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distributed_ml [2018/03/14 22:18] patra |
distributed_ml [2018/04/10 14:36] damaskin |
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=== Asynchronous ML on android devices=== | === Asynchronous ML on android devices=== | ||
- | This project is related to training ML algorithms asynchronously on Android devices. The challenges here are primarily: mobile churn, latency, memory, bandwidth and accuracy. The main goal is building a framework to address these challenges. | + | This project is related to training ML algorithms asynchronously on Android devices. The challenges here are primarily: mobile churn, latency, energy consumption, memory, bandwidth and accuracy. |
Related papers:\\ | Related papers:\\ | ||
[1] __[[http://ttic.uchicago.edu/~kgimpel/papers/gimpel+das+smith.conll10.pdf|Distributed Asynchronous Online Learning for Natural Language Processing]]__ \\ | [1] __[[http://ttic.uchicago.edu/~kgimpel/papers/gimpel+das+smith.conll10.pdf|Distributed Asynchronous Online Learning for Natural Language Processing]]__ \\ | ||
- | [2] __[[http://net.pku.edu.cn/~cuibin/Papers/2017%20sigmod.pdf|Heterogeneity-aware Distributed Parameter Servers]]__ | + | [2] __[[http://net.pku.edu.cn/~cuibin/Papers/2017%20sigmod.pdf|Heterogeneity-aware Distributed Parameter Servers]]__ \\ |
+ | [3] __[[http://proceedings.mlr.press/v70/zhang17e.html|ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning]]__ | ||
=== Multi-output multi-class classification === | === Multi-output multi-class classification === | ||
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[2] __[[http://www.vldb.org/pvldb/vol9/p1695-upadhyaya.pdf|Price-Optimal Querying with Data APIs]]__\\ | [2] __[[http://www.vldb.org/pvldb/vol9/p1695-upadhyaya.pdf|Price-Optimal Querying with Data APIs]]__\\ | ||
[3] __[[http://pages.cs.wisc.edu/~paris/papers/data_pricing.pdf|Query-Based Data Pricing]]__\\ | [3] __[[http://pages.cs.wisc.edu/~paris/papers/data_pricing.pdf|Query-Based Data Pricing]]__\\ | ||
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+ | ===Black-Box Attacks against Recommender Systems=== | ||
+ | A recommender system can be viewed as a black-box that users query with feedback (e.g., ratings, clicks) before getting the output list of recommendations. | ||
+ | The goal is to infer properties of the recommendation algorithm by observing the output from different queries. | ||
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+ | Related papers:\\ | ||
+ | [1] __[[https://www.usenix.org/system/files/conference/usenixsecurity16/sec16_paper_tramer.pdf|Stealing Machine Learning Models via Prediction APIs]]__\\ | ||
+ | [2] __[[https://arxiv.org/pdf/1602.02697v3.pdf|Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples]]__\\ | ||
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**Contact:** __[[http://people.epfl.ch/georgios.damaskinos|Georgios Damaskinos]]__ | **Contact:** __[[http://people.epfl.ch/georgios.damaskinos|Georgios Damaskinos]]__ | ||