Source: https://aicenter.stanford.edu/publications/
Timestamp: 2019-04-25 11:45:44+00:00

Document:
A. Sadeghian, V. Kosaraju, A. Sadeghian, N. Hirose, H. Rezatofighi, and S. Savarese, “SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
R. Semmens, N. Martelaro, P. Kaveti, S. Stent, and W. Ju, “Is Now A Good Time? An Empirical Study of Vehicle-Driver Communication Timing,” ACM Conference for Human Factors in Computing, 2019.
H. Wang, S. Pirk, E. Yumer, V. Kim, O. Sener, S. Sridhar, and L. Guibas, “Learning a Generative Model for Multi-Step Human–Object Interactions from Videos,” in Computer Graphics Forum, 2019.
M. A. Lee, Y. Zhu, K. Srinivasan, P. Shah, S. Savarese, L. Fei-Fei, A. Garg, and J. Bohg, “Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks,” in IEEE International Conference on Robotics and Automation (ICRA), 2019.
K. Solovey, M. Salazar, and M. Pavone, “Scalable and Congestion-aware Routing for Autonomous Mobility-on-Demand via Frank-Wolfe Optimization,” in Robotics: Science and Systems, Freiburg in Breisgau, Germany, 2019.
B. Ivanovic, J. Harrison, A. Sharma, M. Chen, and M. Pavone, “BaRC: Backward Reachability Curriculum for Robotic Reinforcement Learning,” in Proc. IEEE Conf. on Robotics and Automation, Montreal, Canada, 2019.
C. Choy, J. Gwak, and S. Savarese, “4D Spatio-Temporal ConvNet: Minkowski Convolutional Neural Network,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
C. Chang, D. Huang, Y. Sui, L. Fei-Fei, and J. C. Niebles, “D3TW: Discriminative Differentiable Dynamic Time Warping for Weakly Supervised Action Alignment and Segmentation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
K. Salisbury, P. Lowe, and A. Epps, Third Arm: Smart, Wearable, Robotic device, 2019.
R. Krishna, M. Bernstein, and L. Fei-Fei, “Information Maximizing Visual Question Generation,” in IEEE Conference on Computer Vision and Pattern Recognition, 2019.
R. Bhattacharyya, D. Phillips, C. Liu, J. Gupta, K. Driggs-Campbell, and M. Kochenderfer, “Simulating Emergent Properties of Human Driving Behavior Using Multi-Agent Reward Augmented Imitation Learning,” in IEEE International Conference on Robotics and Automation (ICRA), 2019.
H. Wang, S. Sridhar, J. Huang, J. Valentin, S. Song, and L. Guibas, “Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
M. Tsao, D. Milojevic, C. Ruch, M. Salazar, E. Frazzoli, and M. Pavone, “Model Predictive Control of Ride-sharing Autonomous Mobility on Demand Systems,” in Proc. IEEE Conf. on Robotics and Automation, Montreal, Canada, 2019.
E. Biyik, J. Margoliash, S. R. Alimo, and D. Sadigh, “Efficient and Safe Exploration in Deterministic Markov Decision Processes with Unknown Transition Models,” in Proceedings of the American Control Conference (ACC), 2019.
F. Abuzaid, P. Kraft, Suri Sahaana, E. Gan, E. Xu, A. Shenoy, A. Ananthanarayan, J. Sheu, E. Meijer, X. Wu, J. Naughton, P. Bailis, and M. Zaharia, “DIFF: A Relational Interface for Large-Scale Data Explanation,” in International Conference on Very Large Data Bases (VLDB), 2019.
R. Spica, D. Falanga, E. Cristofalo, E. Montijano, D. Scaramuzza, and M. Schwager, “A real-time game theoretic planner for autonomous two-player drone racing,” IEEE Transactions on Robotics, 2018.
R. Spica, D. Falanga, E. Cristofalo, D. Scaramuzza, and M. Schwager, “A real-time game theoretic planner for autonomous two-player drone racing,” in Proceedings of Robotics: Science and Systems, 2018.
H. Nishimura and M. Schwager, “Active motion-based communication for robots with monocular vision,” in Proceedings of the International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 2018.
H. Ren, R. Stewart, J. Song, V. Kuleshov, and S. Ermon, “Adversarial Constraint Learning for Structured Prediction,” in IJCAI, 2018.
