b'Ramesh Raskar'

research

∙ 05/28/2023

Conformal Prediction with Large Language Models for Multi-Choice Question Answering

As large language models continue to be widely developed, robust uncerta...

0 Bhawesh Kumar, et al. ∙

research

∙ 05/27/2023

Federated Conformal Predictors for Distributed Uncertainty Quantification

Conformal prediction is emerging as a popular paradigm for providing rig...

0 Charles Lu, et al. ∙

research

∙ 04/07/2023

Domain Generalization In Robust Invariant Representation

Unsupervised approaches for learning representations invariant to common...

0 Gauri Gupta, et al. ∙

research

∙ 04/03/2023

Role of Transients in Two-Bounce Non-Line-of-Sight Imaging

The goal of non-line-of-sight (NLOS) imaging is to image objects occlude...

0 Siddharth Somasundaram, et al. ∙

research

∙ 12/08/2022

ORCa: Glossy Objects as Radiance Field Cameras

Reflections on glossy objects contain valuable and hidden information ab...

0 Kushagra Tiwary, et al. ∙

research

∙ 10/28/2022

Differentially Private CutMix for Split Learning with Vision Transformer

Recently, vision transformer (ViT) has started to outpace the convention...

0 Seungeun Oh, et al. ∙

research

∙ 09/07/2022

Detection and Mapping of Specular Surfaces Using Multibounce Lidar Returns

We propose methods that use specular, multibounce lidar returns to detec...

0 Connor Henley, et al. ∙

research

∙ 08/25/2022

Fundamentals of Task-Agnostic Data Valuation

We study valuing the data of a data owner/seller for a data seeker/buyer...

0 Mohammad Mohammadi Amiri, et al. ∙

research

∙ 07/20/2022

Differentiable Agent-based Epidemiology

Mechanistic simulators are an indispensable tool for epidemiology to exp...

7 Ayush Chopra, et al. ∙

research

∙ 07/08/2022

Private independence testing across two parties

We introduce π-test, a privacy-preserving algorithm for testing statisti...

5 Praneeth Vepakomma, et al. ∙

research

∙ 07/01/2022

Visual Transformer Meets CutMix for Improved Accuracy, Communication Efficiency, and Data Privacy in Split Learning

This article seeks for a distributed learning solution for the visual tr...

16 Sihun Baek, et al. ∙

research

∙ 06/17/2022

A Roadmap for Greater Public Use of Privacy-Sensitive Government Data: Workshop Report

Government agencies collect and manage a wide range of ever-growing data...

0 Chris Clifton, et al. ∙

research

∙ 04/21/2022

Physics vs. Learned Priors: Rethinking Camera and Algorithm Design for Task-Specific Imaging

Cameras were originally designed using physics-based heuristics to captu...

0 Tzofi Klinghoffer, et al. ∙

research

∙ 04/11/2022

Physically Disentangled Representations

State-of-the-art methods in generative representation learning yield sem...

1 Tzofi Klinghoffer, et al. ∙

research

∙ 03/29/2022

Towards Learning Neural Representations from Shadows

We present a method that learns neural scene representations from only s...

0 Kushagra Tiwary, et al. ∙

research

∙ 01/25/2022

The Privacy-Welfare Trade-off: Effects of Differential Privacy on Influence Welfare in Social Choice

This work studies a fundamental trade-off between privacy and welfare in...

0 Ibrahim Suat Evren, et al. ∙

research

∙ 12/11/2021

Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learning

In recent years, there have been great advances in the field of decentra...

0 Shraman Pal, et al. ∙

research

∙ 10/19/2021

Private measurement of nonlinear correlations between data hosted across multiple parties

We introduce a differentially private method to measure nonlinear correl...

0 Praneeth Vepakomma, et al. ∙

research

∙ 10/09/2021

DeepABM: Scalable, efficient and differentiable agent-based simulations via graph neural networks

We introduce DeepABM, a framework for agent-based modeling that leverage...

0 Ayush Chopra, et al. ∙

research

∙ 08/19/2021

Parallel Quasi-concave set optimization: A new frontier that scales without needing submodularity

Classes of set functions along with a choice of ground set are a bedrock...

0 Praneeth Vepakomma, et al. ∙

research

∙ 05/21/2021

Automatic calibration of time of flight based non-line-of-sight reconstruction

Time of flight based Non-line-of-sight (NLOS) imaging approaches require...

3 Subhash Chandra Sadhu, et al. ∙

research

∙ 05/02/2021

AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning

Wireless channels can be inherently privacy-preserving by distorting the...

123 Yusuke Koda, et al. ∙

research

∙ 03/01/2021

Safepaths: Vaccine Diary Protocol and Decentralized Vaccine Coordination System using a Privacy Preserving User Centric Experience

In this early draft, we present an end-to-end decentralized protocol for...

0 Abhishek Singh, et al. ∙

research

∙ 02/22/2021

Differentially Private Supervised Manifold Learning with Applications like Private Image Retrieval

Differential Privacy offers strong guarantees such as immutable privacy ...

0 Praneeth Vepakomma, et al. ∙

research

∙ 01/20/2021

MIT SafePaths Card (MiSaCa): Augmenting Paper Based Vaccination Cards with Printed Codes

In this early draft, we describe a user-centric, card-based system for v...

