Ruslan Salakhutdinov (original) (raw)

Ruslan Salakhutdinov

Professor
Microsoft Faculty Fellow
Sloan Fellow
Carnegie Mellon University
rsalakhu[at]cs.cmu.edu
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:

• VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks
  Jing Yu Koh, Robert Lo, Lawrence Jang, Vikram Duvvur, Ming Chong Lim, Po-Yu Huang, Graham Neubig, Shuyan Zhou, Ruslan Salakhutdinov, Daniel Fried
  ACL 2014 [arXiv] [Code]
• Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks,
  Murtaza Dalal, Tarun Chiruvolu, Devendra Chaplot, Ruslan Salakhutdinov
  ICLR 2024 [arXiv] [Code]
• Effective Data Augmentation With Diffusion Models
  Brandon Trabucco, Kyle Doherty, Max Gurinas, Ruslan Salakhutdinov
  ICLR 2024 [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
  ICLR 2024 [arXiv] [code]
• Contrastive Difference Predictive Coding
  Chongyi Zheng, Ruslan Salakhutdinov, Benjamin Eysenbach
  ICLR 2024 [arXiv] [code]
• Manifold Preserving Guided Diffusion
  Yutong He, Naoki Murata, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Dongjun Kim♨, Wei-Hsiang Liao, Yuki Mitsufuji, Zico Kolter, Ruslan Salakhutdinov, Stefano Ermon
  ICLR 2024 [arXiv] [code]
• Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data
  Chongyi Zheng, Benjamin Eysenbach, Homer Walke, Patrick Yin, Kuan Fang, Ruslan Salakhutdinov, Sergey Levine
  ICLR 2024 [arXiv] [code]
• Confronting Reward Model Overoptimization with Constrained RLHF
  Ted Moskovitz, Aaditya K. Singh, DJ Strouse, Tuomas Sandholm, Ruslan Salakhutdinov, Anca D. Dragan, Stephen McAleer
  ICLR 2024 [arXiv]
• Generating Images with Multimodal Language Models
  Jing Yu Koh, Daniel Fried, Ruslan Salakhutdinov
  NeurIPS 2023, [arXiv] [code]
• Factorized Contrastive Learning: Going Beyond Multi-view Redundancy
  Paul Pu Liang*, Zihao Deng*, Martin Ma*, James Zou, Louis-Philippe Morency, Ruslan Salakhutdinov
  NeurIPS 2023, [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
  NeurIPS 2023, [arXiv] [code]
• Imitating Task and Motion Planning with Visuomotor Transformers
  Murtaza Dalal*, Ajay Mandlekar, Caelan Garrett, Ankur Handa, Ruslan Salakhutdinov, Dieter Fox
  CoRL 2023, [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]