Yue, Yisong

Combined from CaltechAUTHORS

• Israel, Uriah and Marks, Markus, et el. (2024) A Foundation Model for Cell Segmentation; bioRvix; 2023.11.17.567630; PMCID PMC10690226; 10.1101/2023.11.17.567630
• Yang, Jason and Ducharme, Julie, et el. (2023) DeCOIL: Optimization of Degenerate Codon Libraries for Machine Learning-Assisted Protein Engineering; ACS Synthetic Biology; Vol. 12; No. 8; 2444-2454; 10.1021/acssynbio.3c00301
• Dorobantu, Victor D. and Azizzadenesheli, Kamyar, et el. (2023) Compactly Restrictable Metric Policy Optimization Problems; IEEE Transactions on Automatic Control; Vol. 68; No. 5; 3115-3122; 10.1109/tac.2022.3217269
• Voloshin, Cameron and Verma, Abhinav, et el. (2023) Eventual Discounting Temporal Logic Counterfactual Experience Replay
• Huang, Yujia and Jimenez Rodriguez, Ivan Dario, et el. (2022) FI-ODE: Certified and Robust Forward Invariance in Neural ODEs; 10.48550/arXiv.2210.16940
• Sun, Jennifer J. and Ulmer, Andrew, et el. (2022) The MABe22 Benchmarks for Representation Learning of Multi-Agent Behavior; 10.48550/arXiv.2207.10553
• Sun, Jennifer J. and Tjandrasuwita, Megan, et el. (2022) Neurosymbolic Programming for Science; 10.48550/arXiv.2210.05050
• Tucker, Maegan and Li, Kejun, et el. (2022) POLAR: Preference Optimization and Learning Algorithms for Robotics; 10.48550/arXiv.2208.04404
• Sun, Jennifer J. and Karashchuk, Pierre, et el. (2022) BKinD-3D: Self-Supervised 3D Keypoint Discovery from Multi-View Videos; 10.48550/arXiv.2212.07401
• Dorobantu, Victor D. and Azizzadenesheli, Kamyar, et el. (2022) Compactly Restrictable Metric Policy Optimization Problems; 10.48550/arXiv.arXiv.2207.05850
• Voloshin, Cameron and Le, Hoang M., et el. (2022) Policy Optimization with Linear Temporal Logic Constraints; 10.48550/arXiv.arXiv.2206.09546
• Talukder, Sabera and Sun, Jennifer J., et el. (2022) Deep Neural Imputation: A Framework for Recovering Incomplete Brain Recordings; 10.48550/arXiv.arXiv.2206.08094
• Farhang, Alexander R. and Bernstein, Jeremy D., et el. (2022) Investigating Generalization by Controlling Normalized Margin; Proceedings of Machine Learning Research; Vol. 162; 6324-6336; 10.48550/arXiv.arXiv.2205.03940
• O'Connell, Michael and Shi, Guanya, et el. (2022) Neural-Fly enables rapid learning for agile flight in strong winds; Science Robotics; Vol. 7; No. 66; Art. No. eabm6597; 10.1126/scirobotics.abm6597
• Jimenez Rodriguez, Ivan Dario and Csomay-Shanklin, Noel, et el. (2022) Neural Gaits: Learning Bipedal Locomotion via Control Barrier Functions and Zero Dynamics Policies; Proceedings of Machine Learning Research; Vol. 168; 1060-1072; 10.48550/arXiv.2204.08120
• Shi, Guanya and Hönig, Wolfgang, et el. (2022) Neural-Swarm2: Planning and Control of Heterogeneous Multirotor Swarms Using Learned Interactions; IEEE Transactions on Robotics; Vol. 38; No. 2; 1063-1079; 10.1109/TRO.2021.3098436
• Daftry, Shreyansh and Abcouwer, Neil, et el. (2022) MLNav: Learning to Safely Navigate on Martian Terrains; IEEE Robotics and Automation Letters; Vol. 7; No. 2; 5461-5468; 10.1109/lra.2022.3156654
• Li, Kejun and Tucker, Maegan, et el. (2022) Natural Multicontact Walking for Robotic Assistive Devices via Musculoskeletal Models and Hybrid Zero Dynamics; IEEE Robotics and Automation Letters; Vol. 7; No. 2; 4283-4290; 10.1109/lra.2022.3149568
• Taylor, Andrew J. and Dorobantu, Victor D., et el. (2022) Safety of Sampled-Data Systems with Control Barrier Functions via Approximate Discrete Time Models; 10.48550/arXiv.2203.11470
