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no code implementations • 8 Mar 2023 • Yang Yuan

Can machines think?

no code implementations • 1 Mar 2023 • Yang Yuan

Foundation models like chatGPT have demonstrated remarkable performance on various tasks.

no code implementations • 29 Nov 2022 • Yang Yuan

The first one limits the power of prompt-based learning, saying that the model can solve a downstream task with prompts if and only if the task is representable.

no code implementations • 31 Oct 2022 • Yifan Zhang, Haowei He, Yang Yuan

For many interdisciplinary fields, ML interpretations need to be consistent with what-if scenarios related to the current case, i. e., if one factor changes, how does the model react?

no code implementations • 1 Oct 2022 • Jiaye Teng, Chuan Wen, Dinghuai Zhang, Yoshua Bengio, Yang Gao, Yang Yuan

Conformal prediction is a distribution-free technique for establishing valid prediction intervals.

no code implementations • 6 Jun 2022 • Haowei He, Jiaye Teng, Yang Yuan

Deep neural networks are known to be vulnerable to unseen data: they may wrongly assign high confidence stcores to out-distribuion samples.

no code implementations • 12 Feb 2022 • Jing Xu, Jiaye Teng, Yang Yuan, Andrew Chi-Chih Yao

By considering the entire training trajectory and focusing on early-stopping iterates, compatibility exploits the data and the algorithm information and is therefore a more suitable notion for generalization.

no code implementations • 29 Sep 2021 • Chenzhuang Du, Jiaye Teng, Tingle Li, Yichen Liu, Yue Wang, Yang Yuan, Hang Zhao

We name this problem of multi-modal training, \emph{Modality Laziness}.

no code implementations • ICLR 2022 • Jiaye Teng, Jianhao Ma, Yang Yuan

Generalization is one of the fundamental issues in machine learning.

1 code implementation • 8 Mar 2021 • Jiaye Teng, Zeren Tan, Yang Yuan

It is challenging to deal with censored data, where we only have access to the incomplete information of survival time instead of its exact value.

no code implementations • 23 Nov 2020 • Hao Zhu, Yang Yuan, Guosheng Hu, Xiang Wu, Neil Robertson

IR-Softmax can generalise to any softmax and its variants (which are discriminative for open-set problem) by directly setting the weights as their class centers, naturally solving the data imbalance problem.

1 code implementation • 24 Sep 2020 • Chenwei Wu, Chenzhuang Du, Yang Yuan

In the classical multi-party computation setting, multiple parties jointly compute a function without revealing their own input data.

no code implementations • 4 Jun 2020 • Jiaye Teng, Yang Yuan

First, we apply a machine learning method to fit the ground truth function on the training set and calculate its linear approximation.

no code implementations • 10 Feb 2020 • Yingdong Hu, Liang Zhang, Wei Shan, Xiaoxiao Qin, Jing Qi, Zhenzhou Wu, Yang Yuan

In the big data era, many organizations face the dilemma of data sharing.

no code implementations • NeurIPS 2019 • Piotr Indyk, Ali Vakilian, Yang Yuan

Our experiments show that, for multiple types of data sets, a learned sketch matrix can substantially reduce the approximation loss compared to a random matrix $S$, sometimes by one order of magnitude.

no code implementations • 25 Sep 2019 • Jiaye Teng, Guang-He Lee, Yang Yuan

Robustness is an important property to guarantee the security of machine learning models.

no code implementations • 25 Sep 2019 • Xiyuan Zhang, Yang Yuan, Piotr Indyk

The edit distance between two sequences is an important metric with many applications.

1 code implementation • NeurIPS 2019 • Guang-He Lee, Yang Yuan, Shiyu Chang, Tommi S. Jaakkola

Specifically, an $\ell_2$ bounded adversary cannot alter the ensemble prediction generated by an additive isotropic Gaussian noise, where the radius for the adversary depends on both the variance of the distribution as well as the ensemble margin at the point of interest.

1 code implementation • NeurIPS 2019 • Haowei He, Gao Huang, Yang Yuan

Specifically, at a local minimum there exist many asymmetric directions such that the loss increases abruptly along one side, and slowly along the opposite side--we formally define such minima as asymmetric valleys.

no code implementations • NeurIPS 2018 • Yexiang Xue, Yang Yuan, Zhitian Xu, Ashish Sabharwal

Neural models operating over structured spaces such as knowledge graphs require a continuous embedding of the discrete elements of this space (such as entities) as well as the relationships between them.

no code implementations • ECCV 2018 • Guosheng Hu, Li Liu, Yang Yuan, Zehao Yu, Yang Hua, Zhihong Zhang, Fumin Shen, Ling Shao, Timothy Hospedales, Neil Robertson, Yongxin Yang

To advance subtle expression recognition, we contribute a Large-scale Subtle Emotions and Mental States in the Wild database (LSEMSW).

4 code implementations • ICLR 2018 • Qiantong Xu, Gao Huang, Yang Yuan, Chuan Guo, Yu Sun, Felix Wu, Kilian Weinberger

Evaluating generative adversarial networks (GANs) is inherently challenging.

no code implementations • ICML 2018 • Robert Kleinberg, Yuanzhi Li, Yang Yuan

Stochastic gradient descent (SGD) is widely used in machine learning.

no code implementations • ICCV 2017 • Guosheng Hu, Yang Hua, Yang Yuan, Zhihong Zhang, Zheng Lu, Sankha S. Mukherjee, Timothy M. Hospedales, Neil M. Robertson, Yongxin Yang

To solve this problem, we establish a theoretical equivalence between tensor optimisation and a two-stream gated neural network.

1 code implementation • ICLR 2018 • Elad Hazan, Adam Klivans, Yang Yuan

In particular, we obtain the first quasi-polynomial time algorithm for learning noisy decision trees with polynomial sample complexity.

no code implementations • NeurIPS 2017 • Yuanzhi Li, Yang Yuan

We also show that the identity mapping is necessary for convergence, as it moves the initial point to a better place for optimization.

no code implementations • NeurIPS 2016 • Zeyuan Allen-Zhu, Yang Yuan, Karthik Sridharan

The amount of data available in the world is growing faster than our ability to deal with it.

no code implementations • 30 Dec 2015 • Zeyuan Allen-Zhu, Zheng Qu, Peter Richtárik, Yang Yuan

Accelerated coordinate descent is widely used in optimization due to its cheap per-iteration cost and scalability to large-scale problems.

3 code implementations • 5 Jun 2015 • Zeyuan Allen-Zhu, Yang Yuan

Many classical algorithms are found until several years later to outlive the confines in which they were conceived, and continue to be relevant in unforeseen settings.

1 code implementation • 6 Mar 2015 • Rong Ge, Furong Huang, Chi Jin, Yang Yuan

To the best of our knowledge this is the first work that gives global convergence guarantees for stochastic gradient descent on non-convex functions with exponentially many local minima and saddle points.

no code implementations • 31 Jul 2014 • Wei Chen, Yajun Wang, Yang Yuan, Qinshi Wang

The objective of an online learning algorithm for CMAB is to minimize (\alpha,\beta)-approximation regret, which is the difference between the \alpha{\beta} fraction of the expected reward when always playing the optimal super arm, and the expected reward of playing super arms according to the algorithm.

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