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2020 – today
- 2024
[j10]Gurjeet Sangra Singh
, Luigi Acerbi:
PyBADS: Fast and robust black-box optimization in Python. J. Open Source Softw. 9(96): 5694 (2024)- [c13]Trung Q. Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski:
Input-gradient space particle inference for neural network ensembles. ICLR 2024 - [i15]Trung Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski:
Improving robustness to corruptions with multiplicative weight perturbations. CoRR abs/2406.16540 (2024) - [i14]Marvin Schmitt, Chengkun Li, Aki Vehtari, Luigi Acerbi, Paul-Christian Bürkner, Stefan T. Radev:
Amortized Bayesian Workflow (Extended Abstract). CoRR abs/2409.04332 (2024) - [i13]Petrus Mikkola, Luigi Acerbi, Arto Klami:
Preferential Normalizing Flows. CoRR abs/2410.08710 (2024) - 2023
- [j9]Bobby Huggins, Chengkun Li, Marlon Tobaben, Mikko J. Aarnos, Luigi Acerbi:
PyVBMC: Efficient Bayesian inference in Python. J. Open Source Softw. 8(87): 5428 (2023) - [c12]Daolang Huang, Ayush Bharti, Amauri H. Souza, Luigi Acerbi, Samuel Kaski:
Learning Robust Statistics for Simulation-based Inference under Model Misspecification. NeurIPS 2023 - [c11]Daolang Huang, Manuel Haussmann, Ulpu Remes, St John, Grégoire Clarté, Kevin Sebastian Luck, Samuel Kaski, Luigi Acerbi:
Practical Equivariances via Relational Conditional Neural Processes. NeurIPS 2023 - [i12]Alexander Aushev, Aini Putkonen, Gregoire Clarte, Suyog Chandramouli, Luigi Acerbi, Samuel Kaski, Andrew Howes:
Online simulator-based experimental design for cognitive model selection. CoRR abs/2303.02227 (2023) - [i11]Chengkun Li, Grégoire Clarté, Luigi Acerbi:
Fast post-process Bayesian inference with Sparse Variational Bayesian Monte Carlo. CoRR abs/2303.05263 (2023) - [i10]Bobby Huggins, Chengkun Li, Marlon Tobaben, Mikko J. Aarnos, Luigi Acerbi:
PyVBMC: Efficient Bayesian inference in Python. CoRR abs/2303.09519 (2023) - [i9]Daolang Huang, Ayush Bharti, Amauri H. Souza, Luigi Acerbi, Samuel Kaski:
Learning Robust Statistics for Simulation-based Inference under Model Misspecification. CoRR abs/2305.15871 (2023) - [i8]Trung Q. Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski:
Input gradient diversity for neural network ensembles. CoRR abs/2306.02775 (2023) - [i7]Daolang Huang, Manuel Haussmann, Ulpu Remes, S. T. John, Grégoire Clarté, Kevin Sebastian Luck, Samuel Kaski, Luigi Acerbi:
Practical Equivariances via Relational Conditional Neural Processes. CoRR abs/2306.10915 (2023) - [i6]Gurjeet Sangra Singh, Luigi Acerbi:
PyBADS: Fast and robust black-box optimization in Python. CoRR abs/2306.15576 (2023) - 2022
- [c10]Daniel Augusto de Souza, Diego Mesquita, Samuel Kaski, Luigi Acerbi:
Parallel MCMC Without Embarrassing Failures. AISTATS 2022: 1786-1804 - [c9]Trung Q. Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski:
Tackling covariate shift with node-based Bayesian neural networks. ICML 2022: 21751-21775 - [i5]Daniel Augusto de Souza, Diego Mesquita, Samuel Kaski, Luigi Acerbi:
Parallel MCMC Without Embarrassing Failures. CoRR abs/2202.11154 (2022) - [i4]Trung Q. Trinh, Markus Heinonen, Luigi Acerbi, Samuel Kaski:
Tackling covariate shift with node-based Bayesian neural networks. CoRR abs/2206.02435 (2022) - 2020
- [j8]Yanli Zhou, Luigi Acerbi, Wei Ji Ma:
The role of sensory uncertainty in simple contour integration. PLoS Comput. Biol. 16(11) (2020) - [j7]Bas van Opheusden, Luigi Acerbi, Wei Ji Ma:
Unbiased and efficient log-likelihood estimation with inverse binomial sampling. PLoS Comput. Biol. 16(12) (2020) - [c8]Luigi Acerbi:
Variational Bayesian Monte Carlo with Noisy Likelihoods. NeurIPS 2020 - [c7]Nisheet Patel, Luigi Acerbi, Alexandre Pouget:
Dynamic allocation of limited memory resources in reinforcement learning. NeurIPS 2020 - [i3]Bas van Opheusden, Luigi Acerbi, Wei Ji Ma:
Unbiased and Efficient Log-Likelihood Estimation with Inverse Binomial Sampling. CoRR abs/2001.03985 (2020) - [i2]Luigi Acerbi:
Variational Bayesian Monte Carlo with Noisy Likelihoods. CoRR abs/2006.08655 (2020)
2010 – 2019
- 2019
- [j6]Elyse H. Norton, Luigi Acerbi, Wei Ji Ma, Michael S. Landy:
Human online adaptation to changes in prior probability. PLoS Comput. Biol. 15(7) (2019) - [c6]Xiang Li, Luigi Acerbi, Wei Ji Ma:
Approximate Inference through Sequential Measurements of Likelihoods Accounts for Hick's Law. CogSci 2019: 3510 - 2018
- [j5]Luigi Acerbi, Kalpana Dokka, Dora E. Angelaki, Wei Ji Ma:
Bayesian comparison of explicit and implicit causal inference strategies in multisensory heading perception. PLoS Comput. Biol. 14(7) (2018) - [c5]Luigi Acerbi:
An Exploration of Acquisition and Mean Functions in Variational Bayesian Monte Carlo. AABI 2018: 1-10 - [c4]Luigi Acerbi:
Variational Bayesian Monte Carlo. NeurIPS 2018: 8223-8233 - [i1]Luigi Acerbi:
Variational Bayesian Monte Carlo. CoRR abs/1810.05558 (2018) - 2017
- [c3]Luigi Acerbi, Wei Ji:
Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search. NIPS 2017: 1836-1846 - 2014
- [j4]Luigi Acerbi, Sethu Vijayakumar, Daniel M. Wolpert:
On the Origins of Suboptimality in Human Probabilistic Inference. PLoS Comput. Biol. 10(6) (2014) - [c2]Luigi Acerbi, Wei Ji Ma, Sethu Vijayakumar:
A Framework for Testing Identifiability of Bayesian Models of Perception. NIPS 2014: 1026-1034 - 2013
- [j3]Luigi Acerbi, Alberto Dennunzio, Enrico Formenti:
Surjective multidimensional cellular automata are non-wandering: A combinatorial proof. Inf. Process. Lett. 113(5-6): 156-159 (2013) - 2012
- [j2]Luigi Acerbi, Daniel M. Wolpert, Sethu Vijayakumar:
Internal Representations of Temporal Statistics and Feedback Calibrate Motor-Sensory Interval Timing. PLoS Comput. Biol. 8(11) (2012)
2000 – 2009
- 2009
- [j1]Luigi Acerbi, Alberto Dennunzio, Enrico Formenti:
Conservation of some dynamical properties for operations on cellular automata. Theor. Comput. Sci. 410(38-40): 3685-3693 (2009) - 2007
- [c1]Luigi Acerbi, Alberto Dennunzio, Enrico Formenti:
Shifting and Lifting of Cellular Automata. CiE 2007: 1-10
Coauthor Index
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last updated on 2024-11-19 20:44 CET by the dblp team
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