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This reading group at Oxford Statistics covers area related to Bayesian (nonparametric) methods, network modelling, statistical machine learning, power-laws in empirical data and the analysis of large neural networks. Email François if you are interested in participating, but not in the group.
| Date | Time | Room | Paper/Topic | Presenter | Notes |
|---|---|---|---|---|---|
| 12/01/2025 | 12:00 | Meeting Room 1 | Some ideas from Temporal Graph Neural Tangent Kernel with Graphon-Guaranteed | Valentin | All - Neurips 2024 |
| 19/01/2025 | 12:00 | Length Optimization in Conformal Prediction | Stefano | All - Neurips 2024 | |
| 12:30 | An Information Theoretic Perspective on Conformal Prediction | Kia | All - Neurips 2024 | ||
| 26/01/2025 | 12:00 | Boosted Conformal Prediction Intervals | François | All - Neurips 2024 | |
| 04/02/2025 | 12:00 | Meeting Room 1 | A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks | Valentin | |
| 11/02/2025 | 12:00 | 1.20 | Data thinning for convolution-closed distributions | François | |
| 18/02/2025 | 13:00 | 1.20/MR1 | Prediction-Powered E-Values | Stefano | |
| 25/02/2025 | 12:00 | 1.20/MR1 | Optimal Conformal Prediction for Small Areas | Kia | |
| 04/03/2025 | 12:00 | 1.20/MR1 | A unified recipe for deriving (time-uniform) PAC-Bayes bounds up to page 21 | Valentin | |
| 18/03/2025 | 12:00 | 1.20/MR1 | Some connections between PPI, doubly robust machine learning, the Augmented Inverse Propensity Weighted Estimator, Neyman orthogonality and control variates. The following slides may be useful | François | |
| 01/04/2025 | 12:00 | 1.20/MR1 | Outcome-Informed Weighting for Robust ATE Estimation | Linying Yang | Guest Speaker |
| 29/04/2025 | 12:00 | 1.20/MR1 | Reliable Active Learning via Influence Functions | Stefano | |
| 28/05/2025 | 11:00 | 1.20/MR1 | Influence-Guided Diffusion for Dataset Distillation | Kia | |
| 03/06/2025 | 12:00 | 1.20/MR1 | Bayesian Inference for Vertex-Series-Parallel Partial Orders | Valentin | |
| 10/06/2025 | 12:00 | 1.20/MR1 | Model Collapse Demystified: The Case of Regression | François | |
| 17/06/2025 | 12:00 | 1.20/MR1 | Conformal Prediction Beyond the Seen: A Missing Mass Perspective for Uncertainty Quantification in Generative Models | Kia | |
| 08/07/2025 | 12:00 | 1.20/MR1 | Rényi Neural Process | Stefano |
| Date | Time | Room | Paper/Topic | Presenter | Notes |
|---|---|---|---|---|---|
| 12/01/2024 | 11:00 | Meeting Room 1 | Abide by the law and follow the flow | François | |
| 11:30 | A graphon-signal analysis of GNNs | Valentin | |||
| 12:00 | Thin and Deep Gaussian processes | Stefano | |||
| 12:30 | Revisiting Logistic-softmax Likelihood in Bayesian Meta-Learning for Few-Shot Classification | Kia | |||
| 16/01/2024 | 12:00 | 1.20 | The Quantization Model of Neural Scaling | François | |
| 23/01/2024 | 12:00 | Meeting Room 1 | Sequence Modeling with Multiresolution Convolutional Memory | Stefano | |
| 06/02/2024 | 12:00 | Meeting Room 1 | Bayesian inference of network structure from unreliable data | Valentin | |
| 20/02/2024 | 12:00 | Meeting Room 1 | Posterior Re-calibration for Imbalanced Datasets | Kia | |
| 12/03/2024 | 12:00 | Meeting Room 1 | Active Statistical Inference x Prediction-powered inference | François | |
| 16/04/2024 | 12:00 | Meeting Room 1 | Graphon Neural Networks and the Transferability of Graph Neural Networks | Valentin | |
| 07/05/2024 | 12:00 | Meeting Room 1 | Flow matching for generative modeling | Stefano | |
| 14/05/2024 | 12:00 | Meeting Room 1 | Prediction-Oriented Bayesian Active Learning | Kia | |
| 24/05/2024 | 16:00 | 1.20 | Game-theoretic Statistics and safe anytime-valid inference (Ramdas, Grunwald, Vovk, Shafer, StatScience 2023) | François | |
