Monocular vision based road marking recognition for driver assistance and safety. - Mohak Sukhwani; Suriya Singh; Anirudh Goyal; Aseem Behl; Pritish Mohapatra; Brijendra Kumar Bharti; C. V. Jawahar (ICVES, 2014)

Stories in the Eye: Contextual Visual Interactions for Efficient Video to Language Translation. - Anirudh Goyal; Marius Leordeanu (CoRR, 2015)

Professor Forcing: A New Algorithm for Training Recurrent Networks. - Anirudh Goyal; Alex Lamb; Ying Zhang 033; Saizheng Zhang; Aaron C. Courville; Yoshua Bengio (NIPS, 2016)

Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations. - David Krueger 001; Tegan Maharaj; János Kramár; Mohammad Pezeshki; Nicolas Ballas; Nan Rosemary Ke; Anirudh Goyal; Yoshua Bengio; Hugo Larochelle; Aaron C. Courville; Chris Pal (CoRR, 2016)

An Actor-Critic Algorithm for Sequence Prediction. - Dzmitry Bahdanau; Philemon Brakel; Kelvin Xu; Anirudh Goyal; Ryan Lowe; Joelle Pineau; Aaron C. Courville; Yoshua Bengio (ICLR (Poster), 2017)

Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations. - David Krueger 001; Tegan Maharaj; János Kramár; Mohammad Pezeshki; Nicolas Ballas; Nan Rosemary Ke; Anirudh Goyal; Yoshua Bengio; Aaron C. Courville; Christopher J. Pal (ICLR (Poster), 2017)

Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net. - Anirudh Goyal; Nan Rosemary Ke; Surya Ganguli; Yoshua Bengio (NIPS, 2017)

Z-Forcing: Training Stochastic Recurrent Networks. - Anirudh Goyal; Alessandro Sordoni; Marc-Alexandre Côté; Nan Rosemary Ke; Yoshua Bengio (NIPS, 2017)

Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent Networks. - Nan Rosemary Ke; Anirudh Goyal; Olexa Bilaniuk; Jonathan Binas; Laurent Charlin; Chris Pal; Yoshua Bengio (CoRR, 2017)

ACtuAL: Actor-Critic Under Adversarial Learning. - Anirudh Goyal; Nan Rosemary Ke; Alex Lamb; R. Devon Hjelm; Chris Pal; Joelle Pineau; Yoshua Bengio (CoRR, 2017)

Extending the Framework of Equilibrium Propagation to General Dynamics. - Benjamin Scellier; Anirudh Goyal; Jonathan Binas; Thomas Mesnard; Yoshua Bengio (ICLR (Workshop), 2018)

Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding. - Nan Rosemary Ke; Anirudh Goyal; Olexa Bilaniuk; Jonathan Binas; Michael C. Mozer; Chris Pal; Yoshua Bengio (NeurIPS, 2018)

Recall Traces: Backtracking Models for Efficient Reinforcement Learning. - Anirudh Goyal; Philemon Brakel; William Fedus; Timothy P. Lillicrap; Sergey Levine; Hugo Larochelle; Yoshua Bengio (CoRR, 2018)

Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations. - Alex Lamb; Jonathan Binas; Anirudh Goyal; Dmitriy Serdyuk; Sandeep Subramanian; Ioannis Mitliagkas; Yoshua Bengio (CoRR, 2018)

Generalization of Equilibrium Propagation to Vector Field Dynamics. - Benjamin Scellier; Anirudh Goyal; Jonathan Binas; Thomas Mesnard; Yoshua Bengio (CoRR, 2018)

Sparse Attentive Backtracking: Temporal CreditAssignment Through Reminding. - Nan Rosemary Ke; Anirudh Goyal; Olexa Bilaniuk; Jonathan Binas; Michael C. Mozer; Chris Pal; Yoshua Bengio (CoRR, 2018)