S. Balters, E. L. Murnane, J. A. Landay, and P. E. Paredes, “Breath Booster!: Exploring In-Car, Fast-Paced Breathing Interventions to Enhance Driver Arousal State,” in Proceedings of the 12th EAI International Conference on Pervasive Computing Technologies for Healthcare, 2018, p. 128–137.
Z. Wang, S. Singh, M. Pavone, and M. Schwager, “Cooperative object transport in 3d with multiple quadrotors using no peer communication,” in Proceedings of the International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 2018.
S. Ganguly and O. Khatib, “Experimental studies of contact space model for multi-surface collisions in articulated rigid-body systems,” in Experimental Robotics, 2018 International Symposium on (accepted), 2018.
P. E. Paredes, F. Ordonez, W. Ju, and J. A. Landay, “Fast & furious: Detecting stress with a car steering wheel,” in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 2018.
D. Huang, S. Buch, L. Dery, A. Garg, L. Fei-Fei, and J. C. Niebles, “Finding `It’: Weakly-Supervised Reference-Aware Visual Grounding in Instructional Video,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
Z. Wang, R. Spica, and M. Schwager, “Game theoretic motion planning for multi-robot racing,” in Proceedings of the International Symposium on Distributed Autonomous Robotics Systems (DARS 18), 2018.
B. Ivanovic, E. Schmerling, K. Leung, and M. Pavone, “Generative modeling of multimodal multi-human behavior,” in Proceedings of the International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, 2018.
N. Hirose, A. Sadeghian, M. Vázquez, P. Goebel, and S. Savarese, “GONet: A Semi-Supervised Deep Learning Approach For Traversability Estimation,” arXiv preprint arXiv:1803.03254, 2018.
P. E. Paredes, Y. Zhou, N. A. -H. Hamdan, S. Balters, E. Murnane, W. Ju, and J. A. Landay, “Just breathe: In-car interventions for guided slow breathing,” in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2018.
P. Bailis, E. Gan, S. Madden, D. Narayanan, K. Rong, and S. Suri, “MacroBase: Prioritizing attention in fast data,” ACM TODS, Best of SIGMOD 2017 Special Issue, 2018.
J. Song, H. Ren, D. Sadigh, and S. Ermon, “Multi-agent generative adversarial imitation learning,” in Advances in Neural Information Processing Systems, 2018.
R. P. Bhattacharyya, D. J. Phillips, B. Wulfe, J. Morton, A. Kuefler, and M. J. Kochenderfer, “Multi-agent imitation learning for driving simulation,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018.
E. Schmerling, K. Leung, W. Vollprecht, and M. Pavone, “Multimodal probabilistic model-based planning for human-robot interaction,” in Proceedings of the International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 2018.
D. Huang, S. Nair, D. Xu, Y. Zhu, A. Garg, L. Fei-Fei, S. Savarese, and J. C. Niebles, Neural Task Graphs: Generalizing to Unseen Tasks from a Single Video Demonstration, 2018.
E. Galbally and O. Khatib, “Position, orientation & geometry invariant controller for a screw cap task,” in Robotics Science and Systems Workshop, 2018.
M. Wang, Z. Wang, S. Paudel, and M. Schwager, “Safe distributed lane change maneuvers for multiple autonomous vehicles using buffered input cells,” in Proceedings of the International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 2018.
K. S. Tai, V. Sharan, P. Bailis, and G. Valiant, “Sketching Linear Classifiers over Data Streams,” in SIGMOD, 2018.
L. Fan, Y. Zhu, J. Zhu, Z. Liu, O. Zeng, A. Gupta, J. Creus-Costa, S. Savarese, and L. Fei-Fei, “SURREAL: Open-Source Reinforcement Learning Framework and Robot Manipulation Benchmark,” in Conference on Robot Learning, 2018, p. 767–782.
X. Zang, A. Pokle, M. Vázquez, K. Chen, J. C. Niebles, A. Soto, and S. Savarese, “Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation,” in 2018 Conference on Empirical Methods in Natural Language Processing, 2018.
E. Biyik, D. A. Lazar, R. Pedarsani, and D. Sadigh, “Altruistic Autonomy: Beating Congestion on Shared Roads,” in Workshop on Algorithmic Foundations of Robotics (WAFR), 2018.
E. Biyik and D. Sadigh, “Batch Active Preference-Based Learning of Reward Functions,” in Conference on Robot Learning (CoRL), 2018.
