Weekly Reading Group


October 04, 2021 @ 21:00 JST

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

September 27, 2021 @ 21:00 JST

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

Past Meetings

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]

Show all reading group meetings

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 papers.

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 gather.town.

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]