Sprague-Grundy Values of the $ mathcal{R}$-Wythoff Game. - Albert Gu (Electron. J. Comb., 2015)

Recurrence Width for Structured Dense Matrix Vector Multiplication. - Albert Gu; Rohan Puttagunta; Christopher Ré; Atri Rudra (CoRR, 2016)

The Power of Deferral: Maintaining a Constant-Competitive Steiner Tree Online. - Albert Gu; Anupam Gupta 001; Amit Kumar 001 (SIAM J. Comput., 2016)

Learning Invariance with Compact Transforms. - Anna T. Thomas; Albert Gu; Tri Dao; Atri Rudra; Christopher Ré (ICLR (Workshop), 2018)

Representation Tradeoffs for Hyperbolic Embeddings. - Frederic Sala; Christopher De Sa; Albert Gu; Christopher Ré (ICML, 2018)

Learning Compressed Transforms with Low Displacement Rank. - Anna T. Thomas; Albert Gu; Tri Dao; Atri Rudra; Christopher Ré (NeurIPS, 2018)

A Two-pronged Progress in Structured Dense Matrix Vector Multiplication. - Christopher De Sa; Albert Gu; Rohan Puttagunta; Christopher Ré; Atri Rudra (SODA, 2018)

Learning Mixed-Curvature Representations in Product Spaces. - Albert Gu; Frederic Sala; Beliz Gunel; Christopher Ré (ICLR (Poster), 2019)

Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations. - Tri Dao; Albert Gu; Matthew Eichhorn; Atri Rudra; Christopher Ré (ICML, 2019)

A Kernel Theory of Modern Data Augmentation. - Tri Dao; Albert Gu; Alexander Ratner; Virginia Smith; Chris De Sa; Christopher Ré (ICML, 2019)

Improving the Gating Mechanism of Recurrent Neural Networks. - Albert Gu; Çaglar Gülçehre; Tom Le Paine; Matthew W. Hoffman; Razvan Pascanu (CoRR, 2019)

Sparse Recovery for Orthogonal Polynomial Transforms. - Anna C. Gilbert; Albert Gu; Christopher Ré; Atri Rudra; Mary Wootters (ICALP, 2020)

Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps. - Tri Dao; Nimit Sharad Sohoni; Albert Gu; Matthew Eichhorn; Amit Blonder; Megan Leszczynski; Atri Rudra; Christopher Ré (ICLR, 2020)

Improving the Gating Mechanism of Recurrent Neural Networks. - Albert Gu; Çaglar Gülçehre; Thomas Paine; Matt Hoffman 001; Razvan Pascanu (ICML, 2020)

From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering. - Ines Chami; Albert Gu; Vaggos Chatziafratis; Christopher Ré (NeurIPS, 2020)

HiPPO: Recurrent Memory with Optimal Polynomial Projections. - Albert Gu; Tri Dao; Stefano Ermon; Atri Rudra; Christopher Ré (NeurIPS, 2020)

No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems. - Nimit Sharad Sohoni; Jared Dunnmon; Geoffrey Angus; Albert Gu; Christopher Ré (NeurIPS, 2020)

Model Patching: Closing the Subgroup Performance Gap with Data Augmentation. - Karan Goel; Albert Gu; Sharon Li 001; Christopher Ré (ICLR, 2021)

HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical Projections. - Ines Chami; Albert Gu; Dat Nguyen; Christopher Ré (ICML, 2021)

Catformer: Designing Stable Transformers via Sensitivity Analysis. - Jared Quincy Davis; Albert Gu; Krzysztof Choromanski; Tri Dao; Christopher Ré; Chelsea Finn; Percy Liang (ICML, 2021)

Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers. - Albert Gu; Isys Johnson; Karan Goel; Khaled Saab 002; Tri Dao; Atri Rudra; Christopher Ré (NeurIPS, 2021)

Efficiently Modeling Long Sequences with Structured State Spaces. - Albert Gu; Karan Goel; Christopher Ré (ICLR, 2022)

It's Raw! Audio Generation with State-Space Models. - Karan Goel; Albert Gu; Chris Donahue; Christopher Ré (ICML, 2022)

Diagonal State Spaces are as Effective as Structured State Spaces. - Ankit Gupta 001; Albert Gu; Jonathan Berant (NeurIPS, 2022)

On the Parameterization and Initialization of Diagonal State Space Models. - Albert Gu; Karan Goel; Ankit Gupta 001; Christopher Ré (NeurIPS, 2022)

S4ND: Modeling Images and Videos as Multidimensional Signals with State Spaces. - Eric Nguyen; Karan Goel; Albert Gu; Gordon W. Downs; Preey Shah; Tri Dao; Stephen Baccus; Christopher Ré (NeurIPS, 2022)

Towards a General Purpose CNN for Long Range Dependencies in ND. - David W. Romero; David M. Knigge; Albert Gu; Erik J. Bekkers; Efstratios Gavves; Jakub M. Tomczak; Mark Hoogendoorn (CoRR, 2022)

S4ND: Modeling Images and Videos as Multidimensional Signals Using State Spaces. - Eric Nguyen; Karan Goel; Albert Gu; Gordon W. Downs; Preey Shah; Tri Dao; Stephen A. Baccus; Christopher Ré (CoRR, 2022)

Pretraining Without Attention. - Junxiong Wang; Jing Nathan Yan; Albert Gu; Alexander M. Rush (EMNLP (Findings), 2023)

