Search Results for author: Sen Zhao

Found 10 papers, 1 papers with code

Multidimensional Shape Constraints

no code implementations ICML 2020 Maya Gupta, Erez Louidor, Oleksandr Mangylov, Nobu Morioka, Tamann Narayan, Sen Zhao

We propose new multi-input shape constraints across four intuitive categories: complements, diminishers, dominance, and unimodality constraints.

Additive models

Multi-view Intent Disentangle Graph Networks for Bundle Recommendation

no code implementations23 Feb 2022 Sen Zhao, Wei Wei, Ding Zou, Xianling Mao

Specifically, MIDGN disentangles the user's intents from two different perspectives, respectively: 1) In the global level, MIDGN disentangles the user's intent coupled with inter-bundle items; 2) In the Local level, MIDGN disentangles the user's intent coupled with items within each bundle.

Predicting on the Edge: Identifying Where a Larger Model Does Better

no code implementations15 Feb 2022 Taman Narayan, Heinrich Jiang, Sen Zhao, Sanjiv Kumar

Much effort has been devoted to making large and more accurate models, but relatively little has been put into understanding which examples are benefiting from the added complexity.

Distribution Embedding Networks for Meta-Learning with Heterogeneous Covariate Spaces

no code implementations4 Feb 2022 Lang Liu, Mahdi Milani Fard, Sen Zhao

We provide theoretical insights to show that this architecture allows the embedding and classification blocks to be fixed after pre-training on a diverse set of tasks; only the covariate transformation block with relatively few parameters needs to be updated for each new task.

Classification Meta-Learning

Global Optimization Networks

no code implementations2 Feb 2022 Sen Zhao, Erez Louidor, Olexander Mangylov, Maya Gupta

We consider the problem of estimating a good maximizer of a black-box function given noisy examples.


A recurrent neural network approach for remaining useful life prediction utilizing a novel trend features construction method

no code implementations10 Dec 2021 Sen Zhao, Yong Zhang, Shang Wang, Beitong Zhou, Cheng Cheng

Data-driven methods for remaining useful life (RUL) prediction normally learn features from a fixed window size of a priori of degradation, which may lead to less accurate prediction results on different datasets because of the variance of local features.

Distribution Embedding Network for Meta-Learning with Variable-Length Input

no code implementations1 Jan 2021 Lang Liu, Mahdi Milani Fard, Sen Zhao

We propose Distribution Embedding Network (DEN) for meta-learning, which is designed for applications where both the distribution and the number of features could vary across tasks.

Classification General Classification +1

Metric-Optimized Example Weights

no code implementations ICLR 2019 Sen Zhao, Mahdi Milani Fard, Harikrishna Narasimhan, Maya Gupta

Real-world machine learning applications often have complex test metrics, and may have training and test data that are not identically distributed.

In Defense of the Indefensible: A Very Naive Approach to High-Dimensional Inference

no code implementations16 May 2017 Sen Zhao, Daniela Witten, Ali Shojaie

In this paper, we consider a simple and very na\"{i}ve two-step procedure for this task, in which we (i) fit a lasso model in order to obtain a subset of the variables, and (ii) fit a least squares model on the lasso-selected set.

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