Prateek Mittal

Artificial Intelligence · 2009–2024

A researcher in the Science corpus: 31 papers; trusts 1,009 researchers and is trusted by 43 researchers here, as inferred from the citation graph.

Subfields

Artificial Intelligence — 1,151 citations into it; #5,693 of 35,345 by trust here, #1,924 by citations
Safety, Robustness & Interpretability — 470 citations into it; #1,123 of 2,251 by trust here, #737 by citations
Privacy & Security — 287 citations into it; #541 of 709 by trust here, #503 by citations
Computer Vision and Pattern Recognition — 269 citations into it
Computer Networks and Communications — 213 citations into it; #2,018 of 9,886 by trust here, #2,761 by citations
Signal Processing — 167 citations into it; #6,608 of 7,572 by trust here, #4,560 by citations
Information Systems — 131 citations into it; #1,890 of 8,771 by trust here, #5,078 by citations
Federated & Distributed Learning — 129 citations into it; #190 of 319 by trust here, #26 by citations

Whom Prateek Mittal trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

Who trusts Prateek Mittal overall

Papers from Prateek Mittal's perspective

Ordered by how strongly the researchers Prateek Mittal trusts endorse each paper. Same corpus, a different order for every researcher.

ImageNet classification with deep convolutional neural networks
Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
Gradient-based learning applied to document recognition
Long Short-Term Memory
ImageNet: A large-scale hierarchical image database
Attention is All you Need (Neural Information Processing Systems)
Learning Multiple Layers of Features from Tiny Images
Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

Advances and Open Problems in Federated Learning (2019) — 9,019 citations
Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To! (2023) — 1,288 citations
RobustBench: a standardized adversarial robustness benchmark (2020) — 928 citations
Enhancing robustness of machine learning systems via data transformations (2017) — 246 citations
Analyzing Federated Learning through an Adversarial Lens (2018) — 1,351 citations
Falcon: Honest-Majority Maliciously Secure Framework for Private Deep Learning (2020) — 389 citations
Visual Adversarial Examples Jailbreak Aligned Large Language Models (2023) — 403 citations
DEEProtect: Enabling Inference-based Access Control on Mobile Sensing Applications (2017) — 27 citations
On Your Social Network De-anonymizablity: Quantification and Large Scale Evaluation with Seed Knowledge (2015) — 96 citations
Dependence Makes You Vulnberable: Differential Privacy Under Dependent Tuples (2016) — 213 citations
Preserving Link Privacy in Social Network Based Systems (2012) — 111 citations
Safety Alignment Should Be Made More Than Just a Few Tokens Deep (2024) — 455 citations
SSD: A Unified Framework for Self-Supervised Outlier Detection (2021) — 433 citations
Towards Probabilistic Verification of Machine Unlearning (2020) — 86 citations
Systematic Evaluation of Privacy Risks of Machine Learning Models (2020) — 510 citations
Supplementary : Analyzing Federated Learning through an Adversarial Lens (2019)
Privacy Risks of Securing Machine Learning Models against Adversarial Examples (2019) — 304 citations
Membership Inference Attacks Against Adversarially Robust Deep Learning Models (2019) — 102 citations
Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples (2019) — 27 citations
Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logos (2018) — 70 citations