Papers
Sample Complexity of Distributionally Robust Off-Dynamics Reinforcement Learning with Online Interaction
Yiting He, Zhishuai Liu, Weixin Wang et al.
Olica: Efficient Structured Pruning of Large Language Models without Retraining
Jiujun He, Huazhen Lin
Goal-Oriented Skill Abstraction for Offline Multi-Task Reinforcement Learning
Jinmin He, Kai Li, Yifan Zang et al.
Going Deeper into Locally Differentially Private Graph Neural Networks
Longzhu He, Chaozhuo Li, Peng Tang et al.
Learning Time-Aware Causal Representation for Model Generalization in Evolving Domains
Zhuo He, Shuang Li, Wenze Song et al.
Subspace Optimization for Large Language Models with Convergence Guarantees
Yutong He, Pengrui Li, Yipeng Hu et al.
ReferSplat: Referring Segmentation in 3D Gaussian Splatting
Shuting He, Guangquan Jie, Changshuo Wang et al.
Machines and Mathematical Mutations: Using GNNs to Characterize Quiver Mutation Classes
Jesse He, Helen Jenne, Herman Chau et al.
SPMC: Self-Purifying Federated Backdoor Defense via Margin Contribution
Wenwen He, Wenke Huang, Bin Yang et al.
Double-Filter: Efficient Fine-tuning of Pre-trained Vision-Language Models via Patch&Layer Filtering
Yaoqin He, Junchen Fu, Kaiwen Zheng et al.
Semantic Shift Estimation via Dual-Projection and Classifier Reconstruction for Exemplar-Free Class-Incremental Learning
Run He, Di Fang, Yicheng Xu et al.
DA-KD: Difficulty-Aware Knowledge Distillation for Efficient Large Language Models
Changyi He, Yifu Ding, Jinyang Guo et al.
ZipAR: Parallel Autoregressive Image Generation through Spatial Locality
Yefei He, Feng Chen, Yuanyu He et al.
Random Policy Evaluation Uncovers Policies of Generative Flow Networks
Haoran He, Emmanuel Bengio, Qingpeng Cai et al.
Double Machine Learning for Causal Inference under Shared-State Interference
Chris Hays, Manish Raghavan
Generative Data Mining with Longtail-Guided Diffusion
David S Hayden, Mao Ye, Timur Garipov et al.
Distillation of Discrete Diffusion through Dimensional Correlations
Satoshi Hayakawa, Yuhta Takida, Masaaki Imaizumi et al.
Wasserstein Flow Matching: Generative Modeling Over Families of Distributions
Doron Haviv, Aram-Alexandre Pooladian, Dana Pe’Er et al.
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching
Aaron J Havens, Benjamin Kurt Miller, Bing Yan et al.
Optimal Decision Tree Pruning Revisited: Algorithms and Complexity
Juha Harviainen, Frank Sommer, Manuel Sorge et al.
Controlling Neural Collapse Enhances Out-of-Distribution Detection and Transfer Learning
Md Yousuf Harun, Jhair Gallardo, Christopher Kanan
Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods
Yifan Hao, Xingyuan Pan, Hanning Zhang et al.
Faster and Stronger: When ANN-SNN Conversion Meets Parallel Spiking Calculation
Zecheng Hao, Qichao Ma, Kang Chen et al.
SafeMap: Robust HD Map Construction from Incomplete Observations
Xiaoshuai Hao, Lingdong Kong, Rong Yin et al.
Can MLLMs Reason in Multimodality? EMMA: An Enhanced MultiModal ReAsoning Benchmark
Yunzhuo Hao, Jiawei Gu, Huichen Will Wang et al.