Search Results for author: Svetha Venkatesh

Found 145 papers, 33 papers with code

DeepCoDA: personalized interpretability for compositional health

1 code implementation ICML 2020 Thomas Quinn, Dang Nguyen, Santu Rana, Sunil Gupta, Svetha Venkatesh

Interpretability allows the domain-expert to directly evaluate the model's relevance and reliability, a practice that offers assurance and builds trust.

Revisiting the Dataset Bias Problem from a Statistical Perspective

no code implementations5 Feb 2024 Kien Do, Dung Nguyen, Hung Le, Thao Le, Dang Nguyen, Haripriya Harikumar, Truyen Tran, Santu Rana, Svetha Venkatesh

To overcome this challenge, we propose to approximate \frac{1}{p(u|b)} using a biased classifier trained with "bias amplification" losses.

Attribute

Root Cause Explanation of Outliers under Noisy Mechanisms

no code implementations19 Dec 2023 Phuoc Nguyen, Truyen Tran, Sunil Gupta, Thin Nguyen, Svetha Venkatesh

We then represent the functional form of a target outlier leaf as a function of the node and edge noises.

Attribute

Learn to Unlearn for Deep Neural Networks: Minimizing Unlearning Interference with Gradient Projection

1 code implementation7 Dec 2023 Tuan Hoang, Santu Rana, Sunil Gupta, Svetha Venkatesh

Recent data-privacy laws have sparked interest in machine unlearning, which involves removing the effect of specific training samples from a learnt model as if they were never present in the original training dataset.

Machine Unlearning

LaGR-SEQ: Language-Guided Reinforcement Learning with Sample-Efficient Querying

1 code implementation21 Aug 2023 Thommen George Karimpanal, Laknath Buddhika Semage, Santu Rana, Hung Le, Truyen Tran, Sunil Gupta, Svetha Venkatesh

To address this issue, we introduce SEQ (sample efficient querying), where we simultaneously train a secondary RL agent to decide when the LLM should be queried for solutions.

Decision Making reinforcement-learning +1

Beyond Surprise: Improving Exploration Through Surprise Novelty

1 code implementation9 Aug 2023 Hung Le, Kien Do, Dung Nguyen, Svetha Venkatesh

We present a new computing model for intrinsic rewards in reinforcement learning that addresses the limitations of existing surprise-driven explorations.

Atari Games Retrieval

Predictive Modeling through Hyper-Bayesian Optimization

no code implementations1 Aug 2023 Manisha Senadeera, Santu Rana, Sunil Gupta, Svetha Venkatesh

Specifically, we propose a novel way of integrating model selection and BO for the single goal of reaching the function optima faster.

Bayesian Optimization Model Selection

Persistent-Transient Duality: A Multi-mechanism Approach for Modeling Human-Object Interaction

no code implementations ICCV 2023 Hung Tran, Vuong Le, Svetha Venkatesh, Truyen Tran

To bridge that gap, this work proposes to model two concurrent mechanisms that jointly control human motion: the Persistent process that runs continually on the global scale, and the Transient sub-processes that operate intermittently on the local context of the human while interacting with objects.

Graph Attention Human-Object Interaction Detection +1

Zero-shot Sim2Real Adaptation Across Environments

no code implementations8 Feb 2023 Buddhika Laknath Semage, Thommen George Karimpanal, Santu Rana, Svetha Venkatesh

However, simulators are generally incapable of accurately replicating real-world dynamics, and thus bridging the sim2real gap is an important problem in simulation based learning.

Continuous Control Friction

Gradient Descent in Neural Networks as Sequential Learning in RKBS

no code implementations1 Feb 2023 Alistair Shilton, Sunil Gupta, Santu Rana, Svetha Venkatesh

The study of Neural Tangent Kernels (NTKs) has provided much needed insight into convergence and generalization properties of neural networks in the over-parametrized (wide) limit by approximating the network using a first-order Taylor expansion with respect to its weights in the neighborhood of their initialization values.

Memory-Augmented Theory of Mind Network

no code implementations17 Jan 2023 Dung Nguyen, Phuoc Nguyen, Hung Le, Kien Do, Svetha Venkatesh, Truyen Tran

Social reasoning necessitates the capacity of theory of mind (ToM), the ability to contextualise and attribute mental states to others without having access to their internal cognitive structure.

Attribute

Multi-weather Image Restoration via Domain Translation

no code implementations ICCV 2023 Prashant W. Patil, Sunil Gupta, Santu Rana, Svetha Venkatesh, Subrahmanyam Murala

Therefore, effective restoration of multi-weather degraded images is an essential prerequisite for successful functioning of such systems.

Image Restoration Translation

On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation

no code implementations23 Nov 2022 Thanh Nguyen-Tang, Ming Yin, Sunil Gupta, Svetha Venkatesh, Raman Arora

To the best of our knowledge, these are the first $\tilde{\mathcal{O}}(\frac{1}{K})$ bound and absolute zero sub-optimality bound respectively for offline RL with linear function approximation from adaptive data with partial coverage.

