Bibliografia#
Le opere citate nel libro, in ordine alfabetico. Nel testo le citazioni compaiono come rimandi tra parentesi quadre: ogni rimando porta qui.
Kjersti Aas, Martin Jullum, and Anders Løland. Explaining individual predictions when features are dependent: more accurate approximations to shapley values. Artificial Intelligence, 298:103502, 2021. URL: https://doi.org/10.1016/j.artint.2021.103502.
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS), 308–318. 2016. URL: https://arxiv.org/abs/1607.00133.
Samira Abnar and Willem Zuidema. Quantifying attention flow in transformers. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 4190–4197. 2020.
David H. Ackley, Geoffrey E. Hinton, and Terrence J. Sejnowski. A learning algorithm for boltzmann machines. Cognitive Science, 9(1):147–169, 1985. URL: https://onlinelibrary.wiley.com/doi/abs/10.1207/s15516709cog0901_7, doi:10.1207/s15516709cog0901_7.
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim. Sanity checks for saliency maps. In Advances in Neural Information Processing Systems, volume 31. 2018.
Rishabh Agarwal, Avi Singh, Lei M. Zhang, Bernd Bohnet, Luis Rosias, Stephanie Chan, Biao Zhang, Ankesh Anand, Zaheer Abbas, Azade Nova, John D. Co-Reyes, Eric Chu, Feryal Behbahani, Aleksandra Faust, and Hugo Larochelle. Many-shot in-context learning. In Advances in Neural Information Processing Systems (NeurIPS), volume 37. 2024. Spotlight. URL: https://arxiv.org/abs/2404.11018.
Amey Agrawal, Nitin Kedia, Ashish Panwar, Jayashree Mohan, Nipun Kwatra, Bhargav S. Gulavani, Alexey Tumanov, and Ramachandran Ramjee. Taming throughput-latency tradeoff in LLM inference with Sarathi-Serve. In 18th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 117–134. 2024.
Guillaume Alain and Yoshua Bengio. Understanding intermediate layers using linear classifier probes. arXiv preprint arXiv:1610.01644, 2016. ICLR 2017, workshop track. URL: https://arxiv.org/abs/1610.01644.
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob Menick, Sebastian Borgeaud, Andrew Brock, Aida Nematzadeh, Sahand Sharifzadeh, Mikołaj Bińkowski, Ricardo Barreira, Oriol Vinyals, Andrew Zisserman, and Karen Simonyan. Flamingo: a visual language model for few-shot learning. In Advances in Neural Information Processing Systems (NeurIPS). 2022.
Reem Aleithan, Haoran Xue, Mohammad Mahdi Mohajer, Elijah Nnorom, Gias Uddin, and Song Wang. SWE-Bench+: enhanced coding benchmark for LLMs. arXiv preprint arXiv:2410.06992, 2024. URL: https://arxiv.org/abs/2410.06992.
Shun-Ichi Amari. Learning patterns and pattern sequences by self-organizing nets of threshold elements. IEEE Transactions on Computers, C-21(11):1197–1206, 1972. doi:10.1109/T-C.1972.223477.
Saleema Amershi, Andrew Begel, Christian Bird, Robert DeLine, Harald Gall, Ece Kamar, Nachiappan Nagappan, Besmira Nushi, and Thomas Zimmermann. Software engineering for machine learning: a case study. In IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), 291–300. 2019.
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Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk, Manu Orsini, Sertan Girgin, Raphael Marinier, Léonard Hussenot, Matthieu Geist, Olivier Pietquin, Marcin Michalski, Sylvain Gelly, and Olivier Bachem. What matters in on-policy reinforcement learning? a large-scale empirical study. In International Conference on Learning Representations (ICLR). 2021. URL: https://arxiv.org/abs/2006.05990, arXiv:2006.05990.
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. Machine bias. ProPublica, 2016. URL: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.
Cem Anil, Esin Durmus, Nina Panickssery, Mrinank Sharma, Joe Benton, Sandipan Kundu, and others. Many-shot jailbreaking. In Advances in Neural Information Processing Systems, volume 37. 2024. URL: https://openreview.net/forum?id=cw5mgd71jW.
Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, and others. Chronos: learning the language of time series. Transactions on Machine Learning Research (TMLR), 2024. URL: https://arxiv.org/abs/2403.07815.
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Daniel W. Apley and Jingyu Zhu. Visualizing the effects of predictor variables in black box supervised learning models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 82(4):1059–1086, 2020. URL: https://arxiv.org/abs/1612.08468.
Martin Arjovsky and Léon Bottou. Towards principled methods for training generative adversarial networks. In International Conference on Learning Representations (ICLR). 2017.
Martin Arjovsky, Soumith Chintala, and Léon Bottou. Wasserstein generative adversarial networks. In Proceedings of the 34th International Conference on Machine Learning (ICML). 2017.
Mikel Artetxe, Sebastian Ruder, and Dani Yogatama. On the cross-lingual transferability of monolingual representations. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 4623–4637. 2020.
Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. Self-RAG: learning to retrieve, generate, and critique through self-reflection. In International Conference on Learning Representations (ICLR). 2024.
Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, and Nicolas Ballas. Self-supervised learning from images with a joint-embedding predictive architecture. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2023. URL: https://arxiv.org/abs/2301.08243.
Mido Assran, Adrien Bardes, David Fan, Quentin Garrido, Russell Howes, Mojtaba Komeili, Matthew Muckley, Ammar Rizvi, Claire Roberts, Koustuv Sinha, Artem Zholus, Sergio Arnaud, Abha Gejji, Ada Martin, Francois Robert Hogan, Daniel Dugas, Piotr Bojanowski, Vasil Khalidov, Patrick Labatut, Francisco Massa, Marc Szafraniec, Kapil Krishnakumar, Yong Li, Xiaodong Ma, Sarath Chandar, Franziska Meier, Yann LeCun, Michael Rabbat, and Nicolas Ballas. V-jepa 2: self-supervised video models enable understanding, prediction and planning. arXiv preprint arXiv:2506.09985, 2025. URL: https://arxiv.org/abs/2506.09985.
Anish Athalye, Nicholas Carlini, and David Wagner. Obfuscated gradients give a false sense of security: circumventing defenses to adversarial examples. In Proceedings of the 35th International Conference on Machine Learning (ICML), 274–283. 2018.
Peter Auer, Nicolò Cesa-Bianchi, and Paul Fischer. Finite-time analysis of the multiarmed bandit problem. Machine Learning, 47(2):235–256, 2002.
Alexei Baevski, Yuhao Zhou, Abdelrahman Mohamed, and Michael Auli. Wav2vec 2.0: a framework for self-supervised learning of speech representations. In Advances in Neural Information Processing Systems (NeurIPS), volume 33. 2020.
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. In International Conference on Learning Representations. 2015.
Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen-VL: a versatile vision-language model for understanding, localization, text reading, and beyond. arXiv preprint arXiv:2308.12966, 2023.
Shaojie Bai, J. Zico Kolter, and Vladlen Koltun. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271, 2018. URL: https://arxiv.org/abs/1803.01271.
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, and others. Constitutional ai: harmlessness from ai feedback. arXiv preprint arXiv:2212.08073, 2022. URL: https://arxiv.org/abs/2212.08073.
M. Ballerini, N. Cabibbo, R. Candelier, A. Cavagna, E. Cisbani, I. Giardina, V. Lecomte, A. Orlandi, G. Parisi, A. Procaccini, M. Viale, and V. Zdravkovic. Interaction ruling animal collective behavior depends on topological rather than metric distance: evidence from a field study. Proceedings of the National Academy of Sciences, 105(4):1232–1237, 2008.
Adrien Bardes, Quentin Garrido, Jean Ponce, Xinlei Chen, Michael Rabbat, Yann LeCun, Mahmoud Assran, and Nicolas Ballas. Revisiting feature prediction for learning visual representations from video. Transactions on Machine Learning Research, 2024. URL: https://arxiv.org/abs/2404.08471.
Adrien Bardes, Jean Ponce, and Yann LeCun. VICReg: variance-invariance-covariance regularization for self-supervised learning. In International Conference on Learning Representations (ICLR). 2022. URL: https://arxiv.org/abs/2105.04906.
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Solon Barocas, Moritz Hardt, and Arvind Narayanan. Fairness and Machine Learning: Limitations and Opportunities. MIT Press, 2023. Versione online gratuita su fairmlbook.org. URL: https://fairmlbook.org/.
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Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, and others. Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261, 2018.
Maximilian Beck, Korbinian Pöppel, Phillip Lippe, Richard Kurle, Patrick Blies, Günter Klambauer, Sebastian Böck, and Sepp Hochreiter. xLSTM 7B: a recurrent LLM for fast and efficient inference. In Proceedings of the 42nd International Conference on Machine Learning (ICML). 2025.
Maximilian Beck, Korbinian Pöppel, Markus Spanring, Andreas Auer, Oleksandra Prudnikova, Michael Kopp, Günter Klambauer, Johannes Brandstetter, and Sepp Hochreiter. xLSTM: extended long short-term memory. In Advances in Neural Information Processing Systems (NeurIPS). 2024.
Mikhail Belkin, Daniel Hsu, Siyuan Ma, and Soumik Mandal. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences, 116(32):15849–15854, 2019. doi:10.1073/pnas.1903070116.
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Iz Beltagy, Matthew E. Peters, and Arman Cohan. Longformer: the long-document transformer. arXiv preprint arXiv:2004.05150, 2020.
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Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. On the dangers of stochastic parrots: can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT). 2021.
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Antoine Bordes, Nicolas Usunier, Alberto Garcia-Durán, Jason Weston, and Oksana Yakhnenko. Translating embeddings for modeling multi-relational data. In Advances in Neural Information Processing Systems, volume 26. 2013.
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Trenton Bricken, Adly Templeton, Joshua Batson, Brian Chen, Adam Jermyn, Tom Conerly, Nick Turner, Cem Anil, Carson Denison, Amanda Askell, and others. Towards monosemanticity: decomposing language models with dictionary learning. Transformer Circuits Thread, Anthropic, 2023. URL: https://transformer-circuits.pub/2023/monosemantic-features.
Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choromanski, Tianli Ding, Danny Driess, Avinava Dubey, Chelsea Finn, and others. RT-2: vision-language-action models transfer web knowledge to robotic control. In Proceedings of the 7th Conference on Robot Learning (CoRL), volume 229 of Proceedings of Machine Learning Research, 2165–2183. 2023.
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Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, and others. Language models are few-shot learners. In Advances in Neural Information Processing Systems, volume 33. 2020.
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Charlie Chen, Sebastian Borgeaud, Geoffrey Irving, Jean-Baptiste Lespiau, Laurent Sifre, and John Jumper. Accelerating large language model decoding with speculative sampling. arXiv preprint arXiv:2302.01318, 2023.
Keqin Chen, Zhao Zhang, Weili Zeng, Richong Zhang, Feng Zhu, and Rui Zhao. Shikra: unleashing multimodal LLM's referential dialogue magic. arXiv preprint arXiv:2306.15195, 2023.
Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch. Decision transformer: reinforcement learning via sequence modeling. In Advances in Neural Information Processing Systems (NeurIPS). 2021. URL: https://arxiv.org/abs/2106.01345.
Lin Chen, Jinsong Li, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Zehui Chen, Haodong Duan, Jiaqi Wang, Yu Qiao, Dahua Lin, and Feng Zhao. Are we on the right way for evaluating large vision-language models? arXiv preprint arXiv:2403.20330, 2024.
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba. Evaluating large language models trained on code. OpenAI, arXiv:2107.03374, 2021. URL: https://arxiv.org/abs/2107.03374.
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Alexandra Chouldechova. Fair prediction with disparate impact: a study of bias in recidivism prediction instruments. Big Data, 5(2):153–163, 2017. URL: https://arxiv.org/abs/1703.00056.
Paul F. Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, and Dario Amodei. Deep reinforcement learning from human preferences. In Advances in Neural Information Processing Systems, volume 30. 2017. URL: https://arxiv.org/abs/1706.03741.
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Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D. Manning. What does BERT look at? an analysis of BERT's attention. In Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, 276–286. 2019. URL: https://aclanthology.org/W19-4828/.
Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning. ELECTRA: pre-training text encoders as discriminators rather than generators. In International Conference on Learning Representations (ICLR). 2020. URL: https://arxiv.org/abs/2003.10555.
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