Online optimization covers problems such as online resource allocation, online bipartite matching, adwords (a central problem in e-commerce and advertising), and adwords with separable concave returns. We analyze the worst case competitive ratio of two primal-dual algorithms for a class of online convex (conic) optimization problems that contains the previous examples as special cases defined on the positive orthant. We derive a sufficient condition on the objective function that guarantees a constant worst case competitive ratio (greater than or equal to $\frac{1}{2}$) for monotone objective functions. We provide new examples of online problems on the positive orthant % and the positive semidefinite cone that satisfy the sufficient condition. We show how smoothing can improve the competitive ratio of these algorithms, and in particular for separable functions, we show that the optimal smoothing can be derived by solving a convex optimization problem. This result allows us to directly optimize the competitive ratio bound over a class of smoothing functions, and hence design effective smoothing customized for a given cost function.

Online optimization covers problems such as online resource allocation, online bipartite matching, adwords (a central problem in e-commerce and advertising), and adwords with separable concave returns. We analyze the worst case competitive ratio of two primal-dual algorithms for a class of online convex (conic) optimization problems that contains the previous examples as special cases defined on the positive orthant. We derive a sufficient condition on the objective function that guarantees a constant worst case competitive ratio (greater than or equal to $\frac{1}{2}$) for monotone objective functions. We provide new examples of online problems on the positive orthant % and the positive semidefinite cone that satisfy the sufficient condition. We show how smoothing can improve the competitive ratio of these algorithms, and in particular for separable functions, we show that the optimal smoothing can be derived by solving a convex optimization problem. This result allows us to directly optimize the competitive ratio bound over a class of smoothing functions, and hence design effective smoothing customized for a given cost function.

Online bipartite matching is a fundamental problem in online optimization, extensively studied both in its integral and fractional forms due to its theoretical significance and practical applications, such as online advertising and resource allocation. Motivated by recent progress in learning-augmented algorithms, we study online bipartite fractional matching when the algorithm is given advice in the form of a suggested matching in each iteration. We develop algorithms for both the vertex-weighted and unweighted variants that provably dominate the naive "coin flip" strategy of randomly choosing between the advice-following and advice-free algorithms. Moreover, our algorithm for the vertex-weighted setting extends to the AdWords problem under the small bids assumption, yielding a significant improvement over the seminal work of Mahdian, Nazerzadeh, and Saberi (EC 2007, TALG 2012). Complementing our positive results, we establish a hardness bound on the robustness-consistency tradeoff that is attainable by any algorithm. We empirically validate our algorithms through experiments on synthetic and real-world data.

Artificial intelligence is a hot topic in almost every industry right now, and healthcare is no exception. The big data revolution has transformed manufacturing supply chains, retail advertising and customer service. However, transforming healthcare with AI is a very different and exponentially more difficult challenge. In this article, I'll explain a few reasons why AI in healthcare poses a steeper climb, as well as the potential opportunities that make it worth working toward. Designing and implementing AI tools in healthcare is fundamentally different from using machine learning or big data in other industries.

As an online or physical retail company, your goal is to sell as many products as possible to the public. While traditional advertising methods would have you putting leaflets through doors, the modern marketing approach is all about online ads. Currently, Google Ads is one of the biggest online advertising platforms. However, Google isn't the easiest advertising platform to understand and implement. Especially for those new to the marketing niche, you might be confused by the many different options.

Online optimization covers problems such as online resource allocation, online bipartite matching, adwords (a central problem in e-commerce and advertising), and adwords with separable concave returns. We analyze the worst case competitive ratio of two primal-dual algorithms for a class of online convex (conic) optimization problems that contains the previous examples as special cases defined on the positive orthant. We derive a sufficient condition on the objective function that guarantees a constant worst case competitive ratio (greater than or equal to $\frac{1}{2}$) for monotone objective functions. We provide new examples of online problems on the positive orthant % and the positive semidefinite cone that satisfy the sufficient condition. We show how smoothing can improve the competitive ratio of these algorithms, and in particular for separable functions, we show that the optimal smoothing can be derived by solving a convex optimization problem.

While shopping ads are arguably the most important form of Google advertising for e-commerce accounts, you're missing an opportunity if you ignore regular search ads. Text ads for specific product searches still take up a lot of real estate and can help drive more sales. When I'm preparing to write text ads, I like to see what's currently in the landscape. You know what makes me really happy? Why am I happy with those search results?

Artificial intelligence (AI) and machine learning are new-ish buzzwords. AI is the science, the ability for computers to get smart on their own, doing things that require human intelligence. Machine learning is taking available data and learning through algorithms. And it's incredible how fast things are moving in this space. Computational power has increased exponentially; presenting new opportunities we could not have imagined years ago.

Applications of Artificial Intelligence in Business Posted: 28th June 2018 08:22 In May, Google demonstrated the ability of its artificial intelligent (AI) agent Duplex to have an actual conversation with real life people. It demonstrated it could book a hair appointment but struggled with a more nuanced conversation when attempting to make a restaurant reservation. Whilst there is a lot of hype around AI and a lot of work to be done before an agent passes the Turing Test, the impact AI is having on business should not be underestimated. Voice controlled digital assistants and facial recognition in smart phones are just the beginning. Research firm Tractica estimates that global AI enterprise software revenue will grow from $644 million in 2016 to nearly $39 billion by 2025.

What a momentous year 2017 was for the evolution of machine learning in PPC. Google added in-market audiences for search as well as two new bidding strategies (maximize clicks and maximize conversions), introduced predicted click-through rate and optimized ad rotation to AdWords, and launched Google Attribution. Machine learning is the future of search, without a doubt. As Google's supply of data expands, and their AI research continues to make progress, we can only expect the quality of their machine-learning driven features to improve. The present is a more complicated matter.