Author name cluster

Kien Do

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

17 papers

2 author rows

AAAI Conference 2025 Conference Paper

Learning Structural Causal Models from Ordering: Identifiable Flow Models

Minh Khoa Le
Kien Do
Truyen Tran

In this study, we address causal inference when only observational data and a valid causal ordering from the causal graph are available. We introduce a set of flow models that can recover component-wise, invertible transformation of exogenous variables. Our flow-based methods offer flexible model design while maintaining causal consistency regardless of the number of discretization steps. We propose design improvements that enable simultaneous learning of all causal mechanisms and reduce abduction and prediction complexity to linear O(n) relative to the number of layers, independent of the number of causal variables. Empirically, we demonstrate that our method outperforms previous state-of-the-art approaches and delivers consistent performance across a wide range of structural causal models in answering observational, interventional, and counterfactual questions. Additionally, our method achieves a significant reduction in computational time compared to existing diffusion-based techniques, making it practical for large structural causal models.

PDF Details DOI

AAAI Conference 2025 Conference Paper

Multi-Reference Preference Optimization for Large Language Models

Hung Le
Quan Hung Tran
Dung Nguyen
Kien Do
Saloni Mittal
Kelechi Ogueji
Svetha Venkatesh

How can Large Language Models (LLMs) be aligned with human intentions and values? A typical solution is to gather human preference on model outputs and finetune the LLMs accordingly while ensuring that updates do not deviate too far from a reference model. Recent approaches, such as direct preference optimization (DPO), have eliminated the need for unstable and sluggish reinforcement learning optimization by introducing close-formed supervised losses. However, a significant limitation of the current approach is its design for a single reference model only, neglecting to leverage the collective power of numerous pretrained LLMs. To overcome this limitation, we introduce a novel closed-form formulation for direct preference optimization using multiple reference models. The resulting algorithm, Multi-Reference Preference Optimization (MRPO), leverages broader prior knowledge from diverse reference models, substantially enhancing preference learning capabilities compared to the single-reference DPO. Our experiments demonstrate that LLMs finetuned with MRPO generalize better in various preference data, regardless of data scarcity or abundance. Furthermore, MRPO effectively finetunes LLMs to exhibit superior performance in several downstream natural language processing tasks such as HH-RLHF, GSM8K and TruthfulQA.

PDF Details DOI

AAMAS Conference 2025 Conference Paper

Navigating Social Dilemmas with LLM-based Agents via Consideration of Future Consequences

Dung Nguyen
Hung Le
Kien Do
Sunil Gupta
Svetha Venkatesh
Truyen Tran

Agents built on LLMs have shown versatile capabilities but face difficulties in being cooperative in social dilemma situations. When making decisions under the strain of selecting between long-term consequences and short-term benefits in commonly shared resources, LLM-based agents are vulnerable to the tragedy of the commons, i. e. individuals’ greed exploitation leads to early depletion. We propose LLM agents that consider future consequences to aid them in navigating intertemporal social dilemmas. We introduce two approaches—prompting and intervention—to equip the agent with the ability to consider future consequences when making a decision, which results in a new kind of agent—CFC-Agent. Furthermore, we enable the CFC-Agent to act toward different levels of consideration for future consequences. Our experiments in different settings show that agents that consider future consequences exhibit sustainable behaviour and achieve high common rewards for the population.

PDF

IJCAI Conference 2025 Conference Paper

Navigating Social Dilemmas with LLM-based Agents via Consideration of Future Consequences

Dung Nguyen
Hung Le
Kien Do
Sunil Gupta
Svetha Venkatesh
Truyen Tran

Artificial agents with the aid of large language models (LLMs) are effective in various real-world scenarios but struggle to cooperate in social dilemmas. When making decisions under the strain of selecting between long-term consequences and short-term benefits in commonly shared resources, LLM-based agents often exploit the environment, leading to early depletion. Inspired by the concept of consideration of future consequences (CFC), which is well-known in social psychology, we propose a framework to enable the ability to consider future consequences for LLM-based agents, which results in a new kind of agent that we term the CFC-Agent. We enable the CFC-Agent to act toward different levels of consideration for future consequences. Our first set of experiments, where LLM is directly asked to make decisions, shows that agents considering future consequences exhibit sustainable behaviour and achieve high common rewards for the population. Extensive experiments in complex environments showed that the CFC-Agent can manage a sequence of calls to LLM for reasoning and engaging in communication to cooperate with others to resolve the common dilemma better. Finally, our analysis showed that considering future consequences not only affects the final decision but also improves the conversations between LLM-based agents toward a better resolution of social dilemmas.

PDF Details DOI

ICLR Conference 2025 Conference Paper

Stable Hadamard Memory: Revitalizing Memory-Augmented Agents for Reinforcement Learning

Hung Le 0002
Dung Nguyen 0001
Kien Do
Sunil Gupta 0001
Svetha Venkatesh

Effective decision-making in partially observable environments demands robust memory management. Despite their success in supervised learning, current deep-learning memory models struggle in reinforcement learning environments that are partially observable and long-term. They fail to efficiently capture relevant past information, adapt flexibly to changing observations, and maintain stable updates over long episodes. We theoretically analyze the limitations of existing memory models within a unified framework and introduce the Stable Hadamard Memory, a novel memory model for reinforcement learning agents. Our model dynamically adjusts memory by erasing no longer needed experiences and reinforcing crucial ones computationally efficiently. To this end, we leverage the Hadamard product for calibrating and updating memory, specifically designed to enhance memory capacity while mitigating numerical and learning challenges. Our approach significantly outperforms state-of-the-art memory-based methods on challenging partially observable benchmarks, such as meta-reinforcement learning, long-horizon credit assignment, and POPGym, demonstrating superior performance in handling long-term and evolving contexts.

Details

AAMAS Conference 2024 Conference Paper

Beyond Surprise: Improving Exploration Through Surprise Novelty

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. The reward is the novelty of the surprise rather than the surprise norm. We estimate the surprise novelty as retrieval errors of a memory network wherein the memory stores and reconstructs surprises. Our surprise memory (SM) augments the capability of surprise-based intrinsic motivators, maintaining the agent’s interest in exciting exploration while reducing unwanted attraction to unpredictable or noisy observations. Our experiments demonstrate that the SM combined with various surprise predictors exhibits efficient exploring behaviors and significantly boosts the final performance in sparse reward environments, including Noisy-TV, navigation and challenging Atari games.

PDF

IJCAI Conference 2024 Conference Paper

Diversifying Training Pool Predictability for Zero-shot Coordination: A Theory of Mind Approach

Dung Nguyen
Hung Le
Kien Do
Sunil Gupta
Svetha Venkatesh
Truyen Tran

The challenge in constructing artificial social agents is to enable adaptation ability to novel agents, and is called zero-shot coordination (ZSC). A promising approach is to train the adaptive agents by interacting with a diverse pool of collaborators, assuming that the greater the diversity in other agents seen during training, the better the generalisation. In this paper, we explore an alternative procedure by considering the behavioural predictability of collaborators, i. e. whether their actions and intentions are predictable, and use it to select a diverse set of agents for the training pool. More specifically, we develop a pool of agents through self-play training during which agents' behaviour evolves and has diversity in levels of behavioural predictability (LoBP) through its evolution. We construct an observer to compute the level of behavioural predictability for each version of the collaborators. To do so, the observer is equipped with the theory of mind (ToM) capability to learn to infer the actions and intentions of others. We then use an episodic memory based on the LoBP metric to maintain agents with different levels of behavioural predictability in the pool of agents. Since behaviours that emerge at the later training phase are more complex and meaningful, the memory is updated with the latest versions of training agents. Our extensive experiments demonstrate that LoBP-based diversity training leads to better ZSC than other diversity training methods.

PDF Details DOI

TMLR Journal 2024 Journal Article

Plug, Play, and Generalize: Length Extrapolation with Pointer-Augmented Neural Memory

Hung Le
Dung Nguyen
Kien Do
Svetha Venkatesh
Truyen Tran

We introduce Pointer-Augmented Neural Memory (PANM), a versatile module designed to enhance neural networks' ability to process symbols and extend their capabilities to longer data sequences. PANM integrates an external neural memory utilizing novel physical addresses and pointer manipulation techniques, emulating human and computer-like symbol processing abilities. PANM facilitates operations like pointer assignment, dereferencing, and arithmetic by explicitly employing physical pointers for memory access. This module can be trained end-to-end on sequence data, empowering various sequential models, from simple recurrent networks to large language models (LLMs). Our experiments showcase PANM's exceptional length extrapolation capabilities and its enhancement of recurrent neural networks in symbol processing tasks, including algorithmic reasoning and Dyck language recognition. PANM enables Transformers to achieve up to 100% generalization accuracy in compositional learning tasks and significantly improves performance in mathematical reasoning, question answering, and machine translation. Notably, the generalization effectiveness scales with stronger backbone models, as evidenced by substantial performance gains when we test LLMs finetuned with PANM for tasks up to 10-100 times longer than the training data.