Recall Traces: Backtracking Models for Efficient Reinforcement Learning. - Anirudh Goyal; Philemon Brakel; William Fedus; Soumye Singhal; Timothy P. Lillicrap; Sergey Levine; Hugo Larochelle; Yoshua Bengio (ICLR (Poster), 2019)

InfoBot: Transfer and Exploration via the Information Bottleneck. - Anirudh Goyal; Riashat Islam; Daniel Strouse; Zafarali Ahmed; Hugo Larochelle; Matthew M. Botvinick; Yoshua Bengio; Sergey Levine (ICLR (Poster), 2019)

Modeling the Long Term Future in Model-Based Reinforcement Learning. - Nan Rosemary Ke; Amanpreet Singh; Ahmed Touati; Anirudh Goyal; Yoshua Bengio; Devi Parikh; Dhruv Batra (ICLR (Poster), 2019)

State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations. - Alex Lamb; Jonathan Binas; Anirudh Goyal; Sandeep Subramanian; Ioannis Mitliagkas; Yoshua Bengio; Michael Mozer (ICML, 2019)

Maximum Entropy Generators for Energy-Based Models. - Rithesh Kumar; Anirudh Goyal; Aaron C. Courville; Yoshua Bengio (CoRR, 2019)

Communication Topologies Between Learning Agents in Deep Reinforcement Learning. - Dhaval Adjodah; Dan Calacci; Abhimanyu Dubey; Anirudh Goyal; Peter M. Krafft; Esteban Moro; Alex Pentland (CoRR, 2019)

Learning Dynamics Model in Reinforcement Learning by Incorporating the Long Term Future. - Nan Rosemary Ke; Amanpreet Singh; Ahmed Touati; Anirudh Goyal; Yoshua Bengio; Devi Parikh; Dhruv Batra (CoRR, 2019)

State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations. - Alex Lamb; Jonathan Binas; Anirudh Goyal; Sandeep Subramanian; Ioannis Mitliagkas; Denis Kazakov; Yoshua Bengio; Michael C. Mozer (CoRR, 2019)

Learning Powerful Policies by Using Consistent Dynamics Model. - Shagun Sodhani; Anirudh Goyal; Tristan Deleu; Yoshua Bengio; Sergey Levine; Jian Tang 005 (CoRR, 2019)

Learning the Arrow of Time. - Nasim Rahaman; Steffen Wolf 001; Anirudh Goyal; Roman Remme; Yoshua Bengio (CoRR, 2019)

Learning Neural Causal Models from Unknown Interventions. - Nan Rosemary Ke; Olexa Bilaniuk; Anirudh Goyal; Stefan Bauer; Hugo Larochelle; Chris Pal; Yoshua Bengio (CoRR, 2019)

Leveraging Communication Topologies Between Learning Agents in Deep Reinforcement Learning. - Dhaval Adjodah; Dan Calacci; Abhimanyu Dubey; Anirudh Goyal; P. M. Krafft; Esteban Moro; Alex Pentland (AAMAS, 2020)

A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms. - Yoshua Bengio; Tristan Deleu; Nasim Rahaman; Nan Rosemary Ke; Sébastien Lachapelle; Olexa Bilaniuk; Anirudh Goyal; Christopher J. Pal (ICLR, 2020)

The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget. - Anirudh Goyal; Yoshua Bengio; Matthew M. Botvinick; Sergey Levine (ICLR, 2020)

Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives. - Anirudh Goyal; Shagun Sodhani; Jonathan Binas; Xue Bin Peng; Sergey Levine; Yoshua Bengio (ICLR, 2020)

Learning the Arrow of Time for Problems in Reinforcement Learning. - Nasim Rahaman; Steffen Wolf 001; Anirudh Goyal; Roman Remme; Yoshua Bengio (ICLR, 2020)

Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules. - Sarthak Mittal; Alex Lamb; Anirudh Goyal; Vikram Voleti; Murray Shanahan; Guillaume Lajoie; Michael Mozer; Yoshua Bengio (ICML, 2020)

Small-GAN: Speeding up GAN Training using Core-Sets. - Samarth Sinha; Han Zhang 010; Anirudh Goyal; Yoshua Bengio; Hugo Larochelle; Augustus Odena (ICML, 2020)

Untangling tradeoffs between recurrence and self-attention in artificial neural networks. - Giancarlo Kerg; Bhargav Kanuparthi; Anirudh Goyal; Kyle Goyette; Yoshua Bengio; Guillaume Lajoie (NeurIPS, 2020)

Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples. - Samarth Sinha; Zhengli Zhao; Anirudh Goyal; Colin Raffel; Augustus Odena (NeurIPS, 2020)

Top-K Training of GANs: Improving Generators by Making Critics Less Critical. - Samarth Sinha; Anirudh Goyal; Colin Raffel; Augustus Odena (CoRR, 2020)

Is Independence all you need? On the Generalization of Representations Learned from Correlated Data. - Frederik Träuble; Elliot Creager; Niki Kilbertus; Anirudh Goyal; Francesco Locatello; Bernhard Schölkopf; Stefan Bauer (CoRR, 2020)

Untangling tradeoffs between recurrence and self-attention in neural networks. - Giancarlo Kerg; Bhargav Kanuparthi; Anirudh Goyal; Kyle Goyette; Yoshua Bengio; Guillaume Lajoie (CoRR, 2020)

Object Files and Schemata: Factorizing Declarative and Procedural Knowledge in Dynamical Systems. - Anirudh Goyal; Alex Lamb; Phanideep Gampa; Philippe Beaudoin; Sergey Levine; Charles Blundell; Yoshua Bengio; Michael Mozer (CoRR, 2020)

Maximum Entropy Models for Fast Adaptation. - Samarth Sinha; Anirudh Goyal; Animesh Garg (CoRR, 2020)

S2RMs: Spatially Structured Recurrent Modules. - Nasim Rahaman; Anirudh Goyal; Muhammad Waleed Gondal; Manuel Wuthrich; Stefan Bauer; Yash Sharma 001; Yoshua Bengio; Bernhard Schölkopf (CoRR, 2020)

Inductive Biases for Deep Learning of Higher-Level Cognition. - Anirudh Goyal; Yoshua Bengio (CoRR, 2020)

DIBS: Diversity Inducing Information Bottleneck in Model Ensembles. - Samarth Sinha; Homanga Bharadhwaj; Anirudh Goyal; Hugo Larochelle; Animesh Garg; Florian Shkurti (AAAI, 2021)

Neural Function Modules with Sparse Arguments: A Dynamic Approach to Integrating Information across Layers. - Alex Lamb; Anirudh Goyal; Agnieszka Slowik; Michael Mozer; Philippe Beaudoin; Yoshua Bengio (AISTATS, 2021)

CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning. - Ossama Ahmed; Frederik Träuble; Anirudh Goyal; Alexander Neitz; Manuel Wuthrich; Yoshua Bengio; Bernhard Schölkopf; Stefan Bauer (ICLR, 2021)

Factorizing Declarative and Procedural Knowledge in Structured, Dynamical Environments. - Anirudh Goyal; Alex Lamb; Phanideep Gampa; Philippe Beaudoin; Charles Blundell; Sergey Levine; Yoshua Bengio; Michael Curtis Mozer (ICLR, 2021)

Recurrent Independent Mechanisms. - Anirudh Goyal; Alex Lamb; Jordan Hoffmann; Shagun Sodhani; Sergey Levine; Yoshua Bengio; Bernhard Schölkopf (ICLR, 2021)

Fast And Slow Learning Of Recurrent Independent Mechanisms. - Kanika Madan; Nan Rosemary Ke; Anirudh Goyal; Bernhard Schölkopf; Yoshua Bengio (ICLR, 2021)

