48 citations · 187 across the 13 of their papers we have counts for
13 papers
Game Redesign in No-regret Game Playing
Yuzhe Ma, Young Wu, Xiaojin Zhu
We study the game redesign problem in which an external designer has the ability to change the payoff function in each round, but incurs a design cost for deviating from the origin…
Sequential Attacks on Kalman Filter-based Forward Collision Warning Systems
Yuzhe Ma, Jon Sharp, Ruizhe Wang +2
Kalman Filter (KF) is widely used in various domains to perform sequential learning or variable estimation. In the context of autonomous vehicles, KF constitutes the core component…
Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners
Yun-Shiuan Chuang, Xuezhou Zhang, Yuzhe Ma +3
Successful teaching requires an assumption of how the learner learns - how the learner uses experiences from the world to update their internal states. We investigate what expectat…
Task-agnostic Exploration in Reinforcement Learning
Xuezhou Zhang, Yuzhe ma, Adish Singla
Efficient exploration is one of the main challenges in reinforcement learning (RL). Most existing sample-efficient algorithms assume the existence of a single reward function durin…
The Sample Complexity of Teaching-by-Reinforcement on Q-Learning
Xuezhou Zhang, Shubham Kumar Bharti, Yuzhe Ma +2
We study the sample complexity of teaching, termed as "teaching dimension" (TDim) in the literature, for the teaching-by-reinforcement paradigm, where the teacher guides the studen…
Adaptive Reward-Poisoning Attacks against Reinforcement Learning
Xuezhou Zhang, Yuzhe Ma, Adish Singla +1
In reward-poisoning attacks against reinforcement learning (RL), an attacker can perturb the environment reward into at each step, with the goal of forcing the RL a…
Policy Poisoning in Batch Reinforcement Learning and Control
Yuzhe Ma, Xuezhou Zhang, Wen Sun +1
We study a security threat to batch reinforcement learning and control where the attacker aims to poison the learned policy. The victim is a reinforcement learner / controller whic…
Collaborative and Privacy-Preserving Machine Teaching via Consensus Optimization
Yufei Han, Yuzhe Ma, Christopher Gates +2
In this work, we define a collaborative and privacy-preserving machine teaching paradigm with multiple distributed teachers. We focus on consensus super teaching. It aims at organi…
Data Poisoning against Differentially-Private Learners: Attacks and Defenses
Yuzhe Ma, Xiaojin Zhu, Justin Hsu
Data poisoning attacks aim to manipulate the model produced by a learning algorithm by adversarially modifying the training set. We consider differential privacy as a defensive mea…
Training Set Camouflage
Ayon Sen, Scott Alfeld, Xuezhou Zhang +3
We introduce a form of steganography in the domain of machine learning which we call training set camouflage. Imagine Alice has a training set on an illicit machine learning classi…
Adversarial Attacks on Stochastic Bandits
Kwang-Sung Jun, Lihong Li, Yuzhe Ma +1
We study adversarial attacks that manipulate the reward signals to control the actions chosen by a stochastic multi-armed bandit algorithm. We propose the first attack against two…
Data Poisoning Attacks in Contextual Bandits
Yuzhe Ma, Kwang-Sung Jun, Lihong Li +1
We study offline data poisoning attacks in contextual bandits, a class of reinforcement learning problems with important applications in online recommendation and adaptive medical…
Teacher Improves Learning by Selecting a Training Subset
Yuzhe Ma, Robert Nowak, Philippe Rigollet +2
We call a learner super-teachable if a teacher can trim down an iid training set while making the learner learn even better. We provide sharp super-teaching guarantees on two learn…