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08/09/2023
Intrinsic Motivation via Surprise Memory
We present a new computing model for intrinsic rewards in reinforcement ...
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08/01/2023
Predictive Modeling through Hyper-Bayesian Optimization
Model selection is an integral problem of model based optimization techn...
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07/24/2023
Persistent-Transient Duality: A Multi-mechanism Approach for Modeling Human-Object Interaction
Humans are highly adaptable, swiftly switching between different modes t...
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03/03/2023
BO-Muse: A human expert and AI teaming framework for accelerated experimental design
In this paper we introduce BO-Muse, a new approach to human-AI teaming f...
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02/01/2023
Gradient Descent in Neural Networks as Sequential Learning in RKBS
The study of Neural Tangent Kernels (NTKs) has provided much needed insi...
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01/17/2023
Memory-Augmented Theory of Mind Network
Social reasoning necessitates the capacity of theory of mind (ToM), the ...
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11/23/2022
On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation
Sample-efficient offline reinforcement learning (RL) with linear functio...
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09/21/2022
Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation
Data-free Knowledge Distillation (DFKD) has attracted attention recently...
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07/25/2022
Black-box Few-shot Knowledge Distillation
Knowledge distillation (KD) is an efficient approach to transfer the kno...
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05/25/2022
Guiding Visual Question Answering with Attention Priors
The current success of modern visual reasoning systems is arguably attri...
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05/13/2022
Fast Conditional Network Compression Using Bayesian HyperNetworks
We introduce a conditional compression problem and propose a fast framew...
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04/21/2022
Persistent-Transient Duality in Human Behavior Modeling
We propose to model the persistent-transient duality in human behavior u...
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04/20/2022
Memory-Constrained Policy Optimization
We introduce a new constrained optimization method for policy gradient r...
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04/17/2022
Learning Theory of Mind via Dynamic Traits Attribution
Machine learning of Theory of Mind (ToM) is essential to build social ag...
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03/15/2022
Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization
The expected improvement (EI) algorithm is one of the most popular strat...
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02/24/2022
Towards Effective and Robust Neural Trojan Defenses via Input Filtering
Trojan attacks on deep neural networks are both dangerous and surreptiti...
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12/03/2021
Episodic Policy Gradient Training
We introduce a novel training procedure for policy gradient methods wher...
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11/27/2021
Offline Neural Contextual Bandits: Pessimism, Optimization and Generalization
Offline policy learning (OPL) leverages existing data collected a priori...
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11/03/2021
Model-Based Episodic Memory Induces Dynamic Hybrid Controls
Episodic control enables sample efficiency in reinforcement learning by ...
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10/26/2021
Semantic Host-free Trojan Attack
In this paper, we propose a novel host-free Trojan attack with triggers ...
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10/13/2021
A Field Guide to Scientific XAI: Transparent and Interpretable Deep Learning for Bioinformatics Research
Deep learning has become popular because of its potential to achieve hig...
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08/20/2021
Plug and Play, Model-Based Reinforcement Learning
Sample-efficient generalisation of reinforcement learning approaches hav...
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07/24/2021
Clustering by Maximizing Mutual Information Across Views
We propose a novel framework for image clustering that incorporates join...
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07/24/2021
Combining Online Learning and Offline Learning for Contextual Bandits with Deficient Support
We address policy learning with logged data in contextual bandits. Curre...
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07/18/2021
A New Representation of Successor Features for Transfer across Dissimilar Environments
Transfer in reinforcement learning is usually achieved through generalis...
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05/20/2021
A Spatio-temporal Attention-based Model for Infant Movement Assessment from Videos
The absence or abnormality of fidgety movements of joints or limbs is st...
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05/10/2021
Bayesian Optimistic Optimisation with Exponentially Decaying Regret
Bayesian optimisation (BO) is a well-known efficient algorithm for findi...
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04/11/2021
ALT-MAS: A Data-Efficient Framework for Active Testing of Machine Learning Algorithms
Machine learning models are being used extensively in many important are...
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03/11/2021
On Finite-Sample Analysis of Offline Reinforcement Learning with Deep ReLU Networks
This paper studies the statistical theory of offline reinforcement learn...
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03/03/2021
Learning Asynchronous and Sparse Human-Object Interaction in Videos
Human activities can be learned from video. With effective modeling it i...
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12/17/2020
High Dimensional Level Set Estimation with Bayesian Neural Network
Level Set Estimation (LSE) is an important problem with applications in ...
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12/03/2020
Semi-Supervised Learning with Variational Bayesian Inference and Maximum Uncertainty Regularization
We propose two generic methods for improving semi-supervised learning (S...
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11/19/2020
Logically Consistent Loss for Visual Question Answering
Given an image, a back-ground knowledge, and a set of questions about an...
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10/18/2020
Hierarchical Conditional Relation Networks for Multimodal Video Question Answering
Video QA challenges modelers in multiple fronts. Modeling video necessit...
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09/24/2020
Neurocoder: Learning General-Purpose Computation Using Stored Neural Programs
Artificial Neural Networks are uniquely adroit at machine learning by pr...
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09/16/2020
Theory of Mind with Guilt Aversion Facilitates Cooperative Reinforcement Learning
Guilt aversion induces experience of a utility loss in people if they be...
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09/08/2020
Sequential Subspace Search for Functional Bayesian Optimization Incorporating Experimenter Intuition
We propose an algorithm for Bayesian functional optimisation - that is, ...
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09/05/2020
Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces
Bayesian optimisation is a popular method for efficient optimisation of ...
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08/20/2020
Learning to Abstract and Predict Human Actions
Human activities are naturally structured as hierarchies unrolled over t...
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07/24/2020
Distributional Reinforcement Learning with Maximum Mean Discrepancy
Distributional reinforcement learning (RL) has achieved state-of-the-art...
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06/19/2020
Bayesian Optimization with Missing Inputs
Bayesian optimization (BO) is an efficient method for optimizing expensi...
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06/10/2020
Scalable Backdoor Detection in Neural Networks
Recently, it has been shown that deep learning models are vulnerable to ...
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06/08/2020
Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation
In order to improve the performance of Bayesian optimisation, we develop...
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06/02/2020
DeepCoDA: personalized interpretability for compositional health
Interpretability allows the domain-expert to directly evaluate the model...
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05/18/2020
HyperVAE: A Minimum Description Length Variational Hyper-Encoding Network
We propose a framework called HyperVAE for encoding distributions of dis...
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04/30/2020
Dynamic Language Binding in Relational Visual Reasoning
We present Language-binding Object Graph Network, the first neural reaso...
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03/27/2020
Incorporating Expert Prior in Bayesian Optimisation via Space Warping
Bayesian optimisation is a well-known sample-efficient method for the op...
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02/26/2020
Incorporating Expert Prior Knowledge into Experimental Design via Posterior Sampling
Scientific experiments are usually expensive due to complex experimental...
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02/25/2020
Hierarchical Conditional Relation Networks for Video Question Answering
Video question answering (VideoQA) is challenging as it requires modelin...
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02/10/2020