PDF Details

ECAI Conference 2024 Conference Paper

Revisiting the Dataset Bias Problem from a Statistical Perspective

Kien Do
Dung Nguyen 0001
Hung Le 0002
Thao Le 0003
Dang Nguyen 0002
Haripriya Harikumar
Tran The Truyen
Santu Rana

In this paper, we study the “dataset bias” problem from a statistical standpoint, and identify the main cause of the problem as the strong correlation between a class attribute u and a non-class attribute b in the input x, represented by p(u|b) differing significantly from p(u). Since p(u|b) appears as part of the sampling distributions in the standard maximum log-likelihood (MLL) objective, a model trained on a biased dataset via MLL inherently incorporates such correlation into its parameters, leading to poor generalization to unbiased test data. From this observation, we propose to mitigate dataset bias via either weighting the objective of each sample n by 1 / p(un|bn) or sampling that sample with a weight proportional to 1 / p(un|bn). While both methods are statistically equivalent, the former proves more stable and effective in practice. Additionally, we establish a connection between our debiasing approach and causal reasoning, reinforcing our method’s theoretical foundation. However, when the bias label is unavailable, computing p(u|b) exactly is difficult. To overcome this challenge, we propose to approximate 1 / p(u|b) using a biased classifier trained with “bias amplification” losses. Extensive experiments on various biased datasets demonstrate the superiority of our method over existing debiasing techniques in most settings, validating our theoretical analysis.

Details

AAAI Conference 2023 Conference Paper

Memory-Augmented Theory of Mind Network

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. Recent machine learning approaches to ToM have demonstrated that we can train the observer to read the past and present behaviours of other agents and infer their beliefs (including false beliefs about things that no longer exist), goals, intentions and future actions. The challenges arise when the behavioural space is complex, demanding skilful space navigation for rapidly changing contexts for an extended period. We tackle the challenges by equipping the observer with novel neural memory mechanisms to encode, and hierarchical attention to selectively retrieve information about others. The memories allow rapid, selective querying of distal related past behaviours of others to deliberatively reason about their current mental state, beliefs and future behaviours. This results in ToMMY, a theory of mind model that learns to reason while making little assumptions about the underlying mental processes. We also construct a new suite of experiments to demonstrate that memories facilitate the learning process and achieve better theory of mind performance, especially for high-demand false-belief tasks that require inferring through multiple steps of changes.

PDF Details DOI

IJCAI Conference 2023 Conference Paper

Social Motivation for Modelling Other Agents under Partial Observability in Decentralised Training

Dung Nguyen
Hung Le
Kien Do
Svetha Venkatesh
Truyen Tran

Understanding other agents is a key challenge in constructing artificial social agents. Current works focus on centralised training, wherein agents are allowed to know all the information about others and the environmental state during training. In contrast, this work studies decentralised training, wherein agents must learn the model of other agents in order to cooperate with them under partially-observable conditions, even during training, i. e. learning agents are myopic. The intrinsic motivation for artificial agents is modelled on the concept of human social motivation that entices humans to meet and understand each other, especially when experiencing a utility loss. Our intrinsic motivation encourages agents to stay near each other to obtain better observations and construct a model of others. They do so when their model of other agents is poor, or the overall task performance is bad during the learning phase. This simple but effective method facilitates the processes of modelling others, resulting in an improvement of the performance in cooperative tasks significantly. Our experiments demonstrate that the socially-motivated agent can model others better and promote cooperation across different tasks.

PDF Details DOI

AAAI Conference 2022 Conference Paper

Episodic Policy Gradient Training

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 onthe-fly. Unlike other hyperparameter searches, we formulate hyperparameter scheduling as a standard Markov Decision Process and use episodic memory to store the outcome of used hyperparameters and their training contexts. At any policy update step, the policy learner refers to the stored experiences, and adaptively reconfigures its learning algorithm with the new hyperparameters determined by the memory. This mechanism, dubbed as Episodic Policy Gradient Training (EPGT), enables an episodic learning process, and jointly learns the policy and the learning algorithm’s hyperparameters within a single run. Experimental results on both continuous and discrete environments demonstrate the advantage of using the proposed method in boosting the performance of various policy gradient algorithms.

