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
9 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.
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.
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.
citing papers explorer
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Rank Is Not Capacity: Spectral Occupancy for Latent Graph Models
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.
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ContextualJailbreak: Evolutionary Red-Teaming via Simulated Conversational Priming
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.
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Connections Between Determinantal Point Processes and Gramians in Control
The observability and controllability Gramians parameterized by sensor and actuator node subsets are determinantal point processes.
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Efficient and Accurate Graph Classification with Hyperdimensional Computing on FPGA
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.
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LDDR: Linear-DPP-Based Dynamic-Resolution Frame Sampling for Video MLLMs
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.
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Sampling Pfaffian point processes and the symplectic Arnoldi method
Exact sampling algorithm for Pfaffian point processes via skew-symmetric Cholesky factorization, together with a symplectic Arnoldi method for constructing skew-orthogonal polynomial kernels.
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Adaptive Greedy Frame Selection for Long Video Understanding
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.
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Budget-Aware Routing for Long Clinical Text
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.
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On two ways to use determinantal point processes for Monte Carlo integration
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.