• Cosner, Ryan K. and Jimenez Rodriguez, Ivan D., et el. (2022) Self-Supervised Online Learning for Safety-Critical Control using Stereo Vision; 10.48550/arXiv.2203.01404
• Zhan, Eric and Sun, Jennifer J., et el. (2022) Unsupervised Learning of Neurosymbolic Encoders; 10.48550/arXiv.2107.13132
• Bernstein, Jeremy and Farhang, Alex, et el. (2022) Kernel Interpolation as a Bayes Point Machine; 10.48550/arXiv.2110.04274
• Sun, Jennifer J. and Ryou, Serim, et el. (2022) Self-Supervised Keypoint Discovery in Behavioral Videos; 10.48550/arXiv.2112.05121
• Tseng, Albert and Sun, Jennifer J., et el. (2022) Automatic Synthesis of Diverse Weak Supervision Sources for Behavior Analysis; 10.48550/arXiv.2111.15186
• Cosner, Ryan K. and Tucker, Maegan, et el. (2022) Safety-Aware Preference-Based Learning for Safety-Critical Control; 10.48550/arXiv.2112.08516
• Jimenez Rodriguez, Ivan Dario and Ames, Aaron D., et el. (2022) LyaNet: A Lyapunov Framework for Training Neural ODEs; 10.48550/arXiv.2202.02526
• Tjandrasuwita, Megan and Sun, Jennifer J., et el. (2022) Interpreting Expert Annotation Differences in Animal Behavior; 10.48550/arXiv.2106.06114
• Jimenez Rodriguez, Ivan Dario and Ames, Aaron D., et el. (2022) LyaNet: A Lyapunov Framework for Training Neural ODEs; Proceedings of Machine Learning Research; 18687-18703; 10.48550/arXiv.arXiv.2202.02526
• Taylor, Andrew J. and Dorobantu, Victor D., et el. (2022) Sampled-Data Stabilization with Control Lyapunov Functions via Quadratically Constrained Quadratic Programs; IEEE Control Systems Letters; Vol. 6; 680-685; 10.1109/LCSYS.2021.3085172
• Taylor, Andrew J. and Dorobantu, Victor D., et el. (2021) Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertainty; ISBN 978-1-6654-3659-5; 2021 60th IEEE Conference on Decision and Control (CDC); 6469-6476; 10.1109/CDC45484.2021.9683511
• Gao, Angela F. and Castellanos, Jorge C., et el. (2021) DeepGEM: Generalized Expectation-Maximization for Blind Inversion; ISBN 9781713845393; 35th Conference on Neural Information Processing Systems; 1-12
• Shi, Guanya and Azizzadenesheli, Kamyar, et el. (2021) Meta-Adaptive Nonlinear Control: Theory and Algorithms; 10.48550/arXiv.2106.06098
• Wittmann, Bruce J. and Yue, Yisong, et el. (2021) Informed training set design enables efficient machine learning-assisted directed protein evolution; Cell Systems; Vol. 12; No. 11; 1026-1045; 10.1016/j.cels.2021.07.008
• Jimenez Rodriguez, Ivan D. and Rosolia, Ugo, et el. (2021) Learning to Control an Unstable System with One Minute of Data: Leveraging Gaussian Process Differentiation in Predictive Control; ISBN 978-1-6654-1714-3; 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); 3896-3903; 10.1109/IROS51168.2021.9636786
• Bagherian, Dawna and Gornet, James, et el. (2021) Fine-Grained System Identification of Nonlinear Neural Circuits; ISBN 978-1-4503-8332-5; Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining; 14-24; 10.1145/3447548.3467402
• Ferber, Aaron and Song, Jialin, et el. (2021) Learning Pseudo-Backdoors for Mixed Integer Programs; 10.48550/arXiv.2106.05080
• Liu, Yang and Bernstein, Jeremy, et el. (2021) Learning by Turning: Neural Architecture Aware Optimisation; Proceedings of Machine Learning Research; Vol. 139; 6748-6758; 10.48550/arXiv.2102.07227
• Taylor, Andrew J. and Singletary, Andrew, et el. (2021) A Control Barrier Perspective on Episodic Learning via Projection-to-State Safety; IEEE Control Systems Letters; Vol. 5; No. 3; 1019-1024; 10.1109/LCSYS.2020.3009082
