Search Results for author: Yuzhe ma

Found 24 papers, 3 papers with code

DevelSet: Deep Neural Level Set for Instant Mask Optimization

no code implementations18 Mar 2023 Guojin Chen, Ziyang Yu, Hongduo Liu, Yuzhe ma, Bei Yu

To further enhance printability and fast iterative convergence, we propose a novel deep neural network delicately designed with level set intrinsic principles to facilitate the joint optimization of DNN and GPU accelerated level set optimizer.

AdaOPC: A Self-Adaptive Mask Optimization Framework For Real Design Patterns

no code implementations15 Mar 2023 Wenqian Zhao, Xufeng Yao, Ziyang Yu, Guojin Chen, Yuzhe ma, Bei Yu, Martin D. F. Wong

We inspect the pattern distribution on a design layer and find that different sub-regions have different pattern complexity.

Physics-Informed Optical Kernel Regression Using Complex-valued Neural Fields

no code implementations15 Mar 2023 Guojin Chen, Zehua Pei, HaoYu Yang, Yuzhe ma, Bei Yu, Martin D. F. Wong

Lithography is fundamental to integrated circuit fabrication, necessitating large computation overhead.


Adversarial Attacks on Adversarial Bandits

no code implementations30 Jan 2023 Yuzhe ma, Zhijin Zhou

We also derived a theoretical lower bound on the cumulative attack cost that any victim-agnostic attack algorithm must incur.

Multi-Armed Bandits Recommendation Systems

Rethinking Graph Neural Networks for the Graph Coloring Problem

no code implementations15 Aug 2022 Wei Li, Ruxuan Li, Yuzhe ma, Siu On Chan, David Pan, Bei Yu

Graph coloring, a classical and critical NP-hard problem, is the problem of assigning connected nodes as different colors as possible.

Game Redesign in No-regret Game Playing

no code implementations18 Oct 2021 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 original game.

Rethinking Graph Neural Networks for Graph Coloring

no code implementations1 Jan 2021 Wei Li, Ruxuan Li, Yuzhe ma, Siu On Chan, Bei Yu

To characterize the power of GNNs for the graph coloring problem, we first formalize the discrimination power of GNNs as the capability to assign nodes different colors.

Sequential Attacks on Kalman Filter-based Forward Collision Warning Systems

no code implementations16 Dec 2020 Yuzhe ma, Jon Sharp, Ruizhe Wang, Earlence Fernandes, Xiaojin Zhu

In this paper, we study adversarial attacks on KF as part of the more complex machine-human hybrid system of Forward Collision Warning.

Autonomous Vehicles

Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners

no code implementations5 Sep 2020 Yun-Shiuan Chuang, Xuezhou Zhang, Yuzhe ma, Mark K. Ho, Joseph L. Austerweil, Xiaojin Zhu

To solve the machine teaching optimization problem, we use a deep learning approximation method which simulates learners in the environment and learns to predict how feedback affects the learner's internal states.


Task-agnostic Exploration in Reinforcement Learning

no code implementations NeurIPS 2020 Xuezhou Zhang, Yuzhe ma, Adish Singla

To address these challenges, we propose the \textit{task-agnostic RL} framework: In the exploration phase, the agent first collects trajectories by exploring the MDP without the guidance of a reward function.

Efficient Exploration reinforcement-learning +1

The Sample Complexity of Teaching-by-Reinforcement on Q-Learning

no code implementations16 Jun 2020 Xuezhou Zhang, Shubham Kumar Bharti, Yuzhe ma, Adish Singla, Xiaojin Zhu

Our TDim results provide the minimum number of samples needed for reinforcement learning, and we discuss their connections to standard PAC-style RL sample complexity and teaching-by-demonstration sample complexity results.

