Dominic Richards

Publication Activity (10 Years)

Years Active: 2010-2023
Publications (10 Years): 17

Top Topics

Situational Awareness

Gaussian Process

Top Venues

Publications

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)
Stefanos Eleftheriadis, Dominic Richards, James Hensman
Sparse Gaussian Processes with Spherical Harmonic Features Revisited. CoRR (2023)
Benedict Oakes, Dominic Richards, Jordi Barr, Jason F. Ralph
Double Deep Q Networks for Sensor Management in Space Situational Awareness. CoRR (2022)
Benedict Oakes, Dominic Richards, Jordi Barr, Jason F. Ralph
Double Deep Q Networks for Sensor Management in Space Situational Awareness. FUSION (2022)
Dominic Richards, Mike Rabbat
Learning with Gradient Descent and Weakly Convex Losses. CoRR (2021)
Dominic Richards, Jaouad Mourtada, Lorenzo Rosasco
Asymptotics of Ridge(less) Regression under General Source Condition. AISTATS (2021)
Dominic Richards, Ilja Kuzborskij
Stability & Generalisation of Gradient Descent for Shallow Neural Networks without the Neural Tangent Kernel. CoRR (2021)
Dominic Richards, Edgar Dobriban, Patrick Rebeschini
Comparing Classes of Estimators: When does Gradient Descent Beat Ridge Regression in Linear Models? CoRR (2021)
Dominic Richards, Ilja Kuzborskij
Stability & Generalisation of Gradient Descent for Shallow Neural Networks without the Neural Tangent Kernel. NeurIPS (2021)
Dominic Richards, Mike Rabbat
Learning with Gradient Descent and Weakly Convex Losses. AISTATS (2021)
Dominic Richards, Sahand Negahban, Patrick Rebeschini
Distributed Machine Learning with Sparse Heterogeneous Data. NeurIPS (2021)
Dominic Richards, Patrick Rebeschini
Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent. J. Mach. Learn. Res. 21 (2020)
Dominic Richards, Patrick Rebeschini, Lorenzo Rosasco
Decentralised Learning with Random Features and Distributed Gradient Descent. ICML (2020)
Dominic Richards, Patrick Rebeschini, Lorenzo Rosasco
Decentralised Learning with Random Features and Distributed Gradient Descent. CoRR (2020)
Dominic Richards, Patrick Rebeschini
Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up. CoRR (2019)
Dominic Richards, Patrick Rebeschini
Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up. NeurIPS (2019)
Dominic Richards, Patrick Rebeschini
Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent. CoRR (2018)
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) (2013)
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) (2011)
Dominic Richards, David R. Lester
A monadic approach to automated reasoning for Bluespec SystemVerilog. Innov. Syst. Softw. Eng. 7 (2) (2011)
Dominic Richards, David R. Lester
A Prototype Embedding of Bluespec SystemVerilog in the PVS Theorem Prover. NASA Formal Methods (2010)