Weekly Reading Group
Upcoming
Reading Group. Discussion of the paper by Khan and Swaroop, 2021: [Knowledge-Adaptation Priors]
Reading Group. Discussion of the paper by Pearce et al., 2021: [Understanding Softmax Confidence and Uncertainty]
Past Meetings
Reading Group. Discussion of the paper by Hara et. al., 2019: [Data Cleansing for Models Trained with SGD]
Reading Group. Discussion of the paper by Khan and Rue, 2021: [The Bayesian Learning Rule]
AIP Seminar. Talk by Jiaxin Shi: “Sampling with Mirrored Stein Operators”. [Paper] [Doorkeeper]
Casual chat in gather.town.
Reading Group. Discussion of the paper by Li et al., 2019: [Hessian based analysis of SGD for Deep Nets: Dynamics and Generalization]
Reading Group. Discussion of the paper by Tsividis et al., 2021: [Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration and Planning]
Reading Group. Discussion of the paper by Hobbhahn and Hennig (2021): [Laplace Matching for Fast Approximate Inference in Generalized Linear Models]
Seminar. Leaving presentation by Dharmesh Tailor
Reading group. Discussion of the paper by Raj, Musco and Mackey (2020): [Importance Sampling via Local Sensitivity]
Show all reading group meetings
Reading group. Discussion of ICML 2021 papers.
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]
Reading group. Discussion of several knowledge distillation papers: [Paper 1], [Paper 2], [Paper 3]
Social chat in gathertown.
AIP seminar. Talk by Jooyeon Kim: “Stochastic optimal control and probabilistic modeling for detecting and reducing fake news”
AIP seminar. Talk by Adeline Fermanian on signature for machine learning.
Seminar. Talk by David Frazier. Title: loss-based variational Bayes prediction.
Internal group meeting.
Seminar. Talk by Julyan Arbel. Title: Approximate Bayesian computation with surrogate posteriors [Paper]
Mutual feedback on NeurIPS papers.
Reading group. Discussion of ICLR 2021 papers.
Reading group. On the origin of implicit regularization in stochastic gradient descent. [Paper]
Reading group. Discussion of AISTATS 2021 papers.
Talk. Talk by Dhruva Tirumala: Behavior Priors for Efficient Reinforcement Learning. [Paper]
Tutorial. Tutorial by Thomas Möllenhoff on convex duality: part 2.
Talk + Tutorial. Talk by Fariz Ikhwantri on “Knowledge distillation with DNN2GP” + tutorial by Thomas Möllenhoff on convex duality.
Informal group discussion.
Seminar, unusual day and time! Talk by Daniele Calandriello: “Scalable Determinantal Point Processes for Machine Learning”.
Seminar. Talk by Eric Nalisnick: “Predictive Complexity Priors”. [Paper]
Internal group meeting.
Seminar. Talk by Andrew Foong and David Burt: “On the Expressiveness of Approximate Inference in Bayesian Neural Networks”. [Paper]
Talk by Khimya Khetarpal and Matt Riemer: “Towards Continual Reinforcement Learning: A Review and Perspectives”. [Paper]
Well-being chat.
Seminar. Talk by Blair Bilodeau and Jeffrey Negrea: “Relaxing the I.I.D. Assumption: Adaptively Minimax Optimal Regret via Root-Entropic Regularization”. [Paper]
Mutual feedback on ICML papers.
Catch up meeting.
Seminar, unusual day! Erik.
Reading group. Qi Qian, Hao Li, Juhua Hu: “Efficient Kernel Transfer in Knowledge Distillation”. [Paper]
Seminar. Peter, Thomas. Uncertainty estimation for Bayesian Neural Networks using infinite-width nets.
Pre-ICLR paper discussion.
Reading group. Ben Recht: “A Tour of Reinforcement Learning: The View from Continuous Control”. [Paper]
Team work review for the past 4 months. Siddarth’s talk on functional regularization on the memorable past. [Paper]
Unusual day and time! Dimitri Meunier: “Meta Learning meets Variational Inference. Learning priors with guarantees”.
Feedback discussion on the new team webpage.
Talk by Benjamin Guedj: “A (condensed) primer on PAC-Bayesian Learning”. [Slides]
Talk by François-Xavier Briol: “Stein’s Method for Computational Statistics and Machine Learning”. [Slides]
Research chat and virtual breakfast/lunch/dinner in gather.town.
Siddharth Swaroop: A survey on federated learning.
Happy Buzaaba and Fariz Ikhwantri: A survey on transfer learning. [Slides]
Lab members project overview and discussion.
Pierre’s grant presentation rehearsal and feedback.
Reading group. “Gradient descent for wide two-layer neural networks”. [Blog post]
Reading group. “Generalized Variational Inference: Three arguments for deriving new posteriors”. [Paper]
Reading group. “On the measure of intelligence”. [Paper]
Whole week: comments on ICML tutorials and talks.
Pre-ICML paper discussion.
Talk by Evgenii Egorov. “Involutive MCMC: one way to derive them all”. [Paper]
Talk by Giulia Denevi on Efficient Lifelong Learning Algorithms: Regret Bounds and Statistical Guarantees.
Internal kickoff meeting.
Talk by Peter Nickl on “Variational Bayes for Infinite Mixtures of Local Regressors with Robotics Applications”. [Thesis]
Talk by Gian Maria Marconi on Manifold Regression by Structured Prediction: methodology and applications. [Paper]
Feedback discussion on our NeurIPS submissions.
Talk by Thomas Möllenhoff on Flat Metric Minimization with Applications in Generative Modeling. [Paper]
Talk by Evgenii Egorov on MaxEntropy Pursuit Variational Inference. [Paper]
Reading Group. “Continual Deep Learning by Functional Regularisation of the Memorable Past”. [Paper]
Reading Group. Shalev-Shwartz: “Introduction to online learning”, Chapter 2 (end). [Paper]
Reading Group. Shalev-Shwartz: “Introduction to online learning”, Chapter 2. [Paper]
Reading Group. Shalev-Shwartz: “Introduction to online learning”, Chapter 1. [Paper]