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Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies
Shivakanth Sujit, Pedro H. M. Braga, Jorg Bornschein +1
Reinforcement learning (RL) has shown great promise with algorithms learning in environments with large state and action spaces purely from scalar reward signals. A crucial challen…
Pitfalls of Conditional Batch Normalization for Contextual Multi-Modal Learning
Ivaxi Sheth, Aamer Abdul Rahman, Mohammad Havaei +1
Humans have perfected the art of learning from multiple modalities through sensory organs. Despite their impressive predictive performance on a single modality, neural networks can…
BERT on a Data Diet: Finding Important Examples by Gradient-Based Pruning
Mohsen Fayyaz, Ehsan Aghazadeh, Ali Modarressi +3
Current pre-trained language models rely on large datasets for achieving state-of-the-art performance. However, past research has shown that not all examples in a dataset are equal…
Automatic Evaluation of Excavator Operators using Learned Reward Functions
Pranav Agarwal, Marek Teichmann, Sheldon Andrews +1
Training novice users to operate an excavator for learning different skills requires the presence of expert teachers. Considering the complexity of the problem, it is comparatively…
Learning Latent Structural Causal Models
Jithendaraa Subramanian, Yashas Annadani, Ivaxi Sheth +5
Causal learning has long concerned itself with the accurate recovery of underlying causal mechanisms. Such causal modelling enables better explanations of out-of-distribution data.…
Learning Robust Dynamics through Variational Sparse Gating
Arnav Kumar Jain, Shivakanth Sujit, Shruti Joshi +3
Learning world models from their sensory inputs enables agents to plan for actions by imagining their future outcomes. World models have previously been shown to improve sample-eff…
Locally Constrained Representations in Reinforcement Learning
Somjit Nath, Rushiv Arora, Samira Ebrahimi Kahou
The success of Reinforcement Learning (RL) heavily relies on the ability to learn robust representations from the observations of the environment. In most cases, the representation…
Latent Variable Models for Bayesian Causal Discovery
Jithendaraa Subramanian, Yashas Annadani, Ivaxi Sheth +3
Learning predictors that do not rely on spurious correlations involves building causal representations. However, learning such a representation is very challenging. We, therefore,…
Prioritizing Samples in Reinforcement Learning with Reducible Loss
Shivakanth Sujit, Somjit Nath, Pedro H. M. Braga +1
Most reinforcement learning algorithms take advantage of an experience replay buffer to repeatedly train on samples the agent has observed in the past. Not all samples carry the sa…
FHIST: A Benchmark for Few-shot Classification of Histological Images
Fereshteh Shakeri, Malik Boudiaf, Sina Mohammadi +4
Few-shot learning has recently attracted wide interest in image classification, but almost all the current public benchmarks are focused on natural images. The few-shot paradigm is…