Investigating Twitter as a Source for Studying Behavioral Responses to Epidemics. - Alex Lamb; Michael J. Paul; Mark Dredze (AAAI Fall Symposium: Information Retrieval and Knowledge Discovery in Biomedical Text, 2012)

Separating Fact from Fear: Tracking Flu Infections on Twitter. - Alex Lamb; Michael J. Paul; Mark Dredze (HLT-NAACL, 2013)

Variance Reduction in SGD by Distributed Importance Sampling. - Guillaume Alain; Alex Lamb; Chinnadhurai Sankar; Aaron C. Courville; Yoshua Bengio (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)

Theano: A Python framework for fast computation of mathematical expressions. - Rami Al-Rfou; Guillaume Alain; Amjad Almahairi; Christof Angermüller; Dzmitry Bahdanau; Nicolas Ballas; Frédéric Bastien; Justin Bayer; Anatoly Belikov; Alexander Belopolsky; Yoshua Bengio; Arnaud Bergeron; et al. (CoRR, 2016)

Discriminative Regularization for Generative Models. - Alex Lamb; Vincent Dumoulin; Aaron C. Courville (CoRR, 2016)

Adversarially Learned Inference. - Vincent Dumoulin; Ishmael Belghazi; Ben Poole; Alex Lamb; Martín Arjovsky; Olivier Mastropietro; Aaron C. Courville (ICLR (Poster), 2017)

GibbsNet: Iterative Adversarial Inference for Deep Graphical Models. - Alex Lamb; R. Devon Hjelm; Yaroslav Ganin; Joseph Paul Cohen; Aaron C. Courville; Yoshua Bengio (NIPS, 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)

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)

Manifold Mixup: Encouraging Meaningful On-Manifold Interpolation as a Regularizer. - Vikas Verma; Alex Lamb; Christopher Beckham; Aaron C. Courville; Ioannis Mitliagkas; Yoshua Bengio (CoRR, 2018)

Deep Learning for Classical Japanese Literature. - Tarin Clanuwat; Mikel Bober-Irizar; Asanobu Kitamoto; Alex Lamb; Kazuaki Yamamoto; David Ha (CoRR, 2018)

Interpolated Adversarial Training: Achieving Robust Neural Networks Without Sacrificing Too Much Accuracy. - Alex Lamb; Vikas Verma; Juho Kannala; Yoshua Bengio (AISec@CCS, 2019)

KuroNet: Pre-Modern Japanese Kuzushiji Character Recognition with Deep Learning. - Tarin Clanuwat; Alex Lamb; Asanobu Kitamoto (ICDAR, 2019)

Adversarial Mixup Resynthesizers. - Christopher Beckham; Sina Honari; Alex Lamb; Vikas Verma; Farnoosh Ghadiri; R. Devon Hjelm; Christopher J. Pal (DGS@ICLR, 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)

Manifold Mixup: Better Representations by Interpolating Hidden States. - Vikas Verma; Alex Lamb; Christopher Beckham; Amir Najafi 002; Ioannis Mitliagkas; David Lopez-Paz; Yoshua Bengio (ICML, 2019)

Interpolation Consistency Training for Semi-supervised Learning. - Vikas Verma; Alex Lamb; Juho Kannala; Yoshua Bengio; David Lopez-Paz (IJCAI, 2019)

On Adversarial Mixup Resynthesis. - Christopher Beckham; Sina Honari; Vikas Verma; Alex Lamb; Farnoosh Ghadiri; R. Devon Hjelm; Yoshua Bengio; Chris Pal (NeurIPS, 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)

Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Accuracy. - Alex Lamb; Vikas Verma; Juho Kannala; Yoshua Bengio (CoRR, 2019)

GraphMix: Regularized Training of Graph Neural Networks for Semi-Supervised Learning. - Vikas Verma; Meng Qu; Alex Lamb; Yoshua Bengio; Juho Kannala; Jian Tang 005 (CoRR, 2019)

KaoKore: A Pre-modern Japanese Art Facial Expression Dataset. - Yingtao Tian; Chikahiko Suzuki; Tarin Clanuwat; Mikel Bober-Irizar; Alex Lamb; Asanobu Kitamoto (ICCC, 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)

SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks. - Alex Lamb; Sherjil Ozair; Vikas Verma; David Ha (WACV, 2020)

Jigsaw-VAE: Towards Balancing Features in Variational Autoencoders. - Saeid Asgari Taghanaki; Mohammad Havaei; Alex Lamb; Aditya Sanghi; Ara Danielyan; Tonya Custis (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)

KuroNet: Regularized Residual U-Nets for End-to-End Kuzushiji Character Recognition. - Alex Lamb; Tarin Clanuwat; Asanobu Kitamoto (SN Comput. Sci., 2020)

GraphMix: Improved Training of GNNs for Semi-Supervised Learning. - Vikas Verma; Meng Qu; Kenji Kawaguchi; Alex Lamb; Yoshua Bengio; Juho Kannala; Jian Tang 005 (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)

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)

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

A Brief Introduction to Generative Models. - Alex Lamb (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)

Predicting the Ordering of Characters in Japanese Historical Documents. - Alex Lamb; Tarin Clanuwat; Siyu Han; Mikel Bober-Irizar; Asanobu Kitamoto (CoRR, 2021)

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)

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)

Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization. - Dianbo Liu; Alex Lamb; Xu Ji; Pascal Notsawo; Michael Mozer; Yoshua Bengio; Kenji Kawaguchi (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)

Guaranteed Discovery of Controllable Latent States with Multi-Step Inverse Models. - Alex Lamb; Riashat Islam; Yonathan Efroni; Aniket Didolkar; Dipendra Misra; Dylan J. Foster; Lekan P. Molu; Rajan Chari; Akshay Krishnamurthy; John Langford 001 (CoRR, 2022)

CNT (Conditioning on Noisy Targets): A new Algorithm for Leveraging Top-Down Feedback. - Alexia Jolicoeur-Martineau; Alex Lamb; Vikas Verma; Aniket Didolkar (CoRR, 2022)

Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information. - Riashat Islam; Manan Tomar; Alex Lamb; Yonathan Efroni; Hongyu Zang; Aniket Didolkar; Dipendra Misra; Xin Li 033; Harm van Seijen; Remi Tachet des Combes; John Langford 001 (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)

Neural Active Learning on Heteroskedastic Distributions. - Savya Khosla; Chew Kin Whye; Jordan T. Ash; Cyril Zhang; Kenji Kawaguchi; Alex Lamb (CoRR, 2022)

Towards Data-Driven Offline Simulations for Online Reinforcement Learning. - Shengpu Tang; Felipe Vieira Frujeri; Dipendra Misra; Alex Lamb; John Langford 001; Paul Mineiro; Sebastian Kochman (CoRR, 2022)

Interpolated Adversarial Training: Achieving robust neural networks without sacrificing too much accuracy. - Alex Lamb; Vikas Verma; Kenji Kawaguchi; Alexander Matyasko; Savya Khosla; Juho Kannala; Yoshua Bengio (Neural Networks, 2022)

Interpolation consistency training for semi-supervised learning. - Vikas Verma; Kenji Kawaguchi; Alex Lamb; Juho Kannala; Arno Solin; Yoshua Bengio; David Lopez-Paz (Neural Networks, 2022)

Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization for Heterogeneous Representational Coarseness. - Dianbo Liu; Alex Lamb; Xu Ji; Pascal Tikeng Notsawo Jr.; Michael Mozer; Yoshua Bengio; Kenji Kawaguchi (AAAI, 2023)

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)

Understanding and Improving Neural Active Learning on Heteroskedastic Distributions. - Savya Khosla; Chew Kin Whye; Jordan T. Ash; Cyril Zhang; Kenji Kawaguchi; Alex Lamb (ECAI, 2023)

Principled Offline RL in the Presence of Rich Exogenous Information. - Riashat Islam; Manan Tomar; Alex Lamb; Yonathan Efroni; Hongyu Zang; Aniket Rajiv Didolkar; Dipendra Misra; Xin Li 033; Harm van Seijen; Remi Tachet des Combes; John Langford 001 (ICML, 2023)

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

Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models. - Alex Lamb; Riashat Islam; Yonathan Efroni; Aniket Rajiv Didolkar; Dipendra Misra; Dylan J. Foster; Lekan P. Molu; Rajan Chari; Akshay Krishnamurthy; John Langford 001 (Trans. Mach. Learn. Res., 2023)

Towards Principled Representation Learning from Videos for Reinforcement Learning. - Dipendra Misra; Akanksha Saran; Tengyang Xie; Alex Lamb; John Langford 001 (ICLR, 2024)

PcLast: Discovering Plannable Continuous Latent States. - Anurag Koul; Shivakanth Sujit; Shaoru Chen; Ben Evans; Lili Wu; Byron Xu; Rajan Chari; Riashat Islam; Raihan Seraj; Yonathan Efroni; Lekan P. Molu; Miroslav Dudík; et al. (ICML, 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)

Generalizing Multi-Step Inverse Models for Representation Learning to Finite-Memory POMDPs. - Lili Wu; Ben Evans; Riashat Islam; Raihan Seraj; Yonathan Efroni; Alex Lamb (CoRR, 2024)

Video Occupancy Models. - Manan Tomar; Philippe Hansen-Estruch; Philip Bachman; Alex Lamb; John Langford 001; Matthew E. Taylor; Sergey Levine (CoRR, 2024)

Learning to Achieve Goals with Belief State Transformers. - Edward S. Hu; Kwangjun Ahn; Qinghua Liu; Haoran Xu; Manan Tomar; Ada Langford; Dinesh Jayaraman; Alex Lamb; John Langford 001 (CoRR, 2024)

The Belief State Transformer. - Edward S. Hu; Kwangjun Ahn; Qinghua Liu; Haoran Xu; Manan Tomar; Ada Langford; Dinesh Jayaraman; Alex Lamb; John Langford 001 (ICLR, 2025)

Towards Improving Exploration through Sibling Augmented GFlowNets. - Kanika Madan; Alex Lamb; Emmanuel Bengio; Glen Berseth; Yoshua Bengio (ICLR, 2025)

Efficient Joint Prediction of Multiple Future Tokens. - Kwangjun Ahn; Alex Lamb; John Langford 001 (CoRR, 2025)