Variational Inference for Gaussian Process Models with Linear Complexity
Authors: Ching-An Cheng, Byron Boots
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We run several experiments on regression tasks and show that this decoupled approach greatly outperforms previous sparse variational Gaussian process inference procedures. |
| Researcher Affiliation | Academia | Ching-An Cheng Institute for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta, GA 30332 cacheng@gatech.edu Byron Boots Institute for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta, GA 30332 bboots@cc.gatech.edu |
| Pseudocode | Yes | Algorithm 1 Online Learning with DGPs Parameters: Mα, Mβ, Nm, N Input: M(a, B, α, β, θ) 1: θ0 initialize Hyperparameters( sample Minibatch(D, Nm) ) 2: for t = 1 . . . T do 3: Dt sample Minibatch(D, Nm) 4: M.add Basis(Dt, N , Mα, Mβ) 5: M.update Model(Dt, t) 6: end for |
| Open Source Code | No | The paper does not provide any explicit statement about releasing its source code, nor does it include a link to a code repository for the methodology described. |
| Open Datasets | Yes | Inverse Dynamics of KUKA Robotic Arm This dataset records the inverse dynamics of a KUKA arm performing rhythmic motions at various speeds [17]. Walking Mu Jo Co Mu Jo Co (Multi-Joint dynamics with Contact) is a physics engine for research in robotics, graphics, and animation, created by [25]. |
| Dataset Splits | No | The paper specifies 90% training data and 10% testing data for the KUKA1 and MUJOCO datasets, but it does not explicitly mention a separate validation dataset split. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions "our current Matlab implementation" but does not specify the Matlab version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | The step-size for each stochastic algorithms is scheduled according to γt = γ0(1 + 0.1 t) 1, where γ0 {10 1, 10 2, 10 3} is selected manually for each algorithm to maximize the improvement in objective function after the first 100 iterations. We test each stochastic algorithm for T = 2000 iterations with mini-batches of size Nm = 1024 and the increment size N = 128. Finally, the model sizes used in the experiments are listed as follows: Mα = 1282 and Mβ = 128 for SVDGP; M = 1024 for SVI; M = 256 for i VSGPR; M = 1024, N = 4096 for VSGPR; N = 1024 for GP. |