no code implementations • ICML 2020 • Xi Liu, Ping-Chun Hsieh, Yu Heng Hung, Anirban Bhattacharya, P. Kumar
We propose a new family of bandit algorithms, that are formulated in a general way based on the Biased Maximum Likelihood Estimation (BMLE) method originally appearing in the adaptive control literature.
no code implementations • SMM4H (COLING) 2022 • Xi Liu, Han Zhou, Chang Su
For task 1a, the system achieved an F1 score of 0. 68; for task 1b Overlapping F1 score of 0. 65 and a Strict F1 score of 0. 49.
1 code implementation • 4 Mar 2024 • Buyun Zhang, Liang Luo, Yuxin Chen, Jade Nie, Xi Liu, Daifeng Guo, Yanli Zhao, Shen Li, Yuchen Hao, Yantao Yao, Guna Lakshminarayanan, Ellie Dingqiao Wen, Jongsoo Park, Maxim Naumov, Wenlin Chen
Scaling laws play an instrumental role in the sustainable improvement in model quality.
no code implementations • 1 Mar 2024 • Xi Liu, Ying Guo, Cheng Zhen, Tong Li, Yingying Ao, Pengfei Yan
To achieve coherence between segments, we design a Past Guided Generation Module (PGG) to maintain the consistency of customized listener attributes through the motion prior, and utilize a diffusion-based structure conditioned on the portrait token and the motion prior to realize the controllable generation.
1 code implementation • 25 Nov 2023 • Xi Liu, Gizem Kayar, Ken Perlin
To enable realistic fluid rendering and simulation in a particle-based system, it is essential to construct a mesh from the particle attributes.
no code implementations • 14 Nov 2023 • Hang Yin, Kuang-Hung Liu, Mengying Sun, Yuxin Chen, Buyun Zhang, Jiang Liu, Vivek Sehgal, Rudresh Rajnikant Panchal, Eugen Hotaj, Xi Liu, Daifeng Guo, Jamey Zhang, Zhou Wang, Shali Jiang, Huayu Li, Zhengxing Chen, Wen-Yen Chen, Jiyan Yang, Wei Wen
The large scale of models and tight production schedule requires AutoML to outperform human baselines by only using a small number of model evaluation trials (around 100).
no code implementations • 14 Nov 2023 • Wei Wen, Kuang-Hung Liu, Igor Fedorov, Xin Zhang, Hang Yin, Weiwei Chu, Kaveh Hassani, Mengying Sun, Jiang Liu, Xu Wang, Lin Jiang, Yuxin Chen, Buyun Zhang, Xi Liu, Dehua Cheng, Zhengxing Chen, Guang Zhao, Fangqiu Han, Jiyan Yang, Yuchen Hao, Liang Xiong, Wen-Yen Chen
In industry system, such as ranking system in Meta, it is unclear whether NAS algorithms from the literature can outperform production baselines because of: (1) scale - Meta ranking systems serve billions of users, (2) strong baselines - the baselines are production models optimized by hundreds to thousands of world-class engineers for years since the rise of deep learning, (3) dynamic baselines - engineers may have established new and stronger baselines during NAS search, and (4) efficiency - the search pipeline must yield results quickly in alignment with the productionization life cycle.
no code implementations • 1 Nov 2023 • Tunhou Zhang, Wei Wen, Igor Fedorov, Xi Liu, Buyun Zhang, Fangqiu Han, Wen-Yen Chen, Yiping Han, Feng Yan, Hai Li, Yiran Chen
To optimize search efficiency, DistDNAS distributes the search and aggregates the choice of optimal interaction modules on varying data dates, achieving over 25x speed-up and reducing search cost from 2 days to 2 hours.
1 code implementation • ICCV 2023 • Guangyang Wu, Xiaohong Liu, Kunming Luo, Xi Liu, Qingqing Zheng, Shuaicheng Liu, Xinyang Jiang, Guangtao Zhai, Wenyi Wang
To train and evaluate the proposed AccFlow, we have constructed a large-scale high-quality dataset named CVO, which provides ground-truth optical flow labels between adjacent and distant frames.
no code implementations • 12 Jul 2023 • Xuewei Wang, Qiang Jin, Shengyu Huang, Min Zhang, Xi Liu, Zhengli Zhao, Yukun Chen, Zhengyu Zhang, Jiyan Yang, Ellie Wen, Sagar Chordia, Wenlin Chen, Qin Huang
In order to pass better ads from the early to the final stage ranking, we propose a multi-task learning framework for early stage ranking to capture multiple final stage ranking components (i. e. ads clicks and ads quality events) and their task relations.
