Prasanna Sattigeri

Orcid: 0000-0003-4435-0486

According to our database1, Prasanna Sattigeri authored at least 92 papers between 2009 and 2024.

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Bibliography

2024
Improved Evidential Deep Learning via a Mixture of Dirichlet Distributions.
CoRR, 2024

2023
The incentive gap in data work in the era of large models.
Nat. Mac. Intell., June, 2023

Assessment of Prediction Intervals Using Uncertainty Characteristics Curves.
CoRR, 2023

Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling.
CoRR, 2023

Equivariant Few-Shot Learning from Pretrained Models.
CoRR, 2023

Group Fairness with Uncertainty in Sensitive Attributes.
CoRR, 2023

Effective Human-AI Teams via Learned Natural Language Rules and Onboarding.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Efficient Equivariant Transfer Learning from Pretrained Models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Add-Remove-or-Relabel: Practitioner-Friendly Bias Mitigation via Influential Fairness.
Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023

Reliable Gradient-free and Likelihood-free Prompt Tuning.
Proceedings of the Findings of the Association for Computational Linguistics: EACL 2023, 2023

Who Should Predict? Exact Algorithms For Learning to Defer to Humans.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Post-hoc Uncertainty Learning Using a Dirichlet Meta-Model.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

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

A Maximal Correlation Framework for Fair Machine Learning.
Entropy, 2022

Causal Bandits for Linear Structural Equation Models.
CoRR, 2022

Generating physically-consistent high-resolution climate data with hard-constrained neural networks.
CoRR, 2022

Causal Graphs Underlying Generative Models: Path to Learning with Limited Data.
CoRR, 2022

Intervention target estimation in the presence of latent variables.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Causal Feature Selection for Algorithmic Fairness.
Proceedings of the SIGMOD '22: International Conference on Management of Data, Philadelphia, PA, USA, June 12, 2022

Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Selective Regression under Fairness Criteria.
Proceedings of the International Conference on Machine Learning, 2022

A Maximal Correlation Approach to Imposing Fairness in Machine Learning.
Proceedings of the IEEE International Conference on Acoustics, 2022

Uncertainty Quantification 360: A Hands-on Tutorial.
Proceedings of the CODS-COMAD 2022: 5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD), Bangalore, India, January 8, 2022


2021
Interventional Fairness with Indirect Knowledge of Unobserved Protected Attributes.
Entropy, 2021

Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI.
CoRR, 2021

Uncertainty Characteristics Curves: A Systematic Assessment of Prediction Intervals.
CoRR, 2021

Conditionally independent data generation.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Scalable Intervention Target Estimation in Linear Models.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Leveraging Latent Features for Local Explanations.
Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Fair Selective Classification Via Sufficiency.
Proceedings of the 38th International Conference on Machine Learning, 2021

AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition.
Proceedings of the 9th International Conference on Learning Representations, 2021

Detector-Free Weakly Supervised Grounding by Separation.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021


Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty.
Proceedings of the AIES '21: AAAI/ACM Conference on AI, 2021

StarNet: towards Weakly Supervised Few-Shot Object Detection.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models.
J. Mach. Learn. Res., 2020

Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty.
CoRR, 2020

Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling.
CoRR, 2020

not-so-BigGAN: Generating High-Fidelity Images on a Small Compute Budget.
CoRR, 2020

Fair Data Integration.
CoRR, 2020

Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models.
CoRR, 2020

StarNet: towards weakly supervised few-shot detection and explainable few-shot classification.
CoRR, 2020

Calibrate and Prune: Improving Reliability of Lottery Tickets Through Prediction Calibration.
CoRR, 2020

Optimizing Mode Connectivity via Neuron Alignment.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Improving Reliability of Clinical Models Using Prediction Calibration.
Proceedings of the Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis, 2020

Fairness of Classifiers Across Skin Tones in Dermatology.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020


AR-Net: Adaptive Frame Resolution for Efficient Action Recognition.
Proceedings of the Computer Vision - ECCV 2020, 2020

TAFSSL: Task-Adaptive Feature Sub-Space Learning for Few-Shot Classification.
Proceedings of the Computer Vision - ECCV 2020, 2020

