Learning clustered sub-spaces for sketch-based image retrieval. - Koustav Ghosal; Ameya Prabhu; Riddhiman Dasgupta; Anoop M. Namboodiri (ACPR, 2015)

Towards Sub-Word Level Compositions for Sentiment Analysis of Hindi-English Code Mixed Text. - Aditya Joshi; Ameya Prabhu; Manish Shrivastava 001; Vasudeva Varma (COLING, 2016)

Towards Deep Learning in Hindi NER: An approach to tackle the Labelled Data Sparsity. - Vinayak Athavale; Shreenivas Bharadwaj; Monik Pamecha; Ameya Prabhu; Manish Shrivastava 001 (ICON, 2016)

Adversary Is the Best Teacher: Towards Extremely Compact Neural Networks. - Ameya Prabhu; Harish Krishna; Soham Saha (AAAI, 2018)

Deep Expander Networks: Efficient Deep Networks from Graph Theory. - Ameya Prabhu; Girish Varma; Anoop M. Namboodiri (ECCV (13), 2018)

Hybrid Binary Networks: Optimizing for Accuracy, Efficiency and Memory. - Ameya Prabhu; Vishal Batchu; Rohit Gajawada; Sri Aurobindo Munagala; Anoop M. Namboodiri (WACV, 2018)

Distribution-Aware Binarization of Neural Networks for Sketch Recognition. - Ameya Prabhu; Vishal Batchu; Sri Aurobindo Munagala; Rohit Gajawada; Anoop M. Namboodiri (WACV, 2018)

Sampling Bias in Deep Active Classification: An Empirical Study. - Ameya Prabhu; Charles Dognin; Maneesh Singh 001 (EMNLP/IJCNLP (1), 2019)

"You might also like this model": Data Driven Approach for Recommending Deep Learning Models for Unknown Image Datasets. - Ameya Prabhu; Riddhiman Dasgupta; Anush Sankaran; Srikanth Tamilselvam; Senthil Mani (CoRR, 2019)

STQ-Nets: Unifying Network Binarization and Structured Pruning. - Sri Aurobindo Munagala; Ameya Prabhu; Anoop M. Namboodiri (BMVC, 2020)

GDumb: A Simple Approach that Questions Our Progress in Continual Learning. - Ameya Prabhu; Philip H. S. Torr; Puneet K. Dokania (ECCV (2), 2020)

Simple Unsupervised Multi-Object Tracking. - Shyamgopal Karthik; Ameya Prabhu; Vineet Gandhi (CoRR, 2020)

No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks. - Shyamgopal Karthik; Ameya Prabhu; Puneet K. Dokania; Vineet Gandhi (ICLR, 2021)

CLActive: Episodic Memories for Rapid Active Learning. - Sri Aurobindo Munagala; Sidhant Subramanian; Shyamgopal Karthik; Ameya Prabhu; Anoop M. Namboodiri (CoLLAs, 2022)

Evaluating Inexact Unlearning Requires Revisiting Forgetting. - Shashwat Goel; Ameya Prabhu; Ponnurangam Kumaraguru (CoRR, 2022)

Real-Time Evaluation in Online Continual Learning: A New Hope. - Yasir Ghunaim; Adel Bibi; Kumail Alhamoud; Motasem Alfarra; Hasan Abed Al Kader Hammoud; Ameya Prabhu; Philip H. S. Torr; Bernard Ghanem (CVPR, 2023)

Computationally Budgeted Continual Learning: What Does Matter? - Ameya Prabhu; Hasan Abed Al Kader Hammoud; Puneet K. Dokania; Philip H. S. Torr; Ser-Nam Lim; Bernard Ghanem; Adel Bibi (CVPR, 2023)

Rapid Adaptation in Online Continual Learning: Are We Evaluating It Right? - Hasan Abed Al Kader Hammoud; Ameya Prabhu; Ser-Nam Lim; Philip H. S. Torr; Adel Bibi; Bernard Ghanem (ICCV, 2023)

Real-Time Evaluation in Online Continual Learning: A New Paradigm. - Yasir Ghunaim; Adel Bibi; Kumail Alhamoud; Motasem Alfarra; Hasan Abed Al Kader Hammoud; Ameya Prabhu; Philip H. S. Torr; Bernard Ghanem (CoRR, 2023)

Online Continual Learning Without the Storage Constraint. - Ameya Prabhu; Zhipeng Cai 003; Puneet K. Dokania; Philip H. S. Torr; Vladlen Koltun; Ozan Sener (CoRR, 2023)

From Categories to Classifier: Name-Only Continual Learning by Exploring the Web. - Ameya Prabhu; Hasan Abed Al Kader Hammoud; Ser-Nam Lim; Bernard Ghanem; Philip H. S. Torr; Adel Bibi (CoRR, 2023)

Inverse Scaling: When Bigger Isn't Better. - Ian R. McKenzie; Alexander Lyzhov; Michael Pieler; Alicia Parrish; Aaron Mueller; Ameya Prabhu; Euan McLean; Aaron Kirtland; Alexis Ross; Alisa Liu; Andrew Gritsevskiy; Daniel Wurgaft; et al. (Trans. Mach. Learn. Res., 2023)

From Categories to Classifiers: Name-Only Continual Learning by Exploring the Web. - Ameya Prabhu; Hasan Abed Al Kader Hammoud; Ser-Nam Lim; Bernard Ghanem; Philip Torr 001; Adel Bibi (CoLLAs, 2024)

Data Contamination Report from the 2024 CONDA Shared Task. - Oscar Sainz; Iker García-Ferrero; Alon Jacovi; Jon Ander Campos; Yanai Elazar; Eneko Agirre; Yoav Goldberg; Wei-Lin Chen; Jenny Chim; Leshem Choshen; Luca D'Amico-Wong; Melissa Dell; et al. (CONDA, 2024)

RanDumb: Random Representations Outperform Online Continually Learned Representations. - Ameya Prabhu; Shiven Sinha; Ponnurangam Kumaraguru; Philip Torr 001; Ozan Sener; Puneet K. Dokania (NeurIPS, 2024)

Efficient Lifelong Model Evaluation in an Era of Rapid Progress. - Ameya Prabhu; Vishaal Udandarao; Philip Torr 001; Matthias Bethge; Adel Bibi; Samuel Albanie (NeurIPS, 2024)

CiteME: Can Language Models Accurately Cite Scientific Claims? - Ori Press; Andreas Hochlehnert; Ameya Prabhu; Vishaal Udandarao; Ofir Press; Matthias Bethge (NeurIPS, 2024)

No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance. - Vishaal Udandarao; Ameya Prabhu; Adhiraj Ghosh; Yash Sharma 001; Philip Torr 001; Adel Bibi; Samuel Albanie; Matthias Bethge (NeurIPS, 2024)

A Practitioner's Guide to Real-World Continual Multimodal Pretraining. - Vishaal Udandarao; Karsten Roth; Sebastian Dziadzio; Ameya Prabhu; Mehdi Cherti; Oriol Vinyals; Olivier J. Hénaff; Samuel Albanie; Zeynep Akata; Matthias Bethge (NeurIPS, 2024)

RanDumb: A Simple Approach that Questions the Efficacy of Continual Representation Learning. - Ameya Prabhu; Shiven Sinha; Ponnurangam Kumaraguru; Philip H. S. Torr; Ozan Sener; Puneet K. Dokania (CoRR, 2024)

Lifelong Benchmarks: Efficient Model Evaluation in an Era of Rapid Progress. - Ameya Prabhu; Vishaal Udandarao; Philip Torr 001; Matthias Bethge; Adel Bibi; Samuel Albanie (CoRR, 2024)

Wu's Method can Boost Symbolic AI to Rival Silver Medalists and AlphaGeometry to Outperform Gold Medalists at IMO Geometry. - Shiven Sinha; Ameya Prabhu; Ponnurangam Kumaraguru; Siddharth Bhat; Matthias Bethge (CoRR, 2024)

A Practitioner's Guide to Continual Multimodal Pretraining. - Karsten Roth; Vishaal Udandarao; Sebastian Dziadzio; Ameya Prabhu; Mehdi Cherti; Oriol Vinyals; Olivier J. Hénaff; Samuel Albanie; Matthias Bethge; Zeynep Akata (CoRR, 2024)

Delta-Influence: Unlearning Poisons via Influence Functions. - Wenjie Li; Jiawei Li; Christian Schröder de Witt; Ameya Prabhu; Amartya Sanyal (CoRR, 2024)

Corrective Machine Unlearning. - Shashwat Goel; Ameya Prabhu; Philip Torr 001; Ponnurangam Kumaraguru; Amartya Sanyal (Trans. Mach. Learn. Res., 2024)

kNN-CLIP: Retrieval Enables Training-Free Segmentation on Continually Expanding Large Vocabularies. - Zhongrui Gui; Shuyang Sun; Runjia Li; Jianhao Yuan; Zhaochong An; Karsten Roth; Ameya Prabhu; Philip Torr 001 (Trans. Mach. Learn. Res., 2024)

ONEBench to Test Them All: Sample-Level Benchmarking Over Open-Ended Capabilities. - Adhiraj Ghosh; Sebastian Dziadzio; Ameya Prabhu; Vishaal Udandarao; Samuel Albanie; Matthias Bethge (ACL (1), 2025)

How to Merge Your Multimodal Models Over Time? - Sebastian Dziadzio; Vishaal Udandarao; Karsten Roth; Ameya Prabhu; Zeynep Akata; Samuel Albanie; Matthias Bethge (CVPR, 2025)

Open Problems in Machine Unlearning for AI Safety. - Fazl Barez; Tingchen Fu; Ameya Prabhu; Stephen Casper; Amartya Sanyal; Adel Bibi; Aidan O'Gara; Robert Kirk; Ben Bucknall; Timothy Fist; Luke Ong; Philip Torr 001; et al. (CoRR, 2025)

Humanity's Last Exam. - Long Phan; Alice Gatti; Ziwen Han; Nathaniel Li; Josephina Hu; Hugh Zhang; Sean Shi; Michael Choi; Anish Agrawal; Arnav Chopra; Adam Khoja; Ryan Kim; et al. (CoRR, 2025)

Great Models Think Alike and this Undermines AI Oversight. - Shashwat Goel; Joschka Struber; Ilze Amanda Auzina; Karuna K. Chandra; Ponnurangam Kumaraguru; Douwe Kiela; Ameya Prabhu; Matthias Bethge; Jonas Geiping (CoRR, 2025)

Pretraining Frequency Predicts Compositional Generalization of CLIP on Real-World Tasks. - Thaddäus Wiedemer; Yash Sharma 001; Ameya Prabhu; Matthias Bethge; Wieland Brendel (CoRR, 2025)

Project Alexandria: Towards Freeing Scientific Knowledge from Copyright Burdens via LLMs. - Christoph Schuhmann; Gollam Rabby; Ameya Prabhu; Tawsif Ahmed; Andreas Hochlehnert; Huu Nguyen; Nick Akinci Heidrich; Ludwig Schmidt; Robert Kaczmarczyk; Sören Auer; Jenia Jitsev; Matthias Bethge (CoRR, 2025)

Can Language Models Falsify? Evaluating Algorithmic Reasoning with Counterexample Creation. - Shiven Sinha; Shashwat Goel; Ponnurangam Kumaraguru; Jonas Geiping; Matthias Bethge; Ameya Prabhu (CoRR, 2025)

A Sober Look at Progress in Language Model Reasoning: Pitfalls and Paths to Reproducibility. - Andreas Hochlehnert; Hardik Bhatnagar; Vishaal Udandarao; Samuel Albanie; Ameya Prabhu; Matthias Bethge (CoRR, 2025)

Are We Done with Object-Centric Learning? - Alexander Rubinstein 002; Ameya Prabhu; Matthias Bethge; Seong Joon Oh (CoRR, 2025)

Answer Matching Outperforms Multiple Choice for Language Model Evaluation. - Nikhil Chandak; Shashwat Goel; Ameya Prabhu; Moritz Hardt; Jonas Geiping (CoRR, 2025)

VGGSounder: Audio-Visual Evaluations for Foundation Models. - Daniil Zverev; Thaddäus Wiedemer; Ameya Prabhu; Matthias Bethge; Wieland Brendel; A. Sophia Koepke (CoRR, 2025)