Offline RL reinforcement-learning +1

Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation

no code implementations21 Sep 2022 Kien Do, Hung Le, Dung Nguyen, Dang Nguyen, Haripriya Harikumar, Truyen Tran, Santu Rana, Svetha Venkatesh

Since the EMA generator can be considered as an ensemble of the generator's old versions and often undergoes a smaller change in updates compared to the generator, training on its synthetic samples can help the student recall the past knowledge and prevent the student from adapting too quickly to new updates of the generator.

Data-free Knowledge Distillation

Black-box Few-shot Knowledge Distillation

1 code implementation25 Jul 2022 Dang Nguyen, Sunil Gupta, Kien Do, Svetha Venkatesh

Traditional KD methods require lots of labeled training samples and a white-box teacher (parameters are accessible) to train a good student.

Image Classification Knowledge Distillation

Guiding Visual Question Answering with Attention Priors

no code implementations25 May 2022 Thao Minh Le, Vuong Le, Sunil Gupta, Svetha Venkatesh, Truyen Tran

This grounding guides the attention mechanism inside VQA models through a duality of mechanisms: pre-training attention weight calculation and directly guiding the weights at inference time on a case-by-case basis.

Question Answering Visual Grounding +2

Persistent-Transient Duality in Human Behavior Modeling

no code implementations21 Apr 2022 Hung Tran, Vuong Le, Svetha Venkatesh, Truyen Tran

We propose to model the persistent-transient duality in human behavior using a parent-child multi-channel neural network, which features a parent persistent channel that manages the global dynamics and children transient channels that are initiated and terminated on-demand to handle detailed interactive actions.

Human-Object Interaction Detection motion prediction

Learning to Constrain Policy Optimization with Virtual Trust Region

no code implementations20 Apr 2022 Hung Le, Thommen Karimpanal George, Majid Abdolshah, Dung Nguyen, Kien Do, Sunil Gupta, Svetha Venkatesh

We introduce a constrained optimization method for policy gradient reinforcement learning, which uses a virtual trust region to regulate each policy update.

Atari Games Policy Gradient Methods

Learning to Transfer Role Assignment Across Team Sizes

no code implementations17 Apr 2022 Dung Nguyen, Phuoc Nguyen, Svetha Venkatesh, Truyen Tran

In particular, we train a role assignment network for small teams by demonstration and transfer the network to larger teams, which continue to learn through interaction with the environment.

Management Multi-agent Reinforcement Learning +4

Learning Theory of Mind via Dynamic Traits Attribution

no code implementations17 Apr 2022 Dung Nguyen, Phuoc Nguyen, Hung Le, Kien Do, Svetha Venkatesh, Truyen Tran

Inspired by the observation that humans often infer the character traits of others, then use it to explain behaviour, we propose a new neural ToM architecture that learns to generate a latent trait vector of an actor from the past trajectories.

Future prediction Inductive Bias +1

Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization

no code implementations15 Mar 2022 Hung Tran-The, Sunil Gupta, Santu Rana, Svetha Venkatesh

In particular, whether in the noisy setting, the EI strategy with a standard incumbent converges is still an open question of the Gaussian process bandit optimization problem.

Open-Ended Question Answering

Fast Model-based Policy Search for Universal Policy Networks

no code implementations11 Feb 2022 Buddhika Laknath Semage, Thommen George Karimpanal, Santu Rana, Svetha Venkatesh

Adapting an agent's behaviour to new environments has been one of the primary focus areas of physics based reinforcement learning.

Bayesian Optimisation

Uncertainty Aware System Identification with Universal Policies

no code implementations11 Feb 2022 Buddhika Laknath Semage, Thommen George Karimpanal, Santu Rana, Svetha Venkatesh

Sim2real transfer is primarily concerned with transferring policies trained in simulation to potentially noisy real world environments.

Bayesian Optimisation Continuous Control

Episodic Policy Gradient Training

1 code implementation3 Dec 2021 Hung Le, Majid Abdolshah, Thommen K. George, Kien Do, Dung Nguyen, Svetha Venkatesh

We introduce a novel training procedure for policy gradient methods wherein episodic memory is used to optimize the hyperparameters of reinforcement learning algorithms on-the-fly.

Policy Gradient Methods Scheduling

Kernel Functional Optimisation

1 code implementation NeurIPS 2021 Arun Kumar Anjanapura Venkatesh, Alistair Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh

Traditional methods for kernel selection rely on parametric kernel functions or a combination thereof and although the kernel hyperparameters are tuned, these methods often provide sub-optimal results due to the limitations induced by the parametric forms.

Bayesian Optimisation

Offline Neural Contextual Bandits: Pessimism, Optimization and Generalization

1 code implementation ICLR 2022 Thanh Nguyen-Tang, Sunil Gupta, A. Tuan Nguyen, Svetha Venkatesh

Moreover, we show that our method is more computationally efficient and has a better dependence on the effective dimension of the neural network than an online counterpart.

Multi-Armed Bandits

Balanced Q-learning: Combining the Influence of Optimistic and Pessimistic Targets

no code implementations3 Nov 2021 Thommen George Karimpanal, Hung Le, Majid Abdolshah, Santu Rana, Sunil Gupta, Truyen Tran, Svetha Venkatesh

The optimistic nature of the Q-learning target leads to an overestimation bias, which is an inherent problem associated with standard $Q-$learning.

Q-Learning

Semantic Host-free Trojan Attack

no code implementations26 Oct 2021 Haripriya Harikumar, Kien Do, Santu Rana, Sunil Gupta, Svetha Venkatesh

In this paper, we propose a novel host-free Trojan attack with triggers that are fixed in the semantic space but not necessarily in the pixel space.

Generative Pseudo-Inverse Memory

no code implementations ICLR 2022 Kha Pham, Hung Le, Man Ngo, Truyen Tran, Bao Ho, Svetha Venkatesh

We propose Generative Pseudo-Inverse Memory (GPM), a class of deep generative memory models that are fast to write in and read out.

Denoising

Neural Latent Traversal with Semantic Constraints

no code implementations29 Sep 2021 Majid Abdolshah, Hung Le, Thommen Karimpanal George, Vuong Le, Sunil Gupta, Santu Rana, Svetha Venkatesh

Whilst Generative Adversarial Networks (GANs) generate visually appealing high resolution images, the latent representations (or codes) of these models do not allow controllable changes on the semantic attributes of the generated images.

Plug and Play, Model-Based Reinforcement Learning

no code implementations20 Aug 2021 Majid Abdolshah, Hung Le, Thommen Karimpanal George, Sunil Gupta, Santu Rana, Svetha Venkatesh

This is achieved by representing the global transition dynamics as a union of local transition functions, each with respect to one active object in the scene.

Model-based Reinforcement Learning Object +3

Clustering by Maximizing Mutual Information Across Views

no code implementations ICCV 2021 Kien Do, Truyen Tran, Svetha Venkatesh

We propose a novel framework for image clustering that incorporates joint representation learning and clustering.

Clustering Image Clustering +1

A Spatio-temporal Attention-based Model for Infant Movement Assessment from Videos

1 code implementation20 May 2021 Binh Nguyen-Thai, Vuong Le, Catherine Morgan, Nadia Badawi, Truyen Tran, Svetha Venkatesh

The absence or abnormality of fidgety movements of joints or limbs is strongly indicative of cerebral palsy in infants.

Video Classification

Bayesian Optimistic Optimisation with Exponentially Decaying Regret

no code implementations10 May 2021 Hung Tran-The, Sunil Gupta, Santu Rana, Svetha Venkatesh

Bayesian optimisation (BO) is a well-known efficient algorithm for finding the global optimum of expensive, black-box functions.

Bayesian Optimisation

Intuitive Physics Guided Exploration for Sample Efficient Sim2real Transfer

no code implementations18 Apr 2021 Buddhika Laknath Semage, Thommen George Karimpanal, Santu Rana, Svetha Venkatesh

Physics-based reinforcement learning tasks can benefit from simplified physics simulators as they potentially allow near-optimal policies to be learned in simulation.

Friction

ALT-MAS: A Data-Efficient Framework for Active Testing of Machine Learning Algorithms

no code implementations11 Apr 2021 Huong Ha, Sunil Gupta, Santu Rana, Svetha Venkatesh

Machine learning models are being used extensively in many important areas, but there is no guarantee a model will always perform well or as its developers intended.

BIG-bench Machine Learning Data Augmentation +1

Sample Complexity of Offline Reinforcement Learning with Deep ReLU Networks

no code implementations11 Mar 2021 Thanh Nguyen-Tang, Sunil Gupta, Hung Tran-The, Svetha Venkatesh

To the best of our knowledge, this is the first theoretical characterization of the sample complexity of offline RL with deep neural network function approximation under the general Besov regularity condition that goes beyond {the linearity regime} in the traditional Reproducing Hilbert kernel spaces and Neural Tangent Kernels.

Offline RL reinforcement-learning +1

Learning Asynchronous and Sparse Human-Object Interaction in Videos

no code implementations CVPR 2021 Romero Morais, Vuong Le, Svetha Venkatesh, Truyen Tran

Their interactions are sparse in time hence more faithful to the true underlying nature and more robust in inference and learning.

Human-Object Interaction Detection Object

High Dimensional Level Set Estimation with Bayesian Neural Network

1 code implementation17 Dec 2020 Huong Ha, Sunil Gupta, Santu Rana, Svetha Venkatesh

In particular, we consider two types of LSE problems: (1) \textit{explicit} LSE problem where the threshold level is a fixed user-specified value, and, (2) \textit{implicit} LSE problem where the threshold level is defined as a percentage of the (unknown) maximum of the objective function.

