# Weekly Reading Group

## Upcoming

**December 19, 2022**@ 17:00 JST*Reading Group*. Christmas reading group (paper to be decided, bring cookies).

**December 05, 2022**@ 17:00 JST*Reading Group.* Discussion about interesting papers from NeurIPS 2022 and ICLR 2023.

**November 21, 2022**@ 17:00 JSTInternal group meeting.

## Past Meetings

**November 14, 2022***Talk*. Keigo Nishida (Osaka University) will talk about his work on AdamB: Decoupled Bayes by Backprop With Gaussian Scale Mixture Prior.

**November 07, 2022***Reading Group*. Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective.

**October 25, 2022***Reading Group*. Unusual day (Tuesday)! Discussion of the paper Machine Theory of Mind.

**October 17, 2022***Seminar*. Unusual time. Talk by Cuong N. Nguyen on Generalization Bounds for Deep Transfer Learning Using Majority Predictor Accuracy

**October 04, 2022***Seminar*. Unusual day and time (Tuesday). Talk by Karim Lounici on Meta-Learning Representations with Contextual Linear Bandits

**September 26, 2022***Reading Group*. Discussion of the paper Formalizing the Generalization-Forgetting Trade-off in Continual Learning.

**September 12, 2022***Reading Group*. Discussion of the paper Fortuitous Forgetting in Connectionist Networks.

**August 29, 2022***Reading Group*. Discussion of recent papers on natural-gradient descent Paper 1, Paper 2, Paper 3.

**August 15, 2022***Seminar*. Talk by David Tomàs Cuesta on his lab rotation project.

## Show all reading group meetings

**July 15, 2022***Reading Group*. Unusual day and time (Friday)!! Discussion of notable ICML, ICLR and COLT papers.

**July 14, 2022***Talk*. Unusual day and time (Thursday). Presentation by Itay Evron on “How catastrophic can catastrophic forgetting be in linear regression?”

**June 20, 2022**Internal group meeting.

**May 23, 2022***Seminar*. Talk by Geoffrey Wolfer on Inference in Markov Chain from a Single Finite Trajectory.

**May 09, 2022***Seminar*. Tojo’s term project presentation and research discussion on continual RL.

**April 25, 2022***Reading Group*. Discussion of the Paper by Hassabis et al., Neuroscience-Inspired Artificial Intelligence.

**April 11, 2022***Seminar*. Talk by Vincent Fortuin. Title: On the Importance of Priors in Bayesian Deep Learning.

**March 28, 2022***Reading group*. Discussion of AISTATS 2022 papers.

**March 14, 2022***Reading Group*. Discussion of the Paper by Bubeck and Sellke,
Universal Law of Robustness via Isoperimetry.

**February 28, 2022***Reading Group*. Discussion of the paper
Deep Learning through the Lens of Example Difficulty.

**February 21, 2022***Reading Group*. Discussion of the paper by Kaplan and Friston,
Planning and Navigation as Active Inference.

**February 17, 2022***Seminar*. Talk by Lionel Riou-Durand. Title: Metropolis Adjusted Underdamped Langevin Trajectories: a robust alternative to Hamiltonian Monte-Carlo.

**February 14, 2022***Reading Group*. Discussion of the paper
A Survey of Exploration Methods in Reinforcement Learning.

**February 07, 2022**Internal group meeting.

**January 31, 2022***Reading Group*. Discussion of the paper [Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes].

**January 24, 2022***Reading Group*. Discussion of the paper [Information-Theoretic Generalization Bounds for Stochastic Gradient Descent].

**January 17, 2022**After holiday catch-up and discussion on well-being in ML.

**December 20, 2021***Reading Group*. Discussion of the paper [The Seven Tools of Causal Inference with Reflections on Machine Learning].

**December 06, 2021***Seminar*. Term project talk by Ted Tinker.

**November 29, 2021***Seminar*. Talk by Sébastien Loustau. Title: Deep learning theory for power-efficient algorithms. Slides.

**November 22, 2021***Reading Group*. Discussion of the paper [Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples].

**November 15, 2021***Reading Group*. Discussion of the paper [Dual Parametrization of Sparse Variational Gaussian Processes].

**November 09, 2021***Seminar*. Talk by Ilsang Ohn on Adaptive variational Bayes: Optimality, computation and applications. Register via Doorkeeper.

**November 01, 2021***Reading Group*. Discussion of the paper [The Geometry of Abstract Learned Knowledge in the Hippocampus].

**October 18, 2021***Reading Group*. Discussion of the paper by van der Hoeven et al.: [The Many Faces of Exponential Weights in Online Learning].

**October 11, 2021**Internal group meeting.

**October 04, 2021***Reading Group*. Discussion of the paper by Khan and Swaroop, 2021: [Knowledge-Adaptation Priors]

**September 27, 2021***Reading Group*. Discussion of the paper by Pearce et al., 2021: [Understanding Softmax Confidence and Uncertainty]

**September 13, 2021***Reading Group*. Discussion of the paper by Hara et. al., 2019: [Data Cleansing for Models Trained with SGD]

**September 06, 2021***Reading Group*. Discussion of the paper by Khan and Rue, 2021: [The Bayesian Learning Rule]

**August 30, 2021***AIP Seminar*. Talk by Jiaxin Shi: “Sampling with
Mirrored Stein Operators”. [Paper]
[Doorkeeper]

**August 30, 2021**Casual chat in gather.town.

**August 23, 2021***Reading Group*. Discussion of the paper by Li et al., 2019: [Hessian based analysis of SGD for Deep Nets: Dynamics and Generalization]

