10 citations · 25 across the 13 of their papers we have counts for
15 papers
A Cramér-von Mises Approach to Incentivizing Truthful Data Sharing
Alex Clinton, Thomas Zeng, Yiding Chen +2
Modern data marketplaces and data sharing consortia increasingly rely on incentive mechanisms to encourage agents to contribute data. However, schemes that reward agents based on t…
Scaling Offline RL via Efficient and Expressive Shortcut Models
Nicolas Espinosa-Dice, Yiyi Zhang, Yiding Chen +5
Diffusion and flow models have emerged as powerful generative approaches capable of modeling diverse and multimodal behavior. However, applying these models to offline reinforcemen…
Efficient Controllable Diffusion via Optimal Classifier Guidance
Owen Oertell, Shikun Sun, Yiding Chen +3
The controllable generation of diffusion models aims to steer the model to generate samples that optimize some given objective functions. It is desirable for a variety of applicati…
Convergence Of Consistency Model With Multistep Sampling Under General Data Assumptions
Yiding Chen, Yiyi Zhang, Owen Oertell +1
Diffusion models accomplish remarkable success in data generation tasks across various domains. However, the iterative sampling process is computationally expensive. Consistency mo…
Avoiding scaling in RLHF through Preference-based Exploration
Mingyu Chen, Yiding Chen, Wen Sun +1
Reinforcement Learning from Human Feedback (RLHF) has emerged as a pivotal technique for large language model (LLM) alignment. This paper studies the setting of online RLHF and foc…
Diffusing States and Matching Scores: A New Framework for Imitation Learning
Runzhe Wu, Yiding Chen, Gokul Swamy +2
Adversarial Imitation Learning is traditionally framed as a two-player zero-sum game between a learner and an adversarially chosen cost function, and can therefore be thought of as…
Collaborative Mean Estimation Among Heterogeneous Strategic Agents: Individual Rationality, Fairness, and Truthful Contribution
Alex Clinton, Yiding Chen, Xiaojin Zhu +1
We study a collaborative learning problem where agents aim to estimate a vector by sampling from associated univariate normal distributions…
Minimally Modifying a Markov Game to Achieve Any Nash Equilibrium and Value
Young Wu, Jeremy McMahan, Yiding Chen +3
We study the game modification problem, where a benevolent game designer or a malevolent adversary modifies the reward function of a zero-sum Markov game so that a target determini…
Mechanism Design for Collaborative Normal Mean Estimation
Yiding Chen, Xiaojin Zhu, Kirthevasan Kandasamy
We study collaborative normal mean estimation, where strategic agents collect i.i.d samples from a normal distribution at a cost. They all wish to estimat…
The Game of Hidden Rules: A New Kind of Benchmark Challenge for Machine Learning
Eric Pulick, Shubham Bharti, Yiding Chen +4
As machine learning (ML) is more tightly woven into society, it is imperative that we better characterize ML's strengths and limitations if we are to employ it responsibly. Existin…
Byzantine-Robust Online and Offline Distributed Reinforcement Learning
Yiding Chen, Xuezhou Zhang, Kaiqing Zhang +2
We consider a distributed reinforcement learning setting where multiple agents separately explore the environment and communicate their experiences through a central server. Howeve…
Corruption-Robust Offline Reinforcement Learning
Xuezhou Zhang, Yiding Chen, Jerry Zhu +1
We study the adversarial robustness in offline reinforcement learning. Given a batch dataset consisting of tuples , an adversary is allowed to arbitrarily modify …
Robust Policy Gradient against Strong Data Corruption
Xuezhou Zhang, Yiding Chen, Xiaojin Zhu +1
We study the problem of robust reinforcement learning under adversarial corruption on both rewards and transitions. Our attack model assumes an \textit{adaptive} adversary who can…
Error Lower Bounds of Constant Step-size Stochastic Gradient Descent
Zhiyan Ding, Yiding Chen, Qin Li +1
Stochastic Gradient Descent (SGD) plays a central role in modern machine learning. While there is extensive work on providing error upper bound for SGD, not much is known about SGD…
Optimal Attack against Autoregressive Models by Manipulating the Environment
Yiding Chen, Xiaojin Zhu
We describe an optimal adversarial attack formulation against autoregressive time series forecast using Linear Quadratic Regulator (LQR). In this threat model, the environment evol…