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08/01/2023
SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning
The recent progress in large language models (LLMs), especially the inve...
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07/14/2023
Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions
Accelerating the discovery of novel and more effective therapeutics is a...
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07/11/2023
Geometric Neural Diffusion Processes
Denoising diffusion models have proven to be a flexible and effective pa...
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06/14/2023
Kalman Filter for Online Classification of Non-Stationary Data
In Online Continual Learning (OCL) a learning system receives a stream o...
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05/24/2023
Deep Stochastic Processes via Functional Markov Transition Operators
We introduce Markov Neural Processes (MNPs), a new class of Stochastic P...
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04/04/2023
Incorporating Unlabelled Data into Bayesian Neural Networks
We develop a contrastive framework for learning better prior distributio...
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02/20/2023
Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation
Skip connections and normalisation layers form two standard architectura...
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01/23/2023
Modality-Agnostic Variational Compression of Implicit Neural Representations
We introduce a modality-agnostic neural data compression algorithm based...
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12/28/2022
On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations
KL-regularized reinforcement learning from expert demonstrations has pro...
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07/07/2022
Riemannian Diffusion Schrödinger Bridge
Score-based generative models exhibit state of the art performance on de...
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06/20/2022
When Does Re-initialization Work?
Re-initializing a neural network during training has been observed to im...
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06/09/2022
Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations
Offline reinforcement learning has shown great promise in leveraging lar...
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06/09/2022
Conformal Off-Policy Prediction in Contextual Bandits
Most off-policy evaluation methods for contextual bandits have focused o...
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05/31/2022
Pre-training via Denoising for Molecular Property Prediction
Many important problems involving molecular property prediction from 3D ...
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05/18/2022
Meta-Learning Sparse Compression Networks
Recent work in Deep Learning has re-imagined the representation of data ...
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02/06/2022
Riemannian Score-Based Generative Modeling
Score-based generative models (SGMs) are a novel class of generative mod...
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01/30/2022
COIN++: Data Agnostic Neural Compression
Neural compression algorithms are typically based on autoencoders that r...
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10/27/2021
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels
Gaussian processes are machine learning models capable of learning unkno...
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10/01/2021
Powerpropagation: A sparsity inducing weight reparameterisation
The training of sparse neural networks is becoming an increasingly impor...
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09/28/2021
Interoperability of statistical models in pandemic preparedness: principles and reality
We present "interoperability" as a guiding framework for statistical mod...
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06/25/2021
InteL-VAEs: Adding Inductive Biases to Variational Auto-Encoders via Intermediary Latents
We introduce a simple and effective method for learning VAEs with contro...
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06/18/2021
On Contrastive Representations of Stochastic Processes
Learning representations of stochastic processes is an emerging problem ...
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06/10/2021
Group Equivariant Subsampling
Subsampling is used in convolutional neural networks (CNNs) in the form ...
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06/07/2021
BayesIMP: Uncertainty Quantification for Causal Data Fusion
While causal models are becoming one of the mainstays of machine learnin...
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03/03/2021
COIN: COmpression with Implicit Neural representations
We propose a new simple approach for image compression: instead of stori...
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02/09/2021
Generative Models as Distributions of Functions
Generative models are typically trained on grid-like data such as images...
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12/20/2020
LieTransformer: Equivariant self-attention for Lie Groups
Group equivariant neural networks are used as building blocks of group i...
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11/25/2020
Equivariant Conditional Neural Processes
We introduce Equivariant Conditional Neural Processes (EquivCNPs), a new...
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10/29/2020
Attentive Clustering Processes
Amortized approaches to clustering have recently received renewed attent...
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10/27/2020
Behavior Priors for Efficient Reinforcement Learning
As we deploy reinforcement learning agents to solve increasingly challen...
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09/10/2020
Importance Weighted Policy Learning and Adaption
The ability to exploit prior experience to solve novel problems rapidly ...
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08/07/2020
Bootstrapping Neural Processes
Unlike in the traditional statistical modeling for which a user typicall...
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07/27/2020
On the robustness of effectiveness estimation of nonpharmaceutical interventions against COVID-19 transmission
There remains much uncertainty about the relative effectiveness of diffe...
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07/16/2020
Lottery Tickets in Linear Models: An Analysis of Iterative Magnitude Pruning
We analyse the pruning procedure behind the lottery ticket hypothesis ar...
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07/11/2020
Bayesian Deep Ensembles via the Neural Tangent Kernel
We explore the link between deep ensembles and Gaussian processes (GPs) ...
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06/15/2020
Neural Ensemble Search for Performant and Calibrated Predictions
Ensembles of neural networks achieve superior performance compared to st...
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03/30/2020
Non-exchangeable feature allocation models with sublinear growth of the feature sizes
Feature allocation models are popular models used in different applicati...
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03/04/2020
Simple and Scalable Epistemic Uncertainty Estimation Using a Single Deep Deterministic Neural Network
We propose a method for training a deterministic deep model that can fin...
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02/19/2020
Pruning untrained neural networks: Principles and Analysis
Overparameterized neural networks display state-of-the art performance. ...
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02/13/2020
Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise
Stochastic gradient descent with momentum (SGDm) is one of the most popu...
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12/05/2019
MetaFun: Meta-Learning with Iterative Functional Updates
Few-shot supervised learning leverages experience from previous learning...
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11/01/2019
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments
We introduce a fully stochastic gradient based approach to Bayesian opti...
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10/31/2019
Continual Unsupervised Representation Learning
Continual learning aims to improve the ability of modern learning system...
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10/29/2019
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
Universal probabilistic programming systems (PPSs) provide a powerful an...
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10/20/2019
Amortized Rejection Sampling in Universal Probabilistic Programming
Existing approaches to amortized inference in probabilistic programs wit...
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09/30/2019
Deep Amortized Clustering
We propose a deep amortized clustering (DAC), a neural architecture whic...
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06/17/2019
Stacked Capsule Autoencoders
An object can be seen as a geometrically organized set of interrelated p...
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06/13/2019
Random Tessellation Forests
Space partitioning methods such as random forests and the Mondrian proce...
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06/12/2019
Task Agnostic Continual Learning via Meta Learning
While neural networks are powerful function approximators, they suffer f...
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06/07/2019
