Robust Regression via Hard Thresholding. - Kush Bhatia; Prateek Jain 002; Purushottam Kar (NIPS, 2015)

Sparse Local Embeddings for Extreme Multi-label Classification. - Kush Bhatia; Himanshu Jain; Purushottam Kar; Manik Varma; Prateek Jain 002 (NIPS, 2015)

Locally Non-linear Embeddings for Extreme Multi-label Learning. - Kush Bhatia; Himanshu Jain; Purushottam Kar; Prateek Jain 002; Manik Varma (CoRR, 2015)

Efficient and Consistent Robust Time Series Analysis. - Kush Bhatia; Prateek Jain 002; Parameswaran Kamalaruban; Purushottam Kar (CoRR, 2016)

Lazy Generic Cuts. - Dinesh Khandelwal; Kush Bhatia; Chetan Arora 001; Parag Singla (Comput. Vis. Image Underst., 2016)

Consistent Robust Regression. - Kush Bhatia; Prateek Jain 002; Parameswaran Kamalaruban; Purushottam Kar (NIPS, 2017)

Establishing Appropriate Trust via Critical States. - Sandy H. Huang; Kush Bhatia; Pieter Abbeel; Anca D. Dragan (IROS, 2018)

Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation. - Kush Bhatia; Aldo Pacchiano; Nicolas Flammarion; Peter L. Bartlett; Michael I. Jordan (NeurIPS, 2018)

FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network. - Aditya Kusupati; Manish Singh; Kush Bhatia; Ashish Kumar 007; Prateek Jain 002; Manik Varma (NeurIPS, 2018)

Gen-Oja: A Simple and Efficient Algorithm for Streaming Generalized Eigenvector Computation. - Kush Bhatia; Aldo Pacchiano; Nicolas Flammarion; Peter L. Bartlett; Michael I. Jordan (CoRR, 2018)

Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression. - Arun Sai Suggala; Kush Bhatia; Pradeep Ravikumar; Prateek Jain 002 (COLT, 2019)

Bayesian Robustness: A Nonasymptotic Viewpoint. - Kush Bhatia; Yi-An Ma; Anca D. Dragan; Peter L. Bartlett; Michael I. Jordan (CoRR, 2019)

Preference learning along multiple criteria: A game-theoretic perspective. - Kush Bhatia; Ashwin Pananjady; Peter L. Bartlett; Anca D. Dragan; Martin J. Wainwright (NeurIPS, 2020)

Online learning with dynamics: A minimax perspective. - Kush Bhatia; Karthik Sridharan (NeurIPS, 2020)

Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems. - Dhruv Malik; Ashwin Pananjady; Kush Bhatia; Koulik Khamaru; Peter L. Bartlett; Martin J. Wainwright (J. Mach. Learn. Res., 2020)

Agnostic Learning with Unknown Utilities. - Kush Bhatia; Peter L. Bartlett; Anca D. Dragan; Jacob Steinhardt (ITCS, 2021)

The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models. - Alexander Pan; Kush Bhatia; Jacob Steinhardt (ICLR, 2022)

Congested Bandits: Optimal Routing via Short-term Resets. - Pranjal Awasthi; Kush Bhatia; Sreenivas Gollapudi; Kostas Kollias (ICML, 2022)

Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection. - Kush Bhatia; Nikki Lijing Kuang; Yi-An Ma; Yixin Wang (CoRR, 2022)

Ask Me Anything: A simple strategy for prompting language models. - Simran Arora; Avanika Narayan; Mayee F. Chen; Laurel J. Orr; Neel Guha; Kush Bhatia; Ines Chami; Frederic Sala; Christopher Ré (CoRR, 2022)

Learning when Objectives are Hard to Specify - Kush Bhatia (2022)

Reward Learning as Doubly Nonparametric Bandits: Optimal Design and Scaling Laws. - Kush Bhatia; Wenshuo Guo; Jacob Steinhardt (AISTATS, 2023)

Ask Me Anything: A simple strategy for prompting language models. - Simran Arora; Avanika Narayan; Mayee F. Chen; Laurel J. Orr; Neel Guha; Kush Bhatia; Ines Chami; Christopher Ré (ICLR, 2023)

On the Sensitivity of Reward Inference to Misspecified Human Models. - Joey Hong; Kush Bhatia; Anca D. Dragan (ICLR, 2023)

TART: A plug-and-play Transformer module for task-agnostic reasoning. - Kush Bhatia; Avanika Narayan; Christopher De Sa; Christopher Ré (NeurIPS, 2023)

Skill-it! A data-driven skills framework for understanding and training language models. - Mayee F. Chen; Nicholas Roberts; Kush Bhatia; Jue Wang; Ce Zhang 001; Frederic Sala; Christopher Ré (NeurIPS, 2023)

Embroid: Unsupervised Prediction Smoothing Can Improve Few-Shot Classification. - Neel Guha; Mayee F. Chen; Kush Bhatia; Azalia Mirhoseini; Frederic Sala; Christopher Ré (NeurIPS, 2023)

A case for reframing automated medical image classification as segmentation. - Sarah M. Hooper; Mayee F. Chen; Khaled Saab 002; Kush Bhatia; Curtis P. Langlotz; Christopher Ré (NeurIPS, 2023)

SuperHF: Supervised Iterative Learning from Human Feedback. - Gabriel Mukobi; Peter Chatain; Su Fong; Robert Windesheim; Gitta Kutyniok; Kush Bhatia; Silas Alberti (CoRR, 2023)

The Hedgehog & the Porcupine: Expressive Linear Attentions with Softmax Mimicry. - Michael Zhang; Kush Bhatia; Hermann Kumbong; Christopher Ré (ICLR, 2024)

Cookbook: A framework for improving LLM generative abilities via programmatic data generating templates. - Avanika Narayan; Mayee F. Chen; Kush Bhatia; Christopher Ré (CoRR, 2024)

Automated Rewards via LLM-Generated Progress Functions. - Vishnu Sarukkai; Brennan Shacklett; Zander Majercik; Kush Bhatia; Christopher Ré; Kayvon Fatahalian (CoRR, 2024)