"meta-learning" Papers

Differentially Private Bilevel Optimization: Efficient Algorithms with Near-Optimal Rates

Andrew Lowy, Daogao Liu

End-to-End Implicit Neural Representations for Classification

Alexander Gielisse, Jan van Gemert

Fast Rate Bounds for Multi-Task and Meta-Learning with Different Sample Sizes

Hossein Zakerinia, Christoph Lampert

Few-Shot Image Quality Assessment via Adaptation of Vision-Language Models

Xudong Li, Zihao Huang, Yan Zhang et al.

LiFT: Learning to Fine-Tune via Bayesian Parameter Efficient Meta Fine-Tuning

Minyoung Kim, Timothy Hospedales

MetaBox-v2: A Unified Benchmark Platform for Meta-Black-Box Optimization

Zeyuan Ma, Yue-Jiao Gong, Hongshu Guo et al.

Meta-Dynamical State Space Models for Integrative Neural Data Analysis

Ayesha Vermani, Josue Nassar, Hyungju Jeon et al.

MetaGS: A Meta-Learned Gaussian-Phong Model for Out-of-Distribution 3D Scene Relighting

Yumeng He, Yunbo Wang

MetaOOD: Automatic Selection of OOD Detection Models

Yuehan Qin, Yichi Zhang, Yi Nian et al.

PIED: Physics-Informed Experimental Design for Inverse Problems

Apivich Hemachandra, Gregory Kang Ruey Lau, See-Kiong Ng et al.

Provable Meta-Learning with Low-Rank Adaptations

Jacob Block, Sundararajan Srinivasan, Liam Collins et al.

Why In-Context Learning Models are Good Few-Shot Learners?

Shiguang Wu, Yaqing Wang, Quanming Yao

Adaptive FSS: A Novel Few-Shot Segmentation Framework via Prototype Enhancement

Jing Wang, Jiangyun Li, Chen Chen et al.

A Nearly Optimal Single Loop Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness

Xiaochuan Gong, Jie Hao, Mingrui Liu

Few-Shot Character Understanding in Movies as an Assessment to Meta-Learning of Theory-of-Mind

Mo Yu, Qiujing Wang, Shunchi Zhang et al.

Few-Shot Learning from Augmented Label-Uncertain Queries in Bongard-HOI

Qinqian Lei, Bo Wang, Robby T. Tan

Fine-Grained Prototypes Distillation for Few-Shot Object Detection

Zichen Wang, Bo Yang, Haonan Yue et al.

Human-like Category Learning by Injecting Ecological Priors from Large Language Models into Neural Networks

Akshay Kumar Jagadish, Julian Coda-Forno, Mirko Thalmann et al.

Improving Generalization in Offline Reinforcement Learning via Adversarial Data Splitting

Da Wang, Lin Li, Wei Wei et al.

Learning Low-dimensional Latent Dynamics from High-dimensional Observations: Non-asymptotics and Lower Bounds

Yuyang Zhang, Shahriar Talebi, Na Li

Learning Modality Knowledge Alignment for Cross-Modality Transfer

Wenxuan Ma, Shuang Li, Lincan Cai et al.

Learning to Continually Learn with the Bayesian Principle

Soochan Lee, Hyeonseong Jeon, Jaehyeon Son et al.

Learning Universal Predictors

Jordi Grau-Moya, Tim Genewein, Marcus Hutter et al.

MALIBO: Meta-learning for Likelihood-free Bayesian Optimization

Jiarong Pan, Stefan Falkner, Felix Berkenkamp et al.

Memory Efficient Neural Processes via Constant Memory Attention Block

Leo Feng, Frederick Tung, Hossein Hajimirsadeghi et al.

MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning

Baoquan Zhang, Chuyao Luo, Demin Yu et al.

Meta Evidential Transformer for Few-Shot Open-Set Recognition

Hitesh Sapkota, Krishna Neupane, Qi Yu

Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates

Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo

One Meta-tuned Transformer is What You Need for Few-shot Learning

Xu Yang, Huaxiu Yao, Ying WEI

Test-Time Domain Adaptation by Learning Domain-Aware Batch Normalization

Test-Time Personalization with Meta Prompt for Gaze Estimation

Huan Liu, Julia Qi, Zhenhao Li et al.