Spatially Structured Recurrent Modules. - Nasim Rahaman; Anirudh Goyal; Muhammad Waleed Gondal; Manuel Wuthrich; Stefan Bauer; Yash Sharma 001; Yoshua Bengio; Bernhard Schölkopf (ICLR, 2021)

Robust Representation Learning via Perceptual Similarity Metrics. - Saeid Asgari Taghanaki; Kristy Choi; Amir Hosein Khasahmadi; Anirudh Goyal (ICML, 2021)

On Disentangled Representations Learned from Correlated Data. - Frederik Träuble; Elliot Creager; Niki Kilbertus; Francesco Locatello; Andrea Dittadi; Anirudh Goyal; Bernhard Schölkopf; Stefan Bauer (ICML, 2021)

Real Robot Challenge: A Robotics Competition in the Cloud. - Stefan Bauer; Manuel Wüthrich; Felix Widmaier; Annika Buchholz; Sebastian Stark; Anirudh Goyal; Thomas Steinbrenner; Joel Akpo; Shruti Joshi; Vincent Berenz; Vaibhav Agrawal; Niklas Funk; et al. (NeurIPS (Competition and Demos), 2021)

Neural Production Systems. - Aniket Didolkar; Anirudh Goyal; Nan Rosemary Ke; Charles Blundell; Philippe Beaudoin; Nicolas Heess; Michael Mozer; Yoshua Bengio (NeurIPS, 2021)

Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning. - Nan Rosemary Ke; Aniket Didolkar; Sarthak Mittal; Anirudh Goyal; Guillaume Lajoie; Stefan Bauer; Danilo Jimenez Rezende; Michael Mozer; Yoshua Bengio; Chris Pal (NeurIPS Datasets and Benchmarks, 2021)

Discrete-Valued Neural Communication. - Dianbo Liu; Alex Lamb; Kenji Kawaguchi; Anirudh Goyal; Chen Sun 007; Michael C. Mozer; Yoshua Bengio (NeurIPS, 2021)

Towards Causal Representation Learning. - Bernhard Schölkopf; Francesco Locatello; Stefan Bauer; Nan Rosemary Ke; Nal Kalchbrenner; Anirudh Goyal; Yoshua Bengio (CoRR, 2021)

Transformers with Competitive Ensembles of Independent Mechanisms. - Alex Lamb; Di He 001; Anirudh Goyal; Guolin Ke; Chien-Feng Liao; Mirco Ravanelli; Yoshua Bengio (CoRR, 2021)

Variational Causal Networks: Approximate Bayesian Inference over Causal Structures. - Yashas Annadani; Jonas Rothfuss; Alexandre Lacoste; Nino Scherrer; Anirudh Goyal; Yoshua Bengio; Stefan Bauer (CoRR, 2021)

Learning Neural Causal Models with Active Interventions. - Nino Scherrer; Olexa Bilaniuk; Yashas Annadani; Anirudh Goyal; Patrick Schwab; Bernhard Schölkopf; Michael C. Mozer; Yoshua Bengio; Stefan Bauer; Nan Rosemary Ke (CoRR, 2021)

Toward Causal Representation Learning. - Bernhard Schölkopf; Francesco Locatello; Stefan Bauer; Nan Rosemary Ke; Nal Kalchbrenner; Anirudh Goyal; Yoshua Bengio (Proc. IEEE, 2021)

Uniform Priors for Data-Efficient Learning. - Samarth Sinha; Karsten Roth; Anirudh Goyal; Marzyeh Ghassemi; Zeynep Akata; Hugo Larochelle; Animesh Garg (CVPR Workshops, 2022)

Coordination Among Neural Modules Through a Shared Global Workspace. - Anirudh Goyal; Aniket Rajiv Didolkar; Alex Lamb; Kartikeya Badola; Nan Rosemary Ke; Nasim Rahaman; Jonathan Binas; Charles Blundell; Michael Curtis Mozer; Yoshua Bengio (ICLR, 2022)