| 11/06/2024 | 09:45 | LG.04 | Graphon games: A statistical framework for network games and interventions | Valentin | |
| 18/06/2024 | 14:15 | Meeting Room 1 | Bayesian Prediction-Powered Inference | Stefano | |
| 25/06/2024 | 12:00 | Meeting Room 1 | Minimum-Risk Recalibration of Classifiers | Kia | |
| 01/10/2024 | 12:00 | 1.20 | Tweedie’s Formula and Selection Bias | François | |
| 22/10/2024 | 12:00 | Meeting Room 1 | Robust and Conjugate Gaussian Process Regression (Altamirano et al., 2024) | Stefano | |
| 05/11/2024 | 12:00 | Meeting Room 1 | On Statistical Bias In Active Learning: How and When To Fix It | Kia | |
| 19/11/2024 | 12:00 | Meeting Room 1 | PAC-Bayesian Adversarially Robust Generalization Bounds for GNNs | Valentin | |
| 03/12/2024 | 12:00 | Meeting Room 1 | Revisiting Optimism and Model Complexity in the Wake of Overparameterized Machine Learning I will mostly focus on the material covered in these notes | François |
https://neurips.cc/virtual/2023/papers.html?filter=titles
- ✅ Abide by the law and follow the flow. Marcotte, Gribonval, Peyré. https://arxiv.org/abs/2307.00144
- Score-based Generative Models with Lévy Processes. E. Yoon et al. https://openreview.net/forum?id=0Wp3VHX0Gm
- A Bayesian Take on Gaussian Process Networks. E. Giudice, J. Kuipers, G. Moffa. https://arxiv.org/abs/2306.11380
- ✅ Thin and Deep Gaussian processes. D de Souza et al. https://arxiv.org/abs/2310.11527
- Generalized test utilities for long-tail performance in extreme multi-label classification. E. Schultheis et al. link
- ✅ Revisiting Logistic-softmax Likelihood in Bayesian Meta-Learning for Few-Shot Classification . T. Ke et al. https://openreview.net/forum?id=Z1W0u3Cr74
- Riemannian Laplace approximations for Bayesian neural networks . F. Bergamin et al. https://arxiv.org/abs/2306.07158
- Function Space Bayesian Pseudocoreset for Bayesian Neural Networks. B. Kim et al. https://openreview.net/forum?id=VTtUDU9YNE
- On permutation symmetries in Bayesian neural network posteriors: a variational perspective. S. Rossi et al. https://arxiv.org/abs/2310.10171
- Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network. T. Deleu et al. https://arxiv.org/abs/2305.19366
- Conditional score-based diffusion models for Bayesian inference in infinite dimensions. L. Baldassari et al. https://arxiv.org/abs/2305.19147
- Compression with Bayesian Implicit Neural Representations. Z. Guo et al. https://arxiv.org/abs/2305.19185
- On the Asymptotic Learning Curves of Kernel Ridge Regression under Power-law Decay. Y. Li et al. https://openreview.net/forum?id=E4P5kVSKlT
- Limits, approximation and size transferability for GNNs on sparse graphs via graphops. T. Le and S. Jegelka. https://arxiv.org/abs/2306.04495
- Learning Regularized Monotone Graphon Mean-Field Games. https://openreview.net/forum?id=XF923QPCGw
- Equivariant Neural Operator Learning with Graphon Convolution. https://openreview.net/forum?id=EjiA3uWpnc
- ✅ A graphon-signal analysis of graph neural networks. R. Levie. https://arxiv.org/abs/2305.15987
- Class-Conditional Conformal Prediction with Many Classes. https://proceedings.neurips.cc/paper_files/paper/2023/file/cb931eddd563f8d473c355518ce8601c-Paper-Conference.pdf
- A convergence analysis of gradient descent for deep linear neural networks. Arora et al. ICLR 2019. https://openreview.net/forum?id=SkMQg3C5K7
- On the explicit role of initialization on the convergence and implicit bias of overparametrized linear networks. Min et al., ICML 2021 https://proceedings.mlr.press/v139/min21c.html
- ✅ Graphon Neural Networks and the Transferability of Graph Neural Networks. Ruis, Chamon, Ribeiro. Neurips 2020. https://arxiv.org/abs/2006.03548
