79 citations · 115 across the 16 of their papers we have counts for
16 papers
Generalist Reward Models: Found Inside Large Language Models
Yi-Chen Li, Tian Xu, Yang Yu +6
The alignment of Large Language Models (LLMs) is critically dependent on reward models trained on costly human preference data. While recent work explores bypassing this cost with…
Open3DBench: Open-Source Benchmark for 3D-IC Backend Implementation and PPA Evaluation
Yunqi Shi, Chengrui Gao, Wanqi Ren +6
This work introduces Open3DBench, an open-source 3D-IC backend implementation benchmark built upon the OpenROAD-flow-scripts framework, enabling comprehensive evaluation of power,…
WHALE: Towards Generalizable and Scalable World Models for Embodied Decision-making
Zhilong Zhang, Ruifeng Chen, Junyin Ye +8
World models play a crucial role in decision-making within embodied environments, enabling cost-free explorations that would otherwise be expensive in the real world. To facilitate…
Beimingwu: A Learnware Dock System
Zhi-Hao Tan, Jian-Dong Liu, Xiao-Dong Bi +7
The learnware paradigm proposed by Zhou [2016] aims to enable users to reuse numerous existing well-trained models instead of building machine learning models from scratch, with th…
Learning to Coordinate with Anyone
Lei Yuan, Lihe Li, Ziqian Zhang +5
In open multi-agent environments, the agents may encounter unexpected teammates. Classical multi-agent learning approaches train agents that can only coordinate with seen teammates…
Designing Rewards for Fast Learning
Henry Sowerby, Zhiyuan Zhou, Michael L. Littman
To convey desired behavior to a Reinforcement Learning (RL) agent, a designer must choose a reward function for the environment, arguably the most important knob designers have in…
No-Regret Learning in Time-Varying Zero-Sum Games
Mengxiao Zhang, Peng Zhao, Haipeng Luo +1
Learning from repeated play in a fixed two-player zero-sum game is a classic problem in game theory and online learning. We consider a variant of this problem where the game payoff…
Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees
Chao Qian, Dan-Xuan Liu, Zhi-Hua Zhou
Given a ground set of items, the result diversification problem aims to select a subset with high "quality" and "diversity" while satisfying some constraints. It arises in various…
Reinforcement Learning Experience Reuse with Policy Residual Representation
Wen-Ji Zhou, Yang Yu, Yingfeng Chen +4
Experience reuse is key to sample-efficient reinforcement learning. One of the critical issues is how the experience is represented and stored. Previously, the experience can be st…
Tunneling Neural Perception and Logic Reasoning through Abductive Learning
Wang-Zhou Dai, Qiu-Ling Xu, Yang Yu +1
Perception and reasoning are basic human abilities that are seamlessly connected as part of human intelligence. However, in current machine learning systems, the perception and rea…
Maximizing Submodular or Monotone Approximately Submodular Functions by Multi-objective Evolutionary Algorithms
Chao Qian, Yang Yu, Ke Tang +2
Evolutionary algorithms (EAs) are a kind of nature-inspired general-purpose optimization algorithm, and have shown empirically good performance in solving various real-word optimiz…
A Lower Bound Analysis of Population-based Evolutionary Algorithms for Pseudo-Boolean Functions
Chao Qian, Yang Yu, Zhi-Hua Zhou
Evolutionary algorithms (EAs) are population-based general-purpose optimization algorithms, and have been successfully applied in various real-world optimization tasks. However, pr…
Analyzing Evolutionary Optimization in Noisy Environments
Chao Qian, Yang Yu, Zhi-Hua Zhou
Many optimization tasks have to be handled in noisy environments, where we cannot obtain the exact evaluation of a solution but only a noisy one. For noisy optimization tasks, evol…
Towards Analyzing Crossover Operators in Evolutionary Search via General Markov Chain Switching Theorem
Yang Yu, Chao Qian, Zhi-Hua Zhou
Evolutionary algorithms (EAs), simulating the evolution process of natural species, are used to solve optimization problems. Crossover (also called recombination), originated from…
On the approximation ability of evolutionary optimization with application to minimum set cover
Yang Yu, Xin Yao, Zhi-Hua Zhou
Evolutionary algorithms (EAs) are heuristic algorithms inspired by natural evolution. They are often used to obtain satisficing solutions in practice. In this paper, we investigate…
Weak Evolvability Equals Strong Evolvability
Yang Yu, Zhi-Hua Zhou
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