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Showing papers from ETH Zurich / HKU Show all papers

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NeurIPS 2026FudanETH Zurich / HKUEdith CowanSUTDFederated learning

FedVSSAM: Mitigating Flatness Incompatibility in Sharpness-Aware Federated Learning

FedVSSAM fixes flatness incompatibility in federated sharpness-aware learning via variance-suppressed global directions to boost convergence and generalization.

Bingnan Xiao, Yuan Gao, Bingcong Li, Wei Ni and 2 more

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

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12/20 AI panelreviewers recommend it

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AI panel: 12 of 20 reviewers recommend it
lenient 3/5
medium 7/10
strict 2/5
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NeurIPS 2026U GeorgiaETH Zurich / HKUInstruction tuning

Scalable Variational Bayesian Fine-Tuning of LLMs via Orthogonalized Low-Rank Adapters

PoLAR-VBLL uses orthogonalized low-rank adapters with Bayesian last-layer inference to fine-tune LLMs with scalable, well-calibrated uncertainty quantification.

Haotian Xiang, Bingcong Li, Qin Lu

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

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6/20 AI panelreviewers recommend it

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AI panel: 6 of 20 reviewers recommend it
lenient 4/5
medium 2/10
strict 0/5
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NeurIPS 2026Morgan StanleyETH Zurich / HKUETH ZurichU MinnesotaEfficient & distributed training

ANCRe: Adaptive Neural Connection Reassignment for Efficient Depth Scaling

ANCRe learns residual connectivities from data to fix convergence gaps caused by fixed layouts, accelerating training of deep networks with under 1% overhead.

Yilang Zhang, Bingcong Li, Niao He, Georgios Giannakis

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

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16/20 AI panelreviewers recommend it

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AI panel: 16 of 20 reviewers recommend it
lenient 4/5
medium 9/10
strict 3/5