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]
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