Nathaniel Grammel

Publication Activity (10 Years)

Years Active: 2016-2023
Publications (10 Years): 19

Top Topics

Monotone Boolean Functions

Learning Processes

Prime Implicants

Top Venues

Publications

Calum MacRury, Will Ma, Nathaniel Grammel
On (Random-order) Online Contention Resolution Schemes for the Matching Polytope of (Bipartite) Graphs. SODA (2023)
Calum MacRury, Will Ma, Nathaniel Grammel
On (Random-order) Online Contention Resolution Schemes for the Matching Polytope of (Bipartite) Graphs. CoRR (2022)
Dimitrios Gkenosis, Nathaniel Grammel, Lisa Hellerstein, Devorah Kletenik
The Stochastic Boolean Function Evaluation problem for symmetric Boolean functions. Discret. Appl. Math. 309 (2022)
Nathaniel Grammel, Lisa Hellerstein, Devorah Kletenik, Naifeng Liu
Algorithms for the Unit-Cost Stochastic Score Classification Problem. Algorithmica 84 (10) (2022)
Nathaniel Grammel, Brian Brubach, Will Ma, Aravind Srinivasan
Follow Your Star: New Frameworks for Online Stochastic Matching with Known and Unknown Patience. AISTATS (2021)
Dimitrios Gkenosis, Nathaniel Grammel, Lisa Hellerstein, Devorah Kletenik
The Stochastic Boolean Function Evaluation Problem for Symmetric Boolean Functions. CoRR (2021)
Justin K. Terry, Benjamin Black, Nathaniel Grammel, Mario Jayakumar, Ananth Hari, Ryan Sullivan, Luis S. Santos, Clemens Dieffendahl, Caroline Horsch, Rodrigo Perez-Vicente, Niall L. Williams, Yashas Lokesh, Praveen Ravi
PettingZoo: Gym for Multi-Agent Reinforcement Learning. NeurIPS (2021)
Brian Brubach, Nathaniel Grammel, David G. Harris, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti
Approximating Two-Stage Stochastic Supplier Problems. APPROX-RANDOM (2021)
Brian Brubach, Nathaniel Grammel, Will Ma, Aravind Srinivasan
Improved Guarantees for Offline Stochastic Matching via new Ordered Contention Resolution Schemes. CoRR (2021)
Brian Brubach, Nathaniel Grammel, Will Ma, Aravind Srinivasan
Improved Guarantees for Offline Stochastic Matching via new Ordered Contention Resolution Schemes. NeurIPS (2021)
Justin K. Terry, Nathaniel Grammel, Ananth Hari, Luis Santos, Benjamin Black, Dinesh Manocha
Parameter Sharing is Surprisingly Useful for Multi-Agent Deep Reinforcement Learning. CoRR (2020)
Brian Brubach, Nathaniel Grammel, David G. Harris, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti
Approximation Algorithms for Radius-Based, Two-Stage Stochastic Clustering Problems with Budget Constraints. CoRR (2020)
Justin K. Terry, Nathaniel Grammel
Multi-Agent Informational Learning Processes. CoRR (2020)
Justin K. Terry, Nathaniel Grammel, Benjamin Black, Ananth Hari, Caroline Horsch, Luis Santos
Agent Environment Cycle Games. CoRR (2020)
Brian Brubach, Nathaniel Grammel, Aravind Srinivasan
Vertex-weighted Online Stochastic Matching with Patience Constraints. CoRR (2019)
Dimitrios Gkenosis, Nathaniel Grammel, Lisa Hellerstein, Devorah Kletenik
The Stochastic Score Classification Problem. CoRR (2018)
Dimitrios Gkenosis, Nathaniel Grammel, Lisa Hellerstein, Devorah Kletenik
The Stochastic Score Classification Problem. ESA (2018)
Nathaniel Grammel, Lisa Hellerstein, Devorah Kletenik, Patrick Lin
Scenario Submodular Cover. WAOA (2016)
Nathaniel Grammel, Lisa Hellerstein, Devorah Kletenik, Patrick Lin
Scenario Submodular Cover. CoRR (2016)