FairScholar: Balancing Relevance and Diversity for Scientific Paper Recommendation. - Ankesh Anand; Tanmoy Chakraborty 002; Amitava Das 001 (ECIR, 2017)

We Used Neural Networks to Detect Clickbaits: You Won't Believe What Happened Next! - Ankesh Anand; Tanmoy Chakraborty 002; Noseong Park (ECIR, 2017)

MMGAN: Manifold Matching Generative Adversarial Network for Generating Images. - Noseong Park; Ankesh Anand; Joel Ruben Antony Moniz; Kookjin Lee; Tanmoy Chakraborty 002; Jaegul Choo; Hongkyu Park; Youngmin Kim (CoRR, 2017)

Phishing URL Detection with Oversampling based on Text Generative Adversarial Networks. - Ankesh Anand; Kshitij Gorde; Joel Ruben Antony Moniz; Noseong Park; Tanmoy Chakraborty 002; Bei-Tseng Chu (IEEE BigData, 2018)

HoME: a Household Multimodal Environment. - Simon Brodeur; Ethan Perez; Ankesh Anand; Florian Golemo; Luca Celotti; Florian Strub; Jean Rouat; Hugo Larochelle; Aaron C. Courville (ICLR (Workshop), 2018)

MMGAN: Manifold-Matching Generative Adversarial Networks. - Noseong Park; Ankesh Anand; Joel Ruben Antony Moniz; Kookjin Lee; Jaegul Choo; David Keetae Park; Tanmoy Chakraborty 002; Hongkyu Park; Youngmin Kim (ICPR, 2018)

Blindfold Baselines for Embodied QA. - Ankesh Anand; Eugene Belilovsky; Kyle Kastner; Hugo Larochelle; Aaron C. Courville (CoRR, 2018)

Unsupervised State Representation Learning in Atari. - Ankesh Anand; Evan Racah; Sherjil Ozair; Yoshua Bengio; Marc-Alexandre Côté; R. Devon Hjelm (NeurIPS, 2019)

Data-Efficient Reinforcement Learning with Momentum Predictive Representations. - Max Schwarzer; Ankesh Anand; Rishab Goel; R. Devon Hjelm; Aaron C. Courville; Philip Bachman (CoRR, 2020)

Data-Efficient Reinforcement Learning with Self-Predictive Representations. - Max Schwarzer; Ankesh Anand; Rishab Goel; R. Devon Hjelm; Aaron C. Courville; Philip Bachman (ICLR, 2021)

Pretraining Representations for Data-Efficient Reinforcement Learning. - Max Schwarzer; Nitarshan Rajkumar; Michael Noukhovitch; Ankesh Anand; Laurent Charlin; R. Devon Hjelm; Philip Bachman; Aaron C. Courville (NeurIPS, 2021)

Procedural generalization by planning with self-supervised world models. - Ankesh Anand; Jacob C. Walker; Yazhe Li; Eszter Vértes; Julian Schrittwieser; Sherjil Ozair; Theophane Weber; Jessica B. Hamrick (ICLR, 2022)

Investigating the Role of Model-Based Learning in Exploration and Transfer. - Jacob C. Walker; Eszter Vértes; Yazhe Li; Gabriel Dulac-Arnold; Ankesh Anand; Theophane Weber; Jessica B. Hamrick (ICML, 2023)

Many-Shot In-Context Learning. - Rishabh Agarwal; Avi Singh; Lei Zhang; Bernd Bohnet; Luis Rosias; Stephanie C. Y. Chan; Biao Zhang; Ankesh Anand; Zaheer Abbas; Azade Nova; John D. Co-Reyes; Eric Chu; et al. (NeurIPS, 2024)

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. - Machel Reid; Nikolay Savinov; Denis Teplyashin; Dmitry Lepikhin; Timothy P. Lillicrap; Jean-Baptiste Alayrac; Radu Soricut; Angeliki Lazaridou; Orhan Firat; Julian Schrittwieser; Ioannis Antonoglou; Rohan Anil; et al. (CoRR, 2024)

Many-Shot In-Context Learning. - Rishabh Agarwal; Avi Singh; Lei M. Zhang; Bernd Bohnet; Stephanie Chan 003; Ankesh Anand; Zaheer Abbas; Azade Nova; John D. Co-Reyes; Eric Chu; Feryal M. P. Behbahani; Aleksandra Faust; et al. (CoRR, 2024)

Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. - Avi Singh; John D. Co-Reyes; Rishabh Agarwal; Ankesh Anand; Piyush Patil; Xavier Garcia; Peter J. Liu; James Harrison; Jaehoon Lee 001; Kelvin Xu; Aaron T. Parisi; Abhishek Kumar; et al. (Trans. Mach. Learn. Res., 2024)