Semantic Information Pursuit for Multimodal Data Analysis
Lead Research Organisation:
University of Oxford
Department Name: Statistics
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
People |
ORCID iD |
| Arnaud Doucet (Principal Investigator) |
Publications
Benton J
(2024)
From denoising diffusions to denoising Markov models
in Journal of the Royal Statistical Society Series B: Statistical Methodology
Daudel K.
(2023)
Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics
in Journal of Machine Learning Research
Hellström F
(2023)
Comparing Comparators in Generalization Bounds
Campbell A
(2023)
Trans-Dimensional Generative Modeling via Jump Diffusion Models
Biggs F.
(2023)
Tighter PAC-Bayes Generalisation Bounds by Leveraging Example Difficulty
in Proceedings of Machine Learning Research
Viallard P
(2023)
Learning via Wasserstein-Based High Probability Generalisation Bounds
Viallard P.
(2023)
Learning via Wasserstein-Based High Probability Generalisation Bounds
in Advances in Neural Information Processing Systems
Leroy A.
(2023)
Cluster-Specific Predictions with Multi-Task Gaussian Processes
in Journal of Machine Learning Research
Benton J
(2022)
From Denoising Diffusions to Denoising Markov Models
Leroy A
(2022)
MAGMA: inference and prediction using multi-task Gaussian processes with common mean
in Machine Learning
De Bortoli V.
(2022)
Riemannian Score-Based Generative Modelling
in Advances in Neural Information Processing Systems
Sun S
(2022)
Stability-based PAC-Bayes analysis for multi-view learning algorithms
in Information Fusion
Clerico E
(2022)
Chained Generalisation Bounds
Alquier P
(2022)
Estimation of Copulas via Maximum Mean Discrepancy
in Journal of the American Statistical Association
Haddouche M.
(2022)
Online PAC-Bayes Learning
in Advances in Neural Information Processing Systems
Wang R
(2022)
Multiple Riemannian Manifold-Valued Descriptors Based Image Set Classification With Multi-Kernel Metric Learning
in IEEE Transactions on Big Data
Kaltenbrunner W
(2022)
Innovating peer review, reconfiguring scholarly communication: an analytical overview of ongoing peer review innovation activities
in Journal of Documentation
Mikhailiuk A
(2022)
Consolidated Dataset and Metrics for High-Dynamic-Range Image Quality
in IEEE Transactions on Multimedia
Campbell A
(2022)
A Continuous Time Framework for Discrete Denoising Models
Biggs F.
(2022)
On Margins and Generalisation for Voting Classifiers
in Advances in Neural Information Processing Systems
Phillips A
(2022)
Spectral Diffusion Processes
Paulin D
(2022)
A 4D-Var method with flow-dependent background covariances for the shallow-water equations
in Statistics and Computing
Picard-Weibel A
(2022)
On change of measure inequalities for $f$-divergences
Schrab A.
(2022)
Efficient Aggregated Kernel Tests using Incomplete U-statistics
in Advances in Neural Information Processing Systems
Haddouche M
(2021)
PAC-Bayes Unleashed: Generalisation Bounds with Unbounded Losses.
in Entropy (Basel, Switzerland)
Daudel K.
(2021)
Mixture weights optimisation for Alpha-Divergence Variational Inference
in Advances in Neural Information Processing Systems
Li L
(2021)
Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly.
in Entropy (Basel, Switzerland)
Tadic V
(2021)
Asymptotic Properties of Recursive Particle Maximum Likelihood Estimation
in IEEE Transactions on Information Theory
Deligiannidis G
(2021)
Randomized Hamiltonian Monte Carlo as scaling limit of the bouncy particle sampler and dimension-free convergence rates
in The Annals of Applied Probability
Maddison C
(2021)
Dual Space Preconditioning for Gradient Descent
in SIAM Journal on Optimization
Wang R
(2021)
Graph Embedding Multi-Kernel Metric Learning for Image Set Classification With Grassmannian Manifold-Valued Features
in IEEE Transactions on Multimedia
PĂ©rez-Ortiz M.
(2021)
Tighter risk certificates for neural networks
in Journal of Machine Learning Research
Xu Z
(2021)
STRNet: Triple-stream Spatiotemporal Relation Network for Action Recognition
in International Journal of Automation and Computing
Campbell A
(2021)
Online Variational Filtering and Parameter Learning
Hayou S.
(2021)
Stable ResNet
in Proceedings of Machine Learning Research
Guedj B
(2021)
Still No Free Lunches: The Price to Pay for Tighter PAC-Bayes Bounds.
in Entropy (Basel, Switzerland)
Biggs F
(2021)
Differentiable PAC-Bayes Objectives with Partially Aggregated Neural Networks.
in Entropy (Basel, Switzerland)
Alenlöv J.
