Search Results for author: Prasanna Sattigeri

Found 52 papers, 20 papers with code

Assessment of Prediction Intervals Using Uncertainty Characteristics Curves

no code implementations4 Oct 2023 Jiri Navratil, Benjamin Elder, Matthew Arnold, Soumya Ghosh, Prasanna Sattigeri

Accurate quantification of model uncertainty has long been recognized as a fundamental requirement for trusted AI.

Prediction Intervals

Reliable Gradient-free and Likelihood-free Prompt Tuning

1 code implementation30 Apr 2023 Maohao Shen, Soumya Ghosh, Prasanna Sattigeri, Subhro Das, Yuheng Bu, Gregory Wornell

Due to privacy or commercial constraints, large pre-trained language models (PLMs) are often offered as black-box APIs.

Group Fairness with Uncertainty in Sensitive Attributes

no code implementations16 Feb 2023 Abhin Shah, Maohao Shen, Jongha Jon Ryu, Subhro Das, Prasanna Sattigeri, Yuheng Bu, Gregory W. Wornell

To overcome this limitation, we propose a bootstrap-based algorithm that achieves the target level of fairness despite the uncertainty in sensitive attributes.


Who Should Predict? Exact Algorithms For Learning to Defer to Humans

1 code implementation15 Jan 2023 Hussein Mozannar, Hunter Lang, Dennis Wei, Prasanna Sattigeri, Subhro Das, David Sontag

We show that prior approaches can fail to find a human-AI system with low misclassification error even when there exists a linear classifier and rejector that have zero error (the realizable setting).

Post-hoc Uncertainty Learning using a Dirichlet Meta-Model

1 code implementation14 Dec 2022 Maohao Shen, Yuheng Bu, Prasanna Sattigeri, Soumya Ghosh, Subhro Das, Gregory Wornell

It is known that neural networks have the problem of being over-confident when directly using the output label distribution to generate uncertainty measures.

Image Classification Transfer Learning

Causal Bandits for Linear Structural Equation Models

1 code implementation26 Aug 2022 Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer

Two linear mechanisms, one soft intervention and one observational, are assumed for each node, giving rise to $2^N$ possible interventions.

Thompson Sampling

Physics-Constrained Deep Learning for Climate Downscaling

1 code implementation8 Aug 2022 Paula Harder, Alex Hernandez-Garcia, Venkatesh Ramesh, Qidong Yang, Prasanna Sattigeri, Daniela Szwarcman, Campbell Watson, David Rolnick

In order to conserve physical quantities, here we introduce methods that guarantee statistical constraints are satisfied by a deep learning downscaling model while also improving their performance according to traditional metrics.


Causal Graphs Underlying Generative Models: Path to Learning with Limited Data

no code implementations14 Jul 2022 Samuel C. Hoffman, Kahini Wadhawan, Payel Das, Prasanna Sattigeri, Karthikeyan Shanmugam

In this work, we provide a simple algorithm that relies on perturbation experiments on latent codes of a pre-trained generative autoencoder to uncover a causal graph that is implied by the generative model.

A Maximal Correlation Framework for Fair Machine Learning

no code implementations Entropy 2022 Joshua Lee, Yuheng Bu, Prasanna Sattigeri, Rameswar Panda, Gregory W. Wornell, Leonid Karlinsky and Rogerio Schmidt Feris

As machine learning algorithms grow in popularity and diversify to many industries, ethical and legal concerns regarding their fairness have become increasingly relevant.


Scalable Intervention Target Estimation in Linear Models

1 code implementation NeurIPS 2021 Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer

This paper considers the problem of estimating the unknown intervention targets in a causal directed acyclic graph from observational and interventional data.

Selective Regression Under Fairness Criteria

1 code implementation28 Oct 2021 Abhin Shah, Yuheng Bu, Joshua Ka-Wing Lee, Subhro Das, Rameswar Panda, Prasanna Sattigeri, Gregory W. Wornell

Selective regression allows abstention from prediction if the confidence to make an accurate prediction is not sufficient.

Fairness regression

Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI

1 code implementation2 Jun 2021 Soumya Ghosh, Q. Vera Liao, Karthikeyan Natesan Ramamurthy, Jiri Navratil, Prasanna Sattigeri, Kush R. Varshney, Yunfeng Zhang

In this paper, we describe an open source Python toolkit named Uncertainty Quantification 360 (UQ360) for the uncertainty quantification of AI models.


A Maximal Correlation Approach to Imposing Fairness in Machine Learning

no code implementations30 Dec 2020 Joshua Lee, Yuheng Bu, Prasanna Sattigeri, Rameswar Panda, Gregory Wornell, Leonid Karlinsky, Rogerio Feris

As machine learning algorithms grow in popularity and diversify to many industries, ethical and legal concerns regarding their fairness have become increasingly relevant.

BIG-bench Machine Learning Fairness

Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling

no code implementations25 Oct 2020 Akash Srivastava, Yamini Bansal, Yukun Ding, Cole Hurwitz, Kai Xu, Bernhard Egger, Prasanna Sattigeri, Josh Tenenbaum, David D. Cox, Dan Gutfreund

Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the (aggregate) posterior to encourage statistical independence of the latent factors.


Optimizing Mode Connectivity via Neuron Alignment

1 code implementation NeurIPS 2020 N. Joseph Tatro, Pin-Yu Chen, Payel Das, Igor Melnyk, Prasanna Sattigeri, Rongjie Lai

Yet, current curve finding algorithms do not consider the influence of symmetry in the loss surface created by model weight permutations.

AR-Net: Adaptive Frame Resolution for Efficient Action Recognition

1 code implementation ECCV 2020 Yue Meng, Chung-Ching Lin, Rameswar Panda, Prasanna Sattigeri, Leonid Karlinsky, Aude Oliva, Kate Saenko, Rogerio Feris

Specifically, given a video frame, a policy network is used to decide what input resolution should be used for processing by the action recognition model, with the goal of improving both accuracy and efficiency.

Action Recognition

Causal Feature Selection for Algorithmic Fairness

no code implementations10 Jun 2020 Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri, Kush R. Varshney

In this work, we consider fairness in the integration component of data management, aiming to identify features that improve prediction without adding any bias to the dataset.

Data Integration Fairness +2

Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models

no code implementations27 Apr 2020 Jayaraman J. Thiagarajan, Prasanna Sattigeri, Deepta Rajan, Bindya Venkatesh

The wide-spread adoption of representation learning technologies in clinical decision making strongly emphasizes the need for characterizing model reliability and enabling rigorous introspection of model behavior.

counterfactual Counterfactual Reasoning +3

TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classification

1 code implementation ECCV 2020 Moshe Lichtenstein, Prasanna Sattigeri, Rogerio Feris, Raja Giryes, Leonid Karlinsky

The field of Few-Shot Learning (FSL), or learning from very few (typically $1$ or $5$) examples per novel class (unseen during training), has received a lot of attention and significant performance advances in the recent literature.

Few-Shot Learning General Classification

Calibrate and Prune: Improving Reliability of Lottery Tickets Through Prediction Calibration

no code implementations10 Feb 2020 Bindya Venkatesh, Jayaraman J. Thiagarajan, Kowshik Thopalli, Prasanna Sattigeri

The hypothesis that sub-network initializations (lottery) exist within the initializations of over-parameterized networks, which when trained in isolation produce highly generalizable models, has led to crucial insights into network initialization and has enabled efficient inferencing.

Transfer Learning


no code implementations ICLR 2020 Akash Srivastava, Yamini Bansal, Yukun Ding, Bernhard Egger, Prasanna Sattigeri, Josh Tenenbaum, David D. Cox, Dan Gutfreund

In this work, we tackle a slightly more intricate scenario where the observations are generated from a conditional distribution of some known control variate and some latent noise variate.


Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks

no code implementations NeurIPS 2019 Joshua Lee, Prasanna Sattigeri, Gregory Wornell

However, for practical, privacy, or other reasons, in a variety of applications we may have no control over the individual source task training, nor access to source training samples.

Transfer Learning

Estimating Skin Tone and Effects on Classification Performance in Dermatology Datasets

no code implementations29 Oct 2019 Newton M. Kinyanjui, Timothy Odonga, Celia Cintas, Noel C. F. Codella, Rameswar Panda, Prasanna Sattigeri, Kush R. Varshney

We find that the majority of the data in the the two datasets have ITA values between 34. 5{\deg} and 48{\deg}, which are associated with lighter skin, and is consistent with under-representation of darker skinned populations in these datasets.

