Offline optimization is an emerging problem in many experimental engineering domains including protein, drug or aircraft design, where online experimentation to collect evaluation data is too expensive or dangerous. To avoid that, one has to optimize an unknown function given only its offline evaluation at a fixed set of inputs. A naive solution to this problem is to learn a surrogate model of the unknown function and optimize this surrogate instead. However, such a naive optimizer is prone to erroneous overestimation of the surrogate (possibly due to over-fitting on a biased sample of function evaluation) on inputs outside the offline dataset. Prior approaches addressing this challenge have primarily focused on learning robust surrogate models. However, their search strategies are derived from the surrogate model rather than the actual offline data. To fill this important gap, we introduce a new learning-to-search perspective for offline optimization by reformulating it as an offline reinforcement learning problem. Our proposed policy-guided gradient search approach explicitly learns the best policy for a given surrogate model created from the offline data. Our empirical results on multiple benchmarks demonstrate that the learned optimization policy can be combined with existing offline surrogates to significantly improve the optimization performance.

Offline Model-Based Optimization via Policy-Guided Gradient Search

The increasingly decentralized and private nature of data in our digital society has  motivated the development of personalized, collaborative intelligent systems that enable knowledge aggregation across multiple data owners while accommodating for their data privacy and system constraints. However, collaborative learning has only been investigated in simple and limited settings: isolated task scenarios where learning begins from scratch and does not build on prior expertise; learned model is represented in task-specific forms which are not generalizable to unseen, emerging scenarios; and more often, a universal model representation is assumed across collaborators, ignoring their local compute constraints or input representations. This restricts its practicality in continual learning scenarios with limited task data, which demand continuous adaptation and knowledge transfer across different information silos, tasks, and learning models, as well as the utilization of prior solution expertises. To overcome these limitations, my research has been focused on developing effective and scalable resource-aware collaborative learning frameworks. The current progress and future directions of my research at Washington State University (WSU) are summarized in this letter.

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As the system only allows a single PDF submission, my cover letter (2-page excluding references), my resume and the one-page statement of my talk (1-page excluding references) have been concatenated into a single file in that order. Both my Google scholar profile and webpage are included in my resume, which I also detail below:

Google scholar profile: https://scholar.google.com/citations?user=E-kZZeQAAAAJ&hl=en
Homepage: https://htnghia87.github.io/

Resource-Aware Collaborative Learning across Heterogeneous Systems

This paper investigates the problem of exploiting existing solution models of previous tasks to address a related target task with limited training data. Existing approaches addressing this problem often require access to the internal parameterization of the existing solution models and possibly their training data, which is not possible in many practical settings. To relax this requirement, We approach this problem from a new perspective of black-box re-purposing, which augments the target inputs and leverages their corresponding outputs generated by existing black-box APIs into a feature ensemble. We hypothesize that such feature ensemble can be learned to incorporate and encode relevant black-box knowledge into the feature representation of target data, which will compensate for their scarcity. This hypothesis is confirmed via the reported successes of our proposed black-box ensemble in solving multiple few-shot learning tasks derived from various benchmark datasets. All reported results show consistently that the set of heterogeneous black-box solutions of previous tasks can indeed be reused and combined effectively to solve a reasonably related target task without requiring access to a large training dataset. This is the first step towards enabling new possibilities to further supplement existing techniques in transfer or meta learning with black-box knowledge.

Repurposing Ensemble of Black-Box Models to New Task Domains

Main Track - Machine Learning

technical paper

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-24 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.<br><br><br><br>

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

AAAI 2024

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

Trong Nghia Hoang

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SHORT BIO

Presentations

Offline Model-Based Optimization via Policy-Guided Gradient Search

Resource-Aware Collaborative Learning across Heterogeneous Systems

Repurposing Ensemble of Black-Box Models to New Task Domains

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