OnlineAugment: Online Data Augmentation with Less Domain Knowledge.
Proceedings of the Computer Vision - ECCV 2020, 2020

Treeview and Disentangled Representations for Explaining Deep Neural Networks Decisions.
Proceedings of the 54th Asilomar Conference on Signals, Systems, and Computers, 2020

Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

A Natural Language Processing System for Extracting Evidence of Drug Repurposing from Scientific Publications.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Think Your Artificial Intelligence Software Is Fair? Think Again.
IEEE Softw., 2019

Fairness GAN: Generating datasets with fairness properties using a generative adversarial network.
IBM J. Res. Dev., 2019

AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias.
IBM J. Res. Dev., 2019

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

Estimating Skin Tone and Effects on Classification Performance in Dermatology Datasets.
CoRR, 2019

One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques.
CoRR, 2019

Generating Contrastive Explanations with Monotonic Attribute Functions.
CoRR, 2019

Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018
Optimizing Kernel Machines Using Deep Learning.
IEEE Trans. Neural Networks Learn. Syst., 2018

Understanding Unequal Gender Classification Accuracy from Face Images.
CoRR, 2018

AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias.
CoRR, 2018

Can Deep Clinical Models Handle Real-World Domain Shifts?
CoRR, 2018

Fairness GAN.
CoRR, 2018

Co-regularized Alignment for Unsupervised Domain Adaptation.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Variational Inference of Disentangled Latent Concepts from Unlabeled Observations.
Proceedings of the 6th International Conference on Learning Representations, 2018

Data Driven Techniques for Organizing Scientific Articles Relevant to Biomimicry.
Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 2018

2017
Real-time understanding of humanitarian crises via targeted information retrieval.
IBM J. Res. Dev., 2017

How to foster innovation: A data-driven approach to measuring economic competitiveness.
IBM J. Res. Dev., 2017

Improved Semi-supervised Learning with GANs using Manifold Invariances.
CoRR, 2017

Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

A deep learning approach to multiple kernel fusion.
Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

2016
TreeView: Peeking into Deep Neural Networks Via Feature-Space Partitioning.
CoRR, 2016

Understanding Innovation to Drive Sustainable Development.
CoRR, 2016

Sparsifying Word Representations for Deep Unordered Sentence Modeling.
Proceedings of the 1st Workshop on Representation Learning for NLP, 2016

Robust Local Scaling Using Conditional Quantiles of Graph Similarities.
Proceedings of the IEEE International Conference on Data Mining Workshops, 2016

Stable estimation of Granger-causal factors of country-level innovation.
Proceedings of the 2016 IEEE Global Conference on Signal and Information Processing, 2016

2014
Exploring Latent Structure in Data: Algorithms and Implementations.
PhD thesis, 2014

Automatic image annotation using inverse maps from semantic embeddings.
Proceedings of the 2014 IEEE International Conference on Image Processing, 2014

A scalable feature learning and tag prediction framework for natural environment sounds.
Proceedings of the 48th Asilomar Conference on Signals, Systems and Computers, 2014

2013
Ensemble Sparse Models for Image Analysis
CoRR, 2013

Boosted dictionaries for image restoration based on sparse representations.
Proceedings of the IEEE International Conference on Acoustics, 2013

2012
Supervised local sparse coding of sub-image features for image retrieval.
Proceedings of the 19th IEEE International Conference on Image Processing, 2012

Implementation of a fast image coding and retrieval system using a GPU.
Proceedings of the 2012 IEEE International Conference on Emerging Signal Processing Applications, 2012

Learning dictionaries with graph embedding constraints.
Proceedings of the Conference Record of the Forty Sixth Asilomar Conference on Signals, 2012

2011
Transform domain features for ion-channel signal classification.
Biomed. Signal Process. Control., 2011

Analyte detection using an ion-channel sensor array.
Proceedings of the 17th International Conference on Digital Signal Processing, 2011

2009
Acquiring and Classifying Signals from Nanopores and Ion-Channels.
Proceedings of the Artificial Neural Networks, 2009


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