Vocal Bursts Intensity Prediction

Logically Consistent Loss for Visual Question Answering

no code implementations19 Nov 2020 Anh-Cat Le-Ngo, Truyen Tran, Santu Rana, Sunil Gupta, Svetha Venkatesh

We propose a new model-agnostic logic constraint to tackle this issue by formulating a logically consistent loss in the multi-task learning framework as well as a data organisation called family-batch and hybrid-batch.

Multi-Task Learning Question Answering +1

Neurocoder: Learning General-Purpose Computation Using Stored Neural Programs

no code implementations NeurIPS 2021 Hung Le, Svetha Venkatesh

For the first time a Neural Program is treated as a datum in memory, paving the ways for modular, recursive and procedural neural programming.

Continual Learning Object Recognition

Theory of Mind with Guilt Aversion Facilitates Cooperative Reinforcement Learning

no code implementations16 Sep 2020 Dung Nguyen, Svetha Venkatesh, Phuoc Nguyen, Truyen Tran

In psychological game theory, guilt aversion necessitates modelling of agents that have theory about what other agents think, also known as Theory of Mind (ToM).

reinforcement-learning Reinforcement Learning (RL)

Sequential Subspace Search for Functional Bayesian Optimization Incorporating Experimenter Intuition

no code implementations8 Sep 2020 Alistair Shilton, Sunil Gupta, Santu Rana, Svetha Venkatesh

We propose an algorithm for Bayesian functional optimisation - that is, finding the function to optimise a process - guided by experimenter beliefs and intuitions regarding the expected characteristics (length-scale, smoothness, cyclicity etc.)

Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces

no code implementations NeurIPS 2020 Hung Tran-The, Sunil Gupta, Santu Rana, Huong Ha, Svetha Venkatesh

To this end, we propose a novel BO algorithm which expands (and shifts) the search space over iterations based on controlling the expansion rate thought a hyperharmonic series.

Bayesian Optimisation

Learning to Abstract and Predict Human Actions

1 code implementation20 Aug 2020 Romero Morais, Vuong Le, Truyen Tran, Svetha Venkatesh

We propose Hierarchical Encoder-Refresher-Anticipator, a multi-level neural machine that can learn the structure of human activities by observing a partial hierarchy of events and roll-out such structure into a future prediction in multiple levels of abstraction.

Activity Prediction Future prediction

Distributional Reinforcement Learning via Moment Matching

1 code implementation24 Jul 2020 Thanh Tang Nguyen, Sunil Gupta, Svetha Venkatesh

We consider the problem of learning a set of probability distributions from the empirical Bellman dynamics in distributional reinforcement learning (RL), a class of state-of-the-art methods that estimate the distribution, as opposed to only the expectation, of the total return.

Atari Games Distributional Reinforcement Learning +2

Bayesian Optimization with Missing Inputs

no code implementations19 Jun 2020 Phuc Luong, Dang Nguyen, Sunil Gupta, Santu Rana, Svetha Venkatesh

In real-world applications, BO often faces a major problem of missing values in inputs.

Bayesian Optimization

Scalable Backdoor Detection in Neural Networks

no code implementations10 Jun 2020 Haripriya Harikumar, Vuong Le, Santu Rana, Sourangshu Bhattacharya, Sunil Gupta, Svetha Venkatesh

Recently, it has been shown that deep learning models are vulnerable to Trojan attacks, where an attacker can install a backdoor during training time to make the resultant model misidentify samples contaminated with a small trigger patch.

Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation

1 code implementation8 Jun 2020 Julian Berk, Sunil Gupta, Santu Rana, Svetha Venkatesh

In order to improve the performance of Bayesian optimisation, we develop a modified Gaussian process upper confidence bound (GP-UCB) acquisition function.

Bayesian Optimisation

DeepCoDA: personalized interpretability for compositional health data

1 code implementation2 Jun 2020 Thomas P. Quinn, Dang Nguyen, Santu Rana, Sunil Gupta, Svetha Venkatesh

We define personalized interpretability as a measure of sample-specific feature attribution, and view it as a minimum requirement for a precision health model to justify its conclusions.

Variational Hyper-Encoding Networks

no code implementations18 May 2020 Phuoc Nguyen, Truyen Tran, Sunil Gupta, Santu Rana, Hieu-Chi Dam, Svetha Venkatesh

Given a target distribution, we predict the posterior distribution of the latent code, then use a matrix-network decoder to generate a posterior distribution q(\theta).

Density Estimation Outlier Detection +1

Dynamic Language Binding in Relational Visual Reasoning

1 code implementation30 Apr 2020 Thao Minh Le, Vuong Le, Svetha Venkatesh, Truyen Tran

We present Language-binding Object Graph Network, the first neural reasoning method with dynamic relational structures across both visual and textual domains with applications in visual question answering.

Object Question Answering +2

Incorporating Expert Prior in Bayesian Optimisation via Space Warping

no code implementations27 Mar 2020 Anil Ramachandran, Sunil Gupta, Santu Rana, Cheng Li, Svetha Venkatesh

In this paper, we represent the prior knowledge about the function optimum through a prior distribution.

Bayesian Optimisation

Hierarchical Conditional Relation Networks for Video Question Answering

1 code implementation CVPR 2020 Thao Minh Le, Vuong Le, Svetha Venkatesh, Truyen Tran

Video question answering (VideoQA) is challenging as it requires modeling capacity to distill dynamic visual artifacts and distant relations and to associate them with linguistic concepts.

Audio-Visual Question Answering (AVQA) Question Answering +4

Self-Attentive Associative Memory

1 code implementation ICML 2020 Hung Le, Truyen Tran, Svetha Venkatesh

Heretofore, neural networks with external memory are restricted to single memory with lossy representations of memory interactions.

Memorization Question Answering +1

Distributionally Robust Bayesian Quadrature Optimization

1 code implementation19 Jan 2020 Thanh Tang Nguyen, Sunil Gupta, Huong Ha, Santu Rana, Svetha Venkatesh

We adopt the distributionally robust optimization perspective to this problem by maximizing the expected objective under the most adversarial distribution.

Bayesian Optimization for Categorical and Category-Specific Continuous Inputs

1 code implementation28 Nov 2019 Dang Nguyen, Sunil Gupta, Santu Rana, Alistair Shilton, Svetha Venkatesh

To optimize such functions, we propose a new method that formulates the problem as a multi-armed bandit problem, wherein each category corresponds to an arm with its reward distribution centered around the optimum of the objective function in continuous variables.

Bayesian Optimization BIG-bench Machine Learning +1

Trading Convergence Rate with Computational Budget in High Dimensional Bayesian Optimization

no code implementations27 Nov 2019 Hung Tran-The, Sunil Gupta, Santu Rana, Svetha Venkatesh

Optimising acquisition function in low dimensional subspaces allows our method to obtain accurate solutions within limited computational budget.

Vocal Bursts Intensity Prediction

Cost-aware Multi-objective Bayesian optimisation

no code implementations9 Sep 2019 Majid Abdolshah, Alistair Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh

We introduce a cost-aware multi-objective Bayesian optimisation with non-uniform evaluation cost over objective functions by defining cost-aware constraints over the search space.

Bayesian Optimisation

Accelerating Experimental Design by Incorporating Experimenter Hunches

no code implementations22 Jul 2019 Cheng Li, Santu Rana, Sunil Gupta, Vu Nguyen, Svetha Venkatesh, Alessandra Sutti, David Rubin, Teo Slezak, Murray Height, Mazher Mohammed, Ian Gibson

In this paper, we consider per-variable monotonic trend in the underlying property that results in a unimodal trend in those variables for a target value optimization.

Bayesian Optimization Experimental Design

Neural Reasoning, Fast and Slow, for Video Question Answering

no code implementations10 Jul 2019 Thao Minh Le, Vuong Le, Svetha Venkatesh, Truyen Tran

While recent advances in lingual and visual question answering have enabled sophisticated representations and neural reasoning mechanisms, major challenges in Video QA remain on dynamic grounding of concepts, relations and actions to support the reasoning process.

Natural Questions Question Answering +2

GraphDTA: prediction of drug–target binding affinity using graph convolutional networks

1 code implementation bioRxiv 2019 Thin Nguyen, Hang Le, Svetha Venkatesh

The results show that our proposed method can not only predict the affinity better than non-deep learning models, but also outperform competing deep learning approaches.

Drug Discovery Recommendation Systems

Sparse Spectrum Gaussian Process for Bayesian Optimization

no code implementations21 Jun 2019 Ang Yang, Cheng Li, Santu Rana, Sunil Gupta, Svetha Venkatesh

Since the balance between predictive mean and the predictive variance is the key determinant to the success of Bayesian optimization, the current sparse spectrum methods are less suitable for it.

Thompson Sampling

Multi-objective Bayesian optimisation with preferences over objectives

no code implementations NeurIPS 2019 Majid Abdolshah, Alistair Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh

We present a multi-objective Bayesian optimisation algorithm that allows the user to express preference-order constraints on the objectives of the type "objective A is more important than objective B".

Bayesian Optimisation

Improving Generalization and Stability of Generative Adversarial Networks

1 code implementation ICLR 2019 Hoang Thanh-Tung, Truyen Tran, Svetha Venkatesh

We propose a zero-centered gradient penalty for improving the generalization of the discriminator by pushing it toward the optimal discriminator.

Learning to Remember More with Less Memorization

1 code implementation ICLR 2019 Hung Le, Truyen Tran, Svetha Venkatesh

Memory-augmented neural networks consisting of a neural controller and an external memory have shown potentials in long-term sequential learning.

Memorization Sentiment Analysis +2

Graph Transformation Policy Network for Chemical Reaction Prediction

no code implementations22 Dec 2018 Kien Do, Truyen Tran, Svetha Venkatesh

We address a fundamental problem in chemistry known as chemical reaction product prediction.

Chemical Reaction Prediction

Practical Batch Bayesian Optimization for Less Expensive Functions

no code implementations5 Nov 2018 Vu Nguyen, Sunil Gupta, Santu Rana, Cheng Li, Svetha Venkatesh

Bayesian optimization (BO) and its batch extensions are successful for optimizing expensive black-box functions.

