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ORCIDFurong Huang
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- affiliation: University of Maryland, College Park, USA
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2020 – today
- 2023
[i55]Yanchao Sun, Shuang Ma, Ratnesh Madaan, Rogerio Bonatti, Furong Huang, Ashish Kapoor:
SMART: Self-supervised Multi-task pretrAining with contRol Transformers. CoRR abs/2301.09816 (2023)- [i54]Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel, Mengdi Wang, Furong Huang, Dinesh Manocha:
STEERING: Stein Information Directed Exploration for Model-Based Reinforcement Learning. CoRR abs/2301.12038 (2023) - 2022
- [j9]Jiahao Su, Jingling Li, Xiaoyu Liu, Teresa M. Ranadive, Christopher J. Coley, Tai-Ching Tuan, Furong Huang:
Compact Neural Architecture Designs by Tensor Representations. Frontiers Artif. Intell. 5: 728761 (2022) - [j8]Zheng Liang
, Songqing Li, Siyuan Zhou, Shi Chen, Ying Li, Yanran Chen, Qingbai Zhao, Furong Huang, Chunming Lu, Quanlei Yu, Zhijin Zhou:
Increased or decreased? Interpersonal neural synchronization in group creation. NeuroImage 260: 119448 (2022) - [c30]Xiaoyu Liu, Jiahao Su, Furong Huang:
Tuformer: Data-driven Design of Transformers for Improved Generalization or Efficiency. ICLR 2022 - [c29]Yanchao Sun, Ruijie Zheng, Yongyuan Liang, Furong Huang:
Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL. ICLR 2022 - [c28]Yanchao Sun, Ruijie Zheng, Xiyao Wang, Andrew E. Cohen, Furong Huang:
Transfer RL across Observation Feature Spaces via Model-Based Regularization. ICLR 2022 - [c27]Zhi Zhang, Zhuoran Yang, Han Liu, Pratap Tokekar, Furong Huang:
Reinforcement Learning under a Multi-agent Predictive State Representation Model: Method and Theory. ICLR 2022 - [c26]Jiahao Su, Wonmin Byeon, Furong Huang:
Scaling-up Diverse Orthogonal Convolutional Networks by a Paraunitary Framework. ICML 2022: 20546-20579 - [i53]Yanchao Sun, Ruijie Zheng, Xiyao Wang, Andrew E. Cohen, Furong Huang:
Transfer RL across Observation Feature Spaces via Model-Based Regularization. CoRR abs/2201.00248 (2022) - [i52]Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein:
End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking. CoRR abs/2202.05826 (2022) - [i51]Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel, Brian M. Sadler, Furong Huang, Pratap Tokekar, Dinesh Manocha:
Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning. CoRR abs/2206.01162 (2022) - [i50]Yanchao Sun, Ruijie Zheng, Parisa Hassanzadeh, Yongyuan Liang, Soheil Feizi, Sumitra Ganesh, Furong Huang:
Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems. CoRR abs/2206.10158 (2022) - [i49]Amrit Singh Bedi, Chen Fan, Alec Koppel, Anit Kumar Sahu, Brian M. Sadler, Furong Huang, Dinesh Manocha:
FedBC: Calibrating Global and Local Models via Federated Learning Beyond Consensus. CoRR abs/2206.10815 (2022) - [i48]Bang An, Zora Che, Mucong Ding, Furong Huang:
Transferring Fairness under Distribution Shifts via Fair Consistency Regularization. CoRR abs/2206.12796 (2022) - [i47]Xiyao Wang, Wichayaporn Wongkamjan, Furong Huang:
Live in the Moment: Learning Dynamics Model Adapted to Evolving Policy. CoRR abs/2207.12141 (2022) - [i46]Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie S. Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, Tom Goldstein:
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise. CoRR abs/2208.09392 (2022) - [i45]Xiaofeng Xue, Haokun Mao, Qiong Li, Furong Huang:
An Energy Optimized Specializing DAG Federated Learning based on Event Triggered Communication. CoRR abs/2209.12531 (2022) - [i44]Yongyuan Liang, Yanchao Sun, Ruijie Zheng, Furong Huang:
Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning. CoRR abs/2210.05927 (2022) - [i43]Marco Bornstein, Tahseen Rabbani, Evan Wang, Amrit Singh Bedi, Furong Huang:
SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication. CoRR abs/2210.14026 (2022) - [i42]Kaiwen Yang, Yanchao Sun, Jiahao Su, Fengxiang He, Xinmei Tian, Furong Huang, Tianyi Zhou, Dacheng Tao:
Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach. CoRR abs/2211.00824 (2022) - [i41]Marco Bornstein, Jin-Peng Liu, Jingling Li, Furong Huang:
Escaping From Saddle Points Using Asynchronous Coordinate Gradient Descent. CoRR abs/2211.09908 (2022) - [i40]Paolo Bientinesi, David A. Ham, Furong Huang, Paul H. J. Kelly, P. Sadayappan, Edward Stow:
Tensor Computations: Applications and Optimization (Dagstuhl Seminar 22101). Dagstuhl Reports 12(3): 1-14 (2022) - 2021
- [c25]Yanchao Sun, Xiangyu Yin, Furong Huang:
TempLe: Learning Template of Transitions for Sample Efficient Multi-task RL. AAAI 2021: 9765-9773 - [c24]Huimin Zeng, Chen Zhu, Tom Goldstein, Furong Huang:
Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness under Non-uniform Attacks. AAAI 2021: 10815-10823 - [c23]Yanchao Sun, Da Huo, Furong Huang:
Vulnerability-Aware Poisoning Mechanism for Online RL with Unknown Dynamics. ICLR 2021 - [c22]Tahseen Rabbani, Apollo Jain, Arjun Rajkumar, Furong Huang:
Practical and Fast Momentum-Based Power Methods. MSML 2021: 721-756 - [c21]Sicheng Zhu, Bang An, Furong Huang:
Understanding the Generalization Benefit of Model Invariance from a Data Perspective. NeurIPS 2021: 4328-4341 - [c20]Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, Tom Goldstein:
Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks. NeurIPS 2021: 6695-6706 - [c19]Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John Dickerson, Furong Huang, Tom Goldstein:
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization. NeurIPS 2021: 6733-6746 - [c18]Chen Zhu, Yu Cheng, Zhe Gan, Furong Huang, Jingjing Liu, Tom Goldstein:
MaxVA: Fast Adaptation of Step Sizes by Maximizing Observed Variance of Gradients. ECML/PKDD (3) 2021: 628-643 - [i39]Eitan Borgnia, Jonas Geiping, Valeriia Cherepanova, Liam Fowl, Arjun Gupta, Amin Ghiasi, Furong Huang, Micah Goldblum, Tom Goldstein:
DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data Augmentations. CoRR abs/2103.02079 (2021) - [i38]Chen Chen, Kezhi Kong, Peihong Yu, Juan Luque, Tom Goldstein, Furong Huang:
Insta-RS: Instance-wise Randomized Smoothing for Improved Robustness and Accuracy. CoRR abs/2103.04436 (2021) - [i37]Hyekang Joo, Calvin Bao, Ishan Sen, Furong Huang, Leilani Battle:
Guided Hyperparameter Tuning Through Visualization and Inference. CoRR abs/2105.11516 (2021) - [i36]Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, Tom Goldstein:
Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks. CoRR abs/2106.04537 (2021) - [i35]Yanchao Sun, Ruijie Zheng, Yongyuan Liang, Furong Huang:
Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL. CoRR abs/2106.05087 (2021) - [i34]Jiahao Su, Wonmin Byeon, Furong Huang:
Scaling-up Diverse Orthogonal Convolutional Networks with a Paraunitary Framework. CoRR abs/2106.09121 (2021) - [i33]Huimin Zeng, Jiahao Su, Furong Huang:
Certified Defense via Latent Space Randomized Smoothing with Orthogonal Encoders. CoRR abs/2108.00491 (2021) - [i32]Roman Levin, Manli Shu, Eitan Borgnia, Furong Huang, Micah Goldblum, Tom Goldstein:
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability. CoRR abs/2108.01335 (2021) - [i31]Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Arpit Bansal, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein:
Datasets for Studying Generalization from Easy to Hard Examples. CoRR abs/2108.06011 (2021) - [i30]Tahseen Rabbani, Apollo Jain, Arjun Rajkumar, Furong Huang:
Practical and Fast Momentum-Based Power Methods. CoRR abs/2108.09264 (2021) - [i29]Tahseen Rabbani, Brandon Yushan Feng, Yifan Yang, Arjun Rajkumar, Amitabh Varshney, Furong Huang:
Comfetch: Federated Learning of Large Networks on Memory-Constrained Clients via Sketching. CoRR abs/2109.08346 (2021) - [i28]Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John P. Dickerson, Furong Huang, Tom Goldstein:
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization. CoRR abs/2110.14363 (2021) - [i27]Sicheng Zhu, Bang An, Furong Huang:
Understanding the Generalization Benefit of Model Invariance from a Data Perspective. CoRR abs/2111.05529 (2021) - 2020
- [j7]Jingyuan Ren, Furong Huang, Ying Zhou, Liping Zhuang, Jiahua Xu, Chuanji Gao, Shaozheng Qin, Jing Lu:
The function of the hippocampus and middle temporal gyrus in forming new associations and concepts during the processing of novelty and usefulness features in creative designs. NeuroImage 214: 116751 (2020) - [j6]Xuan Wen, Qiong Li, Haokun Mao, Yi Luo, Bing-Ze Yan, Furong Huang:
Novel reconciliation protocol based on spinal code for continuous-variable quantum key distribution. Quantum Inf. Process. 19(9): 350 (2020) - [c17]Jingling Li, Yanchao Sun, Jiahao Su, Taiji Suzuki, Furong Huang:
Understanding Generalization in Deep Learning via Tensor Methods. AISTATS 2020: 504-515 - [c16]Yanchao Sun, Furong Huang:
Can Agents Learn by Analogy?: An Inferable Model for PAC Reinforcement Learning. AAMAS 2020: 1332-1340 - [c15]W. Ronny Huang, Zeyad Emam, Micah Goldblum, Liam Fowl, Justin K. Terry, Furong Huang, Tom Goldstein:
Understanding Generalization Through Visualizations. ICBINB@NeurIPS 2020: 87-97 - [c14]Jiahao Su, Milan Cvitkovic, Furong Huang:
Sampling-Free Learning of Bayesian Quantized Neural Networks. ICLR 2020 - [c13]Chris Decarolis, Mukul Ram, Seyed Esmaeili, Yu-Xiang Wang, Furong Huang:
An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm. ICML 2020: 2421-2431 - [c12]Alexander Reustle, Tahseen Rabbani, Furong Huang:
Fast GPU Convolution for CP-Decomposed Tensorial Neural Networks. IntelliSys (1) 2020: 468-487 - [c11]Jiahao Su, Shiqi Wang, Furong Huang:
ARMA Nets: Expanding Receptive Field for Dense Prediction. NeurIPS 2020 - [c10]Jiahao Su, Wonmin Byeon, Jean Kossaifi, Furong Huang, Jan Kautz, Anima Anandkumar:
Convolutional Tensor-Train LSTM for Spatio-Temporal Learning. NeurIPS 2020 - [i26]Jingling Li, Yanchao Sun, Jiahao Su, Taiji Suzuki, Furong Huang:
Understanding Generalization in Deep Learning via Tensor Methods. CoRR abs/2001.05070 (2020) - [i25]Yanchao Sun, Xiangyu Yin, Furong Huang:
TempLe: Learning Template of Transitions for Sample Efficient Multi-task RL. CoRR abs/2002.06659 (2020) - [i24]Jiahao Su, Wonmin Byeon, Furong Huang, Jan Kautz, Animashree Anandkumar:
Convolutional Tensor-Train LSTM for Spatio-temporal Learning. CoRR abs/2002.09131 (2020) - [i23]Chen Zhu, Renkun Ni, Ping-Yeh Chiang, Hengduo Li, Furong Huang, Tom Goldstein:
Improving the Tightness of Convex Relaxation Bounds for Training Certifiably Robust Classifiers. CoRR abs/2002.09766 (2020) - [i22]Jiahao Su, Shiqi Wang, Furong Huang:
ARMA Nets: Expanding Receptive Field for Dense Prediction. CoRR abs/2002.11609 (2020) - [i21]Roozbeh Yousefzadeh, Furong Huang:
Using Wavelets and Spectral Methods to Study Patterns in Image-Classification Datasets. CoRR abs/2006.09879 (2020) - [i20]Chen Zhu, Yu Cheng, Zhe Gan, Furong Huang, Jingjing Liu, Tom Goldstein:
Adaptive Learning Rates with Maximum Variation Averaging. CoRR abs/2006.11918 (2020) - [i19]Yanchao Sun, Furong Huang:
Vulnerability-Aware Poisoning Mechanism for Online RL with Unknown Dynamics. CoRR abs/2009.00774 (2020) - [i18]Huimin Zeng, Chen Zhu, Tom Goldstein, Furong Huang:
Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness under Non-uniform Attacks. CoRR abs/2010.12989 (2020) - [i17]Paolo Bientinesi, David A. Ham, Furong Huang, Paul H. J. Kelly, Christian Lengauer, Saday Sadayappan:
Tensor Computations: Applications and Optimization (Dagstuhl Seminar 20111). Dagstuhl Reports 10(3): 58-70 (2020)
2010 – 2019
- 2019
- [j5]Furong Huang, Qingbai Zhao, Zhijin Zhou, Jing Luo:
People got lost in solving a set of similar problems. NeuroImage 186: 192-199 (2019) - [c9]Ali Shafahi, Amin Ghiasi, Mahyar Najibi, Furong Huang, John P. Dickerson, Tom Goldstein:
Batch-wise Logit-Similarity: Generalizing Logit-Squeezing and Label-Smoothing. BMVC 2019: 72 - [c8]Furong Huang, U. N. Niranjan, Ioakeim Perros, Robert Chen, Jimeng Sun, Anima Anandkumar:
Guaranteed Scalable Learning of Latent Tree Models. UAI 2019: 883-893 - [i16]Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan R. Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar:
SysML: The New Frontier of Machine Learning Systems. CoRR abs/1904.03257 (2019) - [i15]W. Ronny Huang, Zeyad Emam, Micah Goldblum, Liam Fowl, Justin K. Terry, Furong Huang, Tom Goldstein:
Understanding Generalization through Visualizations. CoRR abs/1906.03291 (2019) - [i14]Ali Shafahi, Amin Ghiasi, Furong Huang, Tom Goldstein:
Label Smoothing and Logit Squeezing: A Replacement for Adversarial Training? CoRR abs/1910.11585 (2019) - [i13]Jiahao Su, Milan Cvitkovic, Furong Huang:
Sampling-Free Learning of Bayesian Quantized Neural Networks. CoRR abs/1912.02992 (2019) - [i12]Yanchao Sun, Furong Huang:
Can Agents Learn by Analogy? An Inferable Model for PAC Reinforcement Learning. CoRR abs/1912.10329 (2019) - 2018
- [j4]Furong Huang, Shuang Tang, Pei Sun, Jing Luo:
Neural correlates of novelty and appropriateness processing in externally induced constraint relaxation. NeuroImage 172: 381-389 (2018) - [c7]Furong Huang, Jordan T. Ash, John Langford, Robert E. Schapire:
Learning Deep ResNet Blocks Sequentially using Boosting Theory. ICML 2018: 2063-2072 - [i11]Jialin Li, Furong Huang:
Guaranteed Simultaneous Asymmetric Tensor Decomposition via Orthogonalized Alternating Least Squares. CoRR abs/1805.10348 (2018) - [i10]Jiahao Su, Jingling Li, Bobby Bhattacharjee, Furong Huang:
