Gavin Taylor - RankMe

51 papers A* 13A 3C 1Journal 27Unranked 7

Year	Rank	Type	Title / Venue / Authors
2025	A	`conf`	Contrast All The Time: Learning Time Series Representation from Temporal Consistency. ECAI Abdul-Kazeem Shamba, Kerstin Bach, Gavin Taylor
2025	J	`jnl`	eMargin: Revisiting Contrastive Learning with Margin-Based Separation. CoRR Abdul-Kazeem Shamba, Kerstin Bach, Gavin Taylor
2024	J	`jnl`	Dynamic Contrastive Learning for Time Series Representation. CoRR Abdul-Kazeem Shamba, Kerstin Bach, Gavin Taylor
2024	J	`jnl`	Uncertainty-aware autonomous sensing with deep reinforcement learning. Future Gener. Comput. Syst. Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor
2023	J	`jnl`	The National Airworthiness Council artificial intelligence working group (NACAIWG) summit proceedings 2022. Syst. Eng. Jonathon Parry, Donald H. Costello, Jason Rupert, Gavin Taylor
2022	J	`jnl`	Execute Order 66: Targeted Data Poisoning for Reinforcement Learning. CoRR Harrison Foley, Liam Fowl, Tom Goldstein, Gavin Taylor
2022	A*	`conf`	Robust Optimization as Data Augmentation for Large-scale Graphs. CVPR Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, Tom Goldstein
2021	A*	`conf`	LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition. ICLR Valeriia Cherepanova, Micah Goldblum, Harrison Foley, Shiyuan Duan, John P. Dickerson, Gavin Taylor, Tom Goldstein
2021	J	`jnl`	LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition. CoRR Valeriia Cherepanova, Micah Goldblum, Harrison Foley, Shiyuan Duan, John P. Dickerson, Gavin Taylor, Tom Goldstein
2021	J	`jnl`	Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting. CoRR Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor
2021	J	`jnl`	Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting. Sensors Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor
2021	A*	`conf`	Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching. ICLR Jonas Geiping, Liam H. Fowl, W. Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, Tom Goldstein
2020	J	`jnl`	FLAG: Adversarial Data Augmentation for Graph Neural Networks. CoRR Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, Tom Goldstein
2020	J	`jnl`	Information-Driven Adaptive Sensing Based on Deep Reinforcement Learning. CoRR Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor
2020	—	`conf`	Information-driven adaptive sensing based on deep reinforcement learning. IOT Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor
2020	A*	`conf`	MetaPoison: Practical General-purpose Clean-label Data Poisoning. NeurIPS W. Ronny Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, Tom Goldstein
2020	J	`jnl`	MetaPoison: Practical General-purpose Clean-label Data Poisoning. CoRR W. Ronny Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, Tom Goldstein
2020	J	`jnl`	Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching. CoRR Jonas Geiping, Liam Fowl, W. Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, Tom Goldstein
2019	J	`jnl`	Adversarial Training for Free! CoRR Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John P. Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, Tom Goldstein
2019	A*	`conf`	Adversarial training for free! NeurIPS Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John P. Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, Tom Goldstein
2019	—	`conf`	Autonomous Management of Energy-Harvesting IoT Nodes Using Deep Reinforcement Learning. SASO Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor
2019	J	`jnl`	Autonomous Management of Energy-Harvesting IoT Nodes Using Deep Reinforcement Learning. CoRR Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor
2019	—	`conf`	IoT Sensor Gym: Training Autonomous IoT Devices with Deep Reinforcement Learning. IOT Abdulmajid Murad, Kerstin Bach, Frank Alexander Kraemer, Gavin Taylor
2019	A*	`conf`	Transferable Clean-Label Poisoning Attacks on Deep Neural Nets. ICML Chen Zhu, W. Ronny Huang, Hengduo Li, Gavin Taylor, Christoph Studer, Tom Goldstein
2019	J	`jnl`	Transferable Clean-Label Poisoning Attacks on Deep Neural Nets. CoRR Chen Zhu, W. Ronny Huang, Ali Shafahi, Hengduo Li, Gavin Taylor, Christoph Studer, Tom Goldstein