Bayesian Optimization

Bayesian functional optimisation with shape prior

no code implementations19 Sep 2018 Pratibha Vellanki, Santu Rana, Sunil Gupta, David Rubin de Celis Leal, Alessandra Sutti, Murray Height, Svetha Venkatesh

Real world experiments are expensive, and thus it is important to reach a target in minimum number of experiments.

Bayesian Optimisation

Relational dynamic memory networks

no code implementations10 Aug 2018 Trang Pham, Truyen Tran, Svetha Venkatesh

Neural networks excel in detecting regular patterns but are less successful in representing and manipulating complex data structures, possibly due to the lack of an external memory.

Variational Memory Encoder-Decoder

1 code implementation NeurIPS 2018 Hung Le, Truyen Tran, Thin Nguyen, Svetha Venkatesh

Introducing variability while maintaining coherence is a core task in learning to generate utterances in conversation.

Accelerated Bayesian Optimization throughWeight-Prior Tuning

no code implementations21 May 2018 Alistair Shilton, Sunil Gupta, Santu Rana, Pratibha Vellanki, Laurence Park, Cheng Li, Svetha Venkatesh, Alessandra Sutti, David Rubin, Thomas Dorin, Alireza Vahid, Murray Height, Teo Slezak

In this paper we show how such auxiliary data may be used to construct a GP covariance corresponding to a more appropriate weight prior for the objective function.

Test Transfer Learning

High Dimensional Bayesian Optimization Using Dropout

no code implementations15 Feb 2018 Cheng Li, Sunil Gupta, Santu Rana, Vu Nguyen, Svetha Venkatesh, Alistair Shilton

Scaling Bayesian optimization to high dimensions is challenging task as the global optimization of high-dimensional acquisition function can be expensive and often infeasible.

Bayesian Optimization Vocal Bursts Intensity Prediction

Covariance Function Pre-Training with m-Kernels for Accelerated Bayesian Optimisation

no code implementations15 Feb 2018 Alistair Shilton, Sunil Gupta, Santu Rana, Pratibha Vellanki, Cheng Li, Laurence Park, Svetha Venkatesh, Alessandra Sutti, David Rubin, Thomas Dorin, Alireza Vahid, Murray Height

The paper presents a novel approach to direct covariance function learning for Bayesian optimisation, with particular emphasis on experimental design problems where an existing corpus of condensed knowledge is present.

Bayesian Optimisation Experimental Design +1

Dual Control Memory Augmented Neural Networks for Treatment Recommendations

no code implementations11 Feb 2018 Hung Le, Truyen Tran, Svetha Venkatesh

The decoding controller generates a treatment sequence, one treatment option at a time.

Knowledge Graph Embedding with Multiple Relation Projections

no code implementations26 Jan 2018 Kien Do, Truyen Tran, Svetha Venkatesh

Knowledge graphs contain rich relational structures of the world, and thus complement data-driven machine learning in heterogeneous data.

Knowledge Graph Embedding Knowledge Graphs +3

Graph Memory Networks for Molecular Activity Prediction

no code implementations8 Jan 2018 Trang Pham, Truyen Tran, Svetha Venkatesh

GraphMem is capable of jointly training on multiple datasets by using a specific-task query fed to the controller as an input.

Activity Prediction Multi-Task Learning

Process-constrained batch Bayesian optimisation

no code implementations NeurIPS 2017 Pratibha Vellanki, Santu Rana, Sunil Gupta, David Rubin, Alessandra Sutti, Thomas Dorin, Murray Height, Paul Sanders, Svetha Venkatesh

We demonstrate the performance of both pc-BO(basic) and pc-BO(nested) by optimising benchmark test functions, tuning hyper-parameters of the SVM classifier, optimising the heat-treatment process for an Al-Sc alloy to achieve target hardness, and optimising the short polymer fibre production process.

Bayesian Optimisation Test

Finding Algebraic Structure of Care in Time: A Deep Learning Approach

no code implementations21 Nov 2017 Phuoc Nguyen, Truyen Tran, Svetha Venkatesh

The interaction between diseases and treatments at a visit is modeled as the residual of the diseases minus the treatments.

Energy-based Models for Video Anomaly Detection

no code implementations17 Aug 2017 Hung Vu, Dinh Phung, Tu Dinh Nguyen, Anthony Trevors, Svetha Venkatesh

Automated detection of abnormalities in data has been studied in research area in recent years because of its diverse applications in practice including video surveillance, industrial damage detection and network intrusion detection.

Anomaly Detection Feature Engineering +2

High Dimensional Bayesian Optimization with Elastic Gaussian Process

no code implementations ICML 2017 Santu Rana, Cheng Li, Sunil Gupta, Vu Nguyen, Svetha Venkatesh

Bayesian optimization is an efficient way to optimize expensive black-box functions such as designing a new product with highest quality or hyperparameter tuning of a machine learning algorithm.