Learning by Directional Gradient Descent. - David Silver; Anirudh Goyal; Ivo Danihelka; Matteo Hessel; Hado van Hasselt (ICLR, 2022)

Retrieval-Augmented Reinforcement Learning. - Anirudh Goyal; Abram L. Friesen; Andrea Banino; Theophane Weber; Nan Rosemary Ke; Adrià Puigdomènech Badia; Arthur Guez; Mehdi Mirza; Peter Conway Humphreys; Ksenia Konyushkova; Michal Valko; Simon Osindero; et al. (ICML, 2022)

Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence Learning. - Aniket Didolkar; Kshitij Gupta; Anirudh Goyal; Nitesh B. Gundavarapu; Alex Lamb; Nan Rosemary Ke; Yoshua Bengio (NeurIPS, 2022)

Discrete Compositional Representations as an Abstraction for Goal Conditioned Reinforcement Learning. - Riashat Islam; Hongyu Zang; Anirudh Goyal; Alex Lamb; Kenji Kawaguchi; Xin Li 033; Romain Laroche; Yoshua Bengio; Remi Tachet des Combes (NeurIPS, 2022)

Retrieval-Augmented Reinforcement Learning. - Anirudh Goyal; Abram L. Friesen; Andrea Banino; Theophane Weber; Nan Rosemary Ke; Adrià Puigdomènech Badia; Arthur Guez; Mehdi Mirza; Ksenia Konyushkova; Michal Valko; Simon Osindero; Timothy P. Lillicrap; et al. (CoRR, 2022)

Generating Fast and Slow: Scene Decomposition via Reconstruction. - Mihir Prabhudesai; Anirudh Goyal; Deepak Pathak; Katerina Fragkiadaki (CoRR, 2022)

Learning to Induce Causal Structure. - Nan Rosemary Ke; Silvia Chiappa; Jane Wang 001; Jörg Bornschein; Theophane Weber; Anirudh Goyal; Matthew M. Botvinick; Michael Mozer; Danilo Jimenez Rezende (CoRR, 2022)

Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel. - Dianbo Liu; Vedant Shah; Oussama Boussif; Cristian Meo; Anirudh Goyal; Tianmin Shu; Michael Mozer; Nicolas Heess; Yoshua Bengio (CoRR, 2022)

Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence Learning. - Aniket Didolkar; Kshitij Gupta; Anirudh Goyal; Alex Lamb; Nan Rosemary Ke; Yoshua Bengio (CoRR, 2022)

On the Generalization and Adaption Performance of Causal Models. - Nino Scherrer; Anirudh Goyal; Stefan Bauer; Yoshua Bengio; Nan Rosemary Ke (CoRR, 2022)

Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning. - Riashat Islam; Hongyu Zang; Anirudh Goyal; Alex Lamb; Kenji Kawaguchi; Xin Li 033; Romain Laroche; Yoshua Bengio; Remi Tachet des Combes (CoRR, 2022)

Representation Learning in Deep RL via Discrete Information Bottleneck. - Riashat Islam; Hongyu Zang; Manan Tomar; Aniket Didolkar; Md Mofijul Islam; Samin Yeasar Arnob; Tariq Iqbal; Xin Li 033; Anirudh Goyal; Nicolas Heess; Alex Lamb (AISTATS, 2023)

Learning to Induce Causal Structure. - Nan Rosemary Ke; Silvia Chiappa; Jane X. Wang; Jörg Bornschein; Anirudh Goyal; Mélanie Rey; Theophane Weber; Matthew M. Botvinick; Michael Curtis Mozer; Danilo Jimenez Rezende (ICLR, 2023)