D. Kang, P. Bailis, and M. Zaharia, “BlazeIt: Fast Exploratory Video Queries using Neural Networks,” arXiv preprint arXiv:1805.01046, 2018.
J. Harrison, A. Sharma, R. Calandra, and M. Pavone, “Control Adaptation via Meta-Learning Dynamics,” in Workshop on Meta-Learning (MetaLearn 2018), 2018.
J. Lambert, O. Sener, and S. Savarese, “Deep Learning under Privileged Information Using Heteroscedastic Dropout,” CoRR, vol. abs/1805.11614, 2018.
A. Pokle, R. Martín-Martín, P. Goebel, V. Chow, H. M. Ewald, J. Yang, Z. Wang, A. Sadeghian, D. Sadigh, S. Savarese, and M. Vázquez, “Deep Local Trajectory Planning and Control for Robot Navigation,” arXiv preprint, 2018.
L. Yi, H. Huang, D. Liu, E. Kalogerakis, H. Su, and L. Guibas, “Deep Part Induction from Articulated Object Pairs,” ACM Transactions on Graphics (TOG), vol. 37, iss. 6, p. 209, 2018.
K. Fang, T. Wu, D. Yang, S. Savarese, and J. J. Lim, “Demo2Vec: Reasoning Object Affordances From Online Videos,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, p. 2139–2147.
J. Ji, S. Buch, A. Soto, and J. C. Niebles, “End-to-End Joint Semantic Segmentation of Actors and Actions in Video,” in European Conference on Computer Vision (ECCV), 2018.
F. Xia, A. R. Zamir, Z. He, A. Sax, J. Malik, and S. Savarese, “Gibson Env: Real-World Perception for Embodied Agents,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, p. 9068–9079.
R. Ma, A. Gadi Patil, M. Fisher, M. Li, S. Pirk, B. Hua, S. Yeung, X. Tong, L. Guibas, and H. Zhang, “Language-Driven Synthesis of 3D Scenes from Scene Databases,” ACM Transactions on Graphics (Proc. SIGGRAPH ASIA), vol. 37, iss. 6, p. 212:1 – 212:16, 2018.
J. Duchi and H. Namkoong, “Learning Models with Uniform Performance via Distributionally Robust Optimization,” arXiv preprint arXiv:1810.08750, 2018.
K. Fang, Y. Zhu, A. Garg, A. Kurenkov, V. Mehta, L. Fei-Fei, and S. Savarese, “Learning Task-Oriented Grasping for Tool Manipulation from Simulated Self-Supervision,” arXiv preprint arXiv:1806.09266, 2018.
J. Harrison, A. Sharma, and M. Pavone, “Meta-Learning Priors for Efficient Online Bayesian Regression,” Workshop on the Algorithmic Foundations of Robotics (WAFR), 2018.
D. Kang, D. Raghavan, P. Bailis, and M. Zaharia, “Model Assertions for Debugging Machine Learning,” in NIPS ML Systems Workshop, 2018.
K. Leung, E. Schmerling, M. Chen, J. Talbot, J. C. Gerdes, and M. Pavone, “On Infusing Reachability-Based Safety Assurance within Probabilistic Planning Frameworks for Human-Robot Vehicle Interactions,” in Int.\ Symp.\ on Experimental Robotics, 2018.
O. Afolabi, K. Driggs-Campbell, R. Dong, M. Kochenderfer, and S. and Sastry, “People as Sensors: Imputing Maps from Human Actions,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018.
J. Lorenzetti, M. Chen, B. Landry, and M. Pavone, “Reach-Avoid Games Via Mixed-Integer Second-Order Cone Programming,” in Proc. IEEE Conf. on Decision and Control, 2018.
B. Landry, M. Chen, S. Hemley, and M. Pavone, “Reach-Avoid Problems via Sum-of-Squares Optimization and Dynamic Programming,” arXiv preprint arXiv:1807.11553, 2018.
A. Mandlekar, Y. Zhu, A. Garg, J. Booher, M. Spero, A. Tung, J. Gao, J. Emmons, A. Gupta, E. Orbay, S. Savarese, and L. Fei-Fei, “RoboTurk: A Crowdsourcing Platform for Robotic Skill Learning through Imitation,” in Conference on Robot Learning, 2018.
H. Nishimura and M. Schwager, “SACBP: Belief Space Planning for Continuous-Time Dynamical Systems via Stochastic Sequential Action Control,” in The 13th International Workshop on the Algorithmic Foundations of Robotics (WAFR), Mérida, México, 2018.