**August 16, 2021***Reading Group*. Discussion of the paper by Tsividis et al., 2021: [Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration and Planning]

**August 09, 2021***Reading Group*. Discussion of the paper by Hobbhahn and Hennig (2021): [Laplace Matching for Fast Approximate Inference in Generalized Linear Models]

**August 02, 2021***Seminar*. Leaving presentation by Dharmesh Tailor

**July 26, 2021***Reading group*. Discussion of the paper by Raj, Musco and Mackey (2020): [Importance Sampling via Local Sensitivity]

**July 19, 2021***Reading group*. Discussion of ICML 2021 papers.

**July 12, 2021***Reading group*. Discussion of the paper by Azoury and Warmuth (2001): [Relative Loss Bounds for On-Line Density Estimation with the Exponential Family of Distributions]

**July 05, 2021***Reading group*. Discussion of several knowledge distillation papers: [Paper 1], [Paper 2], [Paper 3]

**June 28, 2021**Social chat in gathertown.

**June 22, 2021***AIP seminar*. Talk by Jooyeon Kim: “Stochastic optimal control and probabilistic modeling for detecting and reducing fake news”

**June 07, 2021**Internal group meeting.

**May 31, 2021***Seminar*. Talk by Julyan Arbel. Title: Approximate Bayesian computation with surrogate posteriors [Paper]

**May 17, 2021**Mutual feedback on NeurIPS papers.

**May 10, 2021***Reading group*. Discussion of ICLR 2021 papers.

**April 26, 2021***Reading group*. On the origin of implicit regularization in stochastic gradient descent. [Paper]

**April 19, 2021***Reading group*. Discussion of AISTATS 2021 papers.

**April 12, 2021***Talk*. Talk by Dhruva Tirumala: Behavior Priors for Efficient Reinforcement Learning. [Paper]

**March 29, 2021***Talk + Tutorial*. Talk by Fariz Ikhwantri on “Knowledge distillation with DNN2GP” + tutorial by Thomas Möllenhoff on convex duality.

**March 22, 2021**Informal group discussion.

**March 16, 2021***Seminar, unusual day and time!* Talk by Daniele Calandriello: “Scalable Determinantal Point Processes for Machine Learning”.

**March 01, 2021**Internal group meeting.

**February 22, 2021***Seminar*. Talk by Andrew Foong and David Burt: “On the Expressiveness of Approximate Inference in Bayesian Neural Networks”. [Paper]

**February 15, 2021**Talk by Khimya Khetarpal and Matt Riemer: “Towards Continual Reinforcement Learning: A Review and Perspectives”. [Paper]

**February 08, 2021**Well-being chat.

**February 01, 2021***Seminar*. Talk by Blair Bilodeau and Jeffrey Negrea: “Relaxing the I.I.D. Assumption: Adaptively Minimax Optimal Regret via Root-Entropic Regularization”. [Paper]

**January 25, 2021**Mutual feedback on ICML submissions.

**January 18, 2021**Catch up meeting.

**December 17, 2020***Seminar, unusual day!* Erik.

**November 30, 2020***Reading group*. Qi Qian, Hao Li, Juhua Hu: “Efficient Kernel Transfer in Knowledge Distillation”. [Paper]

**November 16, 2020***Seminar*. Peter, Thomas. Uncertainty estimation for Bayesian Neural Networks using infinite-width nets.

**November 06, 2020**Pre-ICLR paper discussion.

**October 30, 2020***Reading group*. Ben Recht: “A Tour of Reinforcement Learning: The View from Continuous Control”. [Paper]

**October 23, 2020**Team work review for the past 4 months. Siddarth’s talk on functional regularization on the memorable past. [Paper]

**October 22, 2020**Unusual day and time! Dimitri Meunier: “Meta Learning meets Variational Inference. Learning priors with guarantees”.

**October 09, 2020**Feedback discussion on the new team webpage.

**September 25, 2020**Talk by François-Xavier Briol: “Stein’s Method for Computational Statistics and Machine Learning”. [Slides]

**September 18, 2020**Research chat and virtual breakfast/lunch/dinner in gather.town.

**August 28, 2020**Lab members project overview and discussion.

**August 21, 2020**Pierre’s grant presentation rehearsal and feedback.

**August 07, 2020***Reading group.* “Generalized Variational Inference: Three arguments for deriving new posteriors”. [Paper]

**July 17, 2020**Whole week: comments on ICML tutorials and talks.

**July 10, 2020**Pre-ICML paper discussion.

**June 26, 2020**Talk by Giulia Denevi on Efficient Lifelong Learning Algorithms: Regret Bounds and Statistical Guarantees.

**June 12, 2020**Internal kickoff meeting.

**June 05, 2020**Talk by Peter Nickl on “Variational Bayes for Infinite Mixtures of Local Regressors with Robotics Applications”. [Thesis]

**May 29, 2020**Talk by Gian Maria Marconi on Manifold Regression by Structured Prediction: methodology and applications. [Paper]

**May 22, 2020**Feedback discussion on our NeurIPS submissions.

**May 15, 2020**Talk by Thomas Möllenhoff on Flat Metric Minimization with Applications in Generative Modeling. [Paper]

**May 01, 2020***Reading Group*. “Continual Deep Learning by Functional Regularisation of the Memorable Past”. [Paper]

**April 24, 2020***Reading Group*. Shalev-Shwartz: “Introduction to online learning”, Chapter 2 (end). [Paper]