Scott Linderman
31 papers · 2014–2025 · 5 conferences · across top CS/AI conferences
Achievements
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π Academic Marathon (11) π Conference Polyglot (5) π Interdisciplinary Bridge π§ Keyword Pioneer π£ Hot Topic Early Bird
πΊοΈ
Taxonomy Completionist
(37)
π
Conference Polyglot
(5)
π
Academic Marathon
(11)
π¬
Deep Specialist
(14)
π
Keyword Champion
(4)
π
Triple Crown
ποΈ
Keyword Collector
(123)
β‘
Prolific Year
(5)
π
Conference Pioneer
π
Century Club
(31)
π₯
Unstoppable
(12)
π
Trend Setter
Conferences
NIPS (18)
AISTATS (5)
ICML (4)
ICLR (3)
JMLR (1)
Top co-authors
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