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2020 – today
- 2023
[c7]Calum MacRury, Will Ma, Nathaniel Grammel:
On (Random-order) Online Contention Resolution Schemes for the Matching Polytope of (Bipartite) Graphs. SODA 2023: 1995-2014- 2022
- [j2]Nathaniel Grammel, Lisa Hellerstein
, Devorah Kletenik, Naifeng Liu:
Algorithms for the Unit-Cost Stochastic Score Classification Problem. Algorithmica 84(10): 3054-3074 (2022) - [j1]Dimitrios Gkenosis, Nathaniel Grammel, Lisa Hellerstein, Devorah Kletenik:
The Stochastic Boolean Function Evaluation problem for symmetric Boolean functions. Discret. Appl. Math. 309: 269-277 (2022) - [i8]Calum MacRury, Will Ma, Nathaniel Grammel:
On (Random-order) Online Contention Resolution Schemes for the Matching Polytope of (Bipartite) Graphs. CoRR abs/2209.07520 (2022) - 2021
- [c6]Nathaniel Grammel, Brian Brubach, Will Ma, Aravind Srinivasan:
Follow Your Star: New Frameworks for Online Stochastic Matching with Known and Unknown Patience. AISTATS 2021: 2872-2880 - [c5]Brian Brubach, Nathaniel Grammel, David G. Harris, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti:
Approximating Two-Stage Stochastic Supplier Problems. APPROX-RANDOM 2021: 23:1-23:22 - [c4]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: 15032-15043 - [c3]Brian Brubach, Nathaniel Grammel, Will Ma, Aravind Srinivasan:
Improved Guarantees for Offline Stochastic Matching via new Ordered Contention Resolution Schemes. NeurIPS 2021: 27184-27195 - [i7]Brian Brubach, Nathaniel Grammel, Will Ma, Aravind Srinivasan:
Improved Guarantees for Offline Stochastic Matching via new Ordered Contention Resolution Schemes. CoRR abs/2106.06892 (2021) - [i6]Dimitrios Gkenosis, Nathaniel Grammel, Lisa Hellerstein, Devorah Kletenik:
The Stochastic Boolean Function Evaluation Problem for Symmetric Boolean Functions. CoRR abs/2111.08793 (2021) - 2020
- [i5]Justin K. Terry, Nathaniel Grammel, Sanghyun Son, Benjamin Black:
Parameter Sharing For Heterogeneous Agents in Multi-Agent Reinforcement Learning. CoRR abs/2005.13625 (2020) - [i4]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 abs/2008.03325 (2020)
2010 – 2019
- 2019
- [i3]Brian Brubach, Nathaniel Grammel, Aravind Srinivasan:
Vertex-weighted Online Stochastic Matching with Patience Constraints. CoRR abs/1907.03963 (2019) - 2018
- [c2]Dimitrios Gkenosis, Nathaniel Grammel, Lisa Hellerstein, Devorah Kletenik:
The Stochastic Score Classification Problem. ESA 2018: 36:1-36:14 - [i2]Dimitrios Gkenosis, Nathaniel Grammel, Lisa Hellerstein, Devorah Kletenik:
The Stochastic Score Classification Problem. CoRR abs/1806.10660 (2018) - 2016
- [c1]Nathaniel Grammel, Lisa Hellerstein, Devorah Kletenik, Patrick Lin:
Scenario Submodular Cover. WAOA 2016: 116-128 - [i1]Nathaniel Grammel, Lisa Hellerstein, Devorah Kletenik, Patrick Lin:
Scenario Submodular Cover. CoRR abs/1603.03158 (2016)
Coauthor Index
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