default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDDiana Borsa
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
2020 – today
- 2024
[i21]Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Ávila Pires, Yunhao Tang, Clare Lyle, Mark Rowland, Nicolas Heess, Diana Borsa, Arthur Guez, Will Dabney:
A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning. CoRR abs/2406.02035 (2024)- 2023
- [c13]Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo, Yunhao Tang, Rémi Munos, Will Dabney, Diana L. Borsa:
Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition. ICML 2023: 4009-4034 - [c12]Ted Moskovitz, Samo Hromadka, Ahmed Touati, Diana Borsa, Maneesh Sahani:
A State Representation for Diminishing Rewards. NeurIPS 2023 - [i20]Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo, Yunhao Tang, Rémi Munos, Will Dabney, Diana L. Borsa:
Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition. CoRR abs/2305.00654 (2023) - [i19]Ted Moskovitz, Samo Hromadka, Ahmed Touati, Diana Borsa, Maneesh Sahani:
A State Representation for Diminishing Rewards. CoRR abs/2309.03710 (2023) - 2022
- [c11]Miruna Pislar, David Szepesvari, Georg Ostrovski, Diana L. Borsa, Tom Schaul:
When should agents explore? ICLR 2022 - [c10]Angelos Filos, Eszter Vértes, Zita Marinho, Gregory Farquhar, Diana Borsa, Abram L. Friesen, Feryal M. P. Behbahani, Tom Schaul, André Barreto, Simon Osindero:
Model-Value Inconsistency as a Signal for Epistemic Uncertainty. ICML 2022: 6474-6498 - [c9]Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Rémi Munos, André Barreto:
Generalised Policy Improvement with Geometric Policy Composition. ICML 2022: 21272-21307 - [i18]Veronica Chelu, Diana Borsa, Doina Precup, Hado van Hasselt:
Selective Credit Assignment. CoRR abs/2202.09699 (2022) - [i17]Shantanu Thakoor, Mark Rowland, Diana Borsa, Will Dabney, Rémi Munos, André Barreto:
Generalised Policy Improvement with Geometric Policy Composition. CoRR abs/2206.08736 (2022) - 2021
- [c8]Hado van Hasselt, Sephora Madjiheurem, Matteo Hessel, David Silver, André Barreto, Diana Borsa:
Expected Eligibility Traces. AAAI 2021: 9997-10005 - [i16]Tom Schaul, Georg Ostrovski, Iurii Kemaev, Diana Borsa:
Return-based Scaling: Yet Another Normalisation Trick for Deep RL. CoRR abs/2105.05347 (2021) - [i15]André Barreto, Diana Borsa, Shaobo Hou, Gheorghe Comanici, Eser Aygün, Philippe Hamel, Daniel Toyama, Jonathan J. Hunt, Shibl Mourad, David Silver, Doina Precup:
The Option Keyboard: Combining Skills in Reinforcement Learning. CoRR abs/2106.13105 (2021) - [i14]Miruna Pislar, David Szepesvari, Georg Ostrovski, Diana Borsa, Tom Schaul:
When should agents explore? CoRR abs/2108.11811 (2021) - [i13]Angelos Filos, Eszter Vértes, Zita Marinho, Gregory Farquhar, Diana Borsa, Abram L. Friesen, Feryal M. P. Behbahani, Tom Schaul, André Barreto, Simon Osindero:
Model-Value Inconsistency as a Signal for Epistemic Uncertainty. CoRR abs/2112.04153 (2021) - 2020
- [b1]Diana Borsa:
Reinforcement learning in persistent environments: representation learning and transfer. University College London, UK, 2020 - [j1]André Barreto
, Shaobo Hou, Diana Borsa, David Silver, Doina Precup:
Fast reinforcement learning with generalized policy updates. Proc. Natl. Acad. Sci. USA 117(48): 30079-30087 (2020) - [c7]Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney:
Conditional Importance Sampling for Off-Policy Learning. AISTATS 2020: 45-55 - [i12]Hado van Hasselt, Sephora Madjiheurem, Matteo Hessel, David Silver, André Barreto, Diana Borsa:
Expected Eligibility Traces. CoRR abs/2007.01839 (2020) - [i11]Sebastian Flennerhag, Jane X. Wang, Pablo Sprechmann, Francesco Visin, Alexandre Galashov, Steven Kapturowski, Diana L. Borsa, Nicolas Heess, André Barreto, Razvan Pascanu:
Temporal Difference Uncertainties as a Signal for Exploration. CoRR abs/2010.02255 (2020)
2010 – 2019
- 2019
- [c6]Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Rémi Munos, Doina Precup:
The Termination Critic. AISTATS 2019: 2231-2240 - [c5]Diana Borsa, Nicolas Heess, Bilal Piot, Siqi Liu, Leonard Hasenclever, Rémi Munos, Olivier Pietquin:
Observational Learning by Reinforcement Learning. AAMAS 2019: 1117-1124 - [c4]Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Hado van Hasselt, Rémi Munos, David Silver, Tom Schaul:
Universal Successor Features Approximators. ICLR (Poster) 2019 - [c3]André Barreto, Diana Borsa, Shaobo Hou, Gheorghe Comanici, Eser Aygün, Philippe Hamel, Daniel Toyama, Jonathan J. Hunt, Shibl Mourad, David Silver, Doina Precup:
The Option Keyboard: Combining Skills in Reinforcement Learning. NeurIPS 2019: 13031-13041 - [i10]André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Zídek, Rémi Munos:
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. CoRR abs/1901.10964 (2019) - [i9]Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Rémi Munos, Doina Precup:
The Termination Critic. CoRR abs/1902.09996 (2019) - [i8]Tom Schaul, Diana Borsa, Joseph Modayil, Razvan Pascanu:
Ray Interference: a Source of Plateaus in Deep Reinforcement Learning. CoRR abs/1904.11455 (2019) - [i7]Hado van Hasselt, John Quan, Matteo Hessel, Zhongwen Xu, Diana Borsa, André Barreto:
General non-linear Bellman equations. CoRR abs/1907.03687 (2019) - [i6]Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney:
Conditional Importance Sampling for Off-Policy Learning. CoRR abs/1910.07479 (2019) - [i5]Tom Schaul, Diana Borsa, David Ding, David Szepesvari, Georg Ostrovski, Will Dabney, Simon Osindero:
Adapting Behaviour for Learning Progress. CoRR abs/1912.06910 (2019) - 2018
- [c2]André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Zídek, Rémi Munos:
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. ICML 2018: 510-519 - [i4]Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Rémi Munos, Hado van Hasselt, David Silver, Tom Schaul:
Universal Successor Features Approximators. CoRR abs/1812.07626 (2018) - 2017
- [i3]Diana Borsa, Bilal Piot, Rémi Munos, Olivier Pietquin:
Observational Learning by Reinforcement Learning. CoRR abs/1706.06617 (2017) - 2016
- [c1]Alex Gaunt, Diana Borsa, Yoram Bachrach:
Training Neural Nets to Aggregate Crowdsourced Responses. UAI 2016 - [i2]Diana Borsa, Thore Graepel, John Shawe-Taylor:
Learning Shared Representations in Multi-task Reinforcement Learning. CoRR abs/1603.02041 (2016) - 2015
- [i1]Diana Borsa, Thore Graepel, Andrew D. Gordon:
The Wreath Process: A totally generative model of geometric shape based on nested symmetries. CoRR abs/1506.03041 (2015)
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-04 20:01 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
