Reading Group

We hold biweekly “reading-group” meetings (duration: 60 minutes). Every two weeks, a different group member takes the lead, presenting a chosen paper for discussion. Additionally, to the biweekly schedule, we frequently invite external speakers to give talks or tutorials.


August 12, 2024 @ 17:00 JST

Reading Group. The reading group is on a summer break and planned to resume on August 12th. Topic to be decided.

Past Meetings

June 06, 2024

Invited Talk. François-Xavier Briol gives a talk on Robust and Conjugate Gaussian Process Regression. doorkeeper

June 05, 2024

Invited Talk. Patrick Shafto will give a talk on Mathematical foundations for learning agents. doorkeeper

June 03, 2024

Invited Talk. Tom Burns will give a talk on Semantically-correlated memories in a dense associative model. doorkeeper

May 29, 2024

Invited Talk. Talks by Fred Kunstner (Adaptive Methods in Machine Learning and Why Adam Works so Well) and Aaron Mishkin (Optimal Sets and Solution Paths of ReLU Networks). doorkeeper1, doorkeeper2

May 27, 2024

Internal Meeting. Anita introduces her research to the group.

May 20, 2024

Internal Discussion. Mutual feedback on NeurIPS submissions.

April 22, 2024

Reading Group. Dharmesh Tailor: Practice talk on Learning to Defer.

April 08, 2024

Reading Group. Peter Nickl moderates a discussion on compositionality.

April 05, 2024

Talk. Masaki Adachi: “Probabilistic Numerics for Scientific Experts”. doorkeeper

Show all reading group meetings

April 03, 2024

Talk. Rafael Cabral: “A Bayesian workflow for efficient and automatic model checking and robustness analysis”. doorkeeper

March 25, 2024

Reading group. Zhedong will moderate a paper discussion on GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection.

March 11, 2024

Reading group. Zwane Sicelukwanda will give a talk on Safe Robot Motion Generation With Gaussian Processes. doorkeeper

February 26, 2024

Reading group. Hugo will moderate a paper discussion on “A view of Estimation of Distribution Algorithms through the lens of Expectation-Maximization”.

February 16, 2024

Reading group. Pratik Chaudhari gives a talk on ‘A Picture of the Prediction Space of Deep Networks’. doorkeeper

January 29, 2024

Internal meeting. Mutual feedback on the group’s ICML submissions.

January 15, 2024

Internal meeting. Catching up in the new year, discussion on upcoming events and deadlines in 2024.

December 04, 2023

Reading Group. Keigo moderates a discussion on Junk DNA-Hypothesis. The reading group will be on a winter-break afterwards, and resume in the new year.

November 20, 2023

Reading Group. Thomas moderates a discussion on minimum description length, deep-learning and Bayes.

November 06, 2023

Reading Group. Geoffrey moderates a discussion on A Geometric Interpretation of the Metropolis–Hastings Algorithm.

October 24, 2023

Reading Group. Dharmesh moderates a discussion on the paper On Masked Pre-training and the Marginal Likelihood.

September 25, 2023

Discussion. Mutual Feedback on ICLR submissions.

September 11, 2023

Quarterly Internal Group Meeting.

September 08, 2023

Talk. Samuel Kaski: Collaborative Machine Learning for Science [doorkeeper]

September 04, 2023

Talk. Martin Mundt: Self-Expanding Neural Networks [doorkeeper]

September 04, 2023

Talk. Krikamol Muandet: (Im)possibility of Collective Intelligence [doorkeeper]

September 01, 2023

Talk. Geoffrey Wolfer: From Distributions to Markov Chains: Recent Advances in Inference and Geometry. [doorkeeper]

August 25, 2023

Talk. Rajesh Ranganath on out of distribution generalization. [doorkeeper]

August 21, 2023

Long Seminar. Etash Guha gives a tutorial on conformal prediction. Naima Borras speaks about local learning.

August 18, 2023

Talk. So Takao: Improving data-assimilation for weather forecasting: A graph-based Bayesian perspective.