Bayesian Optimization Test +1

Deep Learning to Attend to Risk in ICU

no code implementations17 Jul 2017 Phuoc Nguyen, Truyen Tran, Svetha Venkatesh

At the reasoning layer, evidences across time steps are weighted and combined.

Decision Making ICU Mortality +2

Budgeted Batch Bayesian Optimization With Unknown Batch Sizes

no code implementations15 Mar 2017 Vu Nguyen, Santu Rana, Sunil Gupta, Cheng Li, Svetha Venkatesh

Current batch BO approaches are restrictive in that they fix the number of evaluations per batch, and this can be wasteful when the number of specified evaluations is larger than the number of real maxima in the underlying acquisition function.

Bayesian Optimization BIG-bench Machine Learning +1

Learning Deep Matrix Representations

no code implementations4 Mar 2017 Kien Do, Truyen Tran, Svetha Venkatesh

We derive several new deep networks: (i) feed-forward nets that map an input matrix into an output matrix, (ii) recurrent nets which map a sequence of input matrices into a sequence of output matrices.

EEG Electroencephalogram (EEG) +3

One Size Fits Many: Column Bundle for Multi-X Learning

no code implementations22 Feb 2017 Trang Pham, Truyen Tran, Svetha Venkatesh

Much recent machine learning research has been directed towards leveraging shared statistics among labels, instances and data views, commonly referred to as multi-label, multi-instance and multi-view learning.

MULTI-VIEW LEARNING

Control Matching via Discharge Code Sequences

no code implementations2 Dec 2016 Dang Nguyen, Wei Luo, Dinh Phung, Svetha Venkatesh

In this paper, we consider the patient similarity matching problem over a cancer cohort of more than 220, 000 patients.

Multilevel Anomaly Detection for Mixed Data

no code implementations20 Oct 2016 Kien Do, Truyen Tran, Svetha Venkatesh

We propose MIXMAD, which stands for MIXed data Multilevel Anomaly Detection, an ensemble method that estimates the sparse regions across multiple levels of abstraction of mixed data.

Unsupervised Anomaly Detection

Stabilizing Linear Prediction Models using Autoencoder

no code implementations28 Sep 2016 Shivapratap Gopakumar, Truyen Tran, Dinh Phung, Svetha Venkatesh

Using a linear model as basis for prediction, we achieve feature stability by regularising latent correlation in features.

Column Networks for Collective Classification

1 code implementation15 Sep 2016 Trang Pham, Truyen Tran, Dinh Phung, Svetha Venkatesh

CLN has many desirable theoretical properties: (i) it encodes multi-relations between any two instances; (ii) it is deep and compact, allowing complex functions to be approximated at the network level with a small set of free parameters; (iii) local and relational features are learned simultaneously; (iv) long-range, higher-order dependencies between instances are supported naturally; and (v) crucially, learning and inference are efficient, linear in the size of the network and the number of relations.

Classification General Classification +2

Outlier Detection on Mixed-Type Data: An Energy-based Approach

1 code implementation17 Aug 2016 Kien Do, Truyen Tran, Dinh Phung, Svetha Venkatesh

We evaluate the proposed method on synthetic and real-world datasets and demonstrate that (a) a proper handling mixed-types is necessary in outlier detection, and (b) free-energy of Mv. RBM is a powerful and efficient outlier scoring method, which is highly competitive against state-of-the-arts.

Outlier Detection Vocal Bursts Type Prediction

Faster Training of Very Deep Networks Via p-Norm Gates

no code implementations11 Aug 2016 Trang Pham, Truyen Tran, Dinh Phung, Svetha Venkatesh

Gates are employed in many recent state-of-the-art recurrent models such as LSTM and GRU, and feedforward models such as Residual Nets and Highway Networks.

Machine Translation Translation

Deepr: A Convolutional Net for Medical Records

no code implementations26 Jul 2016 Phuoc Nguyen, Truyen Tran, Nilmini Wickramasinghe, Svetha Venkatesh

On top of the sequence is a convolutional neural net that detects and combines predictive local clinical motifs to stratify the risk.

Feature Engineering

Achieving stable subspace clustering by post-processing generic clustering results

no code implementations27 May 2016 Duc-Son Pham, Ognjen Arandjelovic, Svetha Venkatesh

We propose an effective subspace selection scheme as a post-processing step to improve results obtained by sparse subspace clustering (SSC).

Clustering Face Clustering +1

Learning deep representation of multityped objects and tasks

no code implementations4 Mar 2016 Truyen Tran, Dinh Phung, Svetha Venkatesh

We introduce a deep multitask architecture to integrate multityped representations of multimodal objects.

Image Retrieval Retrieval

Choice by Elimination via Deep Neural Networks

no code implementations17 Feb 2016 Truyen Tran, Dinh Phung, Svetha Venkatesh

We introduce Neural Choice by Elimination, a new framework that integrates deep neural networks into probabilistic sequential choice models for learning to rank.

