Gregory Kang Ruey Lau

Kernelized bandits, also known as Bayesian optimization (BO), has been a prevalent method for optimizing complicated black-box reward functions. Various BO algorithms have been theoretically shown to enjoy upper bounds on their cumulative regret which are sub-linear in the number T of iterations, and a regret lower bound of Ω(sqrtT) has been derived which represents the unavoidable regrets for any classical BO algorithm. Recent works on quantum bandits have shown that with the aid of quantum computing, it is possible to achieve tighter regret upper bounds better than their corresponding classical lower bounds. However, these works are restricted to either multi-armed or linear bandits, and are hence not able to solve sophisticated real-world problems with non-linear reward functions. To this end, we introduce the quantum-Gaussian process-upper confidence bound (Q-GP-UCB) algorithm. To the best of our knowledge, our Q-GP-UCB is the first BO algorithm able to achieve a regret upper bound of O(polylog T), which is significantly smaller than its regret lower bound of Ω(sqrtT) in the classical setting. Moreover, thanks to our novel analysis of the confidence ellipsoid, our Q-GP-UCB with the linear kernel achieves a smaller regret than the quantum linear UCB algorithm from the previous work. We use simulations, as well as an experiment using a real quantum computer, to verify that the theoretical quantum speedup achieved by our Q-GP-UCB is also potentially relevant in practice.

Physics-Informed Neural Networks (PINNs), which incorporate PDEs as soft constraints, train with a composite loss function that contains multiple training point types: different types of \textitcollocation points chosen during training to enforce each PDE and initial/boundary conditions, and \textitexperimental points which are usually costly to obtain via experiments or simulations. Training PINNs using this loss function is challenging as it typically requires selecting large numbers of points of different types, each with different training dynamics. Unlike past works that focused on the selection of either collocation or experimental points, this work introduces \textscPINN Adaptive ColLocation and Experimental points selection (\alg), the first algorithm that \emphjointly optimizes the selection of all training point types, while automatically adjusting the proportion of collocation point types as training progresses. \alg uses information on the interaction among training point types, which had not been considered before, based on an analysis of PINN training dynamics via the Neural Tangent Kernel (NTK). We theoretically show that the criterion used by \alg is related to the PINN generalization error, and empirically demonstrate that \alg is able to outperform existing point selection methods for forward, inverse, and transfer learning problems.

In many inverse problems (IPs) in science and engineering, optimization of design parameters (e.g., sensor placement) with experimental design (ED) methods is performed due to high data acquisition costs when conducting physical experiments, and often has to be done up front due to practical constraints on sensor deployments. However, existing ED methods are often challenging to use in practical PDE-based inverse problems due to significant computational bottlenecks during forward simulation and inverse parameter estimation. This paper presents Physics-Informed Experimental Design (PIED), the first ED framework that makes use of PINNs in a fully differentiable architecture to perform continuous optimization of design parameters for IPs. PIED utilizes techniques such as learning of a shared NN parameter initialization, and approximation of PINN training dynamics during the ED process, for better estimation of the inverse parameters. PIED selects the optimal design parameters for one-shot deployment, allows exploitation of parallel computation unlike existing methods, and is empirically shown to significantly outperform existing ED benchmarks in IPs for both finite-dimensional and function-valued inverse parameters given limited budget for observations.

Protecting intellectual property (IP) of text such as articles and code is increasingly important, especially as sophisticated attacks become possible, such as paraphrasing by large language models (LLMs) or even unauthorized training of LLMs on copyrighted text to infringe such IP. However, existing text watermarking methods are not robust enough against such attacks nor scalable to millions of users for practical implementation. In this paper, we propose Waterfall, the first training-free framework for robust and scalable text watermarking applicable across multiple text types (e.g., articles, code) and languages supportable by LLMs, for general text and LLM data provenance. Waterfall comprises several key innovations, such as being the first to use LLM as paraphrasers for watermarking along with a novel combination of techniques that are surprisingly effective in achieving robust verifiability and scalability. We empirically demonstrate that Waterfall achieves significantly better scalability, robust verifiability, and computational efficiency compared to SOTA article-text watermarking methods, and also showed how it could be directly applied to the watermarking of code.

In this paper, we propose the first training-free framework for robust and scalable text watermarking applicable across multiple text types (e.g., articles, code) and languages, for general as well as LLM text training data provenance. We highlight perspectives on text IP protection, such as using LLMs to enable better IP protection rather than viewing them as just sources of IP infringement, not relying on just major LLM providers, and the benefits of having a general framework that can be easily adapted to defend against new threats.

This position paper proposes a data-centric viewpoint of AI research, focusing on large language models (LLMs). We start by making the key observation that data is instrumental in the developmental (e.g., pretraining and fine-tuning) and inferential stages (e.g., in-context learning) of LLMs, and yet it receives disproportionally low attention from the research community. We identify four specific scenarios centered around data, covering data-centric benchmarks and data curation, data attribution, knowledge transfer, and inference contextualization. In each scenario, we underscore the importance of data, highlight promising research directions, and articulate the potential impacts on the research community and, where applicable, the society as a whole. For instance, we advocate for a suite of data-centric benchmarks tailored to the scale and complexity of data for LLMs. These benchmarks can be used to develop new data curation methods and document research efforts and results, which can help promote openness and transparency in AI and LLM research.