Singapore

Existing feature engineering methods based on large language models (LLMs) have not yet been applied to multi-label learning tasks. They lack the ability to model complex label dependencies and are not specifically adapted to the characteristics of multi-label tasks. To address the above issues, we propose Feature Engineering Automation for Multi-Label Learning (FEAML), an automated feature engineering method for multi-label classification which leverages the code generation capabilities of LLMs. By utilizing metadata and label co-occurrence matrices, LLMs are guided to understand the relationships between data features and task objectives, based on which high-quality features are generated.The newly generated features are evaluated in terms of model accuracy to assess their effectiveness, while Pearson correlation coefficients are used to detect redundancy. FEAML further incorporates the evaluation results as feedback to drive LLMs to continuously optimize code generation in subsequent iterations. By integrating LLMs with a feedback mechanism, FEAML realizes an efficient, interpretable and self-improving feature engineering paradigm. Empirical results on various multi-label datasets demonstrate that our FEAML outperforms other feature engineering methods.

AAAI 2026

The Semantic Architect: How FEAML Bridges Structured Data and LLMs for Multi-Label Tasks

dimensionality reduction/feature selection

multi-class/multi-label learning & extreme classification

multimodal learning

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ACL-IJCNLP 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

technical paper

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

My research lies at the intersection of causality,
reinforcement learning, world models, and multi-agent
systems. I aim to develop causal foundation world models
that enable agents to interpret the past, reason about the
future, and act reliably in dynamic, non-stationary, and
open-ended environments. My work spans causal
representation learning (e.g., CausalVAE), causal reasoning
in large language models, and causality-driven exploration
in open-ended worlds. These contributions have appeared in
leading venues such as NeurIPS, ICML, ICLR, CVPR, and KDD,
and have been recognized through over 770 citations and the
Rising Star in AI award (2024). Looking forward, my agenda
focuses on scalable, trustworthy causal world models for
healthcare, robotics, scientific discovery, and digital
systems.

Toward Causal Foundation World Models: From Representation to Decision-Making

Surface water dynamics play a critical role in Earth’s climate system, influencing ecosystems, agriculture, disaster resilience, and sustainable development. Yet monitoring rivers and surface water at fine spatial and temporal scales remains challenging---especially for narrow or sediment-rich rivers that are poorly captured by low-resolution satellite data. To address this, we introduce RiverScope, a high-resolution dataset developed through collaboration between computer science and hydrology experts. RiverScope comprises 1,145 high-resolution images (covering 2,577 square kilometers) with expert-labeled river and surface water masks, requiring over 100 hours of manual annotation. Each image is co-registered with Sentinel-2, SWOT, and the SWOT River Database (SWORD), enabling the evaluation of cost-accuracy trade-offs across sensors---a key consideration for operational water monitoring. We also establish the first global, high-resolution benchmark for river width estimation, achieving a median error of 7.2 meters---significantly outperforming existing satellite-derived methods. We extensively evaluate deep networks across multiple architectures (e.g., CNNs and transformers), pretraining strategies (e.g., supervised and self-supervised), and training datasets (e.g., ImageNet and satellite imagery). Our best-performing models combine the benefits of transfer learning with the use of all the multispectral PlanetScope channels via learned adaptors. RiverScope provides a valuable resource for fine-scale and multi-sensor hydrological modeling, supporting climate adaptation and sustainable water management.

RiverScope: High-Resolution River Masking Dataset

We now deploy language models in a wide variety of user-facing applications. Typically, these deployments have some specific purpose, like answering questions about documentation or acting as coding assistants, but they require general language understanding. Under these circumstances these models should not be able to answer irrelevant requests such as, poetry generation or questions about physics, etc. Instead we would like language models to only answer to queries corresponding to desired behavior and refuse all other requests, which we refer to as scoping. We conduct a comprehensive empirical evaluation of potential methods from prompting to fine-tuning to preference learning to a recently proposed method for general alignment called Circuit Breakers (CB). Across three families of language models and a broad variety of tasks, we show that it is possible to scope language models. We examine scoping for multiple topics, and fine-grained topics. We ablate diversity of irrelevant queries, layer different techniques, conduct adversarial evaluations and more. Among other results, we find that, when diverse examples of irrelevant queries are available, simple supervised fine-tuning produces the best results, but when such diversity is low, Circuit Breakers perform quite well. One can often get the benefits of both methods by layering them in succession. We intend our study to serve as a practitioner’s guide to scoping language models.

Reducing the Scope of Language Models

Machine learning under limited computational resources has gained increasing attention recently. A common yet challenging scenario is managing multiple time-constrained learning tasks with budgeted computational resources, known as Computational Resource Efficient Learning (CoRE-Learning). To this end, a recently proposed framework, Learning with Adaptive Resource Allocation (LARA), offers a preliminary approach. In this paper, we point out the limitations of LARA, including its reliance on interpolation-based extrapolation methods, the need for a fixed exploration phase, and the use of high-frequency re-estimation and reallocation strategies. To address these issues, we propose Look-ahead and immediate Resource Allocation (LaiRA). Our approach incorporates an efficient Dynamic Kalman Filtering (DKF) for look-ahead feasibility check with limited data and a weight-based online estimator for immediate performance evaluation. For resource allocation, LaiRA constructs an Upper Confidence Bound (UCB) to enable adaptive exploration and introduces an adaptive time-slicing method to reduce task switching costs. Empirical studies validate the effectiveness of our approach.

CoRE-Learning with Look-Ahead and Immediate Resource Allocation

The prevalent class imbalance in real-world graphs significantly affects the performance of Graph Neural Networks (GNNs). Existing methods for analyzing graph imbalance ignore the influence of minority nodes during the dynamic model training process, resulting in performance limitations. In this paper, we focus on minority class information during model training, identifying and defining the minority class forgetting phenomenon that exists in graph imbalanced method training processes. To address this issue, we propose the Graph Imbalance Experience Replay (GIER) Framework. On one hand, the method enhances the model's ability to mine minority node information in historical data, thereby achieving feature completion for minority class nodes. On the other hand, the proposed short-term confidence mechanism allows the model to adaptively calibrate the topological relationships in high confidence nodes, thereby mitigating the model's tendency to propagate erroneous information about minority classes during training. GIER is a unified framework consisting of two synergistic components: Long-term Subgraph Memory (LSM) constructs multi-period feature-representative subgraphs to address distribution imbalance, and Short-term Confidence Calibration (SCC) dynamically reconstructs graph topology through degree-aware node selection and confidence-based filtering to address topological imbalance. Experiments demonstrate that GIER effectively improves the classification performance of GNNs on imbalanced graphs, achieving up to a 3.44\% improvement over the state-of-the-art, and is particularly effective in extreme scenarios with very small minority classes.

GIER: Addressing Class Imbalance in GNNs Through Experience Replay

In this paper we address the problem of discovering causal relationships from observational event sequence data.
Existing methods typically assume that events are instantaneous point events, however in many real-world settings, events have duration.
For example, in healthcare, a patient's symptoms may persist over a time interval and influence clinical actions while ongoing.
To address this, we introduce a causal model for interval-based event sequences that captures rich causal structures, including interactions between events and causal mechanisms that depend on whether other events are ongoing.
We prove that our model is identifiable in the limit and present a practical causal discovery algorithm, Niagara, grounded in the algorithmic Markov condition.
To select among candidate models, we employ a minimum description length (MDL) criterion, enabling robust inference even with limited data.
We validate our approach on synthetic and real data and demonstrate its utility on a real-world medical case study, where it uncovers meaningful causal relationships from noisy, interval-based event data.

Causal Discovery from Interval-Based Event Sequences

Large Language Models (LLMs) face fundamental challenges in long-context reasoning: many documents exceed their finite context windows, while performance on texts that do fit degrades with sequence length, necessitating their augmentation with external memory frameworks. Current solutions, which have evolved from retrieval using semantic embeddings to more sophisticated structured knowledge graphs representations for improved sense-making and associativity, are tailored for fact-based retrieval and fail to build the space-time-anchored narrative representations required for tracking entities through episodic events. To bridge this gap, we propose the \textbf{Generative Semantic Workspace} (GSW), a neuro-inspired generative memory framework that builds structured, interpretable representations of evolving situations, enabling LLMs to reason over evolving roles, actions, and spatiotemporal contexts. Our framework comprises an \textit{Operator}, which maps incoming observations to intermediate semantic structures, and a \textit{Reconciler}, which integrates these into a persistent workspace that enforces temporal, spatial, and logical coherence. On the Episodic Memory Benchmark (EpBench) \cite{huet_episodic_2025} comprising corpora ranging from 100k to 1M tokens in length, GSW outperforms existing RAG based baselines by up to \textbf{20\%}. Furthermore, GSW is highly efficient, reducing query-time context tokens by \textbf{51\%} compared to the next most token-efficient baseline, reducing inference time costs considerably. More broadly, GSW offers a concrete blueprint for endowing LLMs with human-like episodic memory, paving the way for more capable agents that can reason over long horizons.

Beyond Fact Retrieval: Episodic Memory for RAG with Generative Semantic Workspaces

Neural networks have emerged as powerful surrogates for solving partial differential equations (PDEs), offering significant computational speedups over traditional methods. However, these models suffer from a critical limitation: error accumulation during long-term rollouts, where small inaccuracies compound exponentially, eventually causing complete divergence from physically valid solutions. We present PhysicsCorrect, a training-free correction framework that enforces PDE consistency at each prediction step by formulating correction as a linearized inverse problem based on PDE residuals. Our key innovation is an efficient caching strategy that precomputes the Jacobian and its pseudoinverse during an offline warm-up phase, reducing computational overhead by two orders of magnitude compared to standard correction approaches. Across three representative PDE systems, including Navier-Stokes fluid dynamics, wave equations, and the chaotic Kuramoto-Sivashinsky equation, PhysicsCorrect reduces prediction errors by up to 100× while adding negligible inference time (under 5\%). The framework integrates seamlessly with diverse architectures, including Fourier Neural Operators, UNets, and Vision Transformers, effectively transforming unstable neural surrogates into reliable simulation tools that bridge the gap between deep learning's computational efficiency and the physical fidelity demanded by practical scientific applications.

PhysicsCorrect: A Training-Free Approach for Stable Neural PDE Simulations

Quickly and reliably finding accurate inverse kinematics
(IK) solutions is a challenging problem for many robot
manipulators. Existing numerical solvers are widely
applicable but typically only produce a single solution and
rely on local search techniques to minimize nonconvex
objectives. Recent learning-based approaches that
approximate the entire feasible set of solutions have shown
promise in generating multiple fast and accurate IK results
in parallel. However, existing learning-based techniques
have a significant drawback: each robot of interest
requires a specialized model that must be trained from
scratch. To address this key shortcoming, we propose a
novel distance-geometric robot representation coupled with
a graph structure that allows us to leverage the
generalizability of graph neural networks (GNNs). Our
approach, which we call generative graphical IK (GGIK), is
the first learned IK solver that is able to efficiently
yield a large number of diverse solutions in parallel while
also displaying the ability to generalize---a single
learned model can be used to produce IK solutions for a
variety of different robots. When compared to several other
learned IK methods, GGIK provides more accurate solutions
with the same amount of training data. GGIK can also
generalize reasonably well to robot manipulators unseen
during training. In addition, GGIK is able to learn a
constrained distribution that encodes joint limits and
scales well with the number of robot joints and sampled
solutions. Finally, GGIK can be used to complement local IK
solvers by providing a reliable initialization for the
local optimization process.

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