Timezone: »
We propose to organize a workshop on machine learning and combinatorial algorithms. The combination of methods from machine learning and classical AI is an emerging trend. Many researchers have argued that “future AI” methods somehow need to incorporate discrete structures and symbolic/algorithmic reasoning. Additionally, learning-augmented optimization algorithms can impact the broad range of difficult but impactful optimization settings. Coupled learning and combinatorial algorithms have the ability to impact real-world settings such as hardware & software architectural design, self-driving cars, ridesharing, organ matching, supply chain management, theorem proving, and program synthesis among many others. We aim to present diverse perspectives on the integration of machine learning and combinatorial algorithms.
This workshop aims to bring together academic and industrial researchers in order to describe recent advances and build lasting communication channels for the discussion of future research directions pertaining the integration of machine learning and combinatorial algorithms. The workshop will connect researchers with various relevant backgrounds, such as those working on hybrid methods, have particular expertise in combinatorial algorithms, work on problems whose solution likely requires new approaches, as well as everyone interested in learning something about this emerging field of research. We aim to highlight open problems in bridging the gap between machine learning and combinatorial optimization in order to facilitate new research directions.
The workshop will foster the collaboration between the communities by curating a list of problems and challenges to promote the research in the field.
Our technical topics of interest include (but are not limited to):
- Hybrid architectures with combinatorial building blocks
- Attacking hard combinatorial problems with learning
- Neural architectures mimicking combinatorial algorithms
Further information about speakers, paper submissions and schedule are available at the workshop website: https://sites.google.com/view/lmca2020/home .
Sat 3:00 a.m. - 4:30 a.m.
|
Poster Session A: 3:00 AM - 4:30 AM PST
(
Poster Session
)
link »
SlidesLive Video » |
Taras Khakhulin · Ravichandra Addanki · Jinhwi Lee · Jungtaek Kim · Piotr Januszewski · Konrad Czechowski · Francesco Landolfi · Lovro Vrček · Oren Neumann · Claudius Gros · Betty Fabre · Lukas Faber · Lucas Anquetil · Alberto Franzin · Tommaso Bendinelli · Sergey Bartunov
|
Sat 6:50 a.m. - 7:00 a.m.
|
Opening
(
Introduction
)
|
Marin Vlastelica Pogančić · Georg Martius 🔗 |
Sat 7:00 a.m. - 7:25 a.m.
|
Invited Talk (Ellen Vitercik)
(
Talk
)
SlidesLive Video » |
Ellen Vitercik 🔗 |
Sat 7:25 a.m. - 7:50 a.m.
|
Invited Talk (Petar Veličković)
(
Talk
)
SlidesLive Video » |
Petar Veličković 🔗 |
Sat 7:50 a.m. - 8:10 a.m.
|
Q&A for Session
(
Q&A and Discussions
)
|
🔗 |
Sat 8:10 a.m. - 8:18 a.m.
|
Contributed Talk: A Framework For Differentiable Discovery Of Graph Algorithms
(
Contributed Talk
)
SlidesLive Video » |
Hanjun Dai 🔗 |
Sat 8:18 a.m. - 8:26 a.m.
|
Contributed Talk: Learning To Select Nodes In Bounded Suboptimal Conflict-Based Search For Multi-Agent Path Finding
(
Contributed Talk
)
SlidesLive Video » |
Taoan Huang 🔗 |
Sat 8:26 a.m. - 8:35 a.m.
|
Contributed Talk: Neural Algorithms For Graph Navigation
(
Contributed Talk
)
SlidesLive Video » |
Aaron Zweig 🔗 |
Sat 8:35 a.m. - 8:44 a.m.
|
Contributed Talk: Fit The Right Np-Hard Problem: End-To-End Learning Of Integer Programming Constraints
(
Contributed Talk
)
SlidesLive Video » |
Anselm Paulus 🔗 |
Sat 8:44 a.m. - 8:52 a.m.
|
Contributed Talk: Language Generation Via Combinatorial Constraint Satisfaction: A Tree Search Enhanced Monte-Carlo Approach
(
Contributed Talk
)
|
Nan Jiang 🔗 |
Sat 8:52 a.m. - 9:05 a.m.
|
Q&A for Contributed Talks
(
Q&A and Discussions
)
|
🔗 |
Sat 9:05 a.m. - 9:10 a.m.
|
Break
|
🔗 |
Sat 9:10 a.m. - 10:40 a.m.
|
Poster Session B
(
Poster Session
)
link »
SlidesLive Video » |
Ravichandra Addanki · Andreea-Ioana Deac · Yujia Xie · Francesco Landolfi · Antoine Prouvost · Claudius Gros · Renzo Massobrio · Abhishek Cauligi · Simon Alford · Hanjun Dai · Alberto Franzin · Nitish Kumar Panigrahy · Brandon Kates · Iddo Drori · Taoan Huang · Zhou Zhou · Marin Vlastelica · Anselm Paulus · Aaron Zweig · Minsu Cho · Haiyan Yin · Michal Lisicki · Nan Jiang · Haoran Sun
|
Sat 10:40 a.m. - 11:10 a.m.
|
Break
|
🔗 |
Sat 11:10 a.m. - 11:35 a.m.
|
Invited Talk (Zico Kolter)
(
Talk
)
SlidesLive Video » |
J. Zico Kolter 🔗 |
Sat 11:35 a.m. - 12:00 p.m.
|
Invited Talk (Katherine Bouman)
(
Talk
)
|
Katherine Bouman 🔗 |
Sat 12:00 p.m. - 12:25 p.m.
|
Invited Talk (Michal Rolinek)
(
Talk
)
SlidesLive Video » |
Michal Rolinek 🔗 |
Sat 12:25 p.m. - 12:55 p.m.
|
Q&A for Session 2
(
Q&A and Discussions
)
|
🔗 |
Sat 12:55 p.m. - 1:25 p.m.
|
Break
|
🔗 |
Sat 1:25 p.m. - 1:50 p.m.
|
Invited Talk (Armando Solar-Lezama)
(
Talk
)
SlidesLive Video » |
Armando Solar-Lezama 🔗 |
Sat 1:50 p.m. - 2:15 p.m.
|
Invited Talk (Kevin Ellis)
(
Talk
)
SlidesLive Video » |
Kevin Ellis 🔗 |
Sat 2:15 p.m. - 2:40 p.m.
|
Invited Talk (Yuandong Tian)
(
Talk
)
SlidesLive Video » |
Yuandong Tian 🔗 |
Sat 2:40 p.m. - 3:10 p.m.
|
Q&A for Session 3
(
Q&A and Discussions
)
|
🔗 |
Sat 3:10 p.m. - 4:00 p.m.
|
Guided Discussion and Closing
(
Discussion
)
|
🔗 |
-
|
Session A, Poster 2: Neural Large Neighborhood Search
(
Poster
)
SlidesLive Video » |
Ravichandra Addanki 🔗 |
-
|
Session B, Poster 3: Xlvin: Executed Latent Value Iteration Nets
(
Poster
)
SlidesLive Video » |
Andreea-Ioana Deac 🔗 |
-
|
Session B, Poster 4: Differentiable Top-k With Optimal Transport
(
Poster
)
SlidesLive Video » |
Yujia Xie 🔗 |
-
|
Session A, Poster 8: K-Plex Cover Pooling For Graph Neural Networks
(
Poster
)
SlidesLive Video » |
Francesco Landolfi 🔗 |
-
|
Session B, Poster 10: Ecole: A Gym-Like Library For Machine Learning In Combinatorial Optimization Solvers
(
Poster
)
SlidesLive Video » |
Antoine Prouvost 🔗 |
-
|
Session A, Poster 11: Investment Vs. Reward In A Competitive Knapsack Problem
(
Poster
)
SlidesLive Video » |
Claudius Gros 🔗 |
-
|
Session B, Poster 12: Virtual Savant: Learning For Optimization
(
Poster
)
SlidesLive Video » |
Renzo Massobrio 🔗 |
-
|
Session B, Poster 15: CoCo: Learning Strategies For Online Mixed-Integer Control
(
Poster
)
SlidesLive Video » |
Abhishek Cauligi 🔗 |
-
|
Session B, Poster 19: Dreaming With ARC
(
Poster
)
SlidesLive Video » |
Simon Alford 🔗 |
-
|
Session A, Poster 1: Learning Elimination Ordering For Tree Decomposition Problem
(
Poster
)
SlidesLive Video » |
Taras Khakhulin 🔗 |
-
|
Session B, Poster 2: Neural Large Neighborhood Search
(
Poster
)
SlidesLive Video » |
Ravichandra Addanki 🔗 |
-
|
Session B, Poster 20: A Framework For Differentiable Discovery Of Graph Algorithms
(
Poster
)
SlidesLive Video » |
Hanjun Dai 🔗 |
-
|
Session A, Poster 5: Fragment Relation Networks For Geometric Shape Assembly
(
Poster
)
SlidesLive Video » |
Jinhwi Lee 🔗 |
-
|
Session A, Poster 5: Fragment Relation Networks For Geometric Shape Assembly
(
Poster
)
|
Jungtaek Kim 🔗 |
-
|
Session A, Poster 6: Structure And Randomness In Planning And Reinforcement Learning
(
Poster
)
SlidesLive Video » |
Piotr Januszewski 🔗 |
-
|
Session A, Poster 7: Trust, But Verify: Model-Based Exploration In Sparse Reward Environments
(
Poster
)
SlidesLive Video » |
Konrad Czechowski 🔗 |
-
|
Session A, Poster 21: Towards Transferring Algorithm Configurations Across Problems
(
Poster
)
SlidesLive Video » |
Alberto Franzin 🔗 |
-
|
Session B, Poster 8: K-Plex Cover Pooling For Graph Neural Networks
(
Poster
)
SlidesLive Video » |
Francesco Landolfi 🔗 |
-
|
Session A, Poster 9: A Step Towards Neural Genome Assembly
(
Poster
)
SlidesLive Video » |
Lovro Vrček 🔗 |
-
|
Session A, Poster 11: Investment Vs. Reward In A Competitive Knapsack Problem
(
Poster
)
|
Oren Neumann 🔗 |
-
|
Session B, Poster 22: Matching Through Embedding In Dense Graphs
(
Poster
)
SlidesLive Video » |
Nitish Kumar Panigrahy 🔗 |
-
|
Session B, Poster 11: Investment Vs. Reward In A Competitive Knapsack Problem
(
Poster
)
SlidesLive Video » |
Claudius Gros 🔗 |
-
|
Session A, Poster 13: Neural-Driven Multi-Criteria Tree Search For Paraphrase Generation
(
Poster
)
SlidesLive Video » |
Betty Fabre 🔗 |
-
|
Session B, Poster 23: Galaxytsp: A New Billion-Node Benchmark For Tsp ( Poster ) link » | Brandon Kates 🔗 |
-
|
Session A, Poster 16: Learning Lower Bounds For Graph Exploration With Reinforcement Learning
(
Poster
)
SlidesLive Video » |
Lukas Faber 🔗 |
-
|
Session A, Poster 17: Wasserstein Learning Of Determinantal Point Processes
(
Poster
)
SlidesLive Video » |
Lucas Anquetil 🔗 |
-
|
Session B, Poster 21: Towards Transferring Algorithm Configurations Across Problems
(
Poster
)
SlidesLive Video » |
Alberto Franzin 🔗 |
-
|
Session A, Poster 27: A Seq2Seq Approach To Symbolic Regression
(
Poster
)
SlidesLive Video » |
Tommaso Bendinelli 🔗 |
-
|
Session B, Poster 23: Galaxytsp: A New Billion-Node Benchmark For TSP
(
Poster
)
SlidesLive Video » |
Iddo Drori 🔗 |
-
|
Session A, Poster 31: Continuous Latent Search For Combinatorial Optimization
(
Poster
)
|
Sergey Bartunov 🔗 |
-
|
Session B, Poster 24: Learning To Select Nodes In Bounded Suboptimal Conflict-Based Search For Multi-Agent Path Finding
(
Poster
)
|
Taoan Huang 🔗 |
-
|
Session B, Poster 25: Learning For Integer-Constrained Optimization Through Neural Networks With Limited Training
(
Poster
)
SlidesLive Video » |
Zhou Zhou 🔗 |
-
|
Session B, Poster 26: Discrete Planning With Neuro-Algorithmic Policies
(
Poster
)
SlidesLive Video » |
Marin Vlastelica 🔗 |
-
|
Session B, Poster 28: Fit The Right Np-Hard Problem: End-To-End Learning Of Integer Programming Constraints
(
Poster
)
SlidesLive Video » |
Anselm Paulus 🔗 |
-
|
Session B, Poster 29: Neural Algorithms For Graph Navigation
(
Poster
)
SlidesLive Video » |
Aaron Zweig 🔗 |
-
|
Session B, Poster 30: Differentiable Programming For Piecewise Polynomial Functions
(
Poster
)
SlidesLive Video » |
Minsu Cho 🔗 |
-
|
Session B, Poster 32: Reinforcement Learning With Efficient Active Feature Acquisition
(
Poster
)
SlidesLive Video » |
Haiyan Yin 🔗 |
-
|
Session B, Poster 33: Evaluating Curriculum Learning Strategies In Neural Combinatorial Optimization
(
Poster
)
SlidesLive Video » |
Michal Lisicki 🔗 |
-
|
Session B, Poster 34: Language Generation Via Combinatorial Constraint Satisfaction: A Tree Search Enhanced Monte-Carlo Approach
(
Poster
)
SlidesLive Video » |
Nan Jiang 🔗 |
-
|
Session B, Poster 18: Improving Learning To Branch Via Reinforcement Learning
(
Poster
)
SlidesLive Video » |
Haoran Sun 🔗 |
Author Information
Marin Vlastelica (Max Planck Institute for Intelligent Systems)
Marin Vlastelica is a PhD student in the Autonomous Learning group at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. His research interests involve the interplay between combinatorial algorithms and ML, reinforcement learning, and causality with the goal of improving sample efficiency in sequential decision making processes.
