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Shreshth Malik

PhD, started 2021

Shreshth is a DPhil student in the OATML group, supervised by Yarin Gal and Steve Roberts, and is a member of the AIMS CDT. His main research interests include improving the robustness of deep learning, causality, and AI safety/ethics, in order to enable more reliable applications of machine learning in the real world. He also enjoys applying machine learning methods to solve problems in the sciences. Previously, he has worked on understanding the challenges of using deep active learning in practice with Humanloop and David Barber, and predicting the outcomes of material syntheses with Alpha Lee. He holds Master’s degrees in Machine Learning (UCL) and Physical Natural Sciences (University of Cambridge).

Publications while at OATMLNews items mentioning Shreshth MalikReproducibility and CodeBlog Posts

Publications while at OATML:

BatchGFN: Generative Flow Networks for Batch Active Learning

We introduce BatchGFN---a novel approach for pool-based active learning that uses generative flow networks to sample sets of data points proportional to a batch reward. With an appropriate reward function to quantify the utility of acquiring a batch, such as the joint mutual information between the batch and the model parameters, BatchGFN is able to construct highly informative batches for active learning in a principled way. We show our approach enables sampling near-optimal utility batches at inference time with a single forward pass per point in the batch in toy regression problems. This alleviates the computational complexity of batch-aware algorithms and removes the need for greedy approximations to find maximizers for the batch reward. We also present early results for amortizing training across acquisition steps, which will enable scaling to real-world tasks.

Shreshth Malik, Salem Lahlou, Andrew Jesson, Moksh Jain, Nikolay Malkin, Tristan Deleu, Yoshua Bengio, Yarin Gal
Structured Probabilistic Inference & Generative Modeling workshop, ICML 2023

Discovering Long-period Exoplanets using Deep Learning with Citizen Science Labels

Automated planetary transit detection has become vital to prioritize candidates for expert analysis given the scale of modern telescopic surveys. While current methods for short-period exoplanet detection work effectively due to periodicity in the light curves, there lacks a robust approach for detecting single-transit events. However, volunteer-labelled transits recently collected by the Planet Hunters TESS (PHT) project now provide an unprecedented opportunity to investigate a data-driven approach to long-period exoplanet detection. In this work, we train a 1-D convolutional neural network to classify planetary transits using PHT volunteer scores as training data. We find using volunteer scores significantly improves performance over synthetic data, and enables the recovery of known planets at a precision and rate matching that of the volunteers. Importantly, the model also recovers transits found by volunteers but missed by current automated methods.

Shreshth Malik, Nora L. Eisner, Chris J. Lintott, Yarin Gal
Machine Learning and the Physical Sciences workshop, NeurIPS 2022
More publications on Google Scholar.

Blog Posts

OATML Conference papers at NeurIPS 2022

OATML group members and collaborators are proud to present 8 papers at NeurIPS 2022 main conference, and 11 workshop papers. …

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Yarin Gal, Freddie Kalaitzis, Shreshth Malik, Lorenz Kuhn, Gunshi Gupta, Jannik Kossen, Pascal Notin, Andrew Jesson, Panagiotis Tigas, Tim G. J. Rudner, Sebastian Farquhar, Ilia Shumailov, 25 Nov 2022

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