Scott W. Linderman

70 papers A* 22A 5Misc 1Journal 35Unranked 7

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Bayesian Nonparametric Perspective on Mahalanobis Distance for Out of Distribution Detection. Trans. Mach. Learn. Res. Randolph Linderman, Noah Cowan, Yiran Chen, Scott W. Linderman
2026	J	`jnl`	A Unifying Framework for Parallelizing Sequential Models with Linear Dynamical Systems. Trans. Mach. Learn. Res. Xavier Gonzalez, Estefany Kelly Buchanan, Hyun Dong Lee, Jerry Weihong Liu, Ke Alexander Wang, David M. Zoltowski, Leo Kozachkov, Christopher Ré, Scott W. Linderman
2025	J	`jnl`	A Bayesian Nonparametric Perspective on Mahalanobis Distance for Out of Distribution Detection. CoRR Randolph Linderman, Yiran Chen, Scott W. Linderman
2025	J	`jnl`	A Bayesian hierarchical model of trial-to-trial fluctuations in decision criterion. PLoS Comput. Biol. Robin Vloeberghs, Anne E. Urai, Kobe Desender, Scott W. Linderman
2025	J	`jnl`	A Unifying Framework for Parallelizing Sequential Models with Linear Dynamical Systems. CoRR Xavier Gonzalez, Estefany Kelly Buchanan, Hyun Dong Lee, Jerry Weihong Liu, Ke Alexander Wang, David M. Zoltowski, Christopher Ré, Scott W. Linderman
2025	J	`jnl`	An Information Theoretic Perspective on Agentic System Design. CoRR Shizhe He, Avanika Narayan, Ishan S. Khare, Scott W. Linderman, Christopher Ré, Dan Biderman
2025	A*	`conf`	Cost-efficient Collaboration between On-device and Cloud Language Models. ICML Avanika Narayan, Dan Biderman, Sabri Eyuboglu, Avner May, Scott W. Linderman, James Zou, Christopher Ré
2025	J	`jnl`	Decoding inner speech with an end-to-end brain-to-text neural interface. CoRR Yizi Zhang, Linyang He, Chaofei Fan, Tingkai Liu, Han Yu, Trung Le, Jingyuan Li, Scott W. Linderman, Lea Duncker, Francis R. Willett, Nima Mesgarani, Liam Paninski
2025	J	`jnl`	Dynamax: A Python package for probabilistic state space modeling with JAX. J. Open Source Softw. Scott W. Linderman, Peter G. Chang, Giles Harper-Donnelly, Aleyna Kara, Xinglong Li, Gerardo Duran-Martin, Kevin Murphy
2025	J	`jnl`	Minions: Cost-efficient Collaboration Between On-device and Cloud Language Models. CoRR Avanika Narayan, Dan Biderman, Sabri Eyuboglu, Avner May, Scott W. Linderman, James Zou, Christopher Ré
2025	J	`jnl`	Predictability Enables Parallelization of Nonlinear State Space Models. CoRR Xavier Gonzalez, Leo Kozachkov, David M. Zoltowski, Kenneth L. Clarkson, Scott W. Linderman
2025	J	`jnl`	SING: SDE Inference via Natural Gradients. CoRR Amber Hu, Henry Smith, Scott W. Linderman
2025	J	`jnl`	Shrinking the Generation-Verification Gap with Weak Verifiers. CoRR 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
2024	J	`jnl`	Brain-to-Text Benchmark '24: Lessons Learned. CoRR Francis R. Willett, Jingyuan Li, Trung Le, Chaofei Fan, Mingfei Chen, Eli Shlizerman, Yue Chen, Xin Zheng, Tatsuo S. Okubo, Tyler Benster, Hyun Dong Lee, Maxwell Kounga, Estefany Kelly Buchanan, David M. Zoltowski, Scott W. Linderman, Jaimie M. Henderson
2024	J	`jnl`	Informed Correctors for Discrete Diffusion Models. CoRR Yixiu Zhao, Jiaxin Shi, Lester Mackey, Scott W. Linderman
2024	A*	`conf`	Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems. NeurIPS Amber Hu, David M. Zoltowski, Aditya Nair, David Anderson, Lea Duncker, Scott W. Linderman
2024	J	`jnl`	Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems. CoRR Amber Hu, David M. Zoltowski, Aditya Nair, David Anderson, Lea Duncker, Scott W. Linderman
2024	A*	`conf`	Structured flexibility in recurrent neural networks via neuromodulation. NeurIPS Julia Costacurta, Shaunak Bhandarkar, David M. Zoltowski, Scott W. Linderman
2024	A*	`conf`	Towards Scalable and Stable Parallelization of Nonlinear RNNs. NeurIPS Xavier Gonzalez, Andrew Warrington, Jimmy T. H. Smith, Scott W. Linderman
2024	J	`jnl`	Towards Scalable and Stable Parallelization of Nonlinear RNNs. CoRR Xavier Gonzalez, Andrew Warrington, Jimmy T. H. Smith, Scott W. Linderman
