Timezone: »

Efficient List-Decodable Regression using Batches
Abhimanyu Das · Ayush Jain · Weihao Kong · Rajat Sen

Tue Jul 25 02:00 PM -- 04:30 PM (PDT) @ Exhibit Hall 1 #726
We demonstrate the use of batches in studying list-decodable linear regression, in which only $\alpha\in (0,1]$ fraction of batches contain genuine samples from a common distribution and the rest can contain arbitrary or even adversarial samples. When genuine batches have $\ge \tilde\Omega(1/\alpha)$ samples each, our algorithm can efficiently find a small list of potential regression parameters, with a high probability that one of them is close to the true parameter. This is the first polynomial time algorithm for list-decodable linear regression, and its sample complexity scales nearly linearly with the dimension of the covariates. The polynomial time algorithm is made possible by the batch structure and may not be feasible without it, as suggested by a recent Statistical Query lower bound (Diakonikolas et al., 2021b).

Author Information

Abhimanyu Das (Google)
Ayush Jain (UC San Diego)
Weihao Kong (University of Washington)
Rajat Sen (Google Research)

I am a 4th year PhD. student in WNCG, UT Austin. I am advised by [Dr. Sanjay Shakkottai](http://users.ece.utexas.edu/~shakkott/Sanjay_Shakkottai/Contact.html). I received my Bachelors degree in ECE, IIT Kharagpur in 2013. I have spent most of my childhood in Durgapur and Kolkata, West Bengal, India. My research interests include online learning (especially Multi-Armed Bandit problems), causality, learning in queueing systems, recommendation systems and social networks. I like to work on real-world problems that allow rigorous theoretical analysis.

More from the Same Authors