A. Gupta, J. Johnson, L. F. -, S. Savarese, and A. Alahi, “Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks,” CoRR, vol. abs/1803.10892, 2018.
A. R. Zamir, A. Sax, W. B. Shen, L. J. Guibas, J. Malik, and S. Savarese, “Taskonomy: Disentangling Task Transfer Learning,” CoRR, vol. abs/1804.08328, 2018.
D. Sadigh, S. S. Sastry, and S. A. Seshia, “Verifying Robustness of Human-Aware Autonomous Cars,” in Proceedings of the 2nd IFAC Conference on Cyber-Physical and Human Systems, 2018.
D. Xu, S. Nair, Y. Zhu, J. Gao, A. Garg, L. F. -, and S. Savarese, “Neural Task Programming: Learning to Generalize Across Hierarchical Tasks,” in Proceedings of the International Conference on Robotics and Automation (ICRA), 2018.
B. Ivanovic and M. Pavone, “Modeling Multimodal Dynamic Spatiotemporal Graphs,” in arXiv, Montreal, Canada, 2018.
P. E. Paredes, N. A. -H. Hamdan, D. Clark, C. Cai, W. Ju, and J. A. Landay, “Evaluating in-car movements in the design of mindful commute interventions: exploratory study,” Journal of medical Internet research, 2017.
V. Sharan, K. S. Tai, P. Bailis, and G. Valiant, “Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data,” arXiv preprint arXiv:1706.08146, 2017.
Y. Li, J. Song, and S. Ermon, “Infogail: Interpretable imitation learning from visual demonstrations,” in Advances in Neural Information Processing Systems, 2017, p. 3812–3822.
O. Sener and S. Savarese, “A geometric approach to active learning for convolutional neural networks,” in Advances in Neural Information Processing Systems, 2017.
H. Nishimura and M. Schwager, “Active trajectory classification for motion-based communication of robots,” in Robot Communication in the Wild, Robotics: Science and Systems Workshop, 2017.
J. Harrison, A. Garg, B. Ivanovic, Y. Zhu, S. Savarese, L. Fei-Fei, and M. Pavone, “Adapt: zero-shot adaptive policy transfer for stochastic dynamical systems,” arXiv preprint arXiv:1707.04674, 2017.
A. Mandlekar, Y. Zhu, A. Garg, L. Fei-Fei, and S. Savarese, “Adversarially Robust Policy Learning: Active Construction of Physically-Plausible Perturbations,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2017.
K. Rong and P. Bailis, “ASAP: Automatic smoothing for attention prioritization in streaming time-series data,” in VLDB, 2017.
P. Paredes, N. Hamdan, Y. Zhou, F. Ordonez, W. Ju, and J. Landay, “Deepdrive: Deep breathing for commuters,” in ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2017.
P. Bailis, E. Gan, K. Rong, and S. Suri, “Demonstration: MacroBase, A Fast Data Analysis Engine,” in ACM SIGMOD, 2017.
R. Luo, O. Sener, and S. Savarese, “Egocentric rgb-d-thermal: A new framework for complete scene understanding,” in International Conference on 3D Vision, 2017.
S. Buch, V. Escorcia, B. Ghanem, L. Fei-Fei, and J. C. Niebles, “End-to-End, Single-Stream Temporal Action Detection in Untrimmed Videos,” in British Machine Vision Conference (BMVC), 2017.
E. Schmerling and M. Pavone., “Evaluating trajectory collision probability through adaptive importance sampling for safe motion planning,” in Robotics: Science and Systems, 2017.
D. Zhou, Z. Wang, and M. Schwager, “Fast, on-line collision avoidance for dynamic vehicles using buffered voronoi cells,” IEEE Robotics and Automation Letters, 2017.
A. Zamir, T. -L. Wu, L. Sun, W. Shen, J. Malik, and S. Savarese, “Feedback networks,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
A. Majumdar and M. Pavone, “How should a robot assess risk? Towards an axiomatic theory of risk in robotics,” in Int. Symp. on Robotics Research, 2017.
A. Pierson, Z. Wang, and M. Schwager, “Intercepting rogue robots: An algorithm for capturing multiple evaders with multiple pursuers,” IEEE Robotics and Automation Letters, 2017.
