Spectra defines and controls effective capacity in graph embeddings via the Shannon effective rank of a trace-normalized kernel spectrum, making capacity a post-fit property rather than a pre-training hyperparameter.
Determinantal point processes for machine learning.Foundations and Trends® in Machine Learning, 5(2–3):123–286, December 2012
11 Pith papers cite this work. Polarity classification is still indexing.
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ContextualJailbreak uses evolutionary search over simulated primed dialogues with novel mutations to reach 90-100% attack success on open LLMs and transfers to some closed frontier models at 15-90% rates.
The observability and controllability Gramians parameterized by sensor and actuator node subsets are determinantal point processes.
HyperX is the first end-to-end FPGA accelerator for Nyström-based HDC graph classification, delivering 6.85× speedup and 169× energy efficiency over CPU baselines plus 3.4% average accuracy gain on TUDataset benchmarks.
B3O generates large batches by sampling from the acquisition function's Boltzmann distribution, claiming negligible extra regret and better performance than prior batch BO methods on benchmarks and applied tasks.
LDDR proposes a linear DPP-based dynamic-resolution frame sampler that achieves 3x speedup and up to 2.5-point gains on video MLLM benchmarks by selecting non-redundant frames and allocating tokens accordingly.
Exact sampling algorithm for Pfaffian point processes via skew-symmetric Cholesky factorization, together with a symplectic Arnoldi method for constructing skew-orthogonal polynomial kernels.
A question-adaptive greedy frame selector combines SigLIP relevance and DINOv2 coverage under a submodular objective with a text classifier routing to preset trade-offs, yielding accuracy gains on MLVU especially at low frame budgets.
Proposes a minimum measurement standard for LLM-as-a-judge in multi-hop RAG that fixes budgets and requires cluster-aware inference, showing it alters which baseline comparisons remain significant.
RCD balances relevance, coverage, and diversity in a knapsack-constrained selection framework, with experiments showing that selector choice and budget level determine optimal unitization strategies on clinical datasets.
Generalizing two DPP-based Monte Carlo estimators to continuous domains provides variance rates of O(N^{-(1+1/d)}) for a fixed DPP method and O(1/N) for a tailored DPP method, along with new sampling algorithms.
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A Fixed-Budget, Cluster-Aware Standard for LLM-as-a-Judge Evaluation: A Multi-Hop RAG Stress Test
Proposes a minimum measurement standard for LLM-as-a-judge in multi-hop RAG that fixes budgets and requires cluster-aware inference, showing it alters which baseline comparisons remain significant.