2024	J	`jnl`	Towards a theory of learning dynamics in deep state space models. CoRR Jakub Smékal, Jimmy T. H. Smith, Michael Kleinman, Dan Biderman, Scott W. Linderman
2023	A*	`conf`	Convolutional State Space Models for Long-Range Spatiotemporal Modeling. NeurIPS Jimmy T. H. Smith, Shalini De Mello, Jan Kautz, Scott W. Linderman, Wonmin Byeon
2023	J	`jnl`	Convolutional State Space Models for Long-Range Spatiotemporal Modeling. CoRR Jimmy T. H. Smith, Shalini De Mello, Jan Kautz, Scott W. Linderman, Wonmin Byeon
2023	J	`jnl`	Emergence of belief-like representations through reinforcement learning. PLoS Comput. Biol. Jay A. Hennig, Sandra A. Romero Pinto, Takahiro Yamaguchi, Scott W. Linderman, Naoshige Uchida, Samuel J. Gershman
2023	A*	`conf`	NAS-X: Neural Adaptive Smoothing via Twisting. NeurIPS Dieterich Lawson, Michael Li, Scott W. Linderman
2023	J	`jnl`	NAS-X: Neural Adaptive Smoothing via Twisting. CoRR Dieterich Lawson, Michael Li, Scott W. Linderman
2023	A*	`conf`	Revisiting Structured Variational Autoencoders. ICML Yixiu Zhao, Scott W. Linderman
2023	J	`jnl`	Revisiting Structured Variational Autoencoders. CoRR Yixiu Zhao, Scott W. Linderman
2023	A*	`conf`	Simplified State Space Layers for Sequence Modeling. ICLR Jimmy T. H. Smith, Andrew Warrington, Scott W. Linderman
2023	A*	`conf`	Switching Autoregressive Low-rank Tensor Models. NeurIPS Hyun Dong Lee, Andrew Warrington, Joshua I. Glaser, Scott W. Linderman
2023	J	`jnl`	Switching Autoregressive Low-rank Tensor Models. CoRR Hyun Dong Lee, Andrew Warrington, Joshua I. Glaser, Scott W. Linderman
2022	A*	`conf`	Distinguishing discrete and continuous behavioral variability using warped autoregressive HMMs. NeurIPS Julia Costacurta, Lea Duncker, Blue Sheffer, Winthrop Gillis, Caleb Weinreb, Jeffrey E. Markowitz, Sandeep R. Datta, Alex H. Williams, Scott W. Linderman
2022	A*	`conf`	SIXO: Smoothing Inference with Twisted Objectives. NeurIPS Dieterich Lawson, Allan Raventós, Andrew Warrington, Scott W. Linderman
2022	J	`jnl`	SIXO: Smoothing Inference with Twisted Objectives. CoRR Dieterich Lawson, Allan Raventós, Andrew Warrington, Scott W. Linderman
2022	J	`jnl`	Simplified State Space Layers for Sequence Modeling. CoRR Jimmy T. H. Smith, Andrew Warrington, Scott W. Linderman
2022	J	`jnl`	Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models. CoRR Yixin Wang, Anthony Degleris, Alex H. Williams, Scott W. Linderman
2021	A	`conf`	Animal pose estimation from video data with a hierarchical von Mises-Fisher-Gaussian model. AISTATS Libby Zhang, Tim Dunn, Jesse Marshall, Bence Olveczky, Scott W. Linderman
2021	J	`jnl`	Fast deep learning correspondence for neuron tracking and identification in C.elegans using synthetic training. CoRR Xinwei Yu, Matthew S. Creamer, Francesco Randi, Anuj K. Sharma, Scott W. Linderman, Andrew M. Leifer
2021	A*	`conf`	Generalized Shape Metrics on Neural Representations. NeurIPS Alex H. Williams, Erin Kunz, Simon Kornblith, Scott W. Linderman
2021	J	`jnl`	Generalized Shape Metrics on Neural Representations. CoRR Alex H. Williams, Erin Kunz, Simon Kornblith, Scott W. Linderman
2021	A*	`conf`	Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems. NeurIPS Jimmy T. H. Smith, Scott W. Linderman, David Sussillo
2021	J	`jnl`	Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems. CoRR Jimmy T. H. Smith, Scott W. Linderman, David Sussillo
2020	A*	`conf`	A general recurrent state space framework for modeling neural dynamics during decision-making. ICML David M. Zoltowski, Jonathan W. Pillow, Scott W. Linderman
2020	J	`jnl`	Bayesian recurrent state space model for rs-fMRI. CoRR Arunesh Mittal, Scott W. Linderman, John W. Paisley, Paul Sajda
2020	A*	`conf`	Point process models for sequence detection in high-dimensional neural spike trains. NeurIPS Alex H. Williams, Anthony Degleris, Yixin Wang, Scott W. Linderman
