# Manipulating Predictions over Discrete Inputs in Machine Teaching

arXiv: https://arxiv.org/abs/2401.17865 · HTML: https://mlsanity.com/paper/2401.17865/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 49 references that are in the map.
- First posted on arXiv 31 Jan 2024.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks](https://mlsanity.com/paper/1804.00792/) (2018)
- [Weight Poisoning Attacks on Pre-trained Models](https://mlsanity.com/paper/2004.06660/) (2020)
- [Generating Natural Adversarial Examples](https://mlsanity.com/paper/1710.11342/) (2017)
- [Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching](https://mlsanity.com/paper/2009.02276/) (2020)
- [Dynamic Backdoor Attacks Against Machine Learning Models](https://mlsanity.com/paper/2003.03675/) (2020)
- [Stronger Data Poisoning Attacks Break Data Sanitization Defenses](https://mlsanity.com/paper/1811.00741/) (2018)
- [Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks](https://mlsanity.com/paper/1706.05764/) (2017)
- [Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models](https://mlsanity.com/paper/2103.15543/) (2021)
- [Subpopulation Data Poisoning Attacks](https://mlsanity.com/paper/2006.14026/) (2020)
- [Bullseye Polytope: A Scalable Clean-Label Poisoning Attack with Improved Transferability](https://mlsanity.com/paper/2005.00191/) (2020)
- [Generative Poisoning Attack Method Against Neural Networks](https://mlsanity.com/paper/1703.01340/) (2017)
- [An Overview of Backdoor Attacks Against Deep Neural Networks and Possible Defences](https://mlsanity.com/paper/2111.08429/) (2021)
- [Iterative Machine Teaching](https://mlsanity.com/paper/1705.10470/) (2017)
- [Machine Teaching: A New Paradigm for Building Machine Learning Systems](https://mlsanity.com/paper/1707.06742/) (2017)
- [Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder](https://mlsanity.com/paper/2010.02684/) (2020)
- [Active classification with comparison queries](https://mlsanity.com/paper/1704.03564/) (2017)
- [Training Set Debugging Using Trusted Items](https://mlsanity.com/paper/1801.08019/) (2018)
- [Poisoning Attacks with Generative Adversarial Nets](https://mlsanity.com/paper/1906.07773/) (2019)
- [Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoning](https://mlsanity.com/paper/2107.05243/) (2021)
- [Machine Teaching for Bayesian Learners in the Exponential Family](https://mlsanity.com/paper/1306.4947/) (2013)
- [Humpty Dumpty: Controlling Word Meanings via Corpus Poisoning](https://mlsanity.com/paper/2001.04935/) (2020)
- [Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners](https://mlsanity.com/paper/1802.05190/) (2018)

## Abstract (arXiv)

Machine teaching often involves the creation of an optimal (typically minimal) dataset to help a model (referred to as the `student') achieve specific goals given by a teacher. While abundant in the continuous domain, the studies on the effectiveness of machine teaching in the discrete domain are relatively limited. This paper focuses on machine teaching in the discrete domain, specifically on manipulating student models' predictions based on the goals of teachers via changing the training data efficiently. We formulate this task as a combinatorial optimization problem and solve it by proposing an iterative searching algorithm. Our algorithm demonstrates significant numerical merit in the scenarios where a teacher attempts at correcting erroneous predictions to improve the student's models, or maliciously manipulating the model to misclassify some specific samples to the target class aligned with his personal profits. Experimental results show that our proposed algorithm can have superior performance in effectively and efficiently manipulating the predictions of the model, surpassing conventional baselines.