Q-Learning reinforcement-learning +1

Adaptive Reward-Poisoning Attacks against Reinforcement Learning

no code implementations ICML 2020 Xuezhou Zhang, Yuzhe ma, Adish Singla, Xiaojin Zhu

In reward-poisoning attacks against reinforcement learning (RL), an attacker can perturb the environment reward $r_t$ into $r_t+\delta_t$ at each step, with the goal of forcing the RL agent to learn a nefarious policy.

reinforcement-learning Reinforcement Learning (RL)

VLSI Mask Optimization: From Shallow To Deep Learning

no code implementations16 Dec 2019 Haoyu Yang, Wei Zhong, Yuzhe ma, Hao Geng, Ran Chen, Wanli Chen, Bei Yu

VLSI mask optimization is one of the most critical stages in manufacturability aware design, which is costly due to the complicated mask optimization and lithography simulation.

BIG-bench Machine Learning

Policy Poisoning in Batch Reinforcement Learning and Control

1 code implementation NeurIPS 2019 Yuzhe Ma, Xuezhou Zhang, Wen Sun, Xiaojin Zhu

We study a security threat to batch reinforcement learning and control where the attacker aims to poison the learned policy.

reinforcement-learning Reinforcement Learning (RL)

Are Adversarial Perturbations a Showstopper for ML-Based CAD? A Case Study on CNN-Based Lithographic Hotspot Detection

no code implementations25 Jun 2019 Kang Liu, Hao-Yu Yang, Yuzhe ma, Benjamin Tan, Bei Yu, Evangeline F. Y. Young, Ramesh Karri, Siddharth Garg

There is substantial interest in the use of machine learning (ML) based techniques throughout the electronic computer-aided design (CAD) flow, particularly those based on deep learning.

Collaborative and Privacy-Preserving Machine Teaching via Consensus Optimization

no code implementations7 May 2019 Yufei Han, Yuzhe ma, Christopher Gates, Kevin Roundy, Yun Shen

To address these challenges, we formulate collaborative teaching as a consensus and privacy-preserving optimization process to minimize teaching risk.

Privacy Preserving

Data Poisoning against Differentially-Private Learners: Attacks and Defenses

no code implementations23 Mar 2019 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.

Data Poisoning

Adversarial Attacks on Stochastic Bandits

no code implementations NeurIPS 2018 Kwang-Sung Jun, Lihong Li, Yuzhe ma, Xiaojin Zhu

We study adversarial attacks that manipulate the reward signals to control the actions chosen by a stochastic multi-armed bandit algorithm.

Data Poisoning Attacks in Contextual Bandits

no code implementations17 Aug 2018 Yuzhe Ma, Kwang-Sung Jun, Lihong Li, Xiaojin Zhu

We provide a general attack framework based on convex optimization and show that by slightly manipulating rewards in the data, an attacker can force the bandit algorithm to pull a target arm for a target contextual vector.

Data Poisoning Multi-Armed Bandits +2

A Unified Approximation Framework for Compressing and Accelerating Deep Neural Networks

no code implementations26 Jul 2018 Yuzhe Ma, Ran Chen, Wei Li, Fanhua Shang, Wenjian Yu, Minsik Cho, Bei Yu

To address this issue, various approximation techniques have been investigated, which seek for a light weighted network with little performance degradation in exchange of smaller model size or faster inference.

General Classification Image Classification +1

Recent Advances in Convolutional Neural Network Acceleration

no code implementations23 Jul 2018 Qianru Zhang, Meng Zhang, Tinghuan Chen, Zhifei Sun, Yuzhe ma, Bei Yu

We propose a taxonomy in terms of three levels, i. e.~structure level, algorithm level, and implementation level, for acceleration methods.

Image Classification

Cross-layer Optimization for High Speed Adders: A Pareto Driven Machine Learning Approach

1 code implementation18 Jul 2018 Yuzhe Ma, Subhendu Roy, Jin Miao, Jiamin Chen, Bei Yu

In spite of maturity to the modern electronic design automation (EDA) tools, optimized designs at architectural stage may become sub-optimal after going through physical design flow.

Active Learning BIG-bench Machine Learning

Teacher Improves Learning by Selecting a Training Subset

no code implementations25 Feb 2018 Yuzhe Ma, Robert Nowak, Philippe Rigollet, Xuezhou Zhang, Xiaojin Zhu

We call a learner super-teachable if a teacher can trim down an iid training set while making the learner learn even better.

General Classification regression

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