Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging Simple Rules

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In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

Reinforcement learning (RL) is crucial for learning to adapt to new environments. In RL, the prediction error is an important component that compares the expected and actual rewards. Dopamine plays a critical role in encoding these prediction errors. In my ...

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In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

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Informative sample selection in an active learning (AL) setting helps a machine learning system attain optimum performance with minimum labeled samples, thus reducing annotation costs and boosting performance of computer-aided diagnosis systems in the pres ...

Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging Simple Rules

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Understanding generalization and robustness in modern deep learning

Unveiling the complexity of learning and decision-making

Incorporating Projective Geometry into Deep Learning

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Topics in statistical physics of high-dimensional machine learning

Social Opinion Formation and Decision Making Under Communication Trends

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Fusing Pre-existing Knowledge and Machine Learning for Enhanced Building Thermal Modeling and Control

Predicting the long-term collective behaviour of fish pairs with deep learning

Involvement of prelimbic cortex neurons and related circuits in the acquisition of a cooperative learning by pairs of rats

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Evaluating the Impact of Learner Control and Interactivity in Conversational Tutoring Systems for Persuasive Writing

Inverse design of metal-organic frameworks for direct air capture of CO2via deep reinforcement learning

Meta-learning to address diverse Earth observation problems across resolutions

Towards practical reinforcement learning for tokamak magnetic control

On the Generalization of Stochastic Gradient Descent with Momentum

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

GANDALF: Graph-based transformer and Data Augmentation Active Learning Framework with interpretable features for multi-label chest Xray classification