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Pagnoni, Artidoro
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1

WindTunnel: towards differentiable ML pipelines beyond a si..:

Yu, Gyeong-In ; Amizadeh, Saeed ; Kim, Sehoon...
Proceedings of the VLDB Endowment.  15 (2021)  1 - p. 11-20 , 2021
Link: https://doi.org/10.14778..
 
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2

Machine Learning at Microsoft with ML.NET:

, In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining,
Ahmed, Zeeshan ; Amizadeh, Saeed ; Bilenko, Mikhail... - p. 2448-2458 , 2019
Link: https://dl.acm.org/doi/1..
 
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3

QLoRA: Efficient Finetuning of Quantized LLMs:

Dettmers, Tim ; Pagnoni, Artidoro ; Holtzman, Ari.
http://arxiv.org/abs/2305.14314.  , 2023
Link: http://arxiv.org/abs/230..
 
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4

WINDTUNNEL: Towards Differentiable ML Pipelines Beyond a Si..:

Yu, Gyeong-In ; Amizadeh, Saeed ; Kim, Sehoon...
Proceedings of the VLDB Endowment, Vol.15 No.1, pp.11-20.  , 2022
Link: https://hdl.handle.net/1..
 
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5

EvEntS ReaLM: Event Reasoning of Entity States via Language..:

Spiliopoulou, Evangelia ; Pagnoni, Artidoro ; Bisk, Yonatan.
http://arxiv.org/abs/2211.05392.  , 2022
Link: http://arxiv.org/abs/221..
 
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6

Socratic Pretraining: Question-Driven Pretraining for Contr..:

Pagnoni, Artidoro ; Fabbri, Alexander R ; Kryściński, Wojciech.
http://arxiv.org/abs/2212.10449.  , 2022
Link: http://arxiv.org/abs/221..
 
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7

WindTunnel: towards differentiable ML pipelines beyond a si..:

Yu, Gyeong-In ; Amizadeh, Saeed ; Kim, Sehoon...
info:eu-repo/semantics/altIdentifier/doi/10.14778/3485450.3485452.  , 2021
Link: https://hdl.handle.net/2..
 
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8

Understanding Factuality in Abstractive Summarization with ..:

Pagnoni, Artidoro ; Balachandran, Vidhisha ; Tsvetkov, Yulia
http://arxiv.org/abs/2104.13346.  , 2021
Link: http://arxiv.org/abs/210..
 
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9

StructSum: Summarization via Structured Representations:

Balachandran, Vidhisha ; Pagnoni, Artidoro ; Lee, Jay Yoon...
http://arxiv.org/abs/2003.00576.  , 2020
Link: http://arxiv.org/abs/200..
 
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10

Definition Frames: Using Definitions for Hybrid Concept Rep..:

Spiliopoulou, Evangelia ; Pagnoni, Artidoro ; Hovy, Eduard
http://arxiv.org/abs/1909.04793.  , 2019
Link: http://arxiv.org/abs/190..
 
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11

Making Classical Machine Learning Pipelines Differentiable:..:

Yu, Gyeong-In ; Amizadeh, Saeed ; Kim, Sehoon...
http://arxiv.org/abs/1906.03822.  , 2019
Link: http://arxiv.org/abs/190..
 
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12

Analyzing Branch-and-Bound Algorithms for the Multiprocesso..:

Lively, Thomas ; Long, William ; Pagnoni, Artidoro
http://arxiv.org/abs/1901.07070.  , 2019
Link: http://arxiv.org/abs/190..
 
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13

Machine Learning at Microsoft with ML .NET:

Ahmed, Zeeshan ; Amizadeh, Saeed ; Bilenko, Mikhail...
http://arxiv.org/abs/1905.05715.  , 2019
Link: http://arxiv.org/abs/190..
 
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14

PAC Learning Guarantees Under Covariate Shift:

Pagnoni, Artidoro ; Gramatovici, Stefan ; Liu, Samuel
http://arxiv.org/abs/1812.06393.  , 2018
Link: http://arxiv.org/abs/181..
 
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15

Taint Tracking for WebAssembly:

Szanto, Aron ; Tamm, Timothy ; Pagnoni, Artidoro
http://arxiv.org/abs/1807.08349.  , 2018
Link: http://arxiv.org/abs/180..
 
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