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ORCIDDominic Richards
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2020 – today
- 2023
[c9]Nandini Amit Gadhia
, Michalis Smyrnakis, Po-Yu Liu, Damer Blake, Melanie Hay, Anh Nguyen, Dong Xia, Ritesh Krishna, Dominic Richards:
Formulating a method to analyse the differential expression of co-occurrence networks for small-sampled microbiome data. BCB 2023: 9:1-9:6- [i8]Stefanos Eleftheriadis, Dominic Richards, James Hensman:
Sparse Gaussian Processes with Spherical Harmonic Features Revisited. CoRR abs/2303.15948 (2023) - 2022
- [c8]Benedict Oakes, Dominic Richards, Jordi Barr, Jason F. Ralph:
Double Deep Q Networks for Sensor Management in Space Situational Awareness. FUSION 2022: 1-6 - [i7]Benedict Oakes, Dominic Richards, Jordi Barr, Jason F. Ralph:
Double Deep Q Networks for Sensor Management in Space Situational Awareness. CoRR abs/2205.14041 (2022) - 2021
- [b1]Dominic Richards:
Multi-agent learning. University of Oxford, UK, 2021 - [c7]Dominic Richards, Mike Rabbat:
Learning with Gradient Descent and Weakly Convex Losses. AISTATS 2021: 1990-1998 - [c6]Dominic Richards, Jaouad Mourtada, Lorenzo Rosasco:
Asymptotics of Ridge(less) Regression under General Source Condition. AISTATS 2021: 3889-3897 - [c5]Dominic Richards, Ilja Kuzborskij:
Stability & Generalisation of Gradient Descent for Shallow Neural Networks without the Neural Tangent Kernel. NeurIPS 2021: 8609-8621 - [c4]Dominic Richards, Sahand Negahban, Patrick Rebeschini:
Distributed Machine Learning with Sparse Heterogeneous Data. NeurIPS 2021: 18008-18020 - [i6]Dominic Richards, Mike Rabbat:
Learning with Gradient Descent and Weakly Convex Losses. CoRR abs/2101.04968 (2021) - [i5]Dominic Richards, Ilja Kuzborskij:
Stability & Generalisation of Gradient Descent for Shallow Neural Networks without the Neural Tangent Kernel. CoRR abs/2107.12723 (2021) - [i4]Dominic Richards, Edgar Dobriban, Patrick Rebeschini:
Comparing Classes of Estimators: When does Gradient Descent Beat Ridge Regression in Linear Models? CoRR abs/2108.11872 (2021) - 2020
- [j4]Dominic Richards, Patrick Rebeschini:
Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent. J. Mach. Learn. Res. 21: 34:1-34:44 (2020) - [c3]Dominic Richards, Patrick Rebeschini, Lorenzo Rosasco:
Decentralised Learning with Random Features and Distributed Gradient Descent. ICML 2020: 8105-8115 - [i3]Dominic Richards, Patrick Rebeschini, Lorenzo Rosasco:
Decentralised Learning with Random Features and Distributed Gradient Descent. CoRR abs/2007.00360 (2020)
2010 – 2019
- 2019
- [c2]Dominic Richards, Patrick Rebeschini:
Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up. NeurIPS 2019: 1214-1225 - [i2]Dominic Richards, Patrick Rebeschini:
Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up. CoRR abs/1905.03135 (2019) - 2018
- [i1]Dominic Richards, Patrick Rebeschini:
Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent. CoRR abs/1809.06958 (2018) - 2013
- [j3]Javier Navaridas, Steve B. Furber, Jim D. Garside, Xin Jin, Mukaram M. Khan, David R. Lester, Mikel Luján, José Miguel-Alonso, Eustace Painkras, Cameron Patterson, Luis A. Plana, Alexander D. Rast, Dominic Richards, Yebin Shi, Steve Temple, Jian Wu, Shufan Yang:
SpiNNaker: Fault tolerance in a power- and area- constrained large-scale neuromimetic architecture. Parallel Comput. 39(11): 693-708 (2013) - 2011
- [j2]Dominic Richards, David R. Lester:
A monadic approach to automated reasoning for Bluespec SystemVerilog. Innov. Syst. Softw. Eng. 7(2): 85-95 (2011) - [j1]Mukaram M. Khan, Alexander D. Rast, Javier Navaridas, X. Jin, Luis A. Plana, Mikel Luján, Steve Temple, Cameron Patterson, Dominic Richards, John V. Woods, José Miguel-Alonso, Stephen B. Furber:
Event-driven configuration of a neural network CMP system over an homogeneous interconnect fabric. Parallel Comput. 37(8): 392-409 (2011) - 2010
- [c1]Dominic Richards, David R. Lester:
A Prototype Embedding of Bluespec SystemVerilog in the PVS Theorem Prover. NASA Formal Methods 2010: 139-148
Coauthor Index
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