2020	J	`jnl`	Point process models for sequence detection in high-dimensional neural spike trains. CoRR Alex H. Williams, Anthony Degleris, Yixin Wang, Scott W. Linderman
2020	A*	`conf`	Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations. NeurIPS Joshua I. Glaser, Matthew R. Whiteway, John P. Cunningham, Liam Paninski, Scott W. Linderman
2019	A*	`conf`	BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos. NeurIPS 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, Liam Paninski
2019	A*	`conf`	Mutually Regressive Point Processes. NeurIPS Ifigeneia Apostolopoulou, Scott W. Linderman, Kyle Miller, Artur Dubrawski
2019	A*	`conf`	Poisson-Randomized Gamma Dynamical Systems. NeurIPS Aaron Schein, Scott W. Linderman, Mingyuan Zhou, David M. Blei, Hanna M. Wallach
2019	J	`jnl`	Poisson-Randomized Gamma Dynamical Systems. CoRR Aaron Schein, Scott W. Linderman, Mingyuan Zhou, David M. Blei, Hanna M. Wallach
2019	A*	`conf`	Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models. NeurIPS Ruoxi Sun, Scott W. Linderman, Ian Kinsella, Liam Paninski
2019	—	`conf`	Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling. ICLR (Poster) Josue Nassar, Scott W. Linderman, Mónica F. Bugallo, Il Memming Park
2018	—	`conf`	Learning Latent Permutations with Gumbel-Sinkhorn Networks. ICLR (Poster) Gonzalo E. Mena, David Belanger, Scott W. Linderman, Jasper Snoek
2018	J	`jnl`	Learning Latent Permutations with Gumbel-Sinkhorn Networks. CoRR Gonzalo E. Mena, David Belanger, Scott W. Linderman, Jasper Snoek
2018	Misc	`conf`	Learning Structured Neural Dynamics From Single Trial Population Recording. ACSSC Josue Nassar, Scott W. Linderman, Yuan Zhao, Mónica F. Bugallo, Il Memming Park
2018	A*	`conf`	Point process latent variable models of larval zebrafish behavior. NeurIPS Anuj Sharma, Robert Johnson, Florian Engert, Scott W. Linderman
2018	A	`conf`	Reparameterizing the Birkhoff Polytope for Variational Permutation Inference. AISTATS Scott W. Linderman, Gonzalo E. Mena, Hal James Cooper, Liam Paninski, John P. Cunningham
2018	J	`jnl`	Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling. CoRR Josue Nassar, Scott W. Linderman, Mónica F. Bugallo, Il Memming Park
2018	A	`conf`	Variational Sequential Monte Carlo. AISTATS Christian A. Naesseth, Scott W. Linderman, Rajesh Ranganath, David M. Blei
2017	A	`conf`	Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems. AISTATS Scott W. Linderman, Matthew J. Johnson, Andrew C. Miller, Ryan P. Adams, David M. Blei, Liam Paninski
2017	A	`conf`	Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms. AISTATS Christian A. Naesseth, Francisco J. R. Ruiz, Scott W. Linderman, David M. Blei
2017	—	`conf`	Structure-Exploiting variational inference for recurrent switching linear dynamical systems. CAMSAP Scott W. Linderman, Matthew J. Johnson
2016	—	`conf`	Bayesian latent structure discovery from multi-neuron recordings. NIPS Scott W. Linderman, Ryan P. Adams, Jonathan W. Pillow
2016	—	`conf`	Bayesian nonparametric methods for discovering latent structures of rat hippocampal ensemble spikes. MLSP Zhe Chen, Scott W. Linderman, Matthew A. Wilson
2016	J	`jnl`	Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders. J. Mach. Learn. Res. Huseyin Melih Elibol, Vincent Nguyen, Scott W. Linderman, Matthew J. Johnson, Amna Hashmi, Finale Doshi-Velez
2015	—	`conf`	Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation. NIPS Scott W. Linderman, Matthew J. Johnson, Ryan P. Adams
2014	—	`conf`	A framework for studying synaptic plasticity with neural spike train data. NIPS Scott W. Linderman, Christopher H. Stock, Ryan P. Adams
2014	A*	`conf`	Discovering Latent Network Structure in Point Process Data. ICML Scott W. Linderman, Ryan P. Adams
2014	J	`jnl`	Discovering Latent Network Structure in Point Process Data. CoRR Scott W. Linderman, Ryan P. Adams