• Yin, Tianwei and Wu, Zihui, et el. (2021) End-to-End Sequential Sampling and Reconstruction for MR Imaging; 10.48550/arXiv.2105.06460
• Sun, Jennifer J. and Kennedy, Ann, et el. (2021) Task Programming: Learning Data Efficient Behavior Representations; ISBN 978-1-6654-4509-2; 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2875-2884; 10.1109/CVPR46437.2021.00290
• Li, Kejun and Tucker, Maegan, et el. (2021) ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes; ISBN 978-1-7281-9077-8; 2021 IEEE International Conference on Robotics and Automation (ICRA); 3212-3218; 10.1109/ICRA48506.2021.9560840
• Ravi Tej, Akella and Azizzadenesheli, Kamyar, et el. (2021) Deep Bayesian Quadrature Policy Optimization; 10.48550/arXiv.2006.15637
• Sun, Jennifer J. and Karigo, Tomomi, et el. (2021) The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions; 10.48550/arXiv.2104.02710
• Qin, Yidan and Allan, Max, et el. (2021) Learning Invariant Representation of Tasks for Robust Surgical State Estimation; IEEE Robotics and Automation Letters; Vol. 6; No. 2; 3208-3215; 10.1109/LRA.2021.3063014
• Ho, Dimitar and Le, Hoang M., et el. (2021) Online Robust Control of Nonlinear Systems with Large Uncertainty; Proceedings of Machine Learning Research; Vol. 130; 3475-3483; 10.48550/arXiv.2103.11055
• Nakka, Yashwanth Kumar and Liu, Anqi, et el. (2021) Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems; IEEE Robotics and Automation Letters; Vol. 6; No. 2; 389-396; 10.1109/LRA.2020.3044033
• Zhao, Eric and Liu, Anqi, et el. (2021) Active Learning under Label Shift; Proceedings of Machine Learning Research; Vol. 130; 3412-3420; 10.48550/arXiv.2007.08479
• Voloshin, Cameron and Jiang, Nan, et el. (2021) Minimax Model Learning; Proceedings of Machine Learning Research; Vol. 130; 1612-1620; 10.48550/arXiv.2103.02084
• Bernstein, Jeremy and Yue, Yisong (2021) Computing the Information Content of Trained Neural Networks; 10.48550/arXiv.2103.01045
• Abcouwer, Neil and Daftry, Shreyansh, et el. (2021) Machine Learning Based Path Planning for Improved Rover Navigation; ISBN 978-1-7281-7436-5; 2021 IEEE Aerospace Conference; 1-9; 10.1109/AERO50100.2021.9438337
• Liu, Anqi and Liu, Hao, et el. (2021) Disentangling Observed Causal Effects from Latent Confounders using Method of Moments; 10.48550/arXiv.2101.06614
• Cross, Logan and Cockburn, Jeffrey, et el. (2021) Using deep reinforcement learning to reveal how the brain encodes abstract state-space representations in high-dimensional environments; Neuron; Vol. 109; No. 4; 724-738; PMCID PMC7897245; 10.1016/j.neuron.2020.11.021
• Maser, Michael R. and Cui, Alexander Y., et el. (2021) Multilabel Classification Models for the Prediction of Cross-Coupling Reaction Conditions; Journal of Chemical Information and Modeling; Vol. 61; No. 1; 156-166; 10.1021/acs.jcim.0c01234
• Barnum, George and Talukder, Sabera, et el. (2021) On the Benefits of Early Fusion in Multimodal Representation Learning; 10.48550/arXiv.2011.07191
• Talukder, Sabera and Raghavan, Guruprasad, et el. (2021) Architecture Agnostic Neural Networks; 10.48550/arXiv.2011.02712
• Song, Jialin and Lanka, Ravi, et el. (2020) A General Large Neighborhood Search Framework for Solving Integer Programs; ISBN 9781713829546; 34th Conference on Neural Information Processing Systems (NeurIPS 2020); 1-12
• Shi, Guanya and Lin, Yiheng, et el. (2020) Online Optimization with Memory and Competitive Control; ISBN 9781713829546; 34th Conference on Neural Information Processing Systems (NeurIPS 2020); 1-12
• Yu, Chenkai and Shi, Guanya, et el. (2020) The Power of Predictions in Online Control; ISBN 9781713829546; 34th Conference on Neural Information Processing Systems (NeurIPS 2020); 1-11
• Bernstein, Jeremy and Zhao, Jiawei, et el. (2020) Learning compositional functions via multiplicative weight updates; 10.48550/arXiv.2006.14560
• Bernstein, Jeremy and Vahdat, Arash, et el. (2020) On the distance between two neural networks and the stability of learning; ISBN 9781713829546; Advances in Neural Information Processing Systems 33 (NeurIPS 2020); 1-12
• Marino, Joseph and Piché, Alexandre, et el. (2020) Iterative Amortized Policy Optimization; ISBN 9781713829546; 34th Conference on Neural Information Processing Systems (NeurIPS 2020); 1-15
• Kumar, Akash and Singla, Adish, et el. (2020) Average-case Complexity of Teaching Convex Polytopes via Halfspace Queries; 10.48550/arXiv.2006.14677
• Ryou, Serim and Maser, Michael R., et el. (2020) Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions; 10.48550/arXiv.2007.04275
• Shah, Ameesh and Zhan, Eric, et el. (2020) Learning Differentiable Programs with Admissible Neural Heuristics; 10.48550/arXiv.2007.12101
• Yu, Chenkai and Shi, Guanya, et el. (2020) Competitive Control with Delayed Imperfect Information; 10.48550/arXiv.2010.11637
• Marino, Joseph and Piché, Alexandre, et el. (2020) Iterative Amortized Policy Optimization; 10.48550/arXiv.2010.10670
• Prajapat, Manish and Azizzadenesheli, Kamyar, et el. (2020) Competitive Policy Optimization; 10.48550/arXiv.2006.10611
• Wang, Haoxuan and Liu, Anqi, et el. (2020) Distributionally Robust Learning for Unsupervised Domain Adaptation; 10.48550/arXiv.2010.05784
• Tucker, Maegan and Cheng, Myra, et el. (2020) Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits; ISBN 978-1-7281-6212-6; 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); 3423-3430; 10.1109/IROS45743.2020.9341416
• Tucker, Maegan and Novoseller, Ellen, et el. (2020) Preference-Based Learning for Exoskeleton Gait Optimization; ISBN 978-1-7281-7395-5; 2020 IEEE International Conference on Robotics and Automation (ICRA); 2351-2357; 10.1109/ICRA40945.2020.9196661
• Shi, Guanya and Hönig, Wolfgang, et el. (2020) Neural-Swarm: Decentralized Close-Proximity Multirotor Control Using Learned Interactions; ISBN 978-1-7281-7395-5; 2020 IEEE International Conference on Robotics and Automation (ICRA); 3241-3247; 10.1109/ICRA40945.2020.9196800
• Novoseller, Ellen R. and Wei, Yibing, et el. (2020) Dueling Posterior Sampling for Preference-Based Reinforcement Learning; Proceedings of Machine Learning Research; Vol. 124; 1029-1038; 10.48550/arXiv.1908.01289
• Yu, Chenkai and Shi, Guanya, et el. (2020) The Power of Predictions in Online Control; 10.48550/arXiv.2006.07569
• Rivière, Benjamin and Hönig, Wolfgang, et el. (2020) GLAS: Global-to-Local Safe Autonomy Synthesis for Multi-Robot Motion Planning with End-to-End Learning; IEEE Robotics and Automation Letters; Vol. 5; No. 3; 4249-4256; 10.1109/lra.2020.2994035
• Taylor, Andrew J. and Singletary, Andrew, et el. (2020) Learning for Safety-Critical Control with Control Barrier Functions; Proceedings of Machine Learning Research; Vol. 120; 708-717; 10.48550/arXiv.1912.10099
• Song, Jialin and Lanka, Ravi, et el. (2020) A General Large Neighborhood Search Framework for Solving Integer Programs; 10.48550/arXiv.2004.00422
• Song, Jialin and Tokpanov, Yury S., et el. (2020) Mirrored Plasmonic Filter Design via Active Learning of Multi-Fidelity Physical Models; ISBN 9781943580767; 2020 Conference on Lasers and Electro-Optics (CLEO); Art. No. JTu2D.6; 10.1364/cleo_at.2020.jtu2d.6