August 18, 2023

Talk. Molei Tao on Optimization and Sampling in non-Euclidean spaces.

August 14, 2023

Reading group. Lu: Does Label Smoothing Mitigate Label Noise?

July 31, 2023

Reading group. Hugo: Exploring Example Influence in Continual Learning

July 03, 2023

Reading group. Quarterly internal group meeting.

June 12, 2023

Talk. Graham Neubig Is My NLP Model Working? The Answer is Harder Than You Think. doorkeeper link

June 05, 2023

Talk. Alexandre Pouget will speak about the Bayesian brain. doorkeeper link

May 22, 2023

Reading group. Etash and Yuesong’s research overview.

May 08, 2023

Reading group. Joe will give a talk on memory.

April 24, 2023

Reading group. Happy will moderate a discussion on in-context learning.

April 10, 2023

Reading group. Negar will moderate a discussion on Diffusion models as weighted ELBOs.

March 29, 2023

Talk. Talk by Eren Mehmet Kiral on The Lie-Group Bayesian Learning Rule.

March 27, 2023

Discussion. Session on productive time management in academia.

March 13, 2023

Reading group. Gianma will moderate a discussion on implicit bias of SGD.

February 27, 2023

Reading group. Thomas will talk about variational bounds.

February 13, 2023

Internal group meeting.

January 30, 2023

Informal meeting, chatting, catch-up.

January 23, 2023

Mutual feedback on ICML papers.

December 19, 2022

Reading Group. Christmas reading group (paper to be decided, bring cookies).

December 05, 2022

Reading Group. Discussion about interesting papers from NeurIPS 2022 and ICLR 2023.

November 21, 2022

Internal group meeting.

November 14, 2022

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

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.

August 01, 2022

Reading Group. Discussion of Upside-down Reinforcement Learning, Another Paper.

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?”

July 04, 2022

Reading Group. Discussion of the Paper The Art and Science of Cause and Effect.

June 20, 2022

Internal group meeting.

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.

March 07, 2022

Reading Group. Discussion of Ten Simple Rules for Structuring Papers.

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 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 08, 2021

Reading Group. Discussion of the paper [Precision and the Bayesian Brain].

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

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 21, 2021

AIP seminar. Talk by Adeline Fermanian on signature for machine learning.

June 14, 2021

Seminar. Talk by David Frazier. Title: loss-based variational Bayes prediction.

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]

April 05, 2021

Tutorial. Tutorial by Thomas Möllenhoff on convex duality: part 2.

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 08, 2021

Seminar. Talk by Eric Nalisnick: “Predictive Complexity Priors”. [Paper]

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.

October 02, 2020

Talk by Benjamin Guedj: “A (condensed) primer on PAC-Bayesian Learning”. [Slides]

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

September 11, 2020

Siddharth Swaroop: A survey on federated learning.

September 04, 2020

Happy Buzaaba and Fariz Ikhwantri: A survey on transfer learning. [Slides]

August 28, 2020

Lab members project overview and discussion.

August 21, 2020

Pierre’s grant presentation rehearsal and feedback.

August 14, 2020

Reading group. “Gradient descent for wide two-layer neural networks”. [Blog post]

August 07, 2020

Reading group. “Generalized Variational Inference: Three arguments for deriving new posteriors”. [Paper]

July 31, 2020

Reading group. “On the measure of intelligence”. [Paper]

July 17, 2020

Whole week: comments on ICML tutorials and talks.

July 10, 2020

Pre-ICML paper discussion.

July 03, 2020

Talk by Evgenii Egorov. “Involutive MCMC: one way to derive them all”. [Paper]

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 08, 2020

Talk by Evgenii Egorov on MaxEntropy Pursuit Variational Inference. [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]

April 17, 2020

Reading Group. Shalev-Shwartz: “Introduction to online learning”, Chapter 2. [Paper]

April 09, 2020

Reading Group. Shalev-Shwartz: “Introduction to online learning”, Chapter 1. [Paper]

April 03, 2020

Reading Group. “Maximum Entropy Principle”, Jaynes (1957). [Paper I], [Paper II]