

Agentic AI Needs a Systems Theory. - Erik Miehling; Karthikeyan Natesan Ramamurthy; Kush R. Varshney; Matthew Riemer; Djallel Bouneffouf 001; John T. Richards; Amit Dhurandhar; Elizabeth M. Daly; Michael Hind; Prasanna Sattigeri; Dennis Wei; Ambrish Rawat; et al. (CoRR, 2025)