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NeurIPS 2026Meta, transfer & few-shot learning

PaLoRA: Paced Low-Rank Adaptation for Continual Learning

PaLoRA derives an optimal rank-aware pacing law for LoRA continual learning that adaptively restricts gradient scaling to prevent forgetting, improving long-horizon benchmark accuracy by 4%.

Yuxuan Li, Fanhu Zeng, Hao Tang

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published Oct 3, 2026 · ▲ 9 on Hugging Face · Code ★ 2

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NeurIPS 2026MicrosoftMicrosoft ResearchResearch, MicrosoftMicrosoft Research, Redmond, WAMeta, transfer & few-shot learning

Test-Time Learning with an Evolving Library

Weijia Xu, Alessandro Sordoni, Chandan Singh, Zelalem Gero and 3 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026PrincetonMeta, transfer & few-shot learning

Inferring learning rules in deep neural network architectures from animal learning data

Shaunak Bhandarkar, Jonathan Pillow

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026SpotlightYonseiMeta, transfer & few-shot learning

MT-CC: Multi-Group Temperature Scaling for Asymmetric Calibration Behavior in Class-Incremental Learning

Sungjun Yun, Yonghee Choi, Dong-Jun Han

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026FPT SoftwareU California, BerkeleyU Texas at AustinMeta, transfer & few-shot learning

Prediction-only distillation with optimal mixing in ridge-regularized linear and logistic regression

Hien Dang, Pratik Patil, Alessandro Rinaldo

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026The University of Hong KongU HoustonMeta, transfer & few-shot learning

From Generic to Dedicated: A Novel Optimizer for Online Continual Learning

Yongyi Wu, Zheng Wang, Sen Lin

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026IISc SACSpace Applications Centre, ISROIndian Institute of Science, BanMeta, transfer & few-shot learning

TailAdapt: Heavy-Tailed Sparse Variational Adaptation for Long-Tailed Class Incremental Learning

Abhishek Kumar Sinha, Nitant Dube, Soma Biswas

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026GuangzhouPekingDalian University of TechnologyWuhan University of Science and Meta, transfer & few-shot learning

Data-Efficient Learning for Constraint Satisfaction Problems via Relational Biases and Hard Axiom Clamping

Enqiang Zhu, Ke Wang, Yu Zhang, Shengzhi Wang and 2 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026The Hong Kong University of ScieNanyang TechnologicalThe Hong Kong University of ScieHuawei Technologies Ltd.Hong Kong University of Science Meta, transfer & few-shot learning

Curvature-Guided Parameter Initialization for Multi-Task Learning

Linxiao Cao, Zhipeng Zhou, Xutao Huang, Min Zhou and 1 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Harvard, Waddle LabsHarvard CollegeGoogle Deepmind / HarvardMeta, transfer & few-shot learning

Few-shot Task Learning via Compositional Concept Inference

Hanming Ye, Yiding Song, Yilun Du

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Eli Lilly and CompanyMeta, transfer & few-shot learning

DUST: Directional Uncertainty-aware and Scale-invariant Transfer Learning

Abhisek Chakraborty

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026KookminMeta, transfer & few-shot learning

Improving Neural Processes in the Low-Data Regime via Context-Subset Training and Self-Distillation

Hyungi Lee, Jangho Kim

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Seoul NationalMeta, transfer & few-shot learning

Constrained Modulatory Reservoirs for Context-Dependent Computation

Sejeung Choi, Minji Jung, Jinyeong Park, Taek D Chung

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026The Pennsylvania StatePenn StatePennsylvania StateMeta, transfer & few-shot learning

Uncertainty Aware SURE Transfer Learning for Classification Problems

Haoyue Li, Shubo Li, Runze Li

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Maxwell Institute for MathematicU EdinburghHeriot-WattMeta, transfer & few-shot learning

How deep is your network? Deep vs. shallow learning of transfer operators

Mohammad Tabish, Benedict Leimkuhler, Stefan Klus

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026NortheasternMeta, transfer & few-shot learning

DnNP: Denoising Input Uncertainty in Neural Processes

Fatemeh Tohidian, Chengzhi Shi, Soomi Lee, Matthew Elia and 3 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026Nanjingnanjing universityMeta, transfer & few-shot learning

Cross-Architecture Transferability is Width-Confounded:Diagnosis and Subspace Correction

QIN LIU, Chen Zhong, Fengshan Zhao

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026ColumbiaDepartment of Computer Science, Meta, transfer & few-shot learning

Re-evaluating Continual Learning with Few-Shot Adaptation

Few-shot evaluation of continual learning reveals that meta-learning future tasks improves per-shot plasticity and stability across sequences.

Amogh Inamdar, Matthew So, Vici I Milenia, Richard Zemel

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Virginia, CharlottesvilleCiscoCalifornia Institute of TechnoloPurdueU VirginiaMeta, transfer & few-shot learning

Predicting Plasticity in Deep Continual Learning: A Theoretical Perspective

Existing plasticity diagnostics fail to predict trainability, but optimization readiness, combining gradient strength and reliability, lower-bounds optimization gain and predicts plasticity more reliably.

Jiuqi Wang, Jayanth Srinivasa, Claire Chen, Shuze D Liu and 2 more

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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AI panel: 16 of 20 reviewers recommend it
lenient 4/5
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NeurIPS 2026Georgia Institute of TechnologyMeta, transfer & few-shot learning

Trust Region Continual Learning as an Implicit Meta-Learner

Trust region continual learning merges generative replay with Fisher-metric trust regions to implicitly meta-learn rapid task reconvergence without explicit bilevel optimization, outperforming replay and regularization baselines.

Zekun Wang, Anant Gupta, Christopher MacLellan

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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AI panel: 12 of 20 reviewers recommend it
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