18 citations · 28 across the 44 of their papers we have counts for
11 papers · 1 filter
On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation
Thanh Nguyen-Tang, Ming Yin, Sunil Gupta +2
Sample-efficient offline reinforcement learning (RL) with linear function approximation has recently been studied extensively. Much of prior work has yielded the minimax-optimal bo…
Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation
Kien Do, Hung Le, Dung Nguyen +5
Data-free Knowledge Distillation (DFKD) has attracted attention recently thanks to its appealing capability of transferring knowledge from a teacher network to a student network wi…
Black-box Few-shot Knowledge Distillation
Dang Nguyen, Sunil Gupta, Kien Do +1
Knowledge distillation (KD) is an efficient approach to transfer the knowledge from a large "teacher" network to a smaller "student" network. Traditional KD methods require lots of…
Guiding Visual Question Answering with Attention Priors
Thao Minh Le, Vuong Le, Sunil Gupta +2
The current success of modern visual reasoning systems is arguably attributed to cross-modality attention mechanisms. However, in deliberative reasoning such as in VQA, attention i…
Fast Conditional Network Compression Using Bayesian HyperNetworks
Phuoc Nguyen, Truyen Tran, Ky Le +6
We introduce a conditional compression problem and propose a fast framework for tackling it. The problem is how to quickly compress a pretrained large neural network into optimal s…
Learning to Transfer Role Assignment Across Team Sizes
Dung Nguyen, Phuoc Nguyen, Svetha Venkatesh +1
Multi-agent reinforcement learning holds the key for solving complex tasks that demand the coordination of learning agents. However, strong coordination often leads to expensive ex…
Learning to Constrain Policy Optimization with Virtual Trust Region
Hung Le, Thommen Karimpanal George, Majid Abdolshah +4
We introduce a constrained optimization method for policy gradient reinforcement learning, which uses a virtual trust region to regulate each policy update. In addition to using th…
Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization
Hung Tran-The, Sunil Gupta, Santu Rana +1
The expected improvement (EI) algorithm is one of the most popular strategies for optimization under uncertainty due to its simplicity and efficiency. Despite its popularity, the t…
Uncertainty Aware System Identification with Universal Policies
Buddhika Laknath Semage, Thommen George Karimpanal, Santu Rana +1
Sim2real transfer is primarily concerned with transferring policies trained in simulation to potentially noisy real world environments. A common problem associated with sim2real tr…
Fast Model-based Policy Search for Universal Policy Networks
Buddhika Laknath Semage, Thommen George Karimpanal, Santu Rana +1
Adapting an agent's behaviour to new environments has been one of the primary focus areas of physics based reinforcement learning. Although recent approaches such as universal poli…
Towards Effective and Robust Neural Trojan Defenses via Input Filtering
Kien Do, Haripriya Harikumar, Hung Le +6
Trojan attacks on deep neural networks are both dangerous and surreptitious. Over the past few years, Trojan attacks have advanced from using only a single input-agnostic trigger a…