← Authors

Scott Linderman

31 papers · 2014–2025 · 5 conferences · across top CS/AI conferences

Achievements

🌈 Renaissance Researcher (7) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (13) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (37) 🌍 Conference Polyglot (5) πŸƒ Academic Marathon (11) πŸ”¬ Deep Specialist (14) πŸ† Keyword Champion (4) πŸ‘‘ Triple Crown πŸ—ƒοΈ Keyword Collector (123) ⚑ Prolific Year (5) πŸš€ Conference Pioneer πŸ’Ž Century Club (31) πŸ”₯ Unstoppable (12) πŸ“ˆ Trend Setter

Conferences

NIPS (18) AISTATS (5) ICML (4) ICLR (3) JMLR (1)

Top co-authors

Liam Paninski (5) David Blei (4) Ryan P. Adams (3) Andrew Warrington (3) Ryan Adams (2) Gonzalo Mena (2) David Zoltowski (2) Jimmy Smith (2) Jeffrey Markowitz (2) Winthrop Gillis (2)

Keywords

bayesian inference (11) variational inference (11) state space model (5) switching linear dynamical system (4) spike train (4) point process (4) latent variable (3) sequential monte carlo (3) hidden markov model (3) polya-gamma augmentation (2) reparameterization gradient (2) probabilistic model (2) generalized linear model (2) latent variable model (2) neural population (2) time series (2) unsupervised learning (2) autoregressive model (2) tensor decomposition (1) time warping (1)

Papers

Cost-efficient Collaboration between On-device and Cloud Language Models ICML 2025
Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems NIPS 2024
Revisiting Structured Variational Autoencoders ICML 2023
NAS-X: Neural Adaptive Smoothing via Twisting NIPS 2023
Switching Autoregressive Low-rank Tensor Models NIPS 2023
Convolutional State Space Models for Long-Range Spatiotemporal Modeling NIPS 2023
Simplified State Space Layers for Sequence Modeling ICLR 2023
SIXO: Smoothing Inference with Twisted Objectives NIPS 2022
Distinguishing discrete and continuous behavioral variability using warped autoregressive HMMs NIPS 2022
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems NIPS 2021
Animal pose estimation from video data with a hierarchical von Mises-Fisher-Gaussian model AISTATS 2021
Generalized Shape Metrics on Neural Representations NIPS 2021
Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations NIPS 2020
Point process models for sequence detection in high-dimensional neural spike trains NIPS 2020
A general recurrent state space framework for modeling neural dynamics during decision-making ICML 2020
BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos NIPS 2019
Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling ICLR 2019
Poisson-Randomized Gamma Dynamical Systems NIPS 2019
Mutually Regressive Point Processes NIPS 2019
Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models NIPS 2019
Variational Sequential Monte Carlo AISTATS 2018
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference AISTATS 2018
Point process latent variable models of larval zebrafish behavior NIPS 2018
Learning Latent Permutations with Gumbel-Sinkhorn Networks ICLR 2018
Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms AISTATS 2017
Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems AISTATS 2017
Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders JMLR 2016
Bayesian latent structure discovery from multi-neuron recordings NIPS 2016
Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation NIPS 2015
Discovering Latent Network Structure in Point Process Data ICML 2014
A framework for studying synaptic plasticity with neural spike train data NIPS 2014