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Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels
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Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels
Discretization Drift in Two-Player Games
Gradient-based methods for two-player games produce rich dynamics that can solve challenging problems, yet can be difficult to …
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Discretization Drift in Two-Player Games
Riemannian Convex Potential Flows
Modeling distributions on Riemannian manifolds is a crucial component in understanding nonEuclidean data that arises, e.g., in physics …
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Riemannian Convex Potential Flows
Spectral Normalization for Deep Reinforcement Learning: an Optimisation Perspective
Most of the recent deep reinforcement learning advances take an RL-centric perspective and focus on refinements of the training …
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Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities
Advances in algorithmic fairness have largely omitted sexual orientation and gender identity. We explore queer concerns in privacy, …
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A Practical Sparse Approximation for Real Time Recurrent Learning
Recurrent neural networks are usually trained with backpropagation through time, which requires storing a complete history of network …
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Aligning Time Series on Incomparable Spaces
Dynamic time warping (DTW) is a useful method for aligning, comparing and combining time series, but it requires them to live in …
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Aligning Time Series on Incomparable Spaces
Learning Contact Dynamics using Physically Structured Neural Networks
Learning physically structured representations of dynamical systems that include contact between different objects is an important …
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Learning Contact Dynamics using Physically Structured Neural Networks
Matérn Gaussian Processes on Graphs
Gaussian processes are a versatile framework for learning unknown functions in a manner that permits one to utilize prior information …
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Matérn Gaussian Processes on Graphs
Could AI Democratise Education? Socio-Technical Imaginaries of an EdTech Revolution
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