E. Fast, B. Chen, J. Mendelsohn, J. Bassen, and M. Bernstein, “Iris: A Conversational Agent for Complex Tasks,” in arXiv preprint arXiv:1707.05015, 2017.
R. Ma, M. Fisher, B. -S. Hua, S. -K. Weung, X. Tong, L. Guibas, and R. Zhang, “Language-driven synthesis of 3d scenes from scene databases,” in ACM Siggraph Asia, 2017.
S. Pirk, H. Wang, O. Sener, V. Kim, E. Yumer, and L. Guibas, “Learning to generate multi-step human-object interactions from videos,” in ACM Siggraph Asia, 2017.
P. Bailis, E. Gan, S. Madden, D. Narayanan, K. Rong, and S. Suri, “Macrobase: Prioritizing attention in fast data,” in ACM SIGMOD, 2017.
P. Paredes, N. Hamdan, C. Cai, D. Clark, W. Ju, and J. Landay, “Movecommute: Exploring in car mindful movement for commuters,” Journal of Medical Internet Research, 2017.
S. P. Chinchali, S. C. Livingston, and M. Pavone, “Multi-objective optimal control for proactive decision-making with temporal logic models,” in Int. Symp. on Robotics Research, 2017.
M. Jorda, R. Balachandran, J. Ryu, and O. Khatib, “New passivity observers for improved robot force control,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017.
D. Kang, J. Emmons, F. Abuzaid, P. Bailis, and M. Zaharia, “NoScope: Optimizing deep CNN-based queries over video streams at scale,” in arXiv:1703.02529, 2017.
R. Balachandran, M. Jorda, J. Artigas, J. Ryu, and O. Khatib, “Passivity-based stability in explicit force control of robots,” in IEEE International Conference on Robotics and Automation (ICRA), 2017.
Z. N. Sunberg and M. J. Kochenderfer, “POMCPOW: An online algorithm for POMDPs with continuous state, action, and observation spaces,” in arXiv, 2017.
P. Bailis, E. Gan, K. Rong, and S. Suri, “Prioritizing Attention in Fast Data: Challenges and Opportunities,” in CIDR, 2017.
A. Majumdar, S. Singh, A. Mandlekar, and M. Pavone, “Risk-sensitive inversere inforcement learning via coherent risk models,” in Robotics: Science and Systems, 2017.
M. Wang, Z. Wang, S. Paudel, and M. Schwager, “Safe distributed lane change maneuvers for multiple au- tonomous vehicles using buffered input cells,” in Proc. of the International Symposium on Multi-Robot and Multi-Agent Systems (MRS 17), 2017.
E. Gan and P. Bailis, “Scalable Kernel Density Classification via Threshold-Based Pruning,” in ACM SIGMOD, 2017.
A. Sinha, H. Namkoong, and J. C. Duchi, “Simplifying distributional robustness with adversarial training,” in In Submission, 2017.
P. Slade, P. Culbertson, Z. Sunberg, and M. J. Kochenderfer, “Simultaneous active parameter estimation and control using sampling-based Bayesian reinforcement learning,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2017.
S. Buch, V. Escorcia, C. Shen, B. Ghanem, and J. C. Niebles, “SST: Single-Stream Temporal Action Proposals,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
J. C. Duchi, P. W. Glynn, and H. Namkoong, “Statistical estimation of large deviation rates for i.i.d. sub-exponential random walks,” in In Submission, 2017.
Z. N. Sunberg, C. J. Ho, and M. J. Kochenderfer, “The value of inferring the internal state of traffic participants for autonomous freeway driving,” in American Control Conference, 2017.
D. Huang, J. Lim, L. Fei-Fei, and J. C. Niebles, “Unsupervised Visual-Linguistic Reference Resolution in Instructional Videos,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
J. C. Duchi and H. Namkoong, “Variance-based regularization with convex objectives,” in Neural Information Processing Systems 30, 2017.
P. Paredes, S. Balters, K. Qai, F. Ordonez, W. Ju, and J. Landay, “Zenmuter – augmented virtual reality for commuters,” in ACM Symposium on Virtual Reality Software and Technology, 2017.
A. Mandlekar, Y. Zhu, A. Garg, L. Fei-Fei, and S. Savarese, “Adversarially robust policy learning: Active construction of physically-plausible perturbations,” in Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International Conference on, 2017, p. 3932–3939.