2018	A*	`conf`	Visualizing the Loss Landscape of Neural Nets. NeurIPS Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein
2017	A*	`conf`	Adaptive Consensus ADMM for Distributed Optimization. ICML Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein
2017	J	`jnl`	Adaptive Consensus ADMM for Distributed Optimization. CoRR Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein
2017	J	`jnl`	Hoaxing statistical features of the Voynich Manuscript. Cryptologia Gordon Rugg, Gavin Taylor
2017	—	`conf`	Scalable Classifiers with ADMM and Transpose Reduction. AAAI Workshops Gavin Taylor, Zheng Xu, Tom Goldstein
2017	J	`jnl`	Visualizing the Loss Landscape of Neural Nets. CoRR Hao Li, Zheng Xu, Gavin Taylor, Tom Goldstein
2016	—	`conf`	Introduction to the Symposium on AI and the Mitigation of Human Error. AAAI Spring Symposia Ranjeev Mittu, Gavin Taylor, Donald A. Sofge, William F. Lawless
2016	A*	`conf`	Training Neural Networks Without Gradients: A Scalable ADMM Approach. ICML Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit B. Patel, Tom Goldstein
2016	J	`jnl`	Training Neural Networks Without Gradients: A Scalable ADMM Approach. CoRR Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit B. Patel, Tom Goldstein
2016	A	`conf`	Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction. AISTATS Tom Goldstein, Gavin Taylor, Kawika Barabin, Kent Sayre
2015	C	`conf`	Layer-Specific Adaptive Learning Rates for Deep Networks. ICMLA Bharat Singh, Soham De, Yangmuzi Zhang, Thomas A. Goldstein, Gavin Taylor
2015	J	`jnl`	Layer-Specific Adaptive Learning Rates for Deep Networks. CoRR Bharat Singh, Soham De, Yangmuzi Zhang, Thomas A. Goldstein, Gavin Taylor
2015	J	`jnl`	Reports on the 2015 AAAI Spring Symposium Series. AI Mag. Nitin Agarwal, Sean Andrist, Dan Bohus, Fei Fang, Laurie Fenstermacher, Lalana Kagal, Takashi Kido, Christopher Kiekintveld, William F. Lawless, Huan Liu, Andrew McCallum, Hemant Purohit, Oshani Seneviratne, Keiki Takadama, Gavin Taylor
2015	J	`jnl`	Scaling Up Distributed Stochastic Gradient Descent Using Variance Reduction. CoRR Soham De, Gavin Taylor, Tom Goldstein
2015	J	`jnl`	Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction. CoRR Tom Goldstein, Gavin Taylor, Kawika Barabin, Kent Sayre
2015	J	`jnl`	Variance Reduction for Distributed Stochastic Gradient Descent. CoRR Soham De, Gavin Taylor, Tom Goldstein
2014	A*	`conf`	An Analysis of State-Relevance Weights and Sampling Distributions on L1-Regularized Approximate Linear Programming Approximation Accuracy. ICML Gavin Taylor, Connor Geer, David Piekut
2014	J	`jnl`	An Analysis of State-Relevance Weights and Sampling Distributions on L1-Regularized Approximate Linear Programming Approximation Accuracy. CoRR Gavin Taylor, Connor Geer, David Piekut
2014	—	`conf`	Towards Modeling the Behavior of Autonomous Systems and Humans for Trusted Operations. AAAI Spring Symposia Weiqing Gu, Ranjeev Mittu, Julie L. Marble, Gavin Taylor, Ciara Sibley, Joseph T. Coyne, William F. Lawless
2012	J	`jnl`	Value Function Approximation in Noisy Environments Using Locally Smoothed Regularized Approximate Linear Programs CoRR Gavin Taylor, Ronald Parr
2012	A	`conf`	Value Function Approximation in Noisy Environments Using Locally Smoothed Regularized Approximate Linear Programs. UAI Gavin Taylor, Ronald Parr
2010	—	`conf`	An Intensive Introductory Robotics Course Without Prerequisites. Enabling Intelligence through Middleware Julian Mason, Gavin Taylor
2010	J	`jnl`	Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes CoRR Marek Petrik, Gavin Taylor, Ronald Parr, Shlomo Zilberstein
2010	A*	`conf`	Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes. ICML Marek Petrik, Gavin Taylor, Ronald Parr, Shlomo Zilberstein
2009	A*	`conf`	Kernelized value function approximation for reinforcement learning. ICML Gavin Taylor, Ronald Parr
2008	A*	`conf`	An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning. ICML Ronald Parr, Lihong Li, Gavin Taylor, Christopher Painter-Wakefield, Michael L. Littman