(2021)
Pseudo-marginal hamiltonian monte carlo
in Journal of Machine Learning Research
Zhu X
(2021)
Complementary Discriminative Correlation Filters Based on Collaborative Representation for Visual Object Tracking
in IEEE Transactions on Circuits and Systems for Video Technology
Zantedeschi V.
(2021)
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound
in Advances in Neural Information Processing Systems
Mikhailiuk A
(2020)
Consolidated Dataset and Metrics for High-Dynamic-Range Image Quality
Alquier P
(2020)
Estimation of copulas via Maximum Mean Discrepancy
Middleton L
(2020)
Unbiased Markov chain Monte Carlo for intractable target distributions
in Electronic Journal of Statistics
| Title | Estimation of Copulas via Maximum Mean Discrepancy |
| Description | This article deals with robust inference for parametric copula models. Estimation using canonical maximum likelihood might be unstable, especially in the presence of outliers. We propose to use a procedure based on the maximum mean discrepancy (MMD) principle. We derive nonasymptotic oracle inequalities, consistency and asymptotic normality of this new estimator. In particular, the oracle inequality holds without any assumption on the copula family, and can be applied in the presence of outliers or under misspecification. Moreover, in our MMD framework, the statistical inference of copula models for which there exists no density with respect to the Lebesgue measure on [0,1]d, as the Marshall-Olkin copula, becomes feasible. A simulation study shows the robustness of our new procedures, especially compared to pseudo-maximum likelihood estimation. An R package implementing the MMD estimator for copula models is available. Supplementary materials for this article are available online. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://tandf.figshare.com/articles/dataset/Estimation_of_Copulas_via_Maximum_Mean_Discrepancy/19487... |
| Title | Estimation of Copulas via Maximum Mean Discrepancy |
| Description | This article deals with robust inference for parametric copula models. Estimation using canonical maximum likelihood might be unstable, especially in the presence of outliers. We propose to use a procedure based on the maximum mean discrepancy (MMD) principle. We derive nonasymptotic oracle inequalities, consistency and asymptotic normality of this new estimator. In particular, the oracle inequality holds without any assumption on the copula family, and can be applied in the presence of outliers or under misspecification. Moreover, in our MMD framework, the statistical inference of copula models for which there exists no density with respect to the Lebesgue measure on [0,1]d, as the Marshall-Olkin copula, becomes feasible. A simulation study shows the robustness of our new procedures, especially compared to pseudo-maximum likelihood estimation. An R package implementing the MMD estimator for copula models is available. Supplementary materials for this article are available online. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://tandf.figshare.com/articles/dataset/Estimation_of_Copulas_via_Maximum_Mean_Discrepancy/19487... |
| Title | UPIQ: Unified Photometric Image Quality dataset (04.2021) |
| Description | Unified Photometric Image Quality dataset (UPIQ) UPIQ dataset is intended for training and evaluation of full-reference HDR image quality metrics. The dataset contains 84 reference images and 4159 distorted images from four datasets, TID2013 [1] (SDR), LIVE [2] (SDR), Narwaria et al. [3] (HDR) and Korshunov et al. [4] (HDR). Quality scores were obtained by re-aligning existing datasets to a common unified quality scale. This was achieved by collecting additional cross-dataset quality comparisons and re-scaling existing data with a psychometric scaling method. Images in the dataset are represented in absolute photometric and colorimetric units, corresponding to light emitted from a display. This is an updated version of the dataset with the fixed pix_per_deg column. See README.md. [1] Ponomarenko, N., Jin, L., Ieremeiev, O., Lukin, V., Egiazarian, K., Astola, J., Benoit: Image database tid2013: Peculiarities, results and perspectives. Signal Processing: Image Communication 30, 57 - 77 (2015) [2] Sheikh, H., Sabir, M., Bovik, A.: A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms. IEEE Transactions on Image Processing 15(11), 3440-3451 (2006). https://doi.org/10.1109/TIP.2006.881959, http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1709988 [3] Narwaria, M., P. Da Silva, M., Le Callet, P., Pepion, R.: Tone mapping-based high-dynamic-range image compression: study of optimization criterion and perceptual quality. Optical Engineering 52(10) (2013). https://doi.org/10.1117/1.OE.52.10.102008 [4] Korshunov, P., Hanhart, P., Richter, T., Artusi, A., Mantiuk, R., Ebrahimi, T.: Subjective quality assessment database of HDR images compressed with jpeg xt. In: 2015 Seventh International Workshop on Quality of Multimedia Experience (QoMEX). pp. 1-6 (May 2015). https://doi.org/10.1109/QoMEX.2015.7148119 |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://www.repository.cam.ac.uk/handle/1810/321331 |