Timezone: »
Data-efficient learning algorithms are essential in many practical applications where data collection is expensive, e.g., in robotics due to the wear and tear. To address this problem, meta-learning algorithms use prior experience about tasks to learn new, related tasks efficiently. Typically, a set of training tasks is assumed given or randomly chosen. However, this setting does not take into account the sequential nature that naturally arises when training a model from scratch in real-life: how do we collect a set of training tasks in a data-efficient manner? In this work, we introduce task selection based on prior experience into a meta-learning algorithm by conceptualizing the learner and the active meta-learning setting using a probabilistic latent variable model. We provide empirical evidence that our approach improves data-efficiency when compared to strong baselines on simulated robotic experiments.
Author Information
Jean Kaddour (University College London)
Steindor Saemundsson (Imperial College London)
Marc Deisenroth (University College London)

Professor Marc Deisenroth is the DeepMind Chair in Artificial Intelligence at University College London and the Deputy Director of UCL's Centre for Artificial Intelligence. He also holds a visiting faculty position at the University of Johannesburg and Imperial College London. Marc's research interests center around data-efficient machine learning, probabilistic modeling and autonomous decision making. Marc was Program Chair of EWRL 2012, Workshops Chair of RSS 2013, EXPO-Co-Chair of ICML 2020, and Tutorials Co-Chair of NeurIPS 2021. In 2019, Marc co-organized the Machine Learning Summer School in London. He received Paper Awards at ICRA 2014, ICCAS 2016, and ICML 2020. He is co-author of the book [Mathematics for Machine Learning](https://mml-book.github.io) published by Cambridge University Press (2020).
More from the Same Authors
-
2021 : Imitation Learning from Pixel Observations for Continuous Control »
Samuel Cohen · Brandon Amos · Marc Deisenroth · Mikael Henaff · Eugene Vinitsky · Denis Yarats -
2021 : On Combining Expert Demonstrations in Imitation Learning via Optimal Transport »
ilana sebag · Samuel Cohen · Marc Deisenroth -
2021 : Sliced Multi-Marginal Optimal Transport »
Samuel Cohen · Alexander Terenin · Yannik Pitcan · Brandon Amos · Marc Deisenroth · Senanayak Sesh Kumar Karri -
2022 : Actually Sparse Variational Gaussian Processes »
Jake Cunningham · So Takao · Mark van der Wilk · Marc Deisenroth -
2022 : Stop Wasting My Time! Saving Days of ImageNet and BERT Training with Latest Weight Averaging »
Jean Kaddour -
2022 : Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes »
So Takao · Sean Nassimiha · Peter Dudfield · Jack Kelly · Marc Deisenroth -
2022 : Evaluating the Impact of Geometric and Statistical Skews on Out-Of-Distribution Generalization Performance »
Aengus Lynch · Jean Kaddour · Ricardo Silva -
2022 : Optimal Transport for Offline Imitation Learning »
Yicheng Luo · zhengyao Jiang · Samuel Cohen · Edward Grefenstette · Marc Deisenroth -
2022 : Evaluating the Impact of Geometric and Statistical Skews on Out-Of-Distribution Generalization Performance »
Aengus Lynch · Jean Kaddour · Ricardo Silva -
2022 Poster: When Do Flat Minima Optimizers Work? »
Jean Kaddour · Linqing Liu · Ricardo Silva · Matt Kusner -
2021 Poster: Causal Effect Inference for Structured Treatments »
Jean Kaddour · Yuchen Zhu · Qi Liu · Matt Kusner · Ricardo Silva -
2021 Poster: Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels »
Michael Hutchinson · Alexander Terenin · Viacheslav Borovitskiy · So Takao · Yee Teh · Marc Deisenroth -
2020 : GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability »
Arinbjörn Kolbeinsson · Nicholas Jennings · Marc Deisenroth · Daniel Lengyel · Janith Petangoda · Michalis Lazarou · Kate Highnam · John IF Falk -
2020 Poster: Matérn Gaussian Processes on Riemannian Manifolds »
Viacheslav Borovitskiy · Alexander Terenin · Peter Mostowsky · Marc Deisenroth -
2020 Session: Orals & Spotlights Track 25: Probabilistic Models/Statistics »
Marc Deisenroth · Matthew D. Hoffman -
2020 Tutorial: (Track1) There and Back Again: A Tale of Slopes and Expectations Q&A »
Marc Deisenroth · Cheng Soon Ong -
2020 : Discussion Panel: Hugo Larochelle, Finale Doshi-Velez, Devi Parikh, Marc Deisenroth, Julien Mairal, Katja Hofmann, Phillip Isola, and Michael Bowling »
Hugo Larochelle · Finale Doshi-Velez · Marc Deisenroth · Devi Parikh · Julien Mairal · Katja Hofmann · Phillip Isola · Michael Bowling -
2020 Tutorial: (Track1) There and Back Again: A Tale of Slopes and Expectations »
Marc Deisenroth · Cheng Soon Ong -
2019 : Invited Talk - Marc Deisenroth »
Marc Deisenroth -
2019 : Poster session »
Sebastian Farquhar · Erik Daxberger · Andreas Look · Matt Benatan · Ruiyi Zhang · Marton Havasi · Fredrik Gustafsson · James A Brofos · Nabeel Seedat · Micha Livne · Ivan Ustyuzhaninov · Adam Cobb · Felix D McGregor · Patrick McClure · Tim R. Davidson · Gaurush Hiranandani · Sanjeev Arora · Masha Itkina · Didrik Nielsen · William Harvey · Matias Valdenegro-Toro · Stefano Peluchetti · Riccardo Moriconi · Tianyu Cui · Vaclav Smidl · Taylan Cemgil · Jack Fitzsimons · He Zhao · · mariana vargas vieyra · Apratim Bhattacharyya · Rahul Sharma · Geoffroy Dubourg-Felonneau · Jonathan Warrell · Slava Voloshynovskiy · Mihaela Rosca · Jiaming Song · Andrew Ross · Homa Fashandi · Ruiqi Gao · Hooshmand Shokri Razaghi · Joshua Chang · Zhenzhong Xiao · Vanessa Boehm · Giorgio Giannone · Ranganath Krishnan · Joe Davison · Arsenii Ashukha · Jeremiah Liu · Sicong (Sheldon) Huang · Evgenii Nikishin · Sunho Park · Nilesh Ahuja · Mahesh Subedar · · Artyom Gadetsky · Jhosimar Arias Figueroa · Tim G. J. Rudner · Waseem Aslam · Adrián Csiszárik · John Moberg · Ali Hebbal · Kathrin Grosse · Pekka Marttinen · Bang An · Hlynur Jónsson · Samuel Kessler · Abhishek Kumar · Mikhail Figurnov · Omesh Tickoo · Steindor Saemundsson · Ari Heljakka · Dániel Varga · Niklas Heim · Simone Rossi · Max Laves · Waseem Gharbieh · Nicholas Roberts · Luis Armando Pérez Rey · Matthew Willetts · Prithvijit Chakrabarty · Sumedh Ghaisas · Carl Shneider · Wray Buntine · Kamil Adamczewski · Xavier Gitiaux · Suwen Lin · Hao Fu · Gunnar Rätsch · Aidan Gomez · Erik Bodin · Dinh Phung · Lennart Svensson · Juliano Tusi Amaral Laganá Pinto · Milad Alizadeh · Jianzhun Du · Kevin Murphy · Beatrix Benkő · Shashaank Vattikuti · Jonathan Gordon · Christopher Kanan · Sontje Ihler · Darin Graham · Michael Teng · Louis Kirsch · Tomas Pevny · Taras Holotyak -
2018 Poster: Gaussian Process Conditional Density Estimation »
Vincent Dutordoir · Hugh Salimbeni · James Hensman · Marc Deisenroth -
2018 Poster: Maximizing acquisition functions for Bayesian optimization »
James Wilson · Frank Hutter · Marc Deisenroth -
2018 Poster: Orthogonally Decoupled Variational Gaussian Processes »
Hugh Salimbeni · Ching-An Cheng · Byron Boots · Marc Deisenroth -
2017 Poster: Doubly Stochastic Variational Inference for Deep Gaussian Processes »
Hugh Salimbeni · Marc Deisenroth -
2017 Spotlight: Doubly Stochastic Variational Inference for Deep Gaussian Processes »
Hugh Salimbeni · Marc Deisenroth -
2017 Poster: Identification of Gaussian Process State Space Models »
Stefanos Eleftheriadis · Tom Nicholson · Marc Deisenroth · James Hensman -
2015 : Applications of Bayesian Optimization to Systems »
Marc Deisenroth -
2014 Workshop: Novel Trends and Applications in Reinforcement Learning »
Csaba Szepesvari · Marc Deisenroth · Sergey Levine · Pedro Ortega · Brian Ziebart · Emma Brunskill · Naftali Tishby · Gerhard Neumann · Daniel Lee · Sridhar Mahadevan · Pieter Abbeel · David Silver · Vicenç Gómez -
2013 Workshop: Advances in Machine Learning for Sensorimotor Control »
Thomas Walsh · Alborz Geramifard · Marc Deisenroth · Jonathan How · Jan Peters -
2012 Poster: Expectation Propagation in Gaussian Process Dynamical Systems »
Marc Deisenroth · Shakir Mohamed -
2009 Workshop: Probabilistic Approaches for Control and Robotics »
Marc Deisenroth · Hilbert J Kappen · Emo Todorov · Duy Nguyen-Tuong · Carl Edward Rasmussen · Jan Peters