Ashok Kumar Ponnuswami

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We address well-studied problems concerning the learnability of parities and halfspaces in the presence of classification noise. Learning of parities under the uniform distribution with random classification noise, also called the noisy parity problem is a famous open problem in computational learning. We reduce a number of basic problems regarding learning(More)
Modern web search engines are federated --- a user query is sent to the numerous specialized search engines called <i>verticals</i> like web (text documents), News, Image, Video, etc. and the results returned by these engines are then aggregated and composed into a search result page (SERP) and presented to the user. For a specific query, multiple verticals(More)
The Max-Min allocation problem is to distribute indivisi-ble goods to people so as to maximize the minimum utility of the people. We show a (2k − 1)-approximation algorithm for Max-Min when there are k people with subadditive utility functions. We also give a k/α-approximation algorithm (for α ≤ k/2) if the utility functions are additive and the utility of(More)
We study the learnability of several fundamental concept classes in the agnostic learning framework of Haussler [Hau92] and Kearns et al. [KSS94]. We show that under the uniform distribution, agnostically learning parities reduces to learning parities with random classification noise, commonly referred to as the noisy parity problem. Together with the(More)
Modern day federated search engines aggregate heterogeneous types of results from multiple vertical search engines and compose a single search engine result page (SERP). The search engine aggregates the results and produces one ranked list, constraining the vertical results to specific slots on the SERP. The usual way to compare two ranking algorithms is(More)
We consider the problem of minimizing regret with respect to a given set S of pairs of time selection functions and modifications rules. We give an on-line algorithm that has O(T log |S|) regret with respect to S when the algorithm is run for T time steps and there are N actions allowed. This improves the upper bound of O(T N log(|I||F|)) given by Blum and(More)
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