Stateful Active Facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning. - Dianbo Liu; Vedant Shah; Oussama Boussif; Cristian Meo; Anirudh Goyal; Tianmin Shu; Michael Curtis Mozer; Nicolas Heess; Yoshua Bengio (ICLR, 2023)

GFlowOut: Dropout with Generative Flow Networks. - Dianbo Liu; Moksh Jain; Bonaventure F. P. Dossou; Qianli Shen; Salem Lahlou; Anirudh Goyal; Nikolay Malkin; Chris Chinenye Emezue; Dinghuai Zhang; Nadhir Hassen; Xu Ji; Kenji Kawaguchi; et al. (ICML, 2023)

Test-time Adaptation with Slot-Centric Models. - Mihir Prabhudesai; Anirudh Goyal; Sujoy Paul; Sjoerd van Steenkiste; Mehdi S. M. Sajjadi; Gaurav Aggarwal; Thomas Kipf; Deepak Pathak; Katerina Fragkiadaki (ICML, 2023)

Discrete Key-Value Bottleneck. - Frederik Träuble; Anirudh Goyal; Nasim Rahaman; Michael Curtis Mozer; Kenji Kawaguchi; Yoshua Bengio; Bernhard Schölkopf (ICML, 2023)

Leveraging the Third Dimension in Contrastive Learning. - Sumukh K. Aithal; Anirudh Goyal; Alex Lamb; Yoshua Bengio; Michael Mozer (CoRR, 2023)

DiscoGen: Learning to Discover Gene Regulatory Networks. - Nan Rosemary Ke; Sara-Jane Dunn; Jörg Bornschein; Silvia Chiappa; Mélanie Rey; Jean-Baptiste Lespiau; Albin Cassirer; Jane X. Wang; Theophane Weber; David G. T. Barrett; Matthew M. Botvinick; Anirudh Goyal; et al. (CoRR, 2023)

Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior. - Ayush Chakravarthy; Bailey Trang Nguyen; Anirudh Goyal; Yoshua Bengio; Michael C. Mozer (CoRR, 2023)

A Theory for Emergence of Complex Skills in Language Models. - Sanjeev Arora; Anirudh Goyal (CoRR, 2023)

Unlearning via Sparse Representations. - Vedant Shah; Frederik Träuble; Ashish Malik; Hugo Larochelle; Michael Mozer; Sanjeev Arora; Yoshua Bengio; Anirudh Goyal (CoRR, 2023)

Neural Causal Structure Discovery from Interventions. - Nan Rosemary Ke; Olexa Bilaniuk; Anirudh Goyal; Stefan Bauer; Hugo Larochelle; Bernhard Schölkopf; Michael Curtis Mozer; Christopher Pal; Yoshua Bengio (Trans. Mach. Learn. Res., 2023)

A novel privacy protection approach with better human imperceptibility. - Kapil Rana; Aman Pandey; Parth Goyal; Gurinder Singh 001; Puneet Goyal (Appl. Intell., 2023)

Reasoning Robustness of LLMs to Adversarial Typographical Errors. - Esther Gan; Yiran Zhao 006; Liying Cheng; Yancan Mao; Anirudh Goyal; Kenji Kawaguchi; Min-Yen Kan; Michael Shieh (EMNLP, 2024)

Cycle Consistency Driven Object Discovery. - Aniket Rajiv Didolkar; Anirudh Goyal; Yoshua Bengio (ICLR, 2024)

αTC-VAE: On the relationship between Disentanglement and Diversity. - Cristian Meo; Louis Mahon; Anirudh Goyal; Justin Dauwels (ICLR, 2024)

SKILL-MIX: a Flexible and Expandable Family of Evaluations for AI Models. - Dingli Yu; Simran Kaur 001; Arushi Gupta; Jonah Brown-Cohen; Anirudh Goyal; Sanjeev Arora (ICLR, 2024)

Accelerating Greedy Coordinate Gradient and General Prompt Optimization via Probe Sampling. - Yiran Zhao 006; Wenyue Zheng; Tianle Cai; Do Xuan Long; Kenji Kawaguchi; Anirudh Goyal; Michael Qizhe Shieh (NeurIPS, 2024)

Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving. - Aniket Didolkar; Anirudh Goyal; Nan Rosemary Ke; Siyuan Guo; Michal Valko; Timothy P. Lillicrap; Danilo Jimenez Rezende; Yoshua Bengio; Michael C. Mozer; Sanjeev Arora (NeurIPS, 2024)

Keeping LLMs Aligned After Fine-tuning: The Crucial Role of Prompt Templates. - Kaifeng Lyu; Haoyu Zhao; Xinran Gu; Dingli Yu; Anirudh Goyal; Sanjeev Arora (NeurIPS, 2024)

Can Models Learn Skill Composition from Examples? - Haoyu Zhao; Simran Kaur 001; Dingli Yu; Anirudh Goyal; Sanjeev Arora (NeurIPS, 2024)

Can AI Be as Creative as Humans? - Haonan Wang; James Zou 001; Michael Mozer; Anirudh Goyal; Alex Lamb; Linjun Zhang; Weijie J. Su; Zhun Deng; Michael Qizhe Xie; Hannah Brown; Kenji Kawaguchi (CoRR, 2024)

Accelerating Greedy Coordinate Gradient via Probe Sampling. - Yiran Zhao 006; Wenyue Zheng; Tianle Cai; Xuan Long Do; Kenji Kawaguchi; Anirudh Goyal; Michael Shieh (CoRR, 2024)

Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning. - Yuxi Xie; Anirudh Goyal; Wenyue Zheng; Min-Yen Kan; Timothy P. Lillicrap; Kenji Kawaguchi; Michael Shieh (CoRR, 2024)

Learning Beyond Pattern Matching? Assaying Mathematical Understanding in LLMs. - Siyuan Guo; Aniket Didolkar; Nan Rosemary Ke; Anirudh Goyal; Ferenc Huszár; Bernhard Schölkopf (CoRR, 2024)

Bayesian-LoRA: LoRA based Parameter Efficient Fine-Tuning using Optimal Quantization levels and Rank Values trough Differentiable Bayesian Gates. - Cristian Meo; Ksenia Sycheva; Anirudh Goyal; Justin Dauwels (CoRR, 2024)

AI-Assisted Generation of Difficult Math Questions. - Vedant Shah; Dingli Yu; Kaifeng Lyu; Simon Park 002; Nan Rosemary Ke; Michael Mozer; Yoshua Bengio; Sanjeev Arora; Anirudh Goyal (CoRR, 2024)

The Llama 3 Herd of Models. - Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Ahmad Al-Dahle; Aiesha Letman; Akhil Mathur; Alan Schelten; Amy Yang; Angela Fan; Anirudh Goyal; Anthony Hartshorn; et al. (CoRR, 2024)

Zero-Shot Object-Centric Representation Learning. - Aniket Didolkar; Andrii Zadaianchuk; Anirudh Goyal; Michael C. Mozer; Yoshua Bengio; Georg Martius; Maximilian Seitzer (CoRR, 2024)

Narrowing the Focus: Learned Optimizers for Pretrained Models. - Gus Kristiansen; Mark Sandler 002; Andrey Zhmoginov; Nolan Miller; Anirudh Goyal; Jihwan Lee; Max Vladymyrov (CoRR, 2024)

Masked Generative Priors Improve World Models Sequence Modelling Capabilities. - Cristian Meo; Mircea Lica; Zarif Ikram; Akihiro Nakano; Vedant Shah; Aniket Rajiv Didolkar; Dianbo Liu; Anirudh Goyal; Justin Dauwels (CoRR, 2024)

COrAL: Order-Agnostic Language Modeling for Efficient Iterative Refinement. - Yuxi Xie; Anirudh Goyal; Xiaobao Wu; Xunjian Yin; Xiao Xu 005; Min-Yen Kan; Liangming Pan; William Yang Wang (CoRR, 2024)

Object-Centric Temporal Consistency via Conditional Autoregressive Inductive Biases. - Cristian Meo; Akihiro Nakano; Mircea Lica; Aniket Didolkar; Masahiro Suzuki 001; Anirudh Goyal; Mengmi Zhang; Justin Dauwels; Yutaka Matsuo; Yoshua Bengio (CoRR, 2024)

α-TCVAE: On the relationship between Disentanglement and Diversity. - Cristian Meo; Louis Mahon; Anirudh Goyal; Justin Dauwels (CoRR, 2024)

Data for Mathematical Copilots: Better Ways of Presenting Proofs for Machine Learning. - Simon Frieder; Jonas Bayer; Katherine M. Collins; Julius Berner; Jacob Loader; András Juhász; Fabian Ruehle; Sean Welleck; Gabriel Poesia; Ryan-Rhys Griffiths; Adrian Weller; Anirudh Goyal; et al. (CoRR, 2024)

Physical Reasoning and Object Planning for Household Embodied Agents. - Ayush Agrawal; Raghav Prabhakar; Anirudh Goyal; Dianbo Liu (Trans. Mach. Learn. Res., 2024)

Instruct-SkillMix: A Powerful Pipeline for LLM Instruction Tuning. - Simran Kaur 001; Simon Park 002; Anirudh Goyal; Sanjeev Arora (ICLR, 2025)

Understanding and Enhancing Safety Mechanisms of LLMs via Safety-Specific Neuron. - Yiran Zhao 006; Wenxuan Zhang 001; Yuxi Xie; Anirudh Goyal; Kenji Kawaguchi; Michael Shieh (ICLR, 2025)

SWE-Search: Enhancing Software Agents with Monte Carlo Tree Search and Iterative Refinement. - Antonis Antoniades; Albert Örwall; Kexun Zhang; Yuxi Xie; Anirudh Goyal; William Yang Wang (ICLR, 2025)

On the Transfer of Object-Centric Representation Learning. - Aniket Rajiv Didolkar; Andrii Zadaianchuk; Anirudh Goyal; Michael Curtis Mozer; Yoshua Bengio; Georg Martius; Maximilian Seitzer (ICLR, 2025)

A Systematic Examination of Preference Learning through the Lens of Instruction-Following. - Joongwon Kim; Anirudh Goyal; Aston Zhang; Bo Xiong; Rui Hou; Melanie Kambadur; Dhruv Mahajan 001; Hannaneh Hajishirzi; Liang Tan (NAACL (Long Papers), 2025)

Generalizing from SIMPLE to HARD Visual Reasoning: Can We Mitigate Modality Imbalance in VLMs? - Simon Park 002; Abhishek Panigrahi; Yun Cheng; Dingli Yu; Anirudh Goyal; Sanjeev Arora (CoRR, 2025)

Auto-Bench: An Automated Benchmark for Scientific Discovery in LLMs. - Tingting Chen; Srinivas Anumasa; Beibei Lin; Vedant Shah; Anirudh Goyal; Dianbo Liu (CoRR, 2025)

Unnatural Languages Are Not Bugs but Features for LLMs. - Keyu Duan; Yiran Zhao 006; Zhili Feng; Jinjie Ni; Tianyu Pang; Qian Liu 012; Tianle Cai; Longxu Dou; Kenji Kawaguchi; Anirudh Goyal; J. Zico Kolter; Michael Qizhe Shieh (CoRR, 2025)

ASTRO: Teaching Language Models to Reason by Reflecting and Backtracking In-Context. - Joongwon Kim; Anirudh Goyal; Liang Tan; Hannaneh Hajishirzi; Srinivasan Iyer 001; Tianlu Wang (CoRR, 2025)