Cover Image

Speculative Sampling for Parametric Temporal Point Processes

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Biloš, Marin, Schneider, Anderson, Nevmyvaka, Yuriy
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Mechanistic Study of Tabular Foundation Models
by: Biloš, Marin, et al.
Published: (2026)

Add and Thin: Diffusion for Temporal Point Processes
by: Lüdke, David, et al.
Published: (2023)

Variational Schrödinger Diffusion Models
by: Deng, Wei, et al.
Published: (2024)

Technical Report: Full-Stack Fine-Tuning for the Q Programming Language
by: Hogan, Brendan R., et al.
Published: (2025)

Towards Interpretable and Trustworthy Time Series Reasoning: A BlueSky Vision
by: Ning, Kanghui, et al.
Published: (2025)

$\textbf{S}^2$IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting
by: Pan, Zijie, et al.
Published: (2024)

Recurrent Interpolants for Probabilistic Time Series Prediction
by: Chen, Yu, et al.
Published: (2024)

Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting
by: Schuchardt, Jan, et al.
Published: (2025)

Improving Reasoning for Diffusion Language Models via Group Diffusion Policy Optimization
by: Rojas, Kevin, et al.
Published: (2025)

Empowering Time Series Analysis with Large Language Models: A Survey
by: Jiang, Yushan, et al.
Published: (2024)

Structural Knowledge Informed Continual Multivariate Time Series Forecasting
by: Pan, Zijie, et al.
Published: (2024)

TPP-SD: Accelerating Transformer Point Process Sampling with Speculative Decoding
by: Gong, Shukai, et al.
Published: (2025)

Reweighting Improves Conditional Risk Bounds
by: Zhang, Yikai, et al.
Published: (2025)

Variational Schrödinger Momentum Diffusion
by: Rojas, Kevin, et al.
Published: (2025)

AlphaLab: Autonomous Multi-Agent Research Across Optimization Domains with Frontier LLMs
by: Hogan, Brendan R., et al.
Published: (2026)

Deep Generative Sampling in the Dual Divergence Space: A Data-efficient & Interpretative Approach for Generative AI
by: Garg, Sahil, et al.
Published: (2024)

Cubit: Token Mixer with Kernel Ridge Regression
by: Zheng, Chuanyang, et al.
Published: (2026)

Multi-modal Time Series Analysis: A Tutorial and Survey
by: Jiang, Yushan, et al.
Published: (2025)

PRISM: Parametrically Refactoring Inference for Speculative Sampling Draft Models
by: Wang, Xuliang, et al.
Published: (2026)

Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
by: Rasul, Kashif, et al.
Published: (2023)

Chart-RVR: Reinforcement Learning with Verifiable Rewards for Explainable Chart Reasoning
by: Sinha, Sanchit, et al.
Published: (2025)

Small Vocabularies, Big Gains: Pretraining and Tokenization in Time Series Models
by: Roger, Alexis, et al.
Published: (2025)

Reinforcing Multi-Turn Reasoning in LLM Agents via Turn-Level Reward Design
by: Wei, Quan, et al.
Published: (2025)

TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster
by: Ning, Kanghui, et al.
Published: (2025)

Neuro-Symbolic Temporal Point Processes
by: Yang, Yang, et al.
Published: (2024)

Marked Temporal Bayesian Flow Point Processes
by: Chen, Hui, et al.
Published: (2024)

Speculative Sampling with Reinforcement Learning
by: Wang, Chenan, et al.
Published: (2026)

Accelerated Diffusion Models via Speculative Sampling
by: De Bortoli, Valentin, et al.
Published: (2025)

Hyper Hawkes Processes: Interpretable Models of Marked Temporal Point Processes
by: Boyd, Alex, et al.
Published: (2025)

Edit-Based Flow Matching for Temporal Point Processes
by: Lüdke, David, et al.
Published: (2025)

Learning Harmonized Representations for Speculative Sampling
by: Zhang, Lefan, et al.
Published: (2024)

A Bayesian Mixture Model of Temporal Point Processes with Determinantal Point Process Prior
by: Dong, Yiwei, et al.
Published: (2024)

Interpretable Hybrid-Rule Temporal Point Processes
by: Cao, Yunyang, et al.
Published: (2025)

Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches
by: Zhou, Feng, et al.
Published: (2025)

In-Context Learning of Temporal Point Processes with Foundation Inference Models
by: Berghaus, David, et al.
Published: (2025)

On Foundation Models for Temporal Point Processes to Accelerate Scientific Discovery
by: Berghaus, David, et al.
Published: (2025)

Meta-Learning for Neural Network-based Temporal Point Processes
by: Takimoto, Yoshiaki, et al.
Published: (2024)

Preventing Conflicting Gradients in Neural Marked Temporal Point Processes
by: Bosser, Tanguy, et al.
Published: (2024)

EasyTPP: Towards Open Benchmarking Temporal Point Processes
by: Xue, Siqiao, et al.
Published: (2023)

EventFlow: Forecasting Temporal Point Processes with Flow Matching
by: Kerrigan, Gavin, et al.
Published: (2024)