171 citations · 219 across the 17 of their papers we have counts for
18 papers
Self-Speculation for Faster Reasoning Models
Ravisri Valluri, Tung Nguyen, Aditya Grover
Large language models (LLMs) are deployed for increasingly complex tasks involving planning and multi-step decision making, but high-quality performance on these tasks often requir…
Vehicle Routing Problems via Quantum Graph Attention Network Deep Reinforcement Learning
Le Tung Giang, Vu Hoang Viet, Nguyen Xuan Tung +2
The vehicle routing problem (VRP) is a fundamental NP-hard task in intelligent transportation systems with broad applications in logistics and distribution. Deep reinforcement lear…
IndiaWeatherBench: A Dataset and Benchmark for Data-Driven Regional Weather Forecasting over India
Tung Nguyen, Harkanwar Singh, Nilay Naharas +2
Regional weather forecasting is a critical problem for localized climate adaptation, disaster mitigation, and sustainable development. While machine learning has shown impressive p…
Iceberg: Enhancing HLS Modeling with Synthetic Data
Zijian Ding, Tung Nguyen, Weikai Li +3
Deep learning-based prediction models for High-Level Synthesis (HLS) of hardware designs often struggle to generalize. In this paper, we study how to close the generalizability gap…
PhysiX: A Foundation Model for Physics Simulations
Tung Nguyen, Arsh Koneru, Shufan Li +1
Foundation models have achieved remarkable success across video, image, and language domains. By scaling up the number of parameters and training datasets, these models acquire gen…
MedMax: Mixed-Modal Instruction Tuning for Training Biomedical Assistants
Hritik Bansal, Daniel Israel, Siyan Zhao +3
Recent advancements in mixed-modal generative have opened new avenues for developing unified biomedical assistants capable of analyzing biomedical images, answering complex questio…
LICO: Large Language Models for In-Context Molecular Optimization
Tung Nguyen, Aditya Grover
Optimizing black-box functions is a fundamental problem in science and engineering. To solve this problem, many approaches learn a surrogate function that estimates the underlying…
Probing the Decision Boundaries of In-context Learning in Large Language Models
Siyan Zhao, Tung Nguyen, Aditya Grover
In-context learning is a key paradigm in large language models (LLMs) that enables them to generalize to new tasks and domains by simply prompting these models with a few exemplars…
ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction
Juan Nathaniel, Yongquan Qu, Tung Nguyen +4
Accurate prediction of climate in the subseasonal-to-seasonal scale is crucial for disaster preparedness and robust decision making amidst climate change. Yet, forecasting beyond t…
Scaling transformer neural networks for skillful and reliable medium-range weather forecasting
Tung Nguyen, Rohan Shah, Hritik Bansal +6
Weather forecasting is a fundamental problem for anticipating and mitigating the impacts of climate change. Recently, data-driven approaches for weather forecasting based on deep l…
ExPT: Synthetic Pretraining for Few-Shot Experimental Design
Tung Nguyen, Sudhanshu Agrawal, Aditya Grover
Experimental design is a fundamental problem in many science and engineering fields. In this problem, sample efficiency is crucial due to the time, money, and safety costs of real-…
ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling
Tung Nguyen, Jason Jewik, Hritik Bansal +2
Modeling weather and climate is an essential endeavor to understand the near- and long-term impacts of climate change, as well as inform technology and policymaking for adaptation…
Utilization of domain knowledge to improve POMDP belief estimation
Tung Nguyen, Johane Takeuchi
The partially observable Markov decision process (POMDP) framework is a common approach for decision making under uncertainty. Recently, multiple studies have shown that by integra…
Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networks
Tung-Anh Nguyen, Jiayu He, Long Tan Le +2
In the era of Internet of Things (IoT), network-wide anomaly detection is a crucial part of monitoring IoT networks due to the inherent security vulnerabilities of most IoT devices…
ClimaX: A foundation model for weather and climate
Tung Nguyen, Johannes Brandstetter, Ashish Kapoor +2
Most state-of-the-art approaches for weather and climate modeling are based on physics-informed numerical models of the atmosphere. These approaches aim to model the non-linear dyn…
Reliable Conditioning of Behavioral Cloning for Offline Reinforcement Learning
Tung Nguyen, Qinqing Zheng, Aditya Grover
Behavioral cloning (BC) provides a straightforward solution to offline RL by mimicking offline trajectories via supervised learning. Recent advances (Chen et al., 2021; Janner et a…
Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling
Tung Nguyen, Aditya Grover
Neural Processes (NPs) are a popular class of approaches for meta-learning. Similar to Gaussian Processes (GPs), NPs define distributions over functions and can estimate uncertaint…
On the Generalization of Wasserstein Robust Federated Learning
Tung-Anh Nguyen, Tuan Dung Nguyen, Long Tan Le +2
In federated learning, participating clients typically possess non-i.i.d. data, posing a significant challenge to generalization to unseen distributions. To address this, we propos…