604 citations · 2k across the 82 of their papers we have counts for
83 papers
Achieving Fair-Effective Communications and Robustness in Underwater Acoustic Sensor Networks: A Semi-Cooperative Approach
Yu Gou, Tong Zhang, Jun Liu +3
This paper investigates the fair-effective communication and robustness in imperfect and energy-constrained underwater acoustic sensor networks (IC-UASNs). Specifically, we investi…
Rate-Splitting and Sum-DoF for the K-User MISO Broadcast Channel with Mixed CSIT and Order-(K-1) Messages
Shuo Zheng, Tong Zhang, Jingfu Li +4
In this paper, we propose a rate-splitting design and characterize the sum-degrees-of-freedom (DoF) for the K-user multiple-input-single-output (MISO) broadcast channel with mixed…
Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning
Yong Lin, Chen Liu, Chenlu Ye +3
Modern deep learning heavily relies on large labeled datasets, which often comse with high costs in terms of both manual labeling and computational resources. To mitigate these cha…
Spurious Feature Diversification Improves Out-of-distribution Generalization
Yong Lin, Lu Tan, Yifan Hao +5
Generalization to out-of-distribution (OOD) data is a critical challenge in machine learning. Ensemble-based methods, like weight space ensembles that interpolate model parameters,…
May I Ask a Follow-up Question? Understanding the Benefits of Conversations in Neural Network Explainability
Tong Zhang, X. Jessie Yang, Boyang Li
Research in explainable AI (XAI) aims to provide insights into the decision-making process of opaque AI models. To date, most XAI methods offer one-off and static explanations, whi…
Mitigating the Alignment Tax of RLHF
Yong Lin, Hangyu Lin, Wei Xiong +14
LLMs acquire a wide range of abilities during pre-training, but aligning LLMs under Reinforcement Learning with Human Feedback (RLHF) can lead to forgetting pretrained abilities, w…
Integrated Robotics Networks with Co-optimization of Drone Placement and Air-Ground Communications
Menghao Hu, Tong Zhang, Shuai Wang +4
Terrestrial robots, i.e., unmanned ground vehicles (UGVs), and aerial robots, i.e., unmanned aerial vehicles (UAVs), operate in separate spaces. To exploit their complementary feat…
Reverse Diffusion Monte Carlo
Xunpeng Huang, Hanze Dong, Yifan Hao +2
We propose a Monte Carlo sampler from the reverse diffusion process. Unlike the practice of diffusion models, where the intermediary updates -- the score functions -- are learned w…
DoF Upper Bound on Three-User MISO Broadcast Channel with Two Antennas and Mixed CSIT
Shuo Zheng, Tong Zhang, Shuai Wang +4
With delayed and imperfect current channel state information at the transmitter (CSIT), namely mixed CSIT, the sum degrees-of-freedom (sum-DoF) in the two-user multiple-input multi…
Dual Adaptive Representation Alignment for Cross-domain Few-shot Learning
Yifan Zhao, Tong Zhang, Jia Li +1
Few-shot learning aims to recognize novel queries with limited support samples by learning from base knowledge. Recent progress in this setting assumes that the base knowledge and…
Mixture-of-Domain-Adapters: Decoupling and Injecting Domain Knowledge to Pre-trained Language Models Memories
Shizhe Diao, Tianyang Xu, Ruijia Xu +2
Pre-trained language models (PLMs) demonstrate excellent abilities to understand texts in the generic domain while struggling in a specific domain. Although continued pre-training…
A Theoretical Analysis of Optimistic Proximal Policy Optimization in Linear Markov Decision Processes
Han Zhong, Tong Zhang
The proximal policy optimization (PPO) algorithm stands as one of the most prosperous methods in the field of reinforcement learning (RL). Despite its success, the theoretical unde…
What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL?
Rui Yang, Yong Lin, Xiaoteng Ma +3
Offline goal-conditioned RL (GCRL) offers a way to train general-purpose agents from fully offline datasets. In addition to being conservative within the dataset, the generalizatio…
LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models
Shizhe Diao, Rui Pan, Hanze Dong +4
Foundation models have demonstrated a great ability to achieve general human-level intelligence far beyond traditional approaches. As the technique keeps attracting attention from…
Effective Bilevel Optimization via Minimax Reformulation
Xiaoyu Wang, Rui Pan, Renjie Pi +1
Bilevel optimization has found successful applications in various machine learning problems, including hyper-parameter optimization, data cleaning, and meta-learning. However, its…
RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment
Hanze Dong, Wei Xiong, Deepanshu Goyal +7
Generative foundation models are susceptible to implicit biases that can arise from extensive unsupervised training data. Such biases can produce suboptimal samples, skewed outcome…
Environment Invariant Linear Least Squares
Jianqing Fan, Cong Fang, Yihong Gu +1
This paper considers a multi-environment linear regression model in which data from multiple experimental settings are collected. The joint distribution of the response variable an…
Learning in POMDPs is Sample-Efficient with Hindsight Observability
Jonathan N. Lee, Alekh Agarwal, Christoph Dann +1
POMDPs capture a broad class of decision making problems, but hardness results suggest that learning is intractable even in simple settings due to the inherent partial observabilit…
A Heuristic Autonomous Exploration Method Based on Environmental Information Gain During Quadrotor Flight
Tong Zhang, Jiajie Yu, Jiaqi Li +1
Autonomous exploration is a widely studied fundamental application in the field of quadrotors, which requires them to automatically explore unknown space to obtain complete informa…
ADAPT: Action-aware Driving Caption Transformer
Bu Jin, Xinyu Liu, Yupeng Zheng +6
End-to-end autonomous driving has great potential in the transportation industry. However, the lack of transparency and interpretability of the automatic decision-making process hi…
Catalyst Acceleration of Error Compensated Methods Leads to Better Communication Complexity
Xun Qian, Hanze Dong, Tong Zhang +1
Communication overhead is well known to be a key bottleneck in large scale distributed learning, and a particularly successful class of methods which help to overcome this bottlene…
Probabilistic Bilevel Coreset Selection
Xiao Zhou, Renjie Pi, Weizhong Zhang +2
The goal of coreset selection in supervised learning is to produce a weighted subset of data, so that training only on the subset achieves similar performance as training on the en…
ExtremeBERT: A Toolkit for Accelerating Pretraining of Customized BERT
Rui Pan, Shizhe Diao, Jianlin Chen +1
In this paper, we present ExtremeBERT, a toolkit for accelerating and customizing BERT pretraining. Our goal is to provide an easy-to-use BERT pretraining toolkit for the research…
Normalizing Flow with Variational Latent Representation
Hanze Dong, Shizhe Diao, Weizhong Zhang +1
Normalizing flow (NF) has gained popularity over traditional maximum likelihood based methods due to its strong capability to model complex data distributions. However, the standar…