0 Joseph Bae, et al. ∙

research

∙ 01/05/2021

COVID-19 Tests Gone Rogue: Privacy, Efficacy,Mismanagement and Misunderstandings

COVID-19 testing, the cornerstone for effective screening and identifica...

0 Manuel Morales, et al. ∙

research

∙ 01/04/2021

Spatial K-anonymity: A Privacy-preserving Method for COVID-19 Related Geospatial Technologies

There is a growing need for spatial privacy considerations in the many g...

0 Rohan Iyer, et al. ∙

research

∙ 12/21/2020

COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms

The COVID-19 pandemic has challenged scientists and policy-makers intern...

0 Rohan Sukumaran, et al. ∙

research

∙ 12/20/2020

DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks

Recent deep learning models have shown remarkable performance in image c...

18 Abhishek Singh, et al. ∙

research

∙ 12/04/2020

Verifiable Proof of Health using Public Key Cryptography

In the current pandemic, testing continues to be the most important tool...

0 Abhishek Singh, et al. ∙

research

∙ 12/03/2020

Digital Landscape of COVID-19 Testing: Challenges and Opportunities

The COVID-19 Pandemic has left a devastating trail all over the world, i...

0 Darshan Gandhi, et al. ∙

research

∙ 09/26/2020

Proximity Inference with Wifi-Colocation during the COVID-19 Pandemic

In this work we propose a WiFi colocation methodology for digital contac...

0 Mikhail Dmitrienko, et al. ∙

research

∙ 09/25/2020

Target Privacy Threat Modeling for COVID-19 Exposure Notification Systems

The adoption of digital contact tracing (DCT) technology during the COVI...

0 Ananya Gangavarapu, et al. ∙

research

∙ 09/10/2020

Bluetooth based Proximity, Multi-hop Analysis and Bi-directional Trust: Epidemics and More

In this paper, we propose a trust layer on top of Bluetooth and similar ...

0 Ramesh Raskar, et al. ∙

research

∙ 09/04/2020

Proximity Sensing for Contact Tracing

The TC4TL (Too Close For Too Long) challenge is aimed towards designing ...

0 Sheshank Shankar, et al. ∙

research

∙ 08/20/2020

NoPeek: Information leakage reduction to share activations in distributed deep learning

For distributed machine learning with sensitive data, we demonstrate how...

5 Praneeth Vepakomma, et al. ∙

research

∙ 08/15/2020

PPContactTracing: A Privacy-Preserving Contact Tracing Protocol for COVID-19 Pandemic

Several contact tracing solutions have been proposed and implemented all...

0 Priyanka Singh, et al. ∙

research

∙ 08/11/2020

Comparing manual contact tracing and digital contact advice

Manual contact tracing is a top-down solution that starts with contact t...

0 Ramesh Raskar, et al. ∙

research

∙ 08/07/2020

SplitNN-driven Vertical Partitioning

In this work, we introduce SplitNN-driven Vertical Partitioning, a confi...

6 Iker Ceballos, et al. ∙

research

∙ 07/06/2020

Splintering with distributions: A stochastic decoy scheme for private computation

Performing computations while maintaining privacy is an important proble...

11 Praneeth Vepakomma, et al. ∙

research

∙ 07/01/2020

Adding Location and Global Context to the Google/Apple Exposure Notification Bluetooth API

Contact tracing requires a strong understanding of the context of a user...

0 Ramesh Raskar, et al. ∙

research

∙ 07/01/2020

Adding Location and other Context to the Google/Apple Exposure Notification Bluetooth API

Contact tracing requires a strong understanding of the context of a user...

0 Ramesh Raskar, et al. ∙

research

∙ 06/04/2020

COVID-19 Contact-Tracing Mobile Apps: Evaluation and Assessment for Decision Makers

A number of groups, from governments to non-profits, have quickly acted ...

0 Ramesh Raskar, et al. ∙

research

∙ 06/02/2020

Automatic Differentiation for All Photons Imaging to See Inside Volumetric Scattering Media

Imaging through dense scattering media - such as biological tissue, fog,...

9 Tomohiro Maeda, et al. ∙

research

∙ 04/28/2020

Privacy Guidelines for Contact Tracing Applications

Contact tracing is a very powerful method to implement and enforce socia...

0 Manish Shukla, et al. ∙

research

∙ 04/25/2020

Privacy in Deep Learning: A Survey

The ever-growing advances of deep learning in many areas including visio...

9 Fatemehsadat Mirshghallah, et al. ∙

research

∙ 03/31/2020

Assessing Disease Exposure Risk With Location Histories And Protecting Privacy: A Cryptographic Approach In Response To A Global Pandemic

Governments and researchers around the world are implementing digital co...

0 Alex Berke, et al. ∙

research

∙ 03/19/2020

Apps Gone Rogue: Maintaining Personal Privacy in an Epidemic

Containment, the key strategy in quickly halting an epidemic, requires r...

0 Ramesh Raskar, et al. ∙

research

∙ 12/27/2019

Split Learning for collaborative deep learning in healthcare

Shortage of labeled data has been holding the surge of deep learning in ...

9 Maarten G. Poirot, et al. ∙

research

∙ 12/10/2019

Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients...

33 Peter Kairouz, et al. ∙

Ramesh Raskar

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