Linderman, Scott

38 publications

ICML 2025 Cost-Efficient Collaboration Between On-Device and Cloud Language Models Avanika Narayan, Dan Biderman, Sabri Eyuboglu, Avner May, Scott Linderman, James Zou, Christopher Re

ICLRW 2025 Cost-Efficient Collaboration Between On-Device and Cloud Language Models Avanika Narayan, Sabri Eyuboglu, Dan Biderman, Avner May, Scott Linderman, James Zou, Christopher Re

NeurIPS 2025 Extracting Task-Relevant Preserved Dynamics from Contrastive Aligned Neural Recordings Yiqi Jiang, Kaiwen Sheng, Yujia Gao, E. Kelly Buchanan, Yu Shikano, Seung Je Woo, Yixiu Zhao, Tony Hyun Kim, Fatih Dinc, Scott Linderman, Mark Schnitzer

NeurIPS 2025 Identifying Multi-Compartment Hodgkin-Huxley Models with High-Density Extracellular Voltage Recordings Ian Christopher Tanoh, Michael Deistler, Jakob H. Macke, Scott Linderman

NeurIPS 2025 Informed Correctors for Discrete Diffusion Models Yixiu Zhao, Jiaxin Shi, Feng Chen, Shaul Druckmann, Lester Mackey, Scott Linderman

NeurIPS 2025 Parallelizing MCMC Across the Sequence Length David M. Zoltowski, Skyler Wu, Xavier Gonzalez, Leo Kozachkov, Scott Linderman

NeurIPS 2025 Predictability Enables Parallelization of Nonlinear State Space Models Xavier Gonzalez, Leo Kozachkov, David M. Zoltowski, Kenneth L. Clarkson, Scott Linderman

NeurIPS 2025 SING: SDE Inference via Natural Gradients Amber Hu, Henry Smith, Scott Linderman

NeurIPS 2025 Weaver: Shrinking the Generation-Verification Gap by Scaling Compute for Verification Jon Saad-Falcon, E. Kelly Buchanan, Mayee F Chen, Tzu-Heng Huang, Brendan McLaughlin, Tanvir Bhathal, Shang Zhu, Ben Athiwaratkun, Frederic Sala, Scott Linderman, Azalia Mirhoseini, Christopher Re

NeurIPS 2024 Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems Amber Hu, David Zoltowski, Aditya Nair, David Anderson, Lea Duncker, Scott Linderman

NeurIPSW 2024 Sa-SVAE: A Shared and Aligned Structured Variational Autoencoder for Extracting Behaviorally Relevant and Preserved Neural Dynamics Across Animals Yiqi Jiang, Kaiwen Sheng, Seung Je Woo, Yu Shikano, Yixiu Zhao, Canwen Zhang, Scott Linderman, Mark Schnitzer

NeurIPS 2023 Convolutional State Space Models for Long-Range Spatiotemporal Modeling Jimmy Smith, Shalini De Mello, Jan Kautz, Scott Linderman, Wonmin Byeon

NeurIPS 2023 NAS-X: Neural Adaptive Smoothing via Twisting Dieterich Lawson, Michael Li, Scott Linderman

ICML 2023 Revisiting Structured Variational Autoencoders Yixiu Zhao, Scott Linderman

ICLR 2023 Simplified State Space Layers for Sequence Modeling Jimmy T.H. Smith, Andrew Warrington, Scott Linderman

NeurIPS 2023 Switching Autoregressive Low-Rank Tensor Models Hyun Dong Lee, Andrew Warrington, Joshua Glaser, Scott Linderman

NeurIPS 2022 Distinguishing Discrete and Continuous Behavioral Variability Using Warped Autoregressive HMMs Julia Costacurta, Lea Duncker, Blue Sheffer, Winthrop Gillis, Caleb Weinreb, Jeffrey Markowitz, Sandeep R Datta, Alex Williams, Scott Linderman

NeurIPS 2022 SIXO: Smoothing Inference with Twisted Objectives Dieterich Lawson, Allan Raventós, Andrew Warrington, Scott Linderman

CoLLAs 2022 Streaming Inference for Infinite Non-Stationary Clustering Rylan Schaeffer, Gabrielle Kaili-may Liu, Yilun Du, Scott Linderman, Ila R. Fiete

ICLRW 2022 Streaming Inference for Infinite Non-Stationary Clustering Rylan Schaeffer, Gabrielle Kaili-May Liu, Yilun Du, Scott Linderman, Ila R Fiete

AISTATS 2021 Animal Pose Estimation from Video Data with a Hierarchical Von Mises-Fisher-Gaussian Model Libby Zhang, Tim Dunn, Jesse Marshall, Bence Olveczky, Scott Linderman

NeurIPS 2021 Generalized Shape Metrics on Neural Representations Alex H Williams, Erin Kunz, Simon Kornblith, Scott Linderman

NeurIPS 2021 Reverse Engineering Recurrent Neural Networks with Jacobian Switching Linear Dynamical Systems Jimmy Smith, Scott Linderman, David Sussillo

ICML 2020 A General Recurrent State Space Framework for Modeling Neural Dynamics During Decision-Making David Zoltowski, Jonathan Pillow, Scott Linderman

NeurIPS 2020 Point Process Models for Sequence Detection in High-Dimensional Neural Spike Trains Alex Williams, Anthony Degleris, Yixin Wang, Scott Linderman

NeurIPS 2020 Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations Joshua Glaser, Matthew Whiteway, John P. Cunningham, Liam Paninski, Scott Linderman

NeurIPS 2019 BehaveNet: Nonlinear Embedding and Bayesian Neural Decoding of Behavioral Videos Eleanor Batty, Matthew Whiteway, Shreya Saxena, Dan Biderman, Taiga Abe, Simon Musall, Winthrop Gillis, Jeffrey Markowitz, Anne Churchland, John P. Cunningham, Sandeep R Datta, Scott Linderman, Liam Paninski

NeurIPS 2019 Mutually Regressive Point Processes Ifigeneia Apostolopoulou, Scott Linderman, Kyle Miller, Artur Dubrawski

NeurIPS 2019 Poisson-Randomized Gamma Dynamical Systems Aaron Schein, Scott Linderman, Mingyuan Zhou, David Blei, Hanna Wallach

NeurIPS 2019 Scalable Bayesian Inference of Dendritic Voltage via Spatiotemporal Recurrent State Space Models Ruoxi Sun, Scott Linderman, Ian Kinsella, Liam Paninski

ICLR 2019 Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling Josue Nassar, Scott Linderman, Monica Bugallo, Il Memming Park

ICLR 2018 Learning Latent Permutations with Gumbel-Sinkhorn Networks Gonzalo Mena, David Belanger, Scott Linderman, Jasper Snoek

NeurIPS 2018 Point Process Latent Variable Models of Larval Zebrafish Behavior Anuj Sharma, Robert Johnson, Florian Engert, Scott Linderman

NeurIPS 2016 Bayesian Latent Structure Discovery from Multi-Neuron Recordings Scott Linderman, Ryan P. Adams, Jonathan W Pillow

JMLR 2016 Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders Huseyin Melih Elibol, Vincent Nguyen, Scott Linderman, Matthew Johnson, Amna Hashmi, Finale Doshi-Velez

NeurIPS 2015 Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-Gamma Augmentation Scott Linderman, Matthew J Johnson, Ryan P. Adams

NeurIPS 2014 A Framework for Studying Synaptic Plasticity with Neural Spike Train Data Scott Linderman, Christopher H Stock, Ryan P. Adams

ICML 2014 Discovering Latent Network Structure in Point Process Data Scott Linderman, Ryan Adams