Discovering Latent Network Structure in Point Process Data. - Scott W. Linderman; Ryan P. Adams (ICML, 2014)

A framework for studying synaptic plasticity with neural spike train data. - Scott W. Linderman; Christopher H. Stock; Ryan P. Adams (NIPS, 2014)

Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation. - Scott W. Linderman; Matthew J. Johnson 002; Ryan P. Adams (NIPS, 2015)

Bayesian nonparametric methods for discovering latent structures of rat hippocampal ensemble spikes. - Zhe Chen 001; Scott W. Linderman; Matthew A. Wilson (MLSP, 2016)

Bayesian latent structure discovery from multi-neuron recordings. - Scott W. Linderman; Ryan P. Adams; Jonathan W. Pillow (NIPS, 2016)

Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders. - Huseyin Melih Elibol; Vincent Nguyen; Scott W. Linderman; Matthew J. Johnson 002; Amna Hashmi; Finale Doshi-Velez (J. Mach. Learn. Res., 2016)

Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems. - Scott W. Linderman; Matthew J. Johnson 002; Andrew C. Miller; Ryan P. Adams; David M. Blei; Liam Paninski (AISTATS, 2017)

Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms. - Christian A. Naesseth; Francisco J. R. Ruiz; Scott W. Linderman; David M. Blei (AISTATS, 2017)

Structure-Exploiting variational inference for recurrent switching linear dynamical systems. - Scott W. Linderman; Matthew J. Johnson 002 (CAMSAP, 2017)

Learning Structured Neural Dynamics From Single Trial Population Recording. - Josue Nassar; Scott W. Linderman; Yuan Zhao 004; Mónica F. Bugallo; Il Memming Park (ACSSC, 2018)

Reparameterizing the Birkhoff Polytope for Variational Permutation Inference. - Scott W. Linderman; Gonzalo E. Mena; Hal James Cooper; Liam Paninski; John P. Cunningham (AISTATS, 2018)

Variational Sequential Monte Carlo. - Christian A. Naesseth; Scott W. Linderman; Rajesh Ranganath; David M. Blei (AISTATS, 2018)

Learning Latent Permutations with Gumbel-Sinkhorn Networks. - Gonzalo E. Mena; David Belanger 002; Scott W. Linderman; Jasper Snoek (ICLR (Poster), 2018)

Point process latent variable models of larval zebrafish behavior. - Anuj Sharma; Robert Johnson; Florian Engert; Scott W. Linderman (NeurIPS, 2018)

Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling. - Josue Nassar; Scott W. Linderman; Mónica F. Bugallo; Il Memming Park (ICLR (Poster), 2019)

Mutually Regressive Point Processes. - Ifigeneia Apostolopoulou; Scott W. Linderman; Kyle Miller 001; Artur Dubrawski (NeurIPS, 2019)

BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos. - Eleanor Batty; Matthew R. Whiteway; Shreya Saxena; Dan Biderman; Taiga Abe; Simon Musall; Winthrop Gillis; Jeffrey E. Markowitz; Anne Churchland; John P. Cunningham; Sandeep R. Datta; Scott W. Linderman; et al. (NeurIPS, 2019)

Poisson-Randomized Gamma Dynamical Systems. - Aaron Schein; Scott W. Linderman; Mingyuan Zhou; David M. Blei; Hanna M. Wallach (NeurIPS, 2019)

Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models. - Ruoxi Sun 006; Scott W. Linderman; Ian Kinsella; Liam Paninski (NeurIPS, 2019)

A general recurrent state space framework for modeling neural dynamics during decision-making. - David M. Zoltowski; Jonathan W. Pillow; Scott W. Linderman (ICML, 2020)

Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations. - Joshua I. Glaser; Matthew R. Whiteway; John P. Cunningham; Liam Paninski; Scott W. Linderman (NeurIPS, 2020)

Point process models for sequence detection in high-dimensional neural spike trains. - Alex H. Williams; Anthony Degleris; Yixin Wang; Scott W. Linderman (NeurIPS, 2020)

Bayesian recurrent state space model for rs-fMRI. - Arunesh Mittal; Scott W. Linderman; John W. Paisley; Paul Sajda (CoRR, 2020)

Animal pose estimation from video data with a hierarchical von Mises-Fisher-Gaussian model. - Libby Zhang; Tim Dunn; Jesse Marshall; Bence Olveczky; Scott W. Linderman (AISTATS, 2021)

Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems. - Jimmy T. H. Smith; Scott W. Linderman; David Sussillo (NeurIPS, 2021)

Generalized Shape Metrics on Neural Representations. - Alex H. Williams; Erin Kunz; Simon Kornblith; Scott W. Linderman (NeurIPS, 2021)