Tensorized Spectrum Preserving Compression for Neural Networks. CoRR abs/1805.10352 (2018) - 2017
- [i9]Furong Huang, Jordan T. Ash, John Langford, Robert E. Schapire:
Learning Deep ResNet Blocks Sequentially using Boosting Theory. CoRR abs/1706.04964 (2017) - 2016
- [b1]Furong Huang:
Discovery of Latent Factors in High-dimensional Data Using Tensor Methods. University of California, Irvine, USA, 2016 - [i8]Furong Huang:
Discovery of Latent Factors in High-dimensional Data Using Tensor Methods. CoRR abs/1606.03212 (2016) - [i7]Anthony Gitter, Furong Huang, Ragupathyraj Valluvan, Ernest Fraenkel, Animashree Anandkumar:
Unsupervised learning of transcriptional regulatory networks via latent tree graphical models. CoRR abs/1609.06335 (2016) - [i6]Zheng Xu, Furong Huang, Louiqa Raschid, Tom Goldstein:
Non-negative Factorization of the Occurrence Tensor from Financial Contracts. CoRR abs/1612.03350 (2016) - 2015
- [j3]Furong Huang, U. N. Niranjan, Mohammad Umar Hakeem, Animashree Anandkumar:
Online tensor methods for learning latent variable models. J. Mach. Learn. Res. 16: 2797-2835 (2015) - [j2]Furong Huang, Jin Fan, Jing Luo:
The neural basis of novelty and appropriateness in processing of creative chunk decomposition. NeuroImage 113: 122-132 (2015) - [c6]Rong Ge, Furong Huang, Chi Jin, Yang Yuan:
Escaping From Saddle Points - Online Stochastic Gradient for Tensor Decomposition. COLT 2015: 797-842 - [c5]Forough Arabshahi, Furong Huang, Animashree Anandkumar, Carter T. Butts, Sean M. Fitzhugh:
Are You Going to the Party: Depends, Who Else is Coming?: [Learning Hidden Group Dynamics via Conditional Latent Tree Models]. ICDM 2015: 697-702 - [c4]Furong Huang, Animashree Anandkumar:
Convolutional Dictionary Learning through Tensor Factorization. FE@NIPS 2015: 116-129 - [i5]Rong Ge, Furong Huang, Chi Jin, Yang Yuan:
Escaping From Saddle Points - Online Stochastic Gradient for Tensor Decomposition. CoRR abs/1503.02101 (2015) - [i4]Furong Huang, Animashree Anandkumar:
Convolutional Dictionary Learning through Tensor Factorization. CoRR abs/1506.03509 (2015) - 2014
- [i3]Furong Huang, U. N. Niranjan, Animashree Anandkumar:
Integrated Structure and Parameters Learning in Latent Tree Graphical Models. CoRR abs/1406.4566 (2014) - [i2]Forough Arabshahi, Furong Huang, Animashree Anandkumar, Carter T. Butts:
Modeling Dynamic Social Interactions via Conditional Latent Tree Models. CoRR abs/1411.1132 (2014) - 2013
- [c3]Furong Huang, Anima Anandkumar:
FCD: Fast-concurrent-distributed load balancing under switching costs and imperfect observations. INFOCOM 2013: 1896-1904 - [i1]Furong Huang, U. N. Niranjan, Mohammad Umar Hakeem, Prateek Verma, Animashree Anandkumar:
Fast Detection of Overlapping Communities via Online Tensor Methods on GPUs. CoRR abs/1309.0787 (2013) - 2012
- [j1]Animashree Anandkumar, Vincent Y. F. Tan, Furong Huang, Alan S. Willsky:
High-dimensional Gaussian graphical model selection: walk summability and local separation criterion. J. Mach. Learn. Res. 13: 2293-2337 (2012) - [c2]Animashree Anandkumar, Daniel J. Hsu, Furong Huang, Sham M. Kakade:
Learning Mixtures of Tree Graphical Models. NIPS 2012: 1061-1069 - 2010
- [c1]Furong Huang, Wei Wang, Haiyan Luo, Guanding Yu, Zhaoyang Zhang:
Prediction-Based Spectrum Aggregation with Hardware Limitation in Cognitive Radio Networks. VTC Spring 2010: 1-5
Coauthor Index
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