- Generalization Analysis of Message Passing Neural Networks on Large Random Graphs. Neurips 2022. https://arxiv.org/abs/2202.00645
- Online k-means Clustering. Cohen-Addad, Guedj, Kanade, Rom. PMLR. 2021. http://proceedings.mlr.press/v130/cohen-addad21a.html
- Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning. Allen-Zhu and Li. ICLR 2023. https://openreview.net/forum?id=Uuf2q9TfXGA
- ✅ Sequence Modeling with Multiresolution Convolutional Memory. Shi, J., Ke, A. W., and Fox, E. B. ICML 2023. https://arxiv.org/abs/2305.01638
- ✅ Prediction-powered inference. Angelopoulos, A. N., Bates, S., Fannjiang, C., Jordan, M. I., Zrnic, T. Science 382-6671. https://arxiv.org/abs/2301.09633
- ✅ Active Statistical Inference. Zrnic, T., Candès, E. J. arXiv preprint arXiv:2403.03208. https://arxiv.org/abs/2403.03208
- ✅ Bayesian Prediction-Powered Inference. Hofer, R. A., Maynez, J., Dhingra, B., Fisch, A., Globerson, A., Cohen, W. W. arXiv:2405.06034. https://arxiv.org/abs/2405.06034
- ✅ Flow matching for Generative Modeling. Lipman, Y., Chen, R. T. Q., Ben-Hamu, H., Nickel, M., Le, M. ICLR 2023. https://arxiv.org/abs/2210.02747
Conformal prediction
Boosted Conformal Prediction Intervals https://nips.cc/virtual/2024/poster/95004
Large language model validity via enhanced conformal prediction methods https://nips.cc/virtual/2024/poster/95729
Robust Conformal Prediction Using Privileged Information https://nips.cc/virtual/2024/poster/93870 https://arxiv.org/abs/2406.05405
Length Optimization in Conformal Prediction https://nips.cc/virtual/2024/poster/96056
An Information Theoretic Perspective on Conformal Prediction https://nips.cc/virtual/2024/poster/94151
Conformal Prediction for Class-wise Coverage via Augmented Label Rank Calibration https://nips.cc/virtual/2024/poster/94658
Similarity-Navigated Conformal Prediction for Graph Neural Networks https://nips.cc/virtual/2024/poster/94023
NTK - large neural nets
Bounds for the smallest eigenvalue of the NTK for arbitrary spherical data of arbitrary https://nips.cc/virtual/2024/poster/93764
Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood https://nips.cc/virtual/2024/poster/95142
Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization https://nips.cc/virtual/2024/poster/94399
GNNs
Temporal Graph Neural Tangent Kernel with Graphon-Guaranteed https://nips.cc/virtual/2024/poster/96832
What Is Missing For Graph Homophily? Disentangling Graph Homophily For Graph Neural Networks https://nips.cc/virtual/2024/poster/95879
Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks https://nips.cc/virtual/2024/poster/93149
On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks https://nips.cc/virtual/2024/poster/96753
Other
Do Finetti: On Causal Effects for Exchangeable Data https://nips.cc/virtual/2024/poster/96631
Bayesian Nonparametrics Meets Data-Driven Distributionally Robust Optimization https://nips.cc/virtual/2024/poster/96385
A Unified Recipe for Deriving (Time-Uniform) PAC-Bayes Bounds https://nips.cc/virtual/2024/poster/98308
Quasi-Bayes meets Vines https://nips.cc/virtual/2024/poster/94131
https://nips.cc/virtual/2024/poster/95321 Scaling Laws in Linear Regression: Compute, Parameters, and Data
Bias Detection via Signaling https://nips.cc/virtual/2024/poster/96686
LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language https://nips.cc/virtual/2024/poster/95832
Approximately Equivariant Neural Processes https://nips.cc/virtual/2024/poster/94315
Robust Gaussian Processes via Relevance Pursuit https://nips.cc/virtual/2024/poster/96603
Decomposable Transformer Point Processes https://nips.cc/virtual/2024/poster/95355
Is Score Matching Suitable for Estimating Point Processes? https://nips.cc/virtual/2024/poster/95838
Physics-Informed Variational State-Space Gaussian Processes https://nips.cc/virtual/2024/poster/93352