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Published Date

Publication

Online Learning with a Hint

Ofer Dekel, Arthur Flajolet, Nika Haghtalab, Patrick Jaillet

May 2017

Publication

Online Learning for Structured Loss Spaces

Aadirupa Saha, Aditya Gopalan, Siddharth Barman

Association for the Advancement of Artificial Intelligence | May 2017

Publication

Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM

Katrina Ligett, Seth Neel, Aaron Roth, Bo Waggoner, Steven Wu

31st Conference on Neural Information Processing Systems (NIPS 2017) | May 2017

Publication

The Numerics of GANs

Lars Mescheder, Sebastian Nowozin, Andreas Geiger

Advances in Neural Information Processing Systems 30 (NIPS 2017) | May 2017

Project

Publication

Bounding the gap between the McCormick relaxation and the convex hull for bilinear functions

N. Boland, Santanu S. Dey, T. Kalinowski, Marco Molinaro, F. Rigterink

Mathematical Programming | May 2017, Vol 162: pp. 523-535

Publication

Online Auctions and Multi-scale Online Learning

Sébastien Bubeck, Nikhil Devanur, Zhiyi Huang, Rad Niazadeh

18th ACM conference on Economics and Computation (EC’17) | May 2017

Project

Video

A Generic Framework for Mining Top-k Representative Subgraph Patterns

May 25, 2017 | Sayan Ranu

Mining subgraph patterns is an active area of research. Till now, the focus has primarily been on mining all subgraph patterns in the given database. However, due to the exponential subgraph search space, the number…

A Generic Framework for Mining Top-k Representative Subgraph Patterns

1:03:44

Publication

Multi-Task Learning for Contextual Bandits

Aniket Anand Deshmukh, Urun Dogan, Clay Scott

May 2017

Video

Data Predictive Control: Bridging Machine Learning and Controls for Volatile Energy Markets

May 23, 2017 | Achin Jain

In January 2014, the east coast (PJM) electricity grid experienced an increase in the price of electricity from $31/MWh to $2,680/MWh in a matter of 10 minutes. This extreme price volatility has become the new…

Data Predictive Control: Bridging Machine Learning and Controls for Volatile Energy Markets

1:01:49

Video

Proving Performance Properties of Functional Programs

May 22, 2017 | Ravi Madhavan

Static verification of performance properties of programs is an important problem that has attracted a lot of research. However, most existing tools infer best-effort upper bounds and hope that they match users expectations. In this…

Proving Performance Properties of Functional Programs

1:09:39