The first dynamic algorithms for matrix rank and related objects achieve update times scaling with rank r, specifically Õ(r^1.405) per entry update and Õ(r^1.528 + z) per column update, extending to dynamic maximum matching.
Fully Dynamic Maximal Independent Set in Expected Poly-Log Update Time , booktitle =
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A deterministic semi-streaming algorithm achieves an O(Δ)-coloring in O(√log Δ) passes, the first with linear palette size and sublogarithmic passes.
The authors derive the first bit-accurate arithmetic models for matrix multiply-accumulate operations on ten GPU architectures spanning NVIDIA Volta to Blackwell and AMD CDNA1 to CDNA3.
Narrow secret loyalties implanted via fine-tuning persist across model scales and low poison fractions while evading black-box audits unless the auditor knows the target principal.
Unsupervised GNN model learns local updates for approximate MaxIS on dynamic graphs, achieving competitive ratios on 200-1000 node instances and 1.00-1.18x larger solutions than other unsupervised models when generalizing to 100x larger graphs.
GRASP reduces communication in remote control by 12-fold on average (50-fold for continuous actions) by having actors generate actions via guided sampling and local policy learning instead of receiving full actions or rewards.
Module-switching defense disrupts backdoors more effectively than weight averaging with fewer models and remains robust even when some models share the same backdoors.
citing papers explorer
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Dynamic Rank, Basis, and Matching
The first dynamic algorithms for matrix rank and related objects achieve update times scaling with rank r, specifically Õ(r^1.405) per entry update and Õ(r^1.528 + z) per column update, extending to dynamic maximum matching.
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Faster Deterministic Streaming Vertex Coloring
A deterministic semi-streaming algorithm achieves an O(Δ)-coloring in O(√log Δ) passes, the first with linear palette size and sublogarithmic passes.
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Bit-Accurate Modeling of GPU Matrix Multiply-Accumulate Units: Demystifying Numerical Discrepancy and Accuracy
The authors derive the first bit-accurate arithmetic models for matrix multiply-accumulate operations on ten GPU architectures spanning NVIDIA Volta to Blackwell and AMD CDNA1 to CDNA3.
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Narrow Secret Loyalty Dodges Black-Box Audits
Narrow secret loyalties implanted via fine-tuning persist across model scales and low poison fractions while evading black-box audits unless the auditor knows the target principal.
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Unsupervised Learning of Local Updates for Maximum Independent Set in Dynamic Graphs
Unsupervised GNN model learns local updates for approximate MaxIS on dynamic graphs, achieving competitive ratios on 200-1000 node instances and 1.00-1.18x larger solutions than other unsupervised models when generalizing to 100x larger graphs.
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Remote Action Generation: Remote Control with Minimal Communication
GRASP reduces communication in remote control by 12-fold on average (50-fold for continuous actions) by having actors generate actions via guided sampling and local policy learning instead of receiving full actions or rewards.
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Defending against Backdoor Attacks via Module Switching
Module-switching defense disrupts backdoors more effectively than weight averaging with fewer models and remains robust even when some models share the same backdoors.
- Your Neighbors Know: Leveraging Local Neighborhoods for Backdoor Detection in Decentralized Learning