Poster "meta-learning" Papers
33 papers found
Conference
A Meta-Learning Approach to Bayesian Causal Discovery
Anish Dhir, Matthew Ashman, James Requeima et al.
DataRater: Meta-Learned Dataset Curation
Dan Andrei Calian, Greg Farquhar, Iurii Kemaev et al.
Differentially Private Bilevel Optimization: Efficient Algorithms with Near-Optimal Rates
Andrew Lowy, Daogao Liu
Dimension Agnostic Neural Processes
Hyungi Lee, Chaeyun Jang, Dong Bok Lee et al.
Drug-TTA: Test-Time Adaptation for Drug Virtual Screening via Multi-task Meta-Auxiliary Learning
Ao Shen, Ming'zhi Yuan, Yingfan MA et al.
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.
MetaOOD: Automatic Selection of OOD Detection Models
Yuehan Qin, Yichi Zhang, Yi Nian et al.
MetaWriter: Personalized Handwritten Text Recognition Using Meta-Learned Prompt Tuning
Wenhao Gu, Li Gu, Ching Suen et al.
PersonalLLM: Tailoring LLMs to Individual Preferences
Thomas Zollo, Andrew Siah, Naimeng Ye 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.
Provably Efficient Multi-Task Meta Bandit Learning via Shared Representations
Jiabin Lin, Shana Moothedath
qNBO: quasi-Newton Meets Bilevel Optimization
Sheng Fang, Yongjin Liu, Wei Yao et al.
Test Time Scaling for Neural Processes
Hyungi Lee, Moonseok Choi, Hyunsu Kim et al.
Why In-Context Learning Models are Good Few-Shot Learners?
Shiguang Wu, Yaqing Wang, Quanming Yao
A Nearly Optimal Single Loop Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness
Xiaochuan Gong, Jie Hao, Mingrui Liu
Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling
Wonho Bae, Jing Wang, Danica J. Sutherland
Few-Shot Character Understanding in Movies as an Assessment to Meta-Learning of Theory-of-Mind
Mo Yu, Qiujing Wang, Shunchi Zhang 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 to Obstruct Few-Shot Image Classification over Restricted Classes
Amber Yijia Zheng, Chiao-An Yang, Raymond Yeh
Learning Universal Predictors
Jordi Grau-Moya, Tim Genewein, Marcus Hutter et al.
Memory Efficient Neural Processes via Constant Memory Attention Block
Leo Feng, Frederick Tung, Hossein Hajimirsadeghi 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