no code implementations • 26 Apr 2024 • Tung Nguyen, Jeffrey Uhlmann
We propose what we consider to be a measure that is more fundamentally appropriate for assessing RS performance, rank-preference consistency, which simply counts the number of prediction pairs that are inconsistent with the user's expressed product preferences.
1 code implementation • 1 Feb 2024 • Juan Nathaniel, Yongquan Qu, Tung Nguyen, Sungduk Yu, Julius Busecke, Aditya Grover, Pierre Gentine
Thus, we propose ChaosBench, a large-scale, multi-channel, physics-based benchmark for S2S prediction.
no code implementations • 19 Dec 2023 • Tung Nguyen, Eric Nichols, Randy Gomez
Recently, research in human-robot interaction began to consider a robot's influence at the group level.
no code implementations • 6 Dec 2023 • Tung Nguyen, Rohan Shah, Hritik Bansal, Troy Arcomano, Sandeep Madireddy, Romit Maulik, Veerabhadra Kotamarthi, Ian Foster, Aditya Grover
At the core of Stormer is a randomized forecasting objective that trains the model to forecast the weather dynamics over varying time intervals.
1 code implementation • NeurIPS 2023 • Tung Nguyen, Sudhanshu Agrawal, Aditya Grover
In this work, we address the more challenging yet realistic setting of few-shot experimental design, where only a few labeled data points of input designs and their corresponding values are available.
no code implementations • 17 Jul 2023 • Tung Nguyen, Jeffrey Uhlmann
In this paper, we propose a new constraint, called shift-consistency, for solving matrix/tensor completion problems in the context of recommender systems.
no code implementations • 17 Jul 2023 • Tung Nguyen, Jeffrey Uhlmann
In this paper we discuss pre- and post-processing methods to induce desired consistency and/or invariance properties in blackbox systems, e. g., AI-based.
1 code implementation • NeurIPS 2023 • Tung Nguyen, Jason Jewik, Hritik Bansal, Prakhar Sharma, Aditya Grover
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 and mitigation efforts.
no code implementations • 17 Feb 2023 • Tung Nguyen, Johane Takeuchi
The partially observable Markov decision process (POMDP) framework is a common approach for decision making under uncertainty.
1 code implementation • 24 Jan 2023 • Tung Nguyen, Johannes Brandstetter, Ashish Kapoor, Jayesh K. Gupta, Aditya Grover
We develop and demonstrate ClimaX, a flexible and generalizable deep learning model for weather and climate science that can be trained using heterogeneous datasets spanning different variables, spatio-temporal coverage, and physical groundings.
no code implementations • 3 Jan 2023 • Tung Nguyen, Mona Bavarian
Here, we demonstrate how machine learning enables the prediction of comonomers reactivity ratios based on the molecular structure of monomers.
1 code implementation • 11 Oct 2022 • Tung Nguyen, Qinqing Zheng, Aditya Grover
We study CWBC in the context of RvS (Emmons et al., 2021) and Decision Transformers (Chen et al., 2021), and show that CWBC significantly boosts their performance on various benchmarks.
1 code implementation • 9 Jul 2022 • Tung Nguyen, Aditya Grover
We propose Transformer Neural Processes (TNPs), a new member of the NP family that casts uncertainty-aware meta learning as a sequence modeling problem.
no code implementations • 27 Jun 2022 • Tung Nguyen, Sang T. Truong, Jeffrey Uhlmann
In this paper we provide a latent-variable formulation and solution to the recommender system (RS) problem in terms of a fundamental property that any reasonable solution should be expected to satisfy.
no code implementations • 4 Apr 2022 • Tung Nguyen, Jeffrey Uhlmann
We introduce a new consistency-based approach for defining and solving nonnegative/positive matrix and tensor completion problems.
3 code implementations • 14 Jun 2021 • Tung Nguyen, Rui Shu, Tuan Pham, Hung Bui, Stefano Ermon
High-dimensional observations are a major challenge in the application of model-based reinforcement learning (MBRL) to real-world environments.
no code implementations • 1 Jan 2021 • Tung Nguyen, Rui Shu, Tuan Pham, Hung Bui, Stefano Ermon
High-dimensional observations are a major challenge in the application of model-based reinforcement learning (MBRL) to real-world environments.
1 code implementation • ICML 2020 • Rui Shu, Tung Nguyen, Yin-Lam Chow, Tuan Pham, Khoat Than, Mohammad Ghavamzadeh, Stefano Ermon, Hung H. Bui
High-dimensional observations and unknown dynamics are major challenges when applying optimal control to many real-world decision making tasks.
1 code implementation • 27 Jul 2019 • Simon Wiedemann, Heiner Kirchoffer, Stefan Matlage, Paul Haase, Arturo Marban, Talmaj Marinc, David Neumann, Tung Nguyen, Ahmed Osman, Detlev Marpe, Heiko Schwarz, Thomas Wiegand, Wojciech Samek
The field of video compression has developed some of the most sophisticated and efficient compression algorithms known in the literature, enabling very high compressibility for little loss of information.
no code implementations • 27 Apr 2016 • Tung Nguyen, Kazuki Mori, Ruck Thawonmas
In this paper, we present a novel approach that uses deep learning techniques for colorizing grayscale images.