Ruslan Salakhutdinov

Microsoft Faculty Fellow
Sloan Fellow
Carnegie Mellon University
CV Google Scholar  

I am a UPMC professor of Computer Science in the Machine Learning Department, School of Computer Science at Carnegie Mellon University. I work in the field of statistical machine learning (See my CV.)

My research interests include Deep Learning, Probabilistic Graphical Models, and Large-scale Optimization.

Prospective students: Please read this to ensure that I read your email.

Recent Research Highlights:

  • 4 part Deep Learning Tutorial at the Simons Institute, Berkeley
    Part 1:[Slides (pdf)], Part 2:[Slides (pdf)], Part 3:[Slides (pdf)], Part 4:[Slides (pdf)].

  • Deep Learning Tutorial, MLSS, Tübingen, Germany
    Part 1:[Slides (pdf)], Part 2:[Slides (pdf)].

    Recent Papers:

    • Effective Data Augmentation With Diffusion Models
      Brandon Trabucco, Kyle Doherty, Max Gurinas, Ruslan Salakhutdinov
      ICLR 2023 Foundation Models Workshop [arXiv] [code]

    • Generating Images with Multimodal Language Models
      Jing Yu Koh, Daniel Fried, Ruslan Salakhutdinov
      [arXiv] [code]

    • Factorized Contrastive Learning: Going Beyond Multi-view Redundancy
      Paul Pu Liang*, Zihao Deng*, Martin Ma*, James Zou, Louis-Philippe Morency, Ruslan Salakhutdinov
      [arXiv] [code]

    • Multimodal Learning Without Labeled Multimodal Data: Guarantees and Applications
      Paul Pu Liang, Chun Kai Ling, Yun Cheng, Alex Obolenskiy, Yudong Liu, Rohan Pandey, Alex Wilf, Louis-Philippe Morency, Ruslan Salakhutdinov
      [arXiv] [code]

    • Quantifying & Modeling Feature Interactions: An Information Decomposition Framework
      Paul Pu Liang, Yun Cheng, Xiang Fan, Chun Kai Ling, Suzanne Nie, Richard Chen, Zihao Deng, Nicholas Allen, Randy Auerbach, Faisal Mahmood, Ruslan Salakhutdinov, Louis-Philippe Morency
      [arXiv] [code]

    • Imitating Task and Motion Planning with Visuomotor Transformers
      Murtaza Dalal*, Ajay Mandlekar, Caelan Garrett, Ankur Handa, Ruslan Salakhutdinov, Dieter Fox
      [arXiv] [code]

    • Graph Generative Model for Benchmarking Graph Neural Networks
      Minji Yoon, Yue Wu, John Palowitch, Bryan Perozzi, Russ Salakhutdinov
      ICML 2023 [arXiv]

    • A Connection between One-Step RL and Critic Regularization in Reinforcement Learning
      Benjamin Eysenbach, Matthieu Geist, Sergey Levine, and Ruslan Salakhutdinov
      ICML 2023 [arXiv]

    • Grounding Language Models to Images for Multimodal Inputs and Outputs
      Jing Yu Koh, Ruslan Salakhutdinov, Daniel Fried
      ICML 2023 [arXiv] [code]

    • Multimodal Fusion Interactions: A Study of Human and Automatic Quantification
      Paul Pu Liang, Yun Cheng, Ruslan Salakhutdinov, Louis-Philippe Morency
      ICMI 2023 [arXiv] [code]

    • Reasoning over Logically Interacted Conditions for Question Answering
      Haitian Sun, William W. Cohen, Ruslan Salakhutdinov
      ICLR 2023 [arXiv]

    • A Simple Approach for Visual Rearrangement: 3D Mapping and Semantic Search
      Brandon Trabucco, Gunnar Sigurdsson, Robinson Piramuthu, Gaurav S. Sukhatme, Ruslan Salakhutdinov
      ICLR 2023 [arXiv]

    • Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective
      Raj Ghugare, Homanga Bharadhwaj, Benjamin Eysenbach, Sergey Levine, Ruslan Salakhutdinov
      ICLR 2023 [arXiv]

    • MultiViz: An Analysis Benchmark for Visualizing and Understanding Multimodal Models
      Paul Pu Liang, Yiwei Lyu, Gunjan Chhablani, Nihal Jain, Zihao Deng, Xingbo Wang, Louis-Philippe Morency, Ruslan Salakhutdinov
      ICLR 2023 [arXiv]