• Bernstein, Jeremy and Vahdat, Arash, et el. (2020) On the distance between two neural networks and the stability of learning; 10.48550/arXiv.2002.03432
• Park, Jung Yeon and Carr, Kenneth Theo, et el. (2020) Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis; 10.48550/arXiv.2002.05578
• Shi, Guanya and Lin, Yiheng, et el. (2020) Online Optimization with Memory and Competitive Control; 10.48550/arXiv.2002.05318
• Liu, Anqi and Liu, Hao, et el. (2020) Triply Robust Off-Policy Evaluation; 10.48550/arXiv.1911.05811
• Zhan, Eric and Tseng, Albert, et el. (2020) Learning Calibratable Policies using Programmatic Style-Consistency; 10.48550/arXiv.1910.01179
• Voloshin, Cameron and Le, Hoang M., et el. (2020) Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning; 10.48550/arXiv.1911.06854
• Taylor, Andrew J. and Dorobantu, Victor D., et el. (2019) A Control Lyapunov Perspective on Episodic Learning via Projection to State Stability; ISBN 978-1-7281-1398-2; 2019 IEEE 58th Conference on Decision and Control (CDC); 1448-1455; 10.1109/CDC40024.2019.9029226
• Hunziker, Anette and Chen, Yuxin, et el. (2019) Teaching Multiple Concepts to Forgetful Learners; 10.48550/arXiv.1805.08322
• Verma, Abhinav and Le, Hoang M., et el. (2019) Imitation-Projected Policy Gradient for Programmatic Reinforcement Learning; 10.48550/arXiv.1907.05431
• Jin, Baihong and Tan, Yingshui, et el. (2019) An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing; ISBN 978-1-7281-4550-1; 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA); 1008-1015; 10.1109/ICMLA.2019.00171
• Ghosh, Nikhil and Chen, Yuxin, et el. (2019) Landmark Ordinal Embedding; 10.48550/arXiv.1910.12379
• Liu, Yukai and Yu, Rose, et el. (2019) NAOMI: Non-Autoregressive Multiresolution Sequence Imputation; 10.48550/arXiv.1901.10946
• Taylor, Andrew J. and Dorobantu, Victor D., et el. (2019) Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems; ISBN 978-1-7281-4004-9; 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); 6878-6884; 10.1109/IROS40897.2019.8967820
• Song, Jialin and Lanka, Ravi, et el. (2019) Co-training for Policy Learning; 10.48550/arXiv.1907.04484
• Liu, Anqi and Shi, Guanya, et el. (2019) Robust Regression for Safe Exploration in Control; 10.48550/arXiv.1906.05819
• Cheng, Richard and Verma, Abhinav, et el. (2019) Control Regularization for Reduced Variance Reinforcement Learning; Proceedings of Machine Learning Research; Vol. 97; 1141-1150; 10.48550/arXiv.1905.05380
• Ahmadi, Mohamadreza and Wu, Bo, et el. (2019) Barrier Certificates for Assured Machine Teaching; ISBN 978-1-5386-7926-5; 2019 American Control Conference (ACC); 3658-3663; 10.48550/arXiv.1810.00093
• Le, Hoang M. and Voloshin, Cameron, et el. (2019) Batch Policy Learning under Constraints; Proceedings of Machine Learning Research; Vol. 97; 3703-3712; 10.48550/arXiv.1903.08738
• Shi, Guanya and Shi, Xichen, et el. (2019) Neural Lander: Stable Drone Landing Control using Learned Dynamics; ISBN 978-1-5386-6027-0; 2019 International Conference on Robotics and Automation (ICRA); 9784-9790; 10.1109/ICRA.2019.8794351
• Sui, Yanan and Zhuang, Vincent, et el. (2019) Multi-dueling Bandits with Dependent Arms; 10.48550/arXiv.1705.00253
• Song, Jialin and Chen, Yuxin, et el. (2019) A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes; Proceedings of Machine Learning Research; Vol. 89; 3158-3167; 10.48550/arXiv.1811.00755
• Yang, Kevin K. and Chen, Yuxin, et el. (2019) Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design; Proceedings of Machine Learning Research; Vol. 89; 3410-3419; 10.48550/arXiv.1904.08102
• Zhou, Jiaji and Ross, Stephane, et el. (2019) Knapsack Constrained Contextual Submodular List Prediction with Application to Multi-document Summarization; 10.48550/arXiv.1308.3541
• Yu, Rose and Zheng, Stephan, et el. (2019) Long-term Forecasting using Tensor-Train RNNs; 10.48550/arXiv.1711.00073
• Song, Jialin and Lanka, Ravi, et el. (2019) Learning to Search via Retrospective Imitation; 10.48550/arXiv.1804.00846
• Sui, Yanan and Yue, Yisong, et el. (2019) Correlational Dueling Bandits with Application to Clinical Treatment in Large Decision Spaces; 10.48550/arXiv.1707.02375
• Song, Jialin and Tokpanov, Yury S., et el. (2019) Optimizing Photonic Nanostructures via Multi-fidelity Gaussian Processes; 10.48550/arXiv.1811.07707
• Dathathri, Sumanth and Zheng, Stephan, et el. (2019) Detecting Adversarial Examples via Neural Fingerprinting; 10.48550/arXiv.1803.03870
• Zhan, Eric and Zheng, Stephan, et el. (2019) Generative Multi-Agent Behavioral Cloning; 10.48550/arXiv.1803.07612
• Sha, Long and Lucey, Patrick, et el. (2019) Fine-Grained Retrieval of Sports Plays using Tree-Based Alignment of Trajectories; 10.48550/arXiv.1710.02255
• Meier, Men-Andrin and Ross, Zachary E., et el. (2019) Reliable Real-time Seismic Signal/Noise Discrimination with Machine Learning; Journal of Geophysical Research. Solid Earth; Vol. 124; No. 1; 788-800; 10.1029/2018jb016661
• Ross, Zachary E. and Yue, Yisong, et el. (2019) PhaseLink: A Deep Learning Approach to Seismic Phase Association; Journal of Geophysical Research. Solid Earth; Vol. 124; No. 1; 856-869; 10.1029/2018JB016674
• Marino, Joseph and Cvitkovic, Milan, et el. (2018) A General Method for Amortizing Variational Filtering; 10.48550/arXiv.1811.05090
• Zheng, Stephan and Yu, Rose, et el. (2018) Multi-resolution Tensor Learning for Large-Scale Spatial Data; 10.48550/arXiv.1802.06825
• Marino, Joseph and Yue, Yisong, et el. (2018) Iterative Amortized Inference; Proceedings of Machine Learning Research; Vol. 80; 3403-3412; 10.48550/arXiv.1807.09356
• Sui, Yanan and Zhuang, Vincent, et el. (2018) Stagewise Safe Bayesian Optimization with Gaussian Processes; Proceedings of Machine Learning Research; Vol. 80; 4781-4789; 10.48550/arXiv.1806.07555
• Mac Aodha, Oisin and Su, Shihan, et el. (2018) Teaching categories to human learners with visual explanations; ISBN 978-1-5386-6420-9; 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition; 3820-3828; 10.1109/CVPR.2018.00402
• Sha, Long and Lucey, Patrick, et el. (2018) Interactive Sports Analytics: An Intelligent Interface for Utilizing Trajectories for Interactive Sports Play Retrieval and Analytics; ACM Transactions on Computer-Human Interaction; Vol. 25; No. 2; Art. No. 13; 10.1145/3185596
• Chen, Yuxin and Mac Aodha, Oisin, et el. (2018) Near-Optimal Machine Teaching via Explanatory Teaching Sets; Proceedings of Machine Learning Research; Vol. 84; 1970-1978
• Zhan, Eric and Zheng, Stephan, et el. (2018) Generating Multi-Agent Trajectories using Programmatic Weak Supervision; 10.48550/arXiv.1803.07612
• Le, Hoang M. and Jiang, Nan, et el. (2018) Hierarchical Imitation and Reinforcement Learning; Proceedings of Machine Learning Research; Vol. 80; 2917-2926; 10.48550/arXiv.1803.00590
• Chen, Yuxin and Singla, Adish, et el. (2018) Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners; 10.48550/arXiv.1802.05190