Jialin Song (Caltech)
Aaron Ferber (University of Southern California)
Brandon Amos (Facebook AI)
Georg Martius (MPI for Intelligent Systems)
Bistra Dilkina (University of Southern California)
Yisong Yue (Caltech)
More from the Same Authors
-
2020 : Session B, Poster 26: Discrete Planning With Neuro-Algorithmic Policies »
Marin Vlastelica -
2021 : The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions »
Jennifer J Sun · Tomomi Karigo · Dipam Chakraborty · Sharada Mohanty · Benjamin Wild · Quan Sun · Chen Chen · David Anderson · Pietro Perona · Yisong Yue · Ann Kennedy -
2021 : Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning »
Cameron Voloshin · Hoang Le · Nan Jiang · Yisong Yue -
2022 : Meta Optimal Transport »
Brandon Amos · Samuel Cohen · Giulia Luise · Ievgen Redko -
2022 : Neurosymbolic Programming for Science »
Jennifer J Sun · Megan Tjandrasuwita · Atharva Sehgal · Armando Solar-Lezama · Swarat Chaudhuri · Yisong Yue · Omar Costilla Reyes -
2022 : SustainGym: A Benchmark Suite of Reinforcement Learning for Sustainability Applications »
Christopher Yeh · Victor Li · Rajeev Datta · Yisong Yue · Adam Wierman -
2022 : Fifteen-minute Competition Overview Video »
Nico Gürtler · Georg Martius · Pavel Kolev · Sebastian Blaes · Manuel Wuethrich · Markus Wulfmeier · Cansu Sancaktar · Martin Riedmiller · Arthur Allshire · Bernhard Schölkopf · Annika Buchholz · Stefan Bauer -
2022 : Neural All-Pairs Shortest Path for Reinforcement Learning »
Cristina Pinneri · Georg Martius · Andreas Krause -
2022 : Pink Noise Is All You Need: Colored Noise Exploration in Deep Reinforcement Learning »
Onno Eberhard · Jakob Hollenstein · Cristina Pinneri · Georg Martius -
2022 Spotlight: Embrace the Gap: VAEs Perform Independent Mechanism Analysis »
Patrik Reizinger · Luigi Gresele · Jack Brady · Julius von Kügelgen · Dominik Zietlow · Bernhard Schölkopf · Georg Martius · Wieland Brendel · Michel Besserve -
2022 Competition: Real Robot Challenge III - Learning Dexterous Manipulation from Offline Data in the Real World »
Nico Gürtler · Georg Martius · Sebastian Blaes · Pavel Kolev · Cansu Sancaktar · Stefan Bauer · Manuel Wuethrich · Markus Wulfmeier · Martin Riedmiller · Arthur Allshire · Annika Buchholz · Bernhard Schölkopf -
2022 : Panel »
Jeevana Priya Inala · Pushmeet Kohli · Ann Kennedy · Sriram Rajamani · Yisong Yue -
2022 : Invited Talk - Bistra Dilkina - University of Southern California »
Bistra Dilkina -
2022 : Deep Neural Imputation: A Framework for Recovering Incomplete Brain Recordings »
Sabera Talukder · Jennifer J Sun · Matthew Leonard · Bingni Brunton · Yisong Yue -
2022 Poster: Semi-Discrete Normalizing Flows through Differentiable Tessellation »
Ricky T. Q. Chen · Brandon Amos · Maximilian Nickel -
2022 Poster: Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world »
Eugene Vinitsky · Nathan Lichtlé · Xiaomeng Yang · Brandon Amos · Jakob Foerster -
2022 Poster: Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation »
Cansu Sancaktar · Sebastian Blaes · Georg Martius -
2022 Poster: Embrace the Gap: VAEs Perform Independent Mechanism Analysis »
Patrik Reizinger · Luigi Gresele · Jack Brady · Julius von Kügelgen · Dominik Zietlow · Bernhard Schölkopf · Georg Martius · Wieland Brendel · Michel Besserve -
2022 Poster: Policy Optimization with Linear Temporal Logic Constraints »
Cameron Voloshin · Hoang Le · Swarat Chaudhuri · Yisong Yue -
2022 Poster: Theseus: A Library for Differentiable Nonlinear Optimization »
Luis Pineda · Taosha Fan · Maurizio Monge · Shobha Venkataraman · Paloma Sodhi · Ricky T. Q. Chen · Joseph Ortiz · Daniel DeTone · Austin Wang · Stuart Anderson · Jing Dong · Brandon Amos · Mustafa Mukadam -
2021 : Panel B: Safe Learning and Decision Making in Uncertain and Unstructured Environments »
Yisong Yue · J. Zico Kolter · Ivan Dario D Jimenez Rodriguez · Dragos Margineantu · Animesh Garg · Melissa Greeff -
2021 : Learning for Agile Control in the Real World: Challenges and Opportunities »
Yisong Yue · Ivan Dario D Jimenez Rodriguez -
2021 Poster: Meta-Adaptive Nonlinear Control: Theory and Algorithms »
Guanya Shi · Kamyar Azizzadenesheli · Michael O'Connell · Soon-Jo Chung · Yisong Yue -
2021 Poster: DeepGEM: Generalized Expectation-Maximization for Blind Inversion »
Angela Gao · Jorge Castellanos · Yisong Yue · Zachary Ross · Katherine Bouman -
2021 Poster: Iterative Amortized Policy Optimization »
Joseph Marino · Alexandre Piche · Alessandro Davide Ialongo · Yisong Yue -
2020 : Poster Session B »
Ravichandra Addanki · Andreea-Ioana Deac · Yujia Xie · Francesco Landolfi · Antoine Prouvost · Claudius Gros · Renzo Massobrio · Abhishek Cauligi · Simon Alford · Hanjun Dai · Alberto Franzin · Nitish Kumar Panigrahy · Brandon Kates · Iddo Drori · Taoan Huang · Zhou Zhou · Marin Vlastelica · Anselm Paulus · Aaron Zweig · Minsu Cho · Haiyan Yin · Michal Lisicki · Nan Jiang · Haoran Sun -
2020 : Opening »
Marin Vlastelica Pogančić · Georg Martius -
2020 Poster: Online Optimization with Memory and Competitive Control »
Guanya Shi · Yiheng Lin · Soon-Jo Chung · Yisong Yue · Adam Wierman -
2020 Poster: A General Large Neighborhood Search Framework for Solving Integer Linear Programs »
Jialin Song · ravi lanka · Yisong Yue · Bistra Dilkina -
2020 Poster: Learning compositional functions via multiplicative weight updates »
Jeremy Bernstein · Jiawei Zhao · Markus Meister · Ming-Yu Liu · Anima Anandkumar · Yisong Yue -
2020 Poster: Learning Differentiable Programs with Admissible Neural Heuristics »
Ameesh Shah · Eric Zhan · Jennifer J Sun · Abhinav Verma · Yisong Yue · Swarat Chaudhuri -
2020 Poster: On the distance between two neural networks and the stability of learning »
Jeremy Bernstein · Arash Vahdat · Yisong Yue · Ming-Yu Liu -
2020 Poster: The Power of Predictions in Online Control »
Chenkai Yu · Guanya Shi · Soon-Jo Chung · Yisong Yue · Adam Wierman -
2019 : Bistra Dilkina: Graph Representation Learning for Optimization on Graphs »
Bistra Dilkina -
2019 Workshop: Safety and Robustness in Decision-making »
Mohammad Ghavamzadeh · Shie Mannor · Yisong Yue · Marek Petrik · Yinlam Chow -