R. Luo, O. Sener, and S. Savarese, “Scene semantic reconstruction from egocentric rgb-d-thermal videos,” in 3D Vision (3DV), 2017 International Conference on, 2017, p. 593–602.
A. Sadeghian, A. Alahi, and S. Savarese, “Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies,” CoRR, vol. abs/1701.01909, 2017.
A. Alahi, J. Wilson, L. Fei-Fei, and S. Savarese, “Unsupervised camera localization in crowded spaces,” in Robotics and Automation (ICRA), 2017 IEEE International Conference on, 2017, p. 2666–2673.
J. Ho and S. Ermon, “Generative adversarial imitation learning,” in NIPS, 2016.
O. Sener, H. O. Song, A. Saxena, and S. Savarese, “Learning transferrable representations for unsupervised domain adaptation,” in Advances in Neural Information Processing Systems, 2016.
Z. Wang, G. Yang, X. Su, and M. Schwager, “Ouijabots: Omnidirectional robots for cooperative object transport with rotation control using no communication,” in Proc. of the International Conference on Distributed Autonomous Robotics Systems (DARS), 2016.
J. C. Duchi and H. Namkoong, “Robust stochastic optimization: Learning the tails,” in Reliable Machine Learning the Wild Workshop, ICML 2016, 2016.
H. Namkoong and J. C. Duchi, “Stochastic gradient methods for distributionally robust optimization with f-divergences,” in Advances in Neural Information Processing Systems 29, 2016.
A. Sadeghian, A. Alahi, and S. Savarese, “Tracking the untrackable: Learning to track multiple cues with long-term dependencies,” in ECCV, 2016.
I. Lillo, J. C. Niebles, and A. Soto, “A Hierarchical Pose-Based Approach to Complex Action Understanding Using Dictionaries of Actionlets and Motion Poselets,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
E. Fast, W. McGrath, P. Rajpurkar, and M. S. Bernstein, “Augur: Mining Human Behaviors from Fiction to Power Interactive Systems.” 2016.
D. Huang, L. Fei-Fei, and J. C. Niebles, “Connectionist Temporal Modeling for Weakly Supervised Action Labeling,” in European Conference on Computer Vision (ECCV), 2016.
V. Escorcia, F. Caba Heilbron, J. C. Niebles, and B. Ghanem, “DAPs: Deep Action Proposals for Action Understanding,” in European Conference on Computer Vision (ECCV), 2016.
H. Oh Song, Y. Xiang, S. Jegelka, and S. Savarese, “Deep Metric Learning via Lifted Structured Feature Embedding,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
S. Dasgupta, K. Fang, K. Chen, and S. Savarese, “DeLay: Robust Spatial Layout Estimation for Cluttered Indoor Scenes,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
E. Fast, B. Chen, and M. S. Bernstein, “Empath: Understanding Topic Signals in Large-Scale Text,” in CHI, 2016.
F. Caba Heilbron, J. C. Niebles, and B. Ghanem, “Fast Temporal Activity Proposals for Efficient Detection of Human Actions in Untrimmed Videos,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
L. Ballan, F. Castaldo, A. Alahi, F. Palmieri, and S. Savarese, “Knowledge Transfer for Scene-specific Motion Prediction,” in European Conference on Computer Vision (ECCV), 2016.
D. Held, S. Thrun, and S. Savarese, “Learning to Track at 100 FPS with Deep Regression Networks,” in European Conference on Computer Vision (ECCV), 2016.
P. Bailis, D. Narayanan, and S. Madden, “MacroBase: Analytic Monitoring for the Internet of Things,” arXiv preprint arXiv:1603.00567, 2016.
J. Ho, J. K. Gupta, and S. Ermon, “Model-Free Imitation Learning with Policy Optimization,” in Proceedings of The 33rd International Conference on Machine Learning, 2016.
Y. Xiang, W. Kim, W. Chen, J. Ji, C. Choy, H. Su, R. Mottaghi, L. Guibas, and S. Savarese, “ObjectNet3D: A Large Scale Database for 3D Object Recognition,” in European Conference on Computer Vision (ECCV), 2016.
A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese, “Social LSTM: Human Trajectory Prediction in Crowded Spaces,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
J. C. Duchi, P. W. Glynn, and H. Namkoong, “Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach,” In submission to INFORMS, 2016.
P. Paredes, W. Ju, and J. Landay, “The Mindful Commute,” in CHI 2016 Computing and Mental Health Workshop, 2016.

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