Dual mirror descent for online allocation problems
S Balseiro, H Lu, V Mirrokni - International Conference on …, 2020 - proceedings.mlr.press
We consider online allocation problems with concave revenue functions and resource
constraints, which are central problems in revenue management and online advertising. In …
constraints, which are central problems in revenue management and online advertising. In …
Keyword decisions in sponsored search advertising: A literature review and research agenda
In sponsored search advertising (SSA), keywords serve as the basic unit of business model,
linking three stakeholders: consumers, advertisers and search engines. This paper presents …
linking three stakeholders: consumers, advertisers and search engines. This paper presents …
Optimal real-time bidding for display advertising
In this paper we study bid optimisation for real-time bidding (RTB) based display advertising.
RTB allows advertisers to bid on a display ad impression in real time when it is being …
RTB allows advertisers to bid on a display ad impression in real time when it is being …
Display advertising with real-time bidding (RTB) and behavioural targeting
The most significant progress in recent years in online display advertising is what is known
as the Real-Time Bidding (RTB) mechanism to buy and sell ads. RTB essentially facilitates …
as the Real-Time Bidding (RTB) mechanism to buy and sell ads. RTB essentially facilitates …
Learning in repeated auctions with budgets: Regret minimization and equilibrium
SR Balseiro, Y Gur - Management Science, 2019 - pubsonline.informs.org
In online advertising markets, advertisers often purchase ad placements through bidding in
repeated auctions based on realized viewer information. We study how budget-constrained …
repeated auctions based on realized viewer information. We study how budget-constrained …
A survey of general-purpose crowdsourcing techniques
AI Chittilappilly, L Chen… - IEEE Transactions on …, 2016 - ieeexplore.ieee.org
Since Jeff Howe introduced the term Crowdsourcing in 2006, this human-powered problem-
solving paradigm has gained a lot of attention and has been a hot research topic in the field …
solving paradigm has gained a lot of attention and has been a hot research topic in the field …
Online knapsack with frequency predictions
There has been recent interest in using machine-learned predictions to improve the worst-
case guarantees of online algorithms. In this paper we continue this line of work by studying …
case guarantees of online algorithms. In this paper we continue this line of work by studying …
Budget constrained bidding by model-free reinforcement learning in display advertising
D Wu, X Chen, X Yang, H Wang, Q Tan… - Proceedings of the 27th …, 2018 - dl.acm.org
Real-time bidding (RTB) is an important mechanism in online display advertising, where a
proper bid for each page view plays an essential role for good marketing results. Budget …
proper bid for each page view plays an essential role for good marketing results. Budget …
[HTML][HTML] A deep reinforcement learning hyper-heuristic with feature fusion for online packing problems
In recent years, deep reinforcement learning has shown great potential in solving computer
games with sequential decision-making scenarios. Hyper-heuristic is a generic search …
games with sequential decision-making scenarios. Hyper-heuristic is a generic search …
TestEra: A novel framework for automated testing of Java programs
D Marinov, S Khurshid - Proceedings 16th Annual International …, 2001 - ieeexplore.ieee.org
We present TestEra, a novel framework for automated testing of Java programs. TestEra
automatically generates all non-isomorphic test cases within a given input size and …
automatically generates all non-isomorphic test cases within a given input size and …