Search Results for author: Ritwik Sinha

Found 6 papers, 2 papers with code

Privacy Aware Experiments without Cookies

no code implementations3 Nov 2022 Shiv Shankar, Ritwik Sinha, Saayan Mitra, Moumita Sinha, Viswanathan Swaminathan, Sridhar Mahadevan

We propose a two-stage experimental design, where the two brands only need to agree on high-level aggregate parameters of the experiment to test the alternate experiences.

Experimental Design

Time-uniform central limit theory, asymptotic confidence sequences, and anytime-valid causal inference

2 code implementations11 Mar 2021 Ian Waudby-Smith, David Arbour, Ritwik Sinha, Edward H. Kennedy, Aaditya Ramdas

While the CLT approximates the distribution of a sample average by that of a Gaussian at a fixed sample size, we use strong invariance principles (stemming from the seminal 1970s work of Komlos, Major, and Tusnady) to uniformly approximate the entire sample average process by an implicit Gaussian process.

Causal Inference

Botcha: Detecting Malicious Non-Human Traffic in the Wild

no code implementations2 Mar 2021 Sunny Dhamnani, Ritwik Sinha, Vishwa Vinay, Lilly Kumari, Margarita Savova

Malicious bots make up about a quarter of all traffic on the web, and degrade the performance of personalization and recommendation algorithms that operate on e-commerce sites.

Forecasting Granular Audience Size for Online Advertising

no code implementations8 Jan 2019 Ritwik Sinha, Dhruv Singal, Pranav Maneriker, Kushal Chawla, Yash Shrivastava, Deepak Pai, Atanu R. Sinha

Orchestration of campaigns for online display advertising requires marketers to forecast audience size at the granularity of specific attributes of web traffic, characterized by the categorical nature of all attributes (e. g. {US, Chrome, Mobile}).

Time Series Time Series Analysis

Saliency Prediction for Mobile User Interfaces

no code implementations10 Nov 2017 Prakhar Gupta, Shubh Gupta, Ajaykrishnan Jayagopal, Sourav Pal, Ritwik Sinha

However, given the difference in what constitutes a mobile interface, and the usage context of these devices, we postulate that saliency prediction for mobile interface images requires a fresh approach.

Saliency Prediction

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