How to Train your HIPPO: State Space Models with Generalized Orthogonal Basis Projections. - Albert Gu; Isys Johnson; Aman Timalsina; Atri Rudra; Christopher Ré (ICLR, 2023)

Modelling Long Range Dependencies in $N$D: From Task-Specific to a General Purpose CNN. - David M. Knigge; David W. Romero; Albert Gu; Efstratios Gavves; Erik J. Bekkers; Jakub Mikolaj Tomczak; Mark Hoogendoorn; Jan-Jakob Sonke (ICLR, 2023)

Resurrecting Recurrent Neural Networks for Long Sequences. - Antonio Orvieto; Samuel L. Smith; Albert Gu; Anushan Fernando; Çaglar Gülçehre; Razvan Pascanu; Soham De (ICML, 2023)

Structured State Space Models for In-Context Reinforcement Learning. - Chris Lu 001; Yannick Schroecker; Albert Gu; Emilio Parisotto; Jakob N. Foerster; Satinder Singh 001; Feryal M. P. Behbahani (NeurIPS, 2023)

Augmenting conformers with structured state space models for online speech recognition. - Haozhe Shan; Albert Gu; Zhong Meng; Weiran Wang; Krzysztof Choromanski; Tara N. Sainath (CoRR, 2023)

Mamba: Linear-Time Sequence Modeling with Selective State Spaces. - Albert Gu; Tri Dao (CoRR, 2023)

Augmenting Conformers With Structured State-Space Sequence Models For Online Speech Recognition. - Haozhe Shan; Albert Gu; Zhong Meng; Weiran Wang; Krzysztof Choromanski; Tara N. Sainath (ICASSP, 2024)

Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality. - Tri Dao; Albert Gu (ICML, 2024)

Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling. - Yair Schiff; Chia-Hsiang Kao; Aaron Gokaslan; Tri Dao; Albert Gu; Volodymyr Kuleshov (ICML, 2024)

Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models. - Aviv Bick; Kevin Y. Li; Eric P. Xing; J. Zico Kolter; Albert Gu (NeurIPS, 2024)

Hydra: Bidirectional State Space Models Through Generalized Matrix Mixers. - Sukjun Hwang; Aakash Sunil Lahoti; Ratish Puduppully; Tri Dao; Albert Gu (NeurIPS, 2024)

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models. - Soham De; Samuel L. Smith; Anushan Fernando; Aleksandar Botev; George-Cristian Muraru; Albert Gu; Ruba Haroun; Leonard Berrada; Yutian Chen 001; Srivatsan Srinivasan; Guillaume Desjardins; Arnaud Doucet; et al. (CoRR, 2024)

An Empirical Study of Mamba-based Language Models. - Roger Waleffe; Wonmin Byeon; Duncan Riach; Brandon Norick; Vijay Korthikanti; Tri Dao; Albert Gu; Ali Hatamizadeh; Sudhakar Singh 003; Deepak Narayanan; Garvit Kulshreshtha; Vartika Singh; et al. (CoRR, 2024)

Hydra: Bidirectional State Space Models Through Generalized Matrix Mixers. - Sukjun Hwang; Aakash Lahoti; Tri Dao; Albert Gu (CoRR, 2024)

On the Benefits of Memory for Modeling Time-Dependent PDEs. - Ricardo Buitrago Ruiz; Tanya Marwah; Albert Gu; Andrej Risteski (ICLR, 2025)

Towards Codec-LM Co-design for Neural Codec Language Models. - Shih-Lun Wu; Aakash Lahoti; Arjun Desai; Karan Goel; Chris Donahue; Albert Gu (NAACL (Student Research Workshop), 2025)

HybriDNA: A Hybrid Transformer-Mamba2 Long-Range DNA Language Model. - Mingqian Ma; Guoqing Liu; Chuan Cao; Pan Deng; Tri Dao; Albert Gu; Peiran Jin; Zhao Yang 006; Yingce Xia; Renqian Luo; Pipi Hu; Zun Wang; et al. (CoRR, 2025)

Llamba: Scaling Distilled Recurrent Models for Efficient Language Processing. - Aviv Bick; Tobias Katsch; Nimit Sharad Sohoni; Arjun Desai; Albert Gu (CoRR, 2025)

Thinking Slow, Fast: Scaling Inference Compute with Distilled Reasoners. - Daniele Paliotta; Junxiong Wang; Matteo Pagliardini; Kevin Y. Li; Aviv Bick; J. Zico Kolter; Albert Gu; François Fleuret; Tri Dao (CoRR, 2025)

Lyra: An Efficient and Expressive Subquadratic Architecture for Modeling Biological Sequences. - Krithik Ramesh; Sameed M. Siddiqui; Albert Gu; Michael Mitzenmacher; Pardis C. Sabeti (CoRR, 2025)

Understanding the Skill Gap in Recurrent Language Models: The Role of the Gather-and-Aggregate Mechanism. - Aviv Bick; Eric P. Xing; Albert Gu (CoRR, 2025)

Understanding and Improving Length Generalization in Recurrent Models. - Ricardo Buitrago Ruiz; Albert Gu (CoRR, 2025)

Dynamic Chunking for End-to-End Hierarchical Sequence Modeling. - Sukjun Hwang; Brandon Wang; Albert Gu (CoRR, 2025)