85 citations · 295 across the 16 of their papers we have counts for
10 papers · 1 filter
Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset
Alexandre Galashov, Michalis K. Titsias, András György +4
Neural networks are traditionally trained under the assumption that data come from a stationary distribution. However, settings which violate this assumption are becoming more popu…
Kalman Filter for Online Classification of Non-Stationary Data
Michalis K. Titsias, Alexandre Galashov, Amal Rannen-Triki +3
In Online Continual Learning (OCL) a learning system receives a stream of data and sequentially performs prediction and training steps. Important challenges in OCL are concerned wi…
Towards Compute-Optimal Transfer Learning
Massimo Caccia, Alexandre Galashov, Arthur Douillard +6
The field of transfer learning is undergoing a significant shift with the introduction of large pretrained models which have demonstrated strong adaptability to a variety of downst…
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research
Jorg Bornschein, Alexandre Galashov, Ross Hemsley +17
A shared goal of several machine learning communities like continual learning, meta-learning and transfer learning, is to design algorithms and models that efficiently and robustly…
Data augmentation for efficient learning from parametric experts
Alexandre Galashov, Josh Merel, Nicolas Heess
We present a simple, yet powerful data-augmentation technique to enable data-efficient learning from parametric experts for reinforcement and imitation learning. We focus on what w…
Importance Weighted Policy Learning and Adaptation
Alexandre Galashov, Jakub Sygnowski, Guillaume Desjardins +5
The ability to exploit prior experience to solve novel problems rapidly is a hallmark of biological learning systems and of great practical importance for artificial ones. In the m…
Information asymmetry in KL-regularized RL
Alexandre Galashov, Siddhant M. Jayakumar, Leonard Hasenclever +7
Many real world tasks exhibit rich structure that is repeated across different parts of the state space or in time. In this work we study the possibility of leveraging such repeate…
Meta reinforcement learning as task inference
Jan Humplik, Alexandre Galashov, Leonard Hasenclever +3
Humans achieve efficient learning by relying on prior knowledge about the structure of naturally occurring tasks. There is considerable interest in designing reinforcement learning…
Exploiting Hierarchy for Learning and Transfer in KL-regularized RL
Dhruva Tirumala, Hyeonwoo Noh, Alexandre Galashov +6
As reinforcement learning agents are tasked with solving more challenging and diverse tasks, the ability to incorporate prior knowledge into the learning system and to exploit reus…
Neural probabilistic motor primitives for humanoid control
Josh Merel, Leonard Hasenclever, Alexandre Galashov +5
We focus on the problem of learning a single motor module that can flexibly express a range of behaviors for the control of high-dimensional physically simulated humanoids. To do t…