Learning-To-Rank

Collaborative filtering via sparse Markov random fields

no code implementations9 Feb 2016 Truyen Tran, Dinh Phung, Svetha Venkatesh

Recommender systems play a central role in providing individualized access to information and services.

Collaborative Filtering Movie Recommendation +1

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

1 code implementation1 Feb 2016 Trang Pham, Truyen Tran, Dinh Phung, Svetha Venkatesh

We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes.

Discovering topic structures of a temporally evolving document corpus

no code implementations25 Dec 2015 Adham Beykikhoshk, Ognjen Arandjelovic, Dinh Phung, Svetha Venkatesh

In this paper we describe a novel framework for the discovery of the topical content of a data corpus, and the tracking of its complex structural changes across the temporal dimension.

Viewpoint distortion compensation in practical surveillance systems

no code implementations21 Apr 2015 Ognjen Arandjelovic, Duc-Son Pham, Svetha Venkatesh

Our aim is to estimate the perspective-effected geometric distortion of a scene from a video feed.

Groupwise registration of aerial images

no code implementations21 Apr 2015 Ognjen Arandjelovic, Duc-Son Pham, Svetha Venkatesh

This paper addresses the task of time separated aerial image registration.

Image Registration

The adaptable buffer algorithm for high quantile estimation in non-stationary data streams

no code implementations21 Apr 2015 Ognjen Arandjelovic, Duc-Son Pham, Svetha Venkatesh

The need to estimate a particular quantile of a distribution is an important problem which frequently arises in many computer vision and signal processing applications.

Hierarchical Dirichlet process for tracking complex topical structure evolution and its application to autism research literature

no code implementations8 Feb 2015 Adham Beykikhoshk, Ognjen Arandjelovic, Dinh Phung, Svetha Venkatesh

In this paper we describe a novel framework for the discovery of the topical content of a data corpus, and the tracking of its complex structural changes across the temporal dimension.

Boosted Markov Networks for Activity Recognition

no code implementations6 Aug 2014 Truyen Tran, Hung Bui, Svetha Venkatesh

We explore a framework called boosted Markov networks to combine the learning capacity of boosting and the rich modeling semantics of Markov networks and applying the framework for video-based activity recognition.

Activity Recognition feature selection +1

Human Activity Learning and Segmentation using Partially Hidden Discriminative Models

no code implementations6 Aug 2014 Truyen Tran, Hung Bui, Svetha Venkatesh

Learning and understanding the typical patterns in the daily activities and routines of people from low-level sensory data is an important problem in many application domains such as building smart environments, or providing intelligent assistance.

MCMC for Hierarchical Semi-Markov Conditional Random Fields

no code implementations6 Aug 2014 Truyen Tran, Dinh Phung, Svetha Venkatesh, Hung H. Bui

In this contribution, we propose a new approximation technique that may have the potential to achieve sub-cubic time complexity in length and linear time depth, at the cost of some loss of quality.

Thurstonian Boltzmann Machines: Learning from Multiple Inequalities

no code implementations1 Aug 2014 Truyen Tran, Dinh Phung, Svetha Venkatesh

We introduce Thurstonian Boltzmann Machines (TBM), a unified architecture that can naturally incorporate a wide range of data inputs at the same time.

Collaborative Filtering Handwritten Digit Recognition

Learning Structured Outputs from Partial Labels using Forest Ensemble

no code implementations24 Jul 2014 Truyen Tran, Dinh Phung, Svetha Venkatesh

Learning structured outputs with general structures is computationally challenging, except for tree-structured models.

Permutation Models for Collaborative Ranking

no code implementations23 Jul 2014 Truyen Tran, Svetha Venkatesh

Focusing on the core of the collaborative ranking process, the user and their community, we propose new models for representation of the underlying permutations and prediction of ranks.

Collaborative Filtering Collaborative Ranking

Stabilizing Sparse Cox Model using Clinical Structures in Electronic Medical Records

no code implementations23 Jul 2014 Shivapratap Gopakumar, Truyen Tran, Dinh Phung, Svetha Venkatesh

Stability in clinical prediction models is crucial for transferability between studies, yet has received little attention.

feature selection

Learning Rank Functionals: An Empirical Study

no code implementations23 Jul 2014 Truyen Tran, Dinh Phung, Svetha Venkatesh

In practical settings, the task often reduces to estimating a rank functional of an object with respect to a query.

Information Retrieval Learning-To-Rank +3

Tree-based iterated local search for Markov random fields with applications in image analysis

no code implementations22 Jul 2014 Truyen Tran, Dinh Phung, Svetha Venkatesh

The \emph{maximum a posteriori} (MAP) assignment for general structure Markov random fields (MRFs) is computationally intractable.

Image Denoising Stereo Matching +1

Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts

no code implementations9 Jan 2014 Vu Nguyen, Dinh Phung, XuanLong Nguyen, Svetha Venkatesh, Hung Hai Bui

We present a Bayesian nonparametric framework for multilevel clustering which utilizes group-level context information to simultaneously discover low-dimensional structures of the group contents and partitions groups into clusters.

Clustering

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