Search Results for author: Jinghan Yang

Found 7 papers, 5 papers with code

Auto-ICL: In-Context Learning without Human Supervision

1 code implementation15 Nov 2023 Jinghan Yang, Shuming Ma, Furu Wei

In the era of Large Language Models (LLMs), human-computer interaction has evolved towards natural language, offering unprecedented flexibility.

In-Context Learning

Relabeling Minimal Training Subset to Flip a Prediction

no code implementations22 May 2023 Jinghan Yang, Linjie Xu, Lequan Yu

When facing an unsatisfactory prediction from a machine learning model, users can be interested in investigating the underlying reasons and exploring the potential for reversing the outcome.

Binary Classification

Certified Robust Control under Adversarial Perturbations

no code implementations4 Feb 2023 Jinghan Yang, Hunmin Kim, Wenbin Wan, Naira Hovakimyan, Yevgeniy Vorobeychik

Autonomous systems increasingly rely on machine learning techniques to transform high-dimensional raw inputs into predictions that are then used for decision-making and control.

Decision Making

How Many and Which Training Points Would Need to be Removed to Flip this Prediction?

1 code implementation4 Feb 2023 Jinghan Yang, Sarthak Jain, Byron C. Wallace

We consider the problem of identifying a minimal subset of training data $\mathcal{S}_t$ such that if the instances comprising $\mathcal{S}_t$ had been removed prior to training, the categorization of a given test point $x_t$ would have been different.

text-classification Text Classification

PROVES: Establishing Image Provenance using Semantic Signatures

1 code implementation21 Oct 2021 Mingyang Xie, Manav Kulshrestha, Shaojie Wang, Jinghan Yang, Ayan Chakrabarti, Ning Zhang, Yevgeniy Vorobeychik

Modern AI tools, such as generative adversarial networks, have transformed our ability to create and modify visual data with photorealistic results.

Face Verification

Finding Physical Adversarial Examples for Autonomous Driving with Fast and Differentiable Image Compositing

1 code implementation17 Oct 2020 Jinghan Yang, Adith Boloor, Ayan Chakrabarti, Xuan Zhang, Yevgeniy Vorobeychik

We propose a scalable approach for finding adversarial modifications of a simulated autonomous driving environment using a differentiable approximation for the mapping from environmental modifications (rectangles on the road) to the corresponding video inputs to the controller neural network.

Autonomous Driving Bayesian Optimization +2

Protecting Geolocation Privacy of Photo Collections

1 code implementation4 Dec 2019 Jinghan Yang, Ayan Chakrabarti, Yevgeniy Vorobeychik

We study this problem formally as a combinatorial optimization problem in the context of geolocation prediction facilitated by deep learning.

Combinatorial Optimization

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