• Su, Shihan and Chen, Yuxin, et el. (2017) Interpretable Machine Teaching via Feature Feedback
• Le, Hoang M. and Yue, Yisong, et el. (2017) Coordinated Multi-Agent Imitation Learning; Proceedings of Machine Learning Research; Vol. 70; 1995-2003; 10.48550/arXiv.1703.03121
• Taylor, Sarah and Kim, Taehwan, et el. (2017) A deep learning approach for generalized speech animation; ACM Transactions on Graphics; Vol. 36; No. 4; Art. 93; 10.1145/3072959.3073699
• Deng, Zhiwei and Navarathna, Rajitha, et el. (2017) Factorized Variational Autoencoders for Modeling Audience Reactions to Movies; ISBN 978-1-5386-0457-1; 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 10.1109/CVPR.2017.637
• Eyjolfsdottir, Eyrun and Branson, Kristin, et el. (2017) Learning recurrent representations for hierarchical behavior modeling; 10.48550/arXiv.1611.00094
• Le, Hoang M. and Carr, Peter, et el. (2017) Data-Driven Ghosting using Deep Imitation Learning
• Zheng, Stephan and Yue, Yisong, et el. (2016) Generating Long-term Trajectories Using Deep Hierarchical Networks; ISBN 9781510838819; Advances in Neural Information Processing Systems (NIPS 2016); 1551-1559; 10.48550/arXiv.1706.07138
• Ruggero Ronchi, Matteo and Kim, Joon Sik, et el. (2016) A Rotation Invariant Latent Factor Model for Moveme Discovery from Static Poses; ISBN 978-1-5090-5473-2; 16th IEEE International Conference on Data Mining (ICDM); 1179-1184; 10.1109/ICDM.2016.0156
• Le, Hoang M. and Kang, Andrew, et el. (2016) Smooth Imitation Learning for Online Sequence Prediction; Proceedings of Machine Learning Research; Vol. 48; 680-688; 10.48550/arXiv.1606.00968
• Chen, Jianhui and Le, Hoang M., et el. (2016) Learning Online Smooth Predictors for Realtime Camera Planning using Recurrent Decision Trees; ISBN 978-1-4673-8851-1; 2016 IEEE Conference on Computer Vision and Pattern Recognition; 4688-4696; 10.1109/CVPR.2016.507
• Sha, Long and Lucey, Patrick, et el. (2016) Chalkboarding: A New Spatiotemporal Query Paradigm for Sports Play Retrieval; ISBN 978-1-4503-4137-0; Proceedings of the 21st International Conference on Intelligent User Interfaces; 336-347; 10.1145/2856767.2856772
• Krishnan, Kaushik and Marla, Lavanya, et el. (2016) Robust Ambulance Allocation Using Risk-based Metrics; ISBN 978-1-4673-9622-6; 2016 8th International Conference on Communication Systems and Networks (COMSNETS); 1-6; 10.1109/COMSNETS.2016.7439958
• Liu, Siyuan and Yue, Yisong, et el. (2015) Non-Myopic Adaptive Route Planning in Uncertain Congestion Environments; IEEE Transactions on Knowledge and Data Engineering; Vol. 27; No. 9; 2438-2451; 10.1109/TKDE.2015.2411278
• Kim, Taehwan and Taylor, Sarah, et el. (2015) A Decision Tree Framework for Spatiotemporal Sequence Prediction; ISBN 978-1-4503-3664-2; Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 577-586; 10.1145/2783258.2783356
• He, Bryan and Yue, Yisong (2015) Smooth Interactive Submodular Set Cover
• Yue, Yisong and Lucey, Patrick, et el. (2014) Learning Fine-Grained Spatial Models for Dynamic Sports Play Prediction; ISBN 978-1-4799-4274-9; 2014 IEEE International Conference on Data Mining Workshop; 670-679; 10.1109/ICDM.2014.106
• Bialkowski, Alina and Lucey, Patrick, et el. (2014) Identifying Team Style in Soccer Using Formations Learned from Spatiotemporal Tracking Data; ISBN 978-1-4799-4274-9; 2014 IEEE International Conference on Data Mining Workshop; 9-14; 10.1109/ICDMW.2014.167
