Search Results for author: Yuyang Wang

Found 32 papers, 10 papers with code

Robust Probabilistic Time Series Forecasting

1 code implementation24 Feb 2022 Taeho Yoon, Youngsuk Park, Ernest K. Ryu, Yuyang Wang

Probabilistic time series forecasting has played critical role in decision-making processes due to its capability to quantify uncertainties.

Decision Making Probabilistic Time Series Forecasting +1

Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast

no code implementations18 Feb 2022 Yuyang Wang, Rishikesh Magar, Chen Liang, Amir Barati Farimani

On most benchmarks, the generic GNN pre-trained by iMolCLR rivals or even surpasses supervised learning models with sophisticated architecture designs and engineered features.

Contrastive Learning Self-Supervised Learning

Graph-Relational Domain Adaptation

1 code implementation ICLR 2022 Zihao Xu, Hao He, Guang-He Lee, Yuyang Wang, Hao Wang

In this work, we relax such uniform alignment by using a domain graph to encode domain adjacency, e. g., a graph of states in the US with each state as a domain and each edge indicating adjacency, thereby allowing domains to align flexibly based on the graph structure.

Domain Adaptation

GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics

no code implementations18 Dec 2021 Ke Alexander Wang, Danielle Maddix, Yuyang Wang

We consider the problem of probabilistic forecasting over categories with graph structure, where the dynamics at a vertex depends on its local connectivity structure.

AugLiChem: Data Augmentation Library of Chemical Structures for Machine Learning

1 code implementation30 Nov 2021 Rishikesh Magar, Yuyang Wang, Cooper Lorsung, Chen Liang, Hariharan Ramasubramanian, Peiyuan Li, Amir Barati Farimani

Inspired by the success of data augmentations in computer vision and natural language processing, we developed AugLiChem: the data augmentation library for chemical structures.

Data Augmentation

Dynamic Regret for Strongly Adaptive Methods and Optimality of Online KRR

no code implementations22 Nov 2021 Dheeraj Baby, Hilaf Hasson, Yuyang Wang

When the loss functions are strongly convex or exp-concave, we demonstrate that Strongly Adaptive (SA) algorithms can be viewed as a principled way of controlling dynamic regret in terms of path variation $V_T$ of the comparator sequence.

Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting

no code implementations12 Nov 2021 Youngsuk Park, Danielle Maddix, François-Xavier Aubet, Kelvin Kan, Jan Gasthaus, Yuyang Wang

Quantile regression is an effective technique to quantify uncertainty, fit challenging underlying distributions, and often provide full probabilistic predictions through joint learnings over multiple quantile levels.

Time Series Time Series Forecasting

Deep Explicit Duration Switching Models for Time Series

1 code implementation NeurIPS 2021 Abdul Fatir Ansari, Konstantinos Benidis, Richard Kurle, Ali Caner Turkmen, Harold Soh, Alexander J. Smola, Yuyang Wang, Tim Januschowski

We propose the Recurrent Explicit Duration Switching Dynamical System (RED-SDS), a flexible model that is capable of identifying both state- and time-dependent switching dynamics.

Time Series

Correcting Exposure Bias for Link Recommendation

1 code implementation13 Jun 2021 Shantanu Gupta, Hao Wang, Zachary C. Lipton, Yuyang Wang

Link prediction methods are frequently applied in recommender systems, e. g., to suggest citations for academic papers or friends in social networks.

Link Prediction Recommendation Systems

5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning

no code implementations9 Jun 2021 Aldebaro Klautau, Pedro Batista, Nuria Gonzalez-Prelcic, Yuyang Wang, Robert W. Heath Jr

The increasing complexity of configuring cellular networks suggests that machine learning (ML) can effectively improve 5G technologies.


Zero-Shot Recommender Systems

no code implementations18 May 2021 Hao Ding, Yifei Ma, Anoop Deoras, Yuyang Wang, Hao Wang

This poses a chicken-and-egg problem for early-stage products, whose amount of data, in turn, relies on the performance of their RS.

Recommendation Systems Zero-Shot Learning

Variance Reduced Training with Stratified Sampling for Forecasting Models

no code implementations2 Mar 2021 Yucheng Lu, Youngsuk Park, Lifan Chen, Yuyang Wang, Christopher De Sa, Dean Foster

In large-scale time series forecasting, one often encounters the situation where the temporal patterns of time series, while drifting over time, differ from one another in the same dataset.

Time Series Time Series Forecasting

Molecular Contrastive Learning of Representations via Graph Neural Networks

1 code implementation19 Feb 2021 Yuyang Wang, Jianren Wang, Zhonglin Cao, Amir Barati Farimani

In this work, we present MolCLR: Molecular Contrastive Learning of Representations via Graph Neural Networks (GNNs), a self-supervised learning framework that leverages large unlabeled data (~10M unique molecules).

Contrastive Learning Drug Discovery +2

Deep Reinforcement Learning Optimizes Graphene Nanopores for Efficient Desalination

no code implementations19 Jan 2021 Yuyang Wang, Zhonglin Cao, Amir Barati Farimani

Structure and geometry optimization of nanopores on such materials is beneficial for their performances in real-world engineering applications, like water desalination.


Airfoil GAN: Encoding and Synthesizing Airfoils forAerodynamic-aware Shape Optimization

no code implementations12 Jan 2021 Yuyang Wang, Kenji Shimada, Amir Barati Farimani

Our model can (1) encode the existing airfoil into a latent vector and reconstruct the airfoil from that, (2) generate novel airfoils by randomly sampling the latent vectors and mapping the vectors to the airfoil coordinate domain, and (3) synthesize airfoils with desired aerodynamic properties by optimizing learned features via a genetic algorithm.

Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems

3 code implementations20 Nov 2020 Rui Wang, Danielle Maddix, Christos Faloutsos, Yuyang Wang, Rose Yu

While much research on distribution shift has focused on changes in the data domain, our work calls attention to rethink generalization for learning dynamical systems.

Site-specific online compressive beam codebook learning in mmWave vehicular communication

no code implementations11 May 2020 Yuyang Wang, Nitin Jonathan Myers, Nuria González-Prelcic, Robert W. Heath Jr

Furthermore, based on the CS channel measurements, we develop techniques to update and learn such channel AoD statistics at the BS.

Compressive Sensing online learning

Intermittent Demand Forecasting with Deep Renewal Processes

1 code implementation23 Nov 2019 Ali Caner Turkmen, Yuyang Wang, Tim Januschowski

Intermittent demand, where demand occurrences appear sporadically in time, is a common and challenging problem in forecasting.

Point Processes

Deep Factors for Forecasting

no code implementations28 May 2019 Yuyang Wang, Alex Smola, Danielle C. Maddix, Jan Gasthaus, Dean Foster, Tim Januschowski

We provide both theoretical and empirical evidence for the soundness of our approach through a necessary and sufficient decomposition of exchangeable time series into a global and a local part.

Time Series

Deep Factors with Gaussian Processes for Forecasting

no code implementations30 Nov 2018 Danielle C. Maddix, Yuyang Wang, Alex Smola

A large collection of time series poses significant challenges for classical and neural forecasting approaches.

Gaussian Processes Time Series

Gini-regularized Optimal Transport with an Application to Spatio-Temporal Forecasting

no code implementations7 Dec 2017 Lucas Roberts, Leo Razoumov, Lin Su, Yuyang Wang

Moreover, we show that the Gini regularized OT problem converges to the classical OT problem, when the Gini regularized problem is considered as a function of {\lambda}, the regularization parame-ter.

Spatio-Temporal Forecasting

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