no code implementations • 11 May 2023 • Aneeq Zia, Kiran Bhattacharyya, Xi Liu, Max Berniker, Ziheng Wang, Rogerio Nespolo, Satoshi Kondo, Satoshi Kasai, Kousuke Hirasawa, Bo Liu, David Austin, Yiheng Wang, Michal Futrega, Jean-Francois Puget, Zhenqiang Li, Yoichi Sato, Ryo Fujii, Ryo Hachiuma, Mana Masuda, Hideo Saito, An Wang, Mengya Xu, Mobarakol Islam, Long Bai, Winnie Pang, Hongliang Ren, Chinedu Nwoye, Luca Sestini, Nicolas Padoy, Maximilian Nielsen, Samuel Schüttler, Thilo Sentker, Hümeyra Husseini, Ivo Baltruschat, Rüdiger Schmitz, René Werner, Aleksandr Matsun, Mugariya Farooq, Numan Saaed, Jose Renato Restom Viera, Mohammad Yaqub, Neil Getty, Fangfang Xia, Zixuan Zhao, Xiaotian Duan, Xing Yao, Ange Lou, Hao Yang, Jintong Han, Jack Noble, Jie Ying Wu, Tamer Abdulbaki Alshirbaji, Nour Aldeen Jalal, Herag Arabian, Ning Ding, Knut Moeller, Weiliang Chen, Quan He, Muhammad Bilal, Taofeek Akinosho, Adnan Qayyum, Massimo Caputo, Hunaid Vohra, Michael Loizou, Anuoluwapo Ajayi, Ilhem Berrou, Faatihah Niyi-Odumosu, Lena Maier-Hein, Danail Stoyanov, Stefanie Speidel, Anthony Jarc
Unfortunately, obtaining the annotations needed to train machine learning models to identify and localize surgical tools is a difficult task.
1 code implementation • 11 Apr 2023 • Danwei Li, Zhengyu Zhang, Siyang Yuan, Mingze Gao, Weilin Zhang, Chaofei Yang, Xi Liu, Jiyan Yang
However, MTL research faces two challenges: 1) effectively modeling the relationships between tasks to enable knowledge sharing, and 2) jointly learning task-specific and shared knowledge.
no code implementations • 31 Mar 2023 • Ziheng Wang, Conor Perreault, Xi Liu, Anthony Jarc
Endoscopic video recordings are widely used in minimally invasive robot-assisted surgery, but when the endoscope is outside the patient's body, it can capture irrelevant segments that may contain sensitive information.
no code implementations • 10 Dec 2022 • Hsin-En Su, Yen-ju Chen, Ping-Chun Hsieh, Xi Liu
In this paper, we rethink off-policy learning via Coordinate Ascent Policy Optimization (CAPO), an off-policy actor-critic algorithm that decouples policy improvement from the state distribution of the behavior policy without using the policy gradient.
no code implementations • 8 Dec 2022 • Aneeq Zia, Kiran Bhattacharyya, Xi Liu, Ziheng Wang, Max Berniker, Satoshi Kondo, Emanuele Colleoni, Dimitris Psychogyios, Yueming Jin, Jinfan Zhou, Evangelos Mazomenos, Lena Maier-Hein, Danail Stoyanov, Stefanie Speidel, Anthony Jarc
Using this publicly available dataset and results as a springboard, future work may enable more efficient training of surgeons with advances in surgical data science.
no code implementations • 6 Dec 2022 • Wei Hung, Bo-Kai Huang, Ping-Chun Hsieh, Xi Liu
Many real-world continuous control problems are in the dilemma of weighing the pros and cons, multi-objective reinforcement learning (MORL) serves as a generic framework of learning control policies for different preferences over objectives.
no code implementations • 11 Mar 2022 • Buyun Zhang, Liang Luo, Xi Liu, Jay Li, Zeliang Chen, Weilin Zhang, Xiaohan Wei, Yuchen Hao, Michael Tsang, Wenjun Wang, Yang Liu, Huayu Li, Yasmine Badr, Jongsoo Park, Jiyan Yang, Dheevatsa Mudigere, Ellie Wen
To overcome the challenge brought by DHEN's deeper and multi-layer structure in training, we propose a novel co-designed training system that can further improve the training efficiency of DHEN.
1 code implementation • 27 Sep 2021 • Tao Liu, P. R. Kumar, Ruida Zhou, Xi Liu
Motivated by the problem of learning with small sample sizes, this paper shows how to incorporate into support-vector machines (SVMs) those properties that have made convolutional neural networks (CNNs) successful.
no code implementations • 17 Aug 2021 • Xi Liu, Xiabi Liu, Huiyu Li, Xiaopeng Gong
In this paper, we propose a new approach to image co-segmentation through introducing the dense connections into the decoder path of Siamese U-net and presenting a new edge enhanced 3D IOU loss measured over distance maps.
no code implementations • NeurIPS 2021 • Bing-Jing Hsieh, Ping-Chun Hsieh, Xi Liu
While it serves as a natural idea to combine DQN and an existing few-shot learning method, we identify that such a direct combination does not perform well due to severe overfitting, which is particularly critical in BO due to the need of a versatile sampling policy.
no code implementations • 26 Feb 2021 • Aneeq Zia, Kiran Bhattacharyya, Xi Liu, Ziheng Wang, Satoshi Kondo, Emanuele Colleoni, Beatrice van Amsterdam, Razeen Hussain, Raabid Hussain, Lena Maier-Hein, Danail Stoyanov, Stefanie Speidel, Anthony Jarc
Surgical data science is revolutionizing minimally invasive surgery by enabling context-aware applications.
no code implementations • 22 Feb 2021 • Jyun-Li Lin, Wei Hung, Shang-Hsuan Yang, Ping-Chun Hsieh, Xi Liu
Action-constrained reinforcement learning (RL) is a widely-used approach in various real-world applications, such as scheduling in networked systems with resource constraints and control of a robot with kinematic constraints.
no code implementations • 16 Nov 2020 • Akshay Mete, Rahul Singh, Xi Liu, P. R. Kumar
The Reward-Biased Maximum Likelihood Estimate (RBMLE) for adaptive control of Markov chains was proposed to overcome the central obstacle of what is variously called the fundamental "closed-identifiability problem" of adaptive control, the "dual control problem", or, contemporaneously, the "exploration vs. exploitation problem".
no code implementations • 8 Oct 2020 • Yu-Heng Hung, Ping-Chun Hsieh, Xi Liu, P. R. Kumar
Modifying the reward-biased maximum likelihood method originally proposed in the adaptive control literature, we propose novel learning algorithms to handle the explore-exploit trade-off in linear bandits problems as well as generalized linear bandits problems.
no code implementations • 27 Jan 2020 • Xi Liu, Li Li, Ping-Chun Hsieh, Muhe Xie, Yong Ge, Rui Chen
With the explosive growth of online products and content, recommendation techniques have been considered as an effective tool to overcome information overload, improve user experience, and boost business revenue.
no code implementations • 20 Dec 2019 • Xi Liu, Rui Zhang, Yongsheng Zhou, Qianyi Jiang, Qi Song, Nan Li, Kai Zhou, Lei Wang, Dong Wang, Minghui Liao, Mingkun Yang, Xiang Bai, Baoguang Shi, Dimosthenis Karatzas, Shijian Lu, C. V. Jawahar
21 teams submit results for Task 1, 23 teams submit results for Task 2, 24 teams submit results for Task 3, and 13 teams submit results for Task 4.
no code implementations • 2 Jul 2019 • Xi Liu, Ping-Chun Hsieh, Anirban Bhattacharya, P. R. Kumar
To choose the bias-growth rate $\alpha(t)$ in RBMLE, we reveal the nontrivial interplay between $\alpha(t)$ and the regret bound that generally applies in both the Exponential Family as well as the sub-Gaussian/Exponential family bandits.
no code implementations • 14 Jan 2019 • Xi Liu, Muhe Xie, Xidao Wen, Rui Chen, Yong Ge, Nick Duffield, Na Wang
In this paper, we present the first large-scale churn analysis for mobile games that supports both micro-level churn prediction and macro-level churn ranking.
no code implementations • 14 Nov 2018 • Xi Liu, Ping-Chun Hsieh, Nick Duffield, Rui Chen, Muhe Xie, Xidao Wen
Thus the approach of adapting the existing methods to the streaming environment faces non-trivial technical challenges.
1 code implementation • 29 Oct 2018 • Ping-Chun Hsieh, Xi Liu, Anirban Bhattacharya, P. R. Kumar
Sequential decision making for lifetime maximization is a critical problem in many real-world applications, such as medical treatment and portfolio selection.
no code implementations • 20 Aug 2018 • Xi Liu, Muhe Xie, Xidao Wen, Rui Chen, Yong Ge, Nick Duffield, Na Wang
To evaluate the performance of our solution, we collect real-world data from the Samsung Game Launcher platform that includes tens of thousands of games and hundreds of millions of user-app interactions.
no code implementations • 19 Nov 2017 • Arman Hasanzadeh, Xi Liu, Nick Duffield, Krishna R. Narayanan, Byron Chigoy
Building a prediction model for transportation networks is challenging because spatio-temporal dependencies of traffic data in different roads are complex and the graph constructed from road networks is very large.
no code implementations • 13 Apr 2017 • Lin Ma, Caifa Zhou, Xi Liu, Yubin Xu
By verifying the proposed algorithm on embedding Swiss roll from R3 to R2 based on LLE and ISOMAP algorithm, the simulation results show that the proposed adaptive neighboring selection algorithm is feasible and able to find the optimal value of K, making the residual variance relatively small and better visualization of the results.
no code implementations • 10 Jan 2017 • Kele Xu, Xi Liu, Hengxing Cai, Zhifeng Gao
During the last decades, the number of new full-reference image quality assessment algorithms has been increasing drastically.
no code implementations • 26 Aug 2016 • Xi Liu, Badong Chen, Bin Xu, Zongze Wu, Paul Honeine
To improve the robustness of the UKF against impulsive noises, a new filter for nonlinear systems is proposed in this work, namely the maximum correntropy unscented filter (MCUF).
no code implementations • 15 Sep 2015 • Badong Chen, Xi Liu, Haiquan Zhao, José C. Príncipe
Traditional Kalman filter (KF) is derived under the well-known minimum mean square error (MMSE) criterion, which is optimal under Gaussian assumption.