AI Paper Atlas

Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes

Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification

ICML 2023 · Probabilistic and scientific modeling › Uncertainty and Gaussian processes · approach: Structured Gaussian process models · 2 citations (OpenAlex)

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Transform one shared GP sample into class-specific latent functions with input-dependent flowsETGP · characterfontaccuracy: 0.29 -> 0.36Helped
Transform one shared GP sample into class-specific latent functions with input-dependent flowsETGP · devangariaccuracy: 0.93 -> 0.96Helped
Transform one shared GP sample into class-specific latent functions with input-dependent flowsETGP · avilaaccuracy: SVGP: 0.962; TANH: 0.985; LINEAR: 0.970; RBFCORR: 0.988Mixed
Transform one shared GP sample into class-specific latent functions with input-dependent flowsETGP · absenteeismaccuracy: SVGP: 0.314; RBFCORR: 0.319; LINEAR: 0.307; TANH: 0.315No clear effect
Share kernels and inducing points across classes rather than use class-specific SVGP settings (ablation)multi-class SVGP · characterfont and devangariaccuracyHurt
Use fewer inducing points (50 or 100) (ablation)ETGP · vowelaccuracyHelped
Use fewer inducing points (50 or 100) (ablation)ETGP · avila and absenteeismaccuracyNo clear effect
Use SAL flows instead of LINEAR, TANH, or other ETGP flow choices (ablation)ETGP · all five UCI datasetsaccuracyHurt
Use Bayesian rather than point-estimate flow-parameter predictions (ablation)ETGP · small datasetslog-likelihoodHelped
Use Bayesian rather than point-estimate flow-parameter predictions (ablation)ETGP · medium and large datasetsaccuracyNo clear effect
Use an input-dependent flow rather than a non-stationary NNET or ARCCOS kernelETGP · UCI classification datasetsaccuracyHelped
Use ETGP rather than SVGPmulti-class classification · UCI classification datasetstraining time: around one order of magnitudeHelped

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy