@inproceedings{DBLP:conf/aistats/MuttiCR22,

title = {Reward-Free Policy Space Compression for Reinforcement Learning},

author = {Mirco Mutti and Stefano Del Col and Marcello Restelli},

editor = {Gustau Camps-Valls and Francisco J. R. Ruiz and Isabel Valera},

url = {https://proceedings.mlr.press/v151/mutti22a.html},

year  = {2022},

date = {2022-01-01},

booktitle = {International Conference on Artificial Intelligence and Statistics, 

 AISTATS 2022, 28-30 March 2022, Virtual Event},

volume = {151},

pages = {3187--3203},

publisher = {PMLR},

series = {Proceedings of Machine Learning Research},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/aistats/EldowaBR22,

title = {Finite Sample Analysis of Mean-Volatility Actor-Critic for Risk-Averse 

 Reinforcement Learning},

author = {Khaled Eldowa and Lorenzo Bisi and Marcello Restelli},

editor = {Gustau Camps-Valls and Francisco J. R. Ruiz and Isabel Valera},

url = {https://proceedings.mlr.press/v151/eldowa22a.html},

year  = {2022},

date = {2022-01-01},

booktitle = {International Conference on Artificial Intelligence and Statistics, 

 AISTATS 2022, 28-30 March 2022, Virtual Event},

volume = {151},

pages = {10028--10066},

publisher = {PMLR},

series = {Proceedings of Machine Learning Research},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{RivaICAIF2022,

title = {Addressing Non-Stationarity in FX Trading with Online Model Selection of Offline RL Experts},

author = {Antonio Riva and Lorenzo Bisi and Pierre Liotet and Luca Sabbioni and Edoardo Vittori and Marco Pinciroli and Michele Trapletti and Marcello Restelli},

url = {https://doi.org/10.1145/3533271.3561780},

doi = {10.1145/3533271.3561780},

isbn = {9781450393768},

year  = {2022},

date = {2022-01-01},

booktitle = {3rd ACM International Conference on AI in Finance},

pages = {394–402},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {ICAIF '22},

abstract = {Reinforcement learning has proven to be successful in obtaining profitable trading policies; however, the effectiveness of such strategies is strongly conditioned to market stationarity. This hypothesis is challenged by the regime switches frequently experienced by practitioners; thus, when many models are available, validation may become a difficult task. We propose to overcome the issue by explicitly modeling the trading task as a non-stationary reinforcement learning problem. Nevertheless, state-of-the-art RL algorithms for this setting usually require task distribution or dynamics to be predictable, an assumption that can hardly be true in the financial framework. In this work, we propose, instead, a method for the dynamic selection of the best RL agent which is only driven by profit performance. Our modular two-layer approach allows choosing the best strategy among a set of RL models through an online-learning algorithm. While we could select any combination of algorithms in principle, our solution employs two state-of-the-art algorithms: Fitted Q-Iteration (FQI) for the RL layer and Optimistic Adapt ML-Prod (OAMP) for the online learning one. The proposed approach is tested on two simulated FX trading tasks, using actual historical data for the AUS/USD and GBP/USD currency pairs.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{BernasconiICAIF2022,

title = {Dark-Pool Smart Order Routing: A Combinatorial Multi-Armed Bandit Approach},

author = {Martino Bernasconi and Stefano Martino and Edoardo Vittori and Francesco Trovò and Marcello Restelli},

url = {https://doi.org/10.1145/3533271.3561728},

doi = {10.1145/3533271.3561728},

isbn = {9781450393768},

year  = {2022},

date = {2022-01-01},

booktitle = {3rd ACM International Conference on AI in Finance},

pages = {352–360},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {ICAIF '22},

abstract = {We study the problem of developing a Smart Order Routing algorithm that learns how to optimize the dollar volume, i.e., the total value of the traded shares, gained from slicing an order across multiple dark pools. Our work is motivated by two distinct issues: (i) the surge in liquidity fragmentation caused by the rising popularity of electronic trading and by the increasing number of trading venues, and (ii) the growth in popularity of dark pools, an exchange venue characterised by a lack of transparency. This paper critically discusses the known dark pool literature and proposes a novel algorithm, namely the DP-CMAB algorithm, that extends existing solutions by allowing the agent to specify the desired limit price when placing orders. Specifically, we frame the problem of dollar volume optimization in a multi-venue setting as a Combinatorial Multi-Armed Bandit (CMAB) problem, representing a generalization of the well-studied MAB framework. Drawing from the rich MAB and CMAB literature, we present multiple strategies that our algorithm may adopt to select the best allocation options. Furthermore, we analyze how exploiting financial domain knowledge improves the agents’ performance. Finally, we evaluate the DP-CMAB performance in an environment built from real market data and show that our algorithm outperforms state-of-the-art solutions.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/aaai/MetelliPDR21,

title = {Policy Optimization as Online Learning with Mediator Feedback},

author = {Alberto Maria Metelli and Matteo Papini and Pierluca D'Oro and Marcello Restelli},

url = {https://ojs.aaai.org/index.php/AAAI/article/view/17083},

year  = {2021},

date = {2021-01-01},

booktitle = {Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 

 2021, Thirty-Third Conference on Innovative Applications of Artificial 

 Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances 

 in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 

 2021},

pages = {8958--8966},

publisher = {AAAI Press},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/aaai/MuttiPR21,

title = {Task-Agnostic Exploration via Policy Gradient of a Non-Parametric 

 State Entropy Estimate},

author = {Mirco Mutti and Lorenzo Pratissoli and Marcello Restelli},

url = {https://ojs.aaai.org/index.php/AAAI/article/view/17091},

year  = {2021},

date = {2021-01-01},

booktitle = {Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 

 2021, Thirty-Third Conference on Innovative Applications of Artificial 

 Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances 

 in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 

 2021},

pages = {9028--9036},

publisher = {AAAI Press},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/aaai/RamponiR21,

title = {Newton Optimization on Helmholtz Decomposition for Continuous Games},

author = {Giorgia Ramponi and Marcello Restelli},

url = {https://ojs.aaai.org/index.php/AAAI/article/view/17350},

year  = {2021},

date = {2021-01-01},

booktitle = {Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 

 2021, Thirty-Third Conference on Innovative Applications of Artificial 

 Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances 

 in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 

 2021},

pages = {11325--11333},

publisher = {AAAI Press},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/icaif/VittoriLR21,

title = {Monte carlo tree search for trading and hedging},

author = {Edoardo Vittori and Amarildo Likmeta and Marcello Restelli},

editor = {Anisoara Calinescu and Lukasz Szpruch},

url = {https://doi.org/10.1145/3490354.3494402},

doi = {10.1145/3490354.3494402},

year  = {2021},

date = {2021-01-01},

booktitle = {ICAIF'21: 2nd ACM International Conference on AI in Finance, Virtual 

 Event, November 3 - 5, 2021},

pages = {37:1--37:9},

publisher = {ACM},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/icaif/RivaBLSVPTR21,

title = {Learning FX trading strategies with FQI and persistent actions},

author = {Antonio Riva and Lorenzo Bisi and Pierre Liotet and Luca Sabbioni and Edoardo Vittori and Marco Pinciroli and Michele Trapletti and Marcello Restelli},

editor = {Anisoara Calinescu and Lukasz Szpruch},

url = {https://doi.org/10.1145/3490354.3494403},

doi = {10.1145/3490354.3494403},

year  = {2021},

date = {2021-01-01},

booktitle = {ICAIF'21: 2nd ACM International Conference on AI in Finance, Virtual 

 Event, November 3 - 5, 2021},

pages = {38:1--38:9},

publisher = {ACM},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/icml/MetelliRCR21,

title = {Provably Efficient Learning of Transferable Rewards},

author = {Alberto Maria Metelli and Giorgia Ramponi and Alessandro Concetti and Marcello Restelli},

editor = {Marina Meila and Tong Zhang},

url = {http://proceedings.mlr.press/v139/metelli21a.html},

year  = {2021},

date = {2021-01-01},

booktitle = {Proceedings of the 38th International Conference on Machine Learning, 

 ICML 2021, 18-24 July 2021, Virtual Event},

volume = {139},

pages = {7665--7676},

publisher = {PMLR},

series = {Proceedings of Machine Learning Research},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/icml/PapiniTRLP21,

title = {Leveraging Good Representations in Linear Contextual Bandits},

author = {Matteo Papini and Andrea Tirinzoni and Marcello Restelli and Alessandro Lazaric and Matteo Pirotta},

editor = {Marina Meila and Tong Zhang},

url = {http://proceedings.mlr.press/v139/papini21a.html},

year  = {2021},

date = {2021-01-01},

booktitle = {Proceedings of the 38th International Conference on Machine Learning, 

 ICML 2021, 18-24 July 2021, Virtual Event},

volume = {139},

pages = {8371--8380},

publisher = {PMLR},

series = {Proceedings of Machine Learning Research},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/ijcai/PoianiTR21,

title = {Meta-Reinforcement Learning by Tracking Task Non-stationarity},

author = {Riccardo Poiani and Andrea Tirinzoni and Marcello Restelli},

editor = {Zhi-Hua Zhou},

url = {https://doi.org/10.24963/ijcai.2021/399},

doi = {10.24963/ijcai.2021/399},

year  = {2021},

date = {2021-01-01},

booktitle = {Proceedings of the Thirtieth International Joint Conference on Artificial 

 Intelligence, IJCAI 2021, Virtual Event / Montreal, Canada, 19-27 

 August 2021},

pages = {2899--2905},

publisher = {ijcai.org},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/ijcnn/LiotetVR21,

title = {Learning a Belief Representation for Delayed Reinforcement Learning},

author = {Pierre Liotet and Erick Venneri and Marcello Restelli},

url = {https://doi.org/10.1109/IJCNN52387.2021.9534358},

doi = {10.1109/IJCNN52387.2021.9534358},

year  = {2021},

date = {2021-01-01},

booktitle = {International Joint Conference on Neural Networks, IJCNN 2021, Shenzhen, 

 China, July 18-22, 2021},

pages = {1--8},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/nips/MetelliRR21,

title = {Subgaussian and Differentiable Importance Sampling for Off-Policy 

 Evaluation and Learning},

author = {Alberto Maria Metelli and Alessio Russo and Marcello Restelli},

editor = {Marc'Aurelio Ranzato and Alina Beygelzimer and Yann N. Dauphin and Percy Liang and Jennifer Wortman Vaughan},

url = {https://proceedings.neurips.cc/paper/2021/hash/4476b929e30dd0c4e8bdbcc82c6ba23a-Abstract.html},

year  = {2021},

date = {2021-01-01},

booktitle = {Advances in Neural Information Processing Systems 34: Annual Conference 

 on Neural Information Processing Systems 2021, NeurIPS 2021, December 

 6-14, 2021, virtual},

pages = {8119--8132},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/nips/PapiniTPRLP21,

title = {Reinforcement Learning in Linear MDPs: Constant Regret and Representation 

 Selection},

author = {Matteo Papini and Andrea Tirinzoni and Aldo Pacchiano and Marcello Restelli and Alessandro Lazaric and Matteo Pirotta},

editor = {Marc'Aurelio Ranzato and Alina Beygelzimer and Yann N. Dauphin and Percy Liang and Jennifer Wortman Vaughan},

url = {https://proceedings.neurips.cc/paper/2021/hash/8860e834a67da41edd6ffe8a1c58fa55-Abstract.html},

year  = {2021},

date = {2021-01-01},

booktitle = {Advances in Neural Information Processing Systems 34: Annual Conference 

 on Neural Information Processing Systems 2021, NeurIPS 2021, December 

 6-14, 2021, virtual},

pages = {16371--16383},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/nips/RamponiMCR21,

title = {Learning in Non-Cooperative Configurable Markov Decision Processes},

author = {Giorgia Ramponi and Alberto Maria Metelli and Alessandro Concetti and Marcello Restelli},

editor = {Marc'Aurelio Ranzato and Alina Beygelzimer and Yann N. Dauphin and Percy Liang and Jennifer Wortman Vaughan},

url = {https://proceedings.neurips.cc/paper/2021/hash/c0f52c6624ae1359e105c8a5d8cd956a-Abstract.html},

year  = {2021},

date = {2021-01-01},

booktitle = {Advances in Neural Information Processing Systems 34: Annual Conference 

 on Neural Information Processing Systems 2021, NeurIPS 2021, December 

 6-14, 2021, virtual},

pages = {22808--22821},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/pkdd/LucaVTR21,

title = {Conservative Online Convex Optimization},

author = {Martino Bernasconi Luca and Edoardo Vittori and Francesco Trovò and Marcello Restelli},

editor = {Nuria Oliver and Fernando Pérez-Cruz and Stefan Kramer and Jesse Read and José Antonio Lozano},

url = {https://doi.org/10.1007/978-3-030-86486-6_2},

doi = {10.1007/978-3-030-86486-6_2},

year  = {2021},

date = {2021-01-01},

booktitle = {Machine Learning and Knowledge Discovery in Databases. Research Track 

 - European Conference, ECML PKDD 2021, Bilbao, Spain, September 

 13-17, 2021, Proceedings, Part I},

volume = {12975},

pages = {19--34},

publisher = {Springer},

series = {Lecture Notes in Computer Science},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/pkdd/ReCTCBR21,

title = {Exploiting History Data for Nonstationary Multi-armed Bandit},

author = {Gerlando Re and Fabio Chiusano and Francesco Trovò and Diego Carrera and Giacomo Boracchi and Marcello Restelli},

editor = {Nuria Oliver and Fernando Pérez-Cruz and Stefan Kramer and Jesse Read and José Antonio Lozano},

url = {https://doi.org/10.1007/978-3-030-86486-6_4},

doi = {10.1007/978-3-030-86486-6_4},

year  = {2021},

date = {2021-01-01},

booktitle = {Machine Learning and Knowledge Discovery in Databases. Research Track 

 - European Conference, ECML PKDD 2021, Bilbao, Spain, September 

 13-17, 2021, Proceedings, Part I},

volume = {12975},

pages = {51--66},

publisher = {Springer},

series = {Lecture Notes in Computer Science},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/uai/CanonacoSGCRR21,

title = {Time-variant variational transfer for value functions},

author = {Giuseppe Canonaco and Andrea Soprani and Matteo Giuliani and Andrea Castelletti and Manuel Roveri and Marcello Restelli},

editor = {Cassio P. Campos and Marloes H. Maathuis and Erik Quaeghebeur},

url = {https://proceedings.mlr.press/v161/canonaco21a.html},

year  = {2021},

date = {2021-01-01},

booktitle = {Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial 

 Intelligence, UAI 2021, Virtual Event, 27-30 July 2021},

volume = {161},

pages = {876--886},

publisher = {AUAI Press},

series = {Proceedings of Machine Learning Research},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/aaai/DOroMTPR20,

title = {Gradient-Aware Model-Based Policy Search},

author = {Pierluca D'Oro and Alberto Maria Metelli and Andrea Tirinzoni and Matteo Papini and Marcello Restelli},

url = {https://ojs.aaai.org/index.php/AAAI/article/view/5791},

year  = {2020},

date = {2020-01-01},

booktitle = {The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 

 2020, The Thirty-Second Innovative Applications of Artificial Intelligence 

 Conference, IAAI 2020, The Tenth AAAI Symposium on Educational 

 Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, 

 February 7-12, 2020},

pages = {3801--3808},

publisher = {AAAI Press},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/aaai/MuttiR20,

title = {An Intrinsically-Motivated Approach for Learning Highly Exploring 

 and Fast Mixing Policies},

author = {Mirco Mutti and Marcello Restelli},

url = {https://ojs.aaai.org/index.php/AAAI/article/view/5968},

year  = {2020},

date = {2020-01-01},

booktitle = {The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 

 2020, The Thirty-Second Innovative Applications of Artificial Intelligence 

 Conference, IAAI 2020, The Tenth AAAI Symposium on Educational 

 Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, 

 February 7-12, 2020},

pages = {5232--5239},

publisher = {AAAI Press},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/aistats/PapiniBR20,

title = {Balancing Learning Speed and Stability in Policy Gradient via Adaptive 

 Exploration},

author = {Matteo Papini and Andrea Battistello and Marcello Restelli},

editor = {Silvia Chiappa and Roberto Calandra},

url = {http://proceedings.mlr.press/v108/papini20a.html},

year  = {2020},

date = {2020-01-01},

booktitle = {The 23rd International Conference on Artificial Intelligence and Statistics, 

 AISTATS 2020, 26-28 August 2020, Online [Palermo, Sicily, Italy]},

volume = {108},

pages = {1188--1199},

publisher = {PMLR},

series = {Proceedings of Machine Learning Research},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/aistats/RamponiLMTR20,

title = {Truly Batch Model-Free Inverse Reinforcement Learning about Multiple 

 Intentions},

author = {Giorgia Ramponi and Amarildo Likmeta and Alberto Maria Metelli and Andrea Tirinzoni and Marcello Restelli},

editor = {Silvia Chiappa and Roberto Calandra},

url = {http://proceedings.mlr.press/v108/ramponi20a.html},

year  = {2020},

date = {2020-01-01},

booktitle = {The 23rd International Conference on Artificial Intelligence and Statistics, 

 AISTATS 2020, 26-28 August 2020, Online [Palermo, Sicily, Italy]},

volume = {108},

pages = {2359--2369},

publisher = {PMLR},

series = {Proceedings of Machine Learning Research},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/aistats/TirinzoniLR20,

title = {A Novel Confidence-Based Algorithm for Structured Bandits},

author = {Andrea Tirinzoni and Alessandro Lazaric and Marcello Restelli},

editor = {Silvia Chiappa and Roberto Calandra},

url = {http://proceedings.mlr.press/v108/tirinzoni20a.html},

year  = {2020},

date = {2020-01-01},

booktitle = {The 23rd International Conference on Artificial Intelligence and Statistics, 

 AISTATS 2020, 26-28 August 2020, Online [Palermo, Sicily, Italy]},

volume = {108},

pages = {3175--3185},

publisher = {PMLR},

series = {Proceedings of Machine Learning Research},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/atal/NuaraTC0R20,

title = {Driving Exploration by Maximum Distribution in Gaussian Process Bandits},

author = {Alessandro Nuara and Francesco Trovò and Dominic Crippa and Nicola Gatti and Marcello Restelli},

editor = {Amal El Fallah Seghrouchni and Gita Sukthankar and Bo An and Neil Yorke-Smith},

url = {https://dl.acm.org/doi/10.5555/3398761.3398872},

doi = {10.5555/3398761.3398872},

year  = {2020},

date = {2020-01-01},

booktitle = {Proceedings of the 19th International Conference on Autonomous Agents 

 and Multiagent Systems, AAMAS '20, Auckland, New Zealand, May 9-13, 

 2020},

pages = {948--956},

publisher = {International Foundation for Autonomous Agents and Multiagent Systems},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/ecai/CanonacoRR20,

title = {Model-Free Non-Stationarity Detection and Adaptation in Reinforcement 

 Learning},

author = {Giuseppe Canonaco and Marcello Restelli and Manuel Roveri},

editor = {Giuseppe De Giacomo and Alejandro Catalá and Bistra Dilkina and Michela Milano and Senén Barro and Alberto Bugarín and Jérôme Lang},

url = {https://doi.org/10.3233/FAIA200200},

doi = {10.3233/FAIA200200},

year  = {2020},

date = {2020-01-01},

booktitle = {ECAI 2020 - 24th European Conference on Artificial Intelligence, 

 29 August-8 September 2020, Santiago de Compostela, Spain, August 

 29 - September 8, 2020 - Including 10th Conference on Prestigious 

 Applications of Artificial Intelligence (PAIS 2020)},

volume = {325},

pages = {1047--1054},

publisher = {IOS Press},

series = {Frontiers in Artificial Intelligence and Applications},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/icaif/SabbioniRP20,

title = {Fast direct calibration of interest rate derivatives pricing models},

author = {Luca Sabbioni and Marcello Restelli and Andrea Prampolini},

editor = {Tucker Balch},

url = {https://doi.org/10.1145/3383455.3422534},

doi = {10.1145/3383455.3422534},

year  = {2020},

date = {2020-01-01},

booktitle = {ICAIF '20: The First ACM International Conference on AI in Finance, 

 New York, NY, USA, October 15-16, 2020},

pages = {6:1--6:8},

publisher = {ACM},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/icaif/VittoriLTR20,

title = {Dealing with transaction costs in portfolio optimization: online gradient 

 descent with momentum},

author = {Edoardo Vittori and Martino Bernasconi Luca and Francesco Trovò and Marcello Restelli},

editor = {Tucker Balch},

url = {https://doi.org/10.1145/3383455.3422531},

doi = {10.1145/3383455.3422531},

year  = {2020},

date = {2020-01-01},

booktitle = {ICAIF '20: The First ACM International Conference on AI in Finance, 

 New York, NY, USA, October 15-16, 2020},

pages = {11:1--11:8},

publisher = {ACM},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}