BIG-bench Machine Learning General Classification +1

Optimizing Loss Landscape Connectivity via Neuron Alignment

no code implementations25 Sep 2019 N. Joseph Tatro, Pin-Yu Chen, Payel Das, Igor Melnyk, Prasanna Sattigeri, Rongjie Lai

Empirically, this initialization is critical for efficiently learning a simple, planar, low-loss curve between networks that successfully generalizes.

Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors

1 code implementation9 Sep 2019 Jayaraman J. Thiagarajan, Bindya Venkatesh, Prasanna Sattigeri, Peer-Timo Bremer

With rapid adoption of deep learning in critical applications, the question of when and how much to trust these models often arises, which drives the need to quantify the inherent uncertainties.

Object Localization Prediction Intervals +2

Leveraging Latent Features for Local Explanations

2 code implementations29 May 2019 Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri, Yunfeng Zhang, Karthikeyan Shanmugam, Chun-Chen Tu

As the application of deep neural networks proliferates in numerous areas such as medical imaging, video surveillance, and self driving cars, the need for explaining the decisions of these models has become a hot research topic, both at the global and local level.

General Classification Open-Ended Question Answering +1

Understanding Unequal Gender Classification Accuracy from Face Images

no code implementations30 Nov 2018 Vidya Muthukumar, Tejaswini Pedapati, Nalini Ratha, Prasanna Sattigeri, Chai-Wah Wu, Brian Kingsbury, Abhishek Kumar, Samuel Thomas, Aleksandra Mojsilovic, Kush R. Varshney

Recent work shows unequal performance of commercial face classification services in the gender classification task across intersectional groups defined by skin type and gender.

Classification Gender Classification +1

Co-regularized Alignment for Unsupervised Domain Adaptation

no code implementations NeurIPS 2018 Abhishek Kumar, Prasanna Sattigeri, Kahini Wadhawan, Leonid Karlinsky, Rogerio Feris, William T. Freeman, Gregory Wornell

Deep neural networks, trained with large amount of labeled data, can fail to generalize well when tested with examples from a \emph{target domain} whose distribution differs from the training data distribution, referred as the \emph{source domain}.

Unsupervised Domain Adaptation

Understanding Behavior of Clinical Models under Domain Shifts

no code implementations20 Sep 2018 Jayaraman J. Thiagarajan, Deepta Rajan, Prasanna Sattigeri

The hypothesis that computational models can be reliable enough to be adopted in prognosis and patient care is revolutionizing healthcare.

Multi-Label Classification Unsupervised Domain Adaptation

Fairness GAN

no code implementations24 May 2018 Prasanna Sattigeri, Samuel C. Hoffman, Vijil Chenthamarakshan, Kush R. Varshney

In this paper, we introduce the Fairness GAN, an approach for generating a dataset that is plausibly similar to a given multimedia dataset, but is more fair with respect to protected attributes in allocative decision making.

Decision Making Fairness

Optimizing Kernel Machines using Deep Learning

no code implementations15 Nov 2017 Huan Song, Jayaraman J. Thiagarajan, Prasanna Sattigeri, Andreas Spanias

To this end, we develop the DKMO (Deep Kernel Machine Optimization) framework, that creates an ensemble of dense embeddings using Nystrom kernel approximations and utilizes deep learning to generate task-specific representations through the fusion of the embeddings.

Variational Inference of Disentangled Latent Concepts from Unlabeled Observations

2 code implementations ICLR 2018 Abhishek Kumar, Prasanna Sattigeri, Avinash Balakrishnan

Disentangled representations, where the higher level data generative factors are reflected in disjoint latent dimensions, offer several benefits such as ease of deriving invariant representations, transferability to other tasks, interpretability, etc.

Disentanglement Variational Inference

A Deep Learning Approach To Multiple Kernel Fusion

no code implementations28 Dec 2016 Huan Song, Jayaraman J. Thiagarajan, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Andreas Spanias

Kernel fusion is a popular and effective approach for combining multiple features that characterize different aspects of data.

Activity Recognition

TreeView: Peeking into Deep Neural Networks Via Feature-Space Partitioning

no code implementations22 Nov 2016 Jayaraman J. Thiagarajan, Bhavya Kailkhura, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy

In this paper, we take a step in the direction of tackling the problem of interpretability without compromising the model accuracy.

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