• Bialkowski, Alina and Lucey, Patrick, et el. (2014) Large-Scale Analysis of Soccer Matches Using Spatiotemporal Tracking Data; ISBN 978-1-4799-4274-9; 2014 IEEE International Conference on Data Mining Workshop; 725-730; 10.1109/ICDM.2014.133
• Yue, Yisong and Wang, Chong, et el. (2014) Personalized Collaborative Clustering; 10.1145/2566486.2567991
• Ross, Stephane and Zhou, Jiaji, et el. (2013) Learning Policies for Contextual Submodular Prediction; Proceedings of Machine Learning Research; Vol. 28; No. 3; 1364-1372; 10.48550/arXiv.1305.2532
• Liu, Siyuan and Yue, Yisong, et el. (2013) Adaptive Collective Routing Using Gaussian Process Dynamic Congestion Models; ISBN 978-1-4503-2174-7; Proceedings of the 19th ACM SIGKDD international Conference on Knowledge Discovery and Data Mining; 704-712; 10.1145/2487575.2487598
• Yue, Yisong and Broder, Josef, et el. (2012) The K-armed dueling bandits problem; Journal of Computer and System Sciences; Vol. 78; No. 5; 1538-1556; 10.1016/j.jcss.2011.12.028
• Yue, Yisong and Hong, Sue Ann, et el. (2012) Hierarchical Exploration for Accelerating Contextual Bandits; ISBN 978-1-4503-1285-1; ICML'12 Proceedings of the 29th International Coference on International Conference on Machine Learning; 979-986; 10.48550/arXiv.1206.6454
• Chapelle, Olivier and Joachims, Thorsten, et el. (2012) Large-scale validation and analysis of interleaved search evaluation; ACM Transactions on Information Systems; Vol. 30; No. 1; Art. No. 6; 10.1145/2094072.2094078
• Yue, Yisong and Guestrin, Carlos (2011) Linear Submodular Bandits and their Application to Diversified Retrieval; ISBN 9781618395993; Advances in Neural Information Processing Systems 24; 1-9
• Radlinski, Filip and Yue, Yisong (2011) Practical Online Retrieval Evaluation; ISBN 978-1-4503-0757-4; Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval; 1301; 10.1145/2009916.2010171
• Brandt, Christina and Joachims, Thorsten, et el. (2011) Dynamic Ranked Retrieval; ISBN 978-1-4503-0493-1; Proceedings of the 4th ACM international conference on Web search and data mining; 247-256; 10.1145/1935826.1935872
• Yessenalina, Ainur and Yue, Yisong, et el. (2010) Multi-level structured models for document-level sentiment classification
• Yue, Yisong and Gao, Yue, et el. (2010) Learning More Powerful Test Statistics for Click-Based Retrieval Evaluation; ISBN 978-1-4503-0153-4; Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval; 507-514; 10.1145/1835449.1835534
• Yue, Yisong and Patel, Rajan, et el. (2010) Beyond position bias: examining result attractiveness as a source of presentation bias in clickthrough data; ISBN 978-1-60558-799-8; Proceedings of the 19th international conference on World wide web; 1011-1018; 10.1145/1772690.1772793
• Joachims, Thorsten and Hofmann, Thomas, et el. (2009) Predicting Structured Objects with Support Vector Machines; Communications of the ACM; Vol. 52; No. 11; 97-104; 10.1145/1592761.1592783
• Yue, Yisong and Joachims, Thorsten (2009) Interactively Optimizing Information Retrieval Systems as a Dueling Bandits Problem; ISBN 978-1-60558-516-1; Proceedings of the 26th International Conference on Machine Learning; 1201-1208; 10.1145/1553374.1553527
• Yue, Yisong and Joachims, Thorsten (2008) Predicting Diverse Subsets Using Structural SVMs; ISBN 978-1-60558-205-4; Proceedings of the 25th International Conference on Machine Learning; 1224-1231; 10.1145/1390156.1390310
• Yue, Yisong and Finley, Thomas, et el. (2007) A Support Vector Method for Optimizing Average Precision; ISBN 978-1-59593-597-7; Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval; 271-278; 10.1145/1277741.1277790