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Author Information
Daniele Calandriello (INRIA Lille)
Alessandro Lazaric (FACEBOOK)
Michal Valko (Inria Lille - Nord Europe)
Michal is a research scientist in DeepMind Paris and SequeL team at Inria Lille - Nord Europe, France, lead by Philippe Preux and Rémi Munos. He also teaches the course Graphs in Machine Learning at l'ENS Cachan. Michal is primarily interested in designing algorithms that would require as little human supervision as possible. This means 1) reducing the “intelligence” that humans need to input into the system and 2) minimising the data that humans need spend inspecting, classifying, or “tuning” the algorithms. Another important feature of machine learning algorithms should be the ability to adapt to changing environments. That is why he is working in domains that are able to deal with minimal feedback, such as bandit algorithms, semi-supervised learning, and anomaly detection. Most recently he has worked on sequential algorithms with structured decisions where exploiting the structure can lead to provably faster learning. In the past the common thread of Michal's work has been adaptive graph-based learning and its application to the real world applications such as recommender systems, medical error detection, and face recognition. His industrial collaborators include Adobe, Intel, Technicolor, and Microsoft Research. He received his PhD in 2011 from University of Pittsburgh under the supervision of Miloš Hauskrecht and after was a postdoc of Rémi Munos.
Related Events (a corresponding poster, oral, or spotlight)
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2017 Poster: Second-Order Kernel Online Convex Optimization with Adaptive Sketching »
Mon. Aug 7th 08:30 AM -- 12:00 PM Room Gallery #81
More from the Same Authors
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2020 Poster: No-Regret Exploration in Goal-Oriented Reinforcement Learning »
Jean Tarbouriech · Evrard Garcelon · Michal Valko · Matteo Pirotta · Alessandro Lazaric -
2019 : Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret »
Michal Valko -
2019 : Michal Valko: How Negative Dependence Broke the Quadratic Barrier for Learning with Graphs and Kernels »
Michal Valko -
2019 : Poster Session 1 (all papers) »
Matilde Gargiani · Yochai Zur · Chaim Baskin · Evgenii Zheltonozhskii · Liam Li · Ameet Talwalkar · Xuedong Shang · Harkirat Singh Behl · Atilim Gunes Baydin · Ivo Couckuyt · Tom Dhaene · Chieh Lin · Wei Wei · Min Sun · Orchid Majumder · Michele Donini · Yoshihiko Ozaki · Ryan P. Adams · Christian Geißler · Ping Luo · zhanglin peng · · Ruimao Zhang · John Langford · Rich Caruana · Debadeepta Dey · Charles Weill · Xavi Gonzalvo · Scott Yang · Scott Yak · Eugen Hotaj · Vladimir Macko · Mehryar Mohri · Corinna Cortes · Stefan Webb · Jonathan Chen · Martin Jankowiak · Noah Goodman · Aaron Klein · Frank Hutter · Mojan Javaheripi · Mohammad Samragh · Sungbin Lim · Taesup Kim · SUNGWOONG KIM · Michael Volpp · Iddo Drori · Yamuna Krishnamurthy · Kyunghyun Cho · Stanislaw Jastrzebski · Quentin de Laroussilhe · Mingxing Tan · Xiao Ma · Neil Houlsby · Andrea Gesmundo · Zalán Borsos · Krzysztof Maziarz · Felipe Petroski Such · Joel Lehman · Kenneth Stanley · Jeff Clune · Pieter Gijsbers · Joaquin Vanschoren · Felix Mohr · Eyke Hüllermeier · Zheng Xiong · Wenpeng Zhang · Wenwu Zhu · Weijia Shao · Aleksandra Faust · Michal Valko · Michael Y Li · Hugo Jair Escalante · Marcel Wever · Andrey Khorlin · Tara Javidi · Anthony Francis · Saurajit Mukherjee · Jungtaek Kim · Michael McCourt · Saehoon Kim · Tackgeun You · Seungjin Choi · Nicolas Knudde · Alexander Tornede · Ghassen Jerfel -
2019 Poster: Exploiting structure of uncertainty for efficient matroid semi-bandits »
Pierre Perrault · Vianney Perchet · Michal Valko -
2019 Poster: Scale-free adaptive planning for deterministic dynamics & discounted rewards »
Peter Bartlett · Victor Gabillon · Jennifer Healey · Michal Valko -
2019 Oral: Exploiting structure of uncertainty for efficient matroid semi-bandits »
Pierre Perrault · Vianney Perchet · Michal Valko -
2019 Oral: Scale-free adaptive planning for deterministic dynamics & discounted rewards »
Peter Bartlett · Victor Gabillon · Jennifer Healey · Michal Valko -
2018 Poster: Improved large-scale graph learning through ridge spectral sparsification »
Daniele Calandriello · Alessandro Lazaric · Ioannis Koutis · Michal Valko -
2018 Oral: Improved large-scale graph learning through ridge spectral sparsification »
Daniele Calandriello · Alessandro Lazaric · Ioannis Koutis · Michal Valko -
2017 : Faster graph bandit learning using information about the neighbors »
Michal Valko -
2017 : Panel Discussion »
Balaraman Ravindran · Chelsea Finn · Alessandro Lazaric · Katja Hofmann · Marc Bellemare -
2017 : Explorartion methods for options »
Alessandro Lazaric -
2017 Poster: Active Learning for Accurate Estimation of Linear Models »
Carlos Riquelme Ruiz · Mohammad Ghavamzadeh · Alessandro Lazaric -
2017 Poster: Zonotope hit-and-run for efficient sampling from projection DPPs »
Guillaume Gautier · Rémi Bardenet · Michal Valko -
2017 Talk: Zonotope hit-and-run for efficient sampling from projection DPPs »
Guillaume Gautier · Rémi Bardenet · Michal Valko -
2017 Talk: Active Learning for Accurate Estimation of Linear Models »
Carlos Riquelme Ruiz · Mohammad Ghavamzadeh · Alessandro Lazaric