Fast deep learning correspondence for neuron tracking and identification in C.elegans using synthetic training. - Xinwei Yu; Matthew S. Creamer; Francesco Randi; Anuj K. Sharma; Scott W. Linderman; Andrew M. Leifer (CoRR, 2021)

Distinguishing discrete and continuous behavioral variability using warped autoregressive HMMs. - Julia Costacurta; Lea Duncker; Blue Sheffer; Winthrop Gillis; Caleb Weinreb; Jeffrey E. Markowitz; Sandeep R. Datta; Alex H. Williams; Scott W. Linderman (NeurIPS, 2022)

SIXO: Smoothing Inference with Twisted Objectives. - Dieterich Lawson; Allan Raventós; Andrew Warrington; Scott W. Linderman (NeurIPS, 2022)

Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models. - Yixin Wang; Anthony Degleris; Alex H. Williams; Scott W. Linderman (CoRR, 2022)

Simplified State Space Layers for Sequence Modeling. - Jimmy T. H. Smith; Andrew Warrington; Scott W. Linderman (ICLR, 2023)

Revisiting Structured Variational Autoencoders. - Yixiu Zhao; Scott W. Linderman (ICML, 2023)

NAS-X: Neural Adaptive Smoothing via Twisting. - Dieterich Lawson; Michael Li; Scott W. Linderman (NeurIPS, 2023)

Switching Autoregressive Low-rank Tensor Models. - Hyun Dong Lee; Andrew Warrington; Joshua I. Glaser; Scott W. Linderman (NeurIPS, 2023)

Convolutional State Space Models for Long-Range Spatiotemporal Modeling. - Jimmy T. H. Smith; Shalini De Mello; Jan Kautz; Scott W. Linderman; Wonmin Byeon (NeurIPS, 2023)

Emergence of belief-like representations through reinforcement learning. - Jay A. Hennig; Sandra A. Romero Pinto; Takahiro Yamaguchi; Scott W. Linderman; Naoshige Uchida; Samuel J. Gershman (PLoS Comput. Biol., 2023)

Structured flexibility in recurrent neural networks via neuromodulation. - Julia Costacurta; Shaunak Bhandarkar; David M. Zoltowski; Scott W. Linderman (NeurIPS, 2024)

Towards Scalable and Stable Parallelization of Nonlinear RNNs. - Xavier Gonzalez; Andrew Warrington; Jimmy T. H. Smith; Scott W. Linderman (NeurIPS, 2024)

Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems. - Amber Hu; David M. Zoltowski; Aditya Nair; David Anderson; Lea Duncker; Scott W. Linderman (NeurIPS, 2024)

Towards a theory of learning dynamics in deep state space models. - Jakub Smékal; Jimmy T. H. Smith; Michael Kleinman; Dan Biderman; Scott W. Linderman (CoRR, 2024)

Informed Correctors for Discrete Diffusion Models. - Yixiu Zhao; Jiaxin Shi; Lester Mackey; Scott W. Linderman (CoRR, 2024)

Brain-to-Text Benchmark '24: Lessons Learned. - Francis R. Willett; Jingyuan Li; Trung Le 002; Chaofei Fan; Mingfei Chen; Eli Shlizerman; Yue Chen; Xin Zheng; Tatsuo S. Okubo; Tyler Benster; Hyun Dong Lee; Maxwell Kounga; et al. (CoRR, 2024)

A Bayesian Nonparametric Perspective on Mahalanobis Distance for Out of Distribution Detection. - Randolph Linderman; Yiran Chen 001; Scott W. Linderman (CoRR, 2025)

Minions: Cost-efficient Collaboration Between On-device and Cloud Language Models. - Avanika Narayan; Dan Biderman; Sabri Eyuboglu; Avner May; Scott W. Linderman; James Zou 001; Christopher Ré (CoRR, 2025)

Dynamax: A Python package for probabilistic state space modeling with JAX. - Scott W. Linderman; Peter G. Chang; Giles Harper-Donnelly; Aleyna Kara; Xinglong Li; Gerardo Duran-Martin; Kevin Murphy 002 (J. Open Source Softw., 2025)

SING: SDE Inference via Natural Gradients. - Amber Hu; Henry Smith; Scott W. Linderman (CoRR, 2025)

Shrinking the Generation-Verification Gap with Weak Verifiers. - Jon Saad-Falcon; Estefany Kelly Buchanan; Mayee F. Chen; Tzu-Heng Huang; Brendan McLaughlin; Tanvir Bhathal; Shang Zhu; Ben Athiwaratkun; Frederic Sala; Scott W. Linderman; Azalia Mirhoseini; Christopher Ré (CoRR, 2025)