Interested in / Focusing on :
📯Help #MLTwitter!
— Nitin Kishore (@Nitin_wysiwyg) July 5, 2023
Being a generalist was exhausting😔. So now, I'll focus on:
1️⃣ Heterogenous #GNNs + #TimeSeries Forecasting
2️⃣ RL with Human Feedback (#RLHF)
3️⃣ NLP -Large Language Models (#LLMs)
I'd appreciate any must read #papers / tips /advice! 📚🚀 #AI #MachineLearning
I have practical expertise and experience in :
Here is my : 1) Google Scholar 2) Google Patents
Please click the links below ⬇️ to explore the papers further and access the pdf and support materials
Published in US Patents, 2021
Built an end-to-end automated pipeline for “Inappropriate content detection” for UGC(user generated content) items on Walmart eCommerce using Vader Sentiment, Topic Modelling, Snorkel and BERT. Built as part of the Trust and Safety framework. This framework creates auto-generated keyword based rules that are obtained through Topic Modelling, over a multi-processed scraper that pulls in RSS feeds of news articles and summaries
Published in INFORMS Journal on Applied Analytics, 2021
Using RL to simulate price markdowns in stores, personalized to region and demographic based optimal policy in order to balance contradictory multi-objective optimization scenario. We need to reduce the price to increase sell through rate of bad performing products, but this decay has to be done in a slow controlled duration, so as to avoid triggering a replenishment before shelf change cycle. This involves reward design with multiple cost penalties. For single price change scenario we devised a variant of the Black Scholes Equation
Recommended citation: Nitin Kishore Sai Samala, Yixian Chen, Prakhar Mehrotra, Kamilia Ahmadi, Viresh Jivane, Linsey Pang (2021) A Multiobjective Optimization for Clearance in Walmart Brick-and-Mortar Stores. INFORMS Journal on Applied Analytics 51(1):76-89. https://doi.org/10.1287/inte.2020.1065 https://doi.org/10.1287/inte.2020.1065
Published in Umass Amherst, 2018
Used Capsule Network as discriminator for a generative adversarial network (GAN), trained using several hacks, which outperformed CNN-GANs at modeling image data distribution of mnist, cifar10 and celebA
Published in Umass Amherst, 2018
Tackled two difficult variants of Document Summarization, the single input summarization and extractive summarization (compressed sentence or EDU) on The New York Times Annotated Corpus(NYT50)