2019 Poster: End to end learning and optimization on graphs »
Bryan Wilder · Eric Ewing · Bistra Dilkina · Milind Tambe -
2019 Poster: Control What You Can: Intrinsically Motivated Task-Planning Agent »
Sebastian Blaes · Marin Vlastelica Pogančić · Jiajie Zhu · Georg Martius -
2019 Poster: Imitation-Projected Programmatic Reinforcement Learning »
Abhinav Verma · Hoang Le · Yisong Yue · Swarat Chaudhuri -
2019 Poster: Differentiable Convex Optimization Layers »
Akshay Agrawal · Brandon Amos · Shane Barratt · Stephen Boyd · Steven Diamond · J. Zico Kolter -
2019 Poster: NAOMI: Non-Autoregressive Multiresolution Sequence Imputation »
Yukai Liu · Rose Yu · Stephan Zheng · Eric Zhan · Yisong Yue -
2019 Poster: Teaching Multiple Concepts to a Forgetful Learner »
Anette Hunziker · Yuxin Chen · Oisin Mac Aodha · Manuel Gomez Rodriguez · Andreas Krause · Pietro Perona · Yisong Yue · Adish Singla -
2019 Poster: Landmark Ordinal Embedding »
Nikhil Ghosh · Yuxin Chen · Yisong Yue -
2018 : Contributed Work »
Thaer Moustafa Dieb · Aditya Balu · Amir H. Khasahmadi · Viraj Shah · Boris Knyazev · Payel Das · Garrett Goh · Georgy Derevyanko · Gianni De Fabritiis · Reiko Hagawa · John Ingraham · David Belanger · Jialin Song · Kim Nicoli · Miha Skalic · Michelle Wu · Niklas Gebauer · Peter Bjørn Jørgensen · Ryan-Rhys Griffiths · Shengchao Liu · Sheshera Mysore · Hai Leong Chieu · Philippe Schwaller · Bart Olsthoorn · Bianca-Cristina Cristescu · Wei-Cheng Tseng · Seongok Ryu · Iddo Drori · Kevin Yang · Soumya Sanyal · Zois Boukouvalas · Rishi Bedi · Arindam Paul · Sambuddha Ghosal · Daniil Bash · Clyde Fare · Zekun Ren · Ali Oskooei · Minn Xuan Wong · Paul Sinz · Théophile Gaudin · Wengong Jin · Paul Leu -
2018 : Yisong Yue »
Yisong Yue -
2018 Poster: Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners »
Yuxin Chen · Adish Singla · Oisin Mac Aodha · Pietro Perona · Yisong Yue -
2018 Poster: L4: Practical loss-based stepsize adaptation for deep learning »
Michal Rolinek · Georg Martius -
2018 Poster: A General Method for Amortizing Variational Filtering »
Joseph Marino · Milan Cvitkovic · Yisong Yue -
2017 : Coffee break and Poster Session II »
Mohamed Kane · Albert Haque · Vagelis Papalexakis · John Guibas · Peter Li · Carlos Arias · Eric Nalisnick · Padhraic Smyth · Frank Rudzicz · Xia Zhu · Theodore Willke · Noemie Elhadad · Hans Raffauf · Harini Suresh · Paroma Varma · Yisong Yue · Ognjen (Oggi) Rudovic · Luca Foschini · Syed Rameel Ahmad · Hasham ul Haq · Valerio Maggio · Giuseppe Jurman · Sonali Parbhoo · Pouya Bashivan · Jyoti Islam · Mirco Musolesi · Chris Wu · Alexander Ratner · Jared Dunnmon · Cristóbal Esteban · Aram Galstyan · Greg Ver Steeg · Hrant Khachatrian · Marc Górriz · Mihaela van der Schaar · Anton Nemchenko · Manasi Patwardhan · Tanay Tandon -
2016 Poster: Generating Long-term Trajectories Using Deep Hierarchical Networks »
Stephan Zheng · Yisong Yue · Patrick Lucey -
2015 Poster: Smooth Interactive Submodular Set Cover »
Bryan He · Yisong Yue -
2015 Demonstration: Data-Driven Speech Animation »
Yisong Yue · Iain Matthews