PDF Details

AAMAS Conference 2022 Conference Paper

Learning Theory of Mind via Dynamic Traits Attribution

Dung Nguyen
Phuoc Nguyen
Hung Le
Kien Do
Svetha Venkatesh
Truyen Tran

Machine learning of Theory of Mind (ToM) is essential to build social agents that co-live with humans and other agents. This capacity, once acquired, will help machines infer the mental states of others from observed contextual action trajectories, enabling future prediction of goals, intention, actions and successor representations. The underlying mechanism for such a prediction remains unclear, however. 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. This trait vector then multiplicatively modulates the prediction mechanism via a ‘fast weights’ scheme in the prediction neural network, which reads the current context and predicts the behaviour. We empirically show that the fast weights provide a good inductive bias to model the character traits of agents and hence improves mindreading ability. On the indirect assessment of false-belief understanding, the new ToM model enables more efficient helping behaviours.

PDF

NeurIPS Conference 2022 Conference Paper

Learning to Constrain Policy Optimization with Virtual Trust Region

Thai 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 two trust regions to regulate each policy update. In addition to using the proximity of one single old policy as the first trust region as done by prior works, we propose forming a second trust region by constructing another virtual policy that represents a wide range of past policies. We then enforce the new policy to stay closer to the virtual policy, which is beneficial if the old policy performs poorly. We propose a mechanism to automatically build the virtual policy from a memory buffer of past policies, providing a new capability for dynamically selecting appropriate trust regions during the optimization process. Our proposed method, dubbed Memory-Constrained Policy Optimization (MCPO), is examined in diverse environments, including robotic locomotion control, navigation with sparse rewards and Atari games, consistently demonstrating competitive performance against recent on-policy constrained policy gradient methods.

PDF Details

NeurIPS Conference 2022 Conference Paper

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

Kien Do
Thai Hung Le
Dung Nguyen
Dang Nguyen
HARIPRIYA HARIKUMAR
Truyen Tran
Santu Rana
Svetha Venkatesh

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 without using training data. The main idea is to use a generator to synthesize data for training the student. As the generator gets updated, the distribution of synthetic data will change. Such distribution shift could be large if the generator and the student are trained adversarially, causing the student to forget the knowledge it acquired at the previous steps. To alleviate this problem, we propose a simple yet effective method called Momentum Adversarial Distillation (MAD) which maintains an exponential moving average (EMA) copy of the generator and uses synthetic samples from both the generator and the EMA generator to train the student. 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 the new updates of the generator. Our experiments on six benchmark datasets including big datasets like ImageNet and Places365 demonstrate the superior performance of MAD over competing methods for handling the large distribution shift problem. Our method also compares favorably to existing DFKD methods and even achieves state-of-the-art results in some cases.

PDF Details

AAAI Conference 2021 Conference Paper

Semi-Supervised Learning with Variational Bayesian Inference and Maximum Uncertainty Regularization

Kien Do
Truyen Tran
Svetha Venkatesh

We propose two generic methods for improving semisupervised learning (SSL). The first integrates weight perturbation (WP) into existing “consistency regularization” (CR) based methods. We implement WP by leveraging variational Bayesian inference (VBI). The second method proposes a novel consistency loss called “maximum uncertainty regularization” (MUR). While most consistency losses act on perturbations in the vicinity of each data point, MUR actively searches for “virtual” points situated beyond this region that cause the most uncertain class predictions. This allows MUR to impose smoothness on a wider area in the input-output manifold. Our experiments show clear improvements in classification errors of various CR based methods when they are combined with VBI or MUR or both.

PDF Details

ICLR Conference 2020 Conference Paper

Theory and Evaluation Metrics for Learning Disentangled Representations

Kien Do
Tran The Truyen

We make two theoretical contributions to disentanglement learning by (a) defining precise semantics of disentangled representations, and (b) establishing robust metrics for evaluation. First, we characterize the concept “disentangled representations” used in supervised and unsupervised methods along three dimensions–informativeness, separability and interpretability–which can be expressed and quantified explicitly using information-theoretic constructs. This helps explain the behaviors of several well-known disentanglement learning models. We then propose robust metrics for measuring informativeness, separability and interpretability. Through a comprehensive suite of experiments, we show that our metrics correctly characterize the representations learned by different methods and are consistent with qualitative (visual) results. Thus, the metrics allow disentanglement learning methods to be compared on a fair ground. We also empirically uncovered new interesting properties of VAE-based methods and interpreted them with our formulation. These findings are promising and hopefully will encourage the design of more theoretically driven models for learning disentangled representations.

Details