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)].
- I am teaching a Intermdiate Deep Learning class, Fall 2019.
Recent Papers:
-
Capsules with Inverted Dot-Product Attention Routing
Yao-Hung Hubert Tsai, Nitish Srivastava, Hanlin Goh, Ruslan Salakhutdinov
ICLR 2020, [arXiv] -
Learning To Explore Using Active Neural Mapping
Devendra Singh Chaplot, Saurabh Gupta, Dhiraj Gandhi, Abhinav Gupta, Ruslan Salakhutdinov
ICLR 2020, [arXiv] -
Differentiable Reasoning over a Virtual Knowledge Base
Bhuwan Dhingra, Manzil Zaheer, Vidhisha Balachandran, Graham Neubig, Ruslan Salakhutdinov, William W. Cