Scott Linderman
31 papers · 2014–2025 · 5 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (43) π Academic Marathon Β· Half π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Gold π£ Hot Topic Early Bird
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Triple Crown
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Keyword Champion
(5)
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Deep Specialist
(16)
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Renaissance Researcher
(6)
π₯
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Prolific Year Β· Productive
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Conference Pioneer
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Century Club Β· Bronze
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Trend Setter
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Keyword Collector
(135)
Top co-authors
Research topics
Machine Learning
(29)
Artificial Intelligence
(21)
Optimization & Theory
(19)
Bayesian & Probabilistic Β· Artificial Intelligence
(16)
Core Methods
(11)
Deep Learning
(10)
Bayesian & Probabilistic Β· Machine Learning
(9)
Core AI
(7)
Interdisciplinary
(5)
Mathematics & Optimization
(5)
Architectures
(5)
Models
(5)
Healthcare & Medicine
(4)
Learning Types
(4)
Optimization
(4)
Keywords
bayesian inference
(12)
variational inference
(11)
state space model
(6)
switching linear dynamical system
(5)
latent variable
(4)
spike train
(4)
point process
(4)
latent variable model
(3)
sequential monte carlo
(3)
hidden markov model
(3)
generalized linear model
(2)
autoregressive model
(2)
time series analysis
(2)
reparameterization gradient
(2)
probabilistic model
(2)
time series
(2)
gibbs sampling
(2)
neural population
(2)
unsupervised learning
(2)
polya-gamma augmentation
(2)
Papers
Cost-efficient Collaboration between On-device and Cloud Language Models
ICML 2025
Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems
NeurIPS 2024
Convolutional State Space Models for Long-Range Spatiotemporal Modeling
NeurIPS 2023
NAS-X: Neural Adaptive Smoothing via Twisting
NeurIPS 2023
Revisiting Structured Variational Autoencoders
ICML 2023
Simplified State Space Layers for Sequence Modeling
ICLR 2023
Switching Autoregressive Low-rank Tensor Models
NeurIPS 2023
Distinguishing discrete and continuous behavioral variability using warped autoregressive HMMs
NeurIPS 2022
SIXO: Smoothing Inference with Twisted Objectives
NeurIPS 2022
Animal pose estimation from video data with a hierarchical von Mises-Fisher-Gaussian model
AISTATS 2021
Generalized Shape Metrics on Neural Representations
NeurIPS 2021
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems
NeurIPS 2021
A general recurrent state space framework for modeling neural dynamics during decision-making
ICML 2020
Point process models for sequence detection in high-dimensional neural spike trains
NeurIPS 2020
Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations
NeurIPS 2020
BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos
NeurIPS 2019
Mutually Regressive Point Processes
NeurIPS 2019
Poisson-Randomized Gamma Dynamical Systems
NeurIPS 2019
Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models
NeurIPS 2019
Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling
ICLR 2019
Learning Latent Permutations with Gumbel-Sinkhorn Networks
ICLR 2018
Point process latent variable models of larval zebrafish behavior
NeurIPS 2018
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference
AISTATS 2018
Variational Sequential Monte Carlo
AISTATS 2018
Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems
AISTATS 2017
Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms
AISTATS 2017
Bayesian latent structure discovery from multi-neuron recordings
NeurIPS 2016
Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders
JMLR 2016
Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation
NeurIPS 2015
A framework for studying synaptic plasticity with neural spike train data
NeurIPS 2014
Discovering Latent Network Structure in Point Process Data
ICML 2014