6 papers
Feature Priming in Online Linear Regression: Sparse-Regret Lower Bounds and a Tight Univariate Rate
Huibo Xu, Shi Fu, Qixin Zhang +1
In high-dimensional online prediction, the best predictor may depend on only a few features, so regret should scale with sparsity rather than the ambient dimension. Feature priming…
Multinoulli Extension: A Lossless Continuous Relaxation for Partition-Constrained Subset Selection
Qixin Zhang, Wei Huang, Yan Sun +3
Identifying the most representative subset for a close-to-submodular objective while satisfying the predefined partition constraint is a fundamental task with numerous applications…
Near-Oracle KV Selection via Pre-hoc Sparsity for Long-Context Inference
Yifei Gao, Lei Wang, Rong-Cheng Tu +3
A core bottleneck in large language model (LLM) inference is the cost of attending over the ever-growing key-value (KV) cache. Although near-oracle top-k KV selection can preserve…
Time Is All It Takes: Spike-Retiming Attacks on Event-Driven Spiking Neural Networks
Yi Yu, Qixin Zhang, Shuhan Ye +6
Spiking neural networks (SNNs) compute with discrete spikes and exploit temporal structure, yet most adversarial attacks change intensities or event counts instead of timing. We st…
Efficient Differentiable Causal Discovery via Reliable Super-Structure Learning
Pingchuan Ma, Qixin Zhang, Shuai Wang +1
Recently, differentiable causal discovery has emerged as a promising approach to improve the accuracy and efficiency of existing methods. However, when applied to high-dimensional…
Sparse by Rule: Probability-Based N:M Pruning for Spiking Neural Networks
Shuhan Ye, Yi Yu, Qixin Zhang +4
Brain-inspired Spiking neural networks (SNNs) promise energy-efficient intelligence via event-driven, sparse computation, but deeper architectures inflate parameters and computatio…