Christopher Pal
Distinguished Scientist
AI Research Partnerships & Ecosystem
Christopher Pal is a Distinguished Scientist at ServiceNow AI Research. He has been involved in AI and machine learning research for over twenty-five years and has published extensively on key aspects of artificial intelligence, machine learning and deep learning, including work on: large language models (LLMs), reasoning, robotics, computer vision, and generative modelling techniques. He is also a Full Professor at Polytechnique Montréal, a Canada CIFAR AI Chair, and an adjunct professor in the Department of Computer Science and Operations Research (DIRO) at the Université de Montréal. He has a PhD in computer science from the University of Waterloo, is associated with the IVADO, and is one of the core founding faculty members of Mila, the Quebec AI Institute.
Intérêts
- Machine Learning
- Computer Vision
- Natural Language Processing
- Reinforcement Learning
- Deep Learning
Publications
DRBench: A Realistic Benchmark for Enterprise Deep Research.
International Conference on Learning Representations,
2026.
Grounding Computer Use Agents on Human Demonstrations.
International Conference on Learning Representations,
2026.
StarFlow: Generating Structured Workflow Outputs From Sketch Images.
European Chapter of the Association for Computational Linguistics (EACL),
2026.
AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Document Understanding.
Neural Information Processing Systems (NeurIPS),
2025.
Rendering-Aware Reinforcement Learning for Vector Graphics Generation.
Neural Information Processing Systems (NeurIPS),
2025.
The Promise of RL for Autoregressive Image Editing.
Neural Information Processing Systems (NeurIPS),
2025.
WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation.
Conference on Empirical Methods in Natural Language Processing (EMNLP),
2025.
BigCharts-R1: Enhanced Chart Reasoning with Visual Reinforcement Finetuning.
Conference on Language Modeling (COLM),
2025.
LLMs can learn self-restraint through iterative self-reflection.
Transactions on Machine Learning Research (TMLR),
2025.
AgentAda: Skill-Adaptive Data Analytics for Tailored Insight Discovery.
Workshop at the Annual Meeting of the Association for Computational Linguistics (ACL),
2025.
UI-Vision: A Desktop-centric GUI Benchmark for Visual Perception and Interaction.
International Conference on Machine Learning (ICML),
2025.
WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation.
Workshop at the Computer Vision and Pattern Recognition Conference (CVPR),
2025.
StarVector: Generating Scalable Vector Graphics Code from Images and Text.
Computer Vision and Pattern Recognition (CVPR),
2025.
AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Understanding.
Workshop at the International Conference of Learning Representation (ICLR),
2025.
WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation.
Workshop at the International Conference of Learning Representation (ICLR),
2025.
BigDocs: An Open and Permissively-Licensed Dataset for Training Multimodal Models on Document and Code Tasks.
International Conference of Learning Representations (ICLR),
2025.
InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation.
International Conference of Learning Representations (ICLR),
2025.
Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced Reasoning.
International Conference of Learning Representations (ICLR),
2025.
LitLLMs, LLMs for Literature Review: Are We There Yet?.
Transactions on Machine Learning Research (TMLR),
2025.
StarVector: Generating Scalable Vector Graphics Code from Images and Text.
AAAI Demos,
2025.
BigDocs: A Permissively-Licensed Dataset for Training Vision-Language Models on Document and Code Tasks.
Workshop at the Neural Information Processing Systems (NeurIPS),
2024.
RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content.
NeurIPS Datasets and Benchmarks Track (NeurIPS Datasets),
2024.
XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference.
Workshop at the Neural Information Processing Systems (NeurIPS),
2024.
XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference.
Conference on Empirical Methods in Natural Language Processing (EMNLP),
2024.
Exploring validation metrics for offline model-based optimisation with diffusion models.
Transactions on Machine Learning Research (TMLR),
2024.
IntentGPT: Few-shot Intent Discovery with Large Language Models.
Workshop at the International Conference of Learning Representation (ICLR),
2024.
Self-evaluation and self-prompting to improve the reliability of LLMs.
Workshop at the International Conference of Learning Representation (ICLR),
2024.
Workflow discovery in low data regimes.
International Conference of Learning Representations (ICLR),
2024.
Language Decision Transformers with Exponential Tilt for Interactive Text Environments.
ArXiv,
2024.
Capture the Flag: Uncovering Data Insights with Large Language Models.
Workshop at the Neural Information Processing Systems (NeurIPS),
2023.
Are Diffusion Models Vision-And-Language Reasoners?.
Conference on Neural Information Processing Systems (NeurIPS),
2023.
Bridging the Gap Between Target Networks and Functional Regularization.
Transactions on Machine Learning Research (TMLR),
2023.
Workflow discovery in low data regimes.
Workshop at the International Conference on Machine Learning (ICML),
2023.
Multilingual Code Retrieval Without Paired Data: A New Benchmark and Experiments.
Workshop at the International Conference on Learning Representations (ICLR),
2023.
Towards Learning to Imitate from a Single Video Demonstration.
Journal of Machine Learning Research (JMLR),
2023.
Workflow discovery in low data regimes.
Transactions on Machine Learning Research,
2023.
Attention-based Neural Cellular Automata.
Conference on Neural Information Processing Systems (NeurIPS),
2022.
Implicit Offline Reinforcement Learning via Supervised Learning.
Workshop at the Neural Information Processing Systems (NeurIPS),
2022.
Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation.
Conference on Neural Information Processing Systems (NeurIPS),
2022.
Neural Attentive Circuits.
Conference on Neural Information Processing Systems (NeurIPS),
2022.
Does entity abstraction help generative Transformers reason? .
Transactions on Machine Learning Research,
2022.
Overcoming challenges in leveraging GANs for few-shot data augmentation.
Workshop at the Conference on Lifelong Learning Agents (CoLLAs),
2022.
Direct Behavior Specification via Constrained Reinforcement Learning.
International Conference on Machine Learning (ICML),
2022.
A Probabilistic Perspective on Reinforcement Learning via Supervised Learning.
Workshop at the International Conference on Learning Representations (ICLR),
2022.
Latent Variable Sequential Set Transformers for Joint Multi-Agent Motion Prediction.
International Conference on Learning Representations (ICLR),
2022.
Learning to Guide and to Be Guided in the Architect-Builder Problem.
International Conference on Learning Representations (ICLR),
2022.
Visual Question Answering From Another Perspective: CLEVR Mental Rotation Tests.
Pattern Recognition (PR),
2022.
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning.
Conference on Neural Information Processing Systems (NeurIPS),
2021.
DuoRAT: Towards Simpler Text-to-SQL Models.
North American Chapter of the Association for Computational Linguistics (NAACL),
2021.
Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data.
International Conference on Learning Representations (ICLR),
2021.
Predicting Infectiousness for Proactive Contact Tracing.
International Conference on Learning Representations (ICLR),
2021.
Reinforcement Learning with Random Delays.
International Conference on Learning Representations (ICLR),
2021.
Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization.
Conference on Neural Information Processing Systems (NeurIPS),
2020.
Measuring Systematic Generalization in Neural Proof Generation with Transformers.
Conference on Neural Information Processing Systems (NeurIPS),
2020.
Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning.
Conference on Neural Information Processing Systems (NeurIPS),
2020.
On Extractive and Abstractive Neural Document Summarization with Transformer Language Models.
Conference on Empirical Methods in Natural Language Processing (EMNLP),
2020.
AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation.
International Conference on Machine Learning (ICML),
2020.
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms.
International Conference on Learning Representations (ICLR),
2020.
Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents.
International Conference on Learning Representations (ICLR),
2020.
Reinforced Active Learning for Image Segmentation.
International Conference on Learning Representations (ICLR),
2020.
Neural Multisensory Scene Inference.
Conference on Neural Information Processing Systems (NeurIPS),
2019.
On Adversarial Mixup Resynthesis.
Conference on Neural Information Processing Systems (NeurIPS),
2019.
Real-Time Reinforcement Learning.
Conference on Neural Information Processing Systems (NeurIPS),
2019.
Retrieving Signals in the Frequency Domain with Deep Complex Extractors.
Workshop at the Neural Information Processing Systems (NeurIPS),
2019.
Active Domain Randomization.
Conference on Robot Learning (CoRL),
2019.
SEVN: A Sidewalk Simulation Environment for Visual Navigation.
Conference on Robot Learning (CoRL),
2019.
On the impressive performance of randomly weighted encoders in summarization tasks.
Annual Meeting of the Association for Computational Linguistics (ACL),
2019.
Structure Learning for Neural Module Networks.
Annual Meeting of the Association for Computational Linguistics (ACL),
2019.
Adversarial Mixup Resynthesizers.
Workshop at the International Conference on Learning Representations (ICLR),
2019.
Planning with Latent SImulated Trajectories.
Workshop at the International Conference on Learning Representations (ICLR),
2019.
Probabilistic Planning with Sequential Monte Carlo Methods.
International Conference on Learning Representations (ICLR),
2019.
Towards Standardization of Data Licenses: The Montreal Data License.
Workshop at the International Conference on Learning Representations (ICLR),
2019.
Recurrent Transition Networks for Character Locomotion.
Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia (SIGGRAPH Asia),
2018.
Fashion-Gen: The Generative Fashion Dataset and Challenge.
Women in Machine Learning (WiML),
2018.
Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding.
Conference on Neural Information Processing Systems (NeurIPS),
2018.
Towards Deep Conversational Recommendations.
Conference on Neural Information Processing Systems (NeurIPS),
2018.
Towards Text Generation with Adversarially Learned Neural Outlines.
Conference on Neural Information Processing Systems (NeurIPS),
2018.
Unsupervised Depth Estimation, 3D Face Rotation and Replacement.
Conference on Neural Information Processing Systems (NeurIPS),
2018.
Fashion-Gen: The Generative Fashion Dataset and Challenge.
Workshop at the International Conference on Machine Learning (ICML),
2018.
Deep Complex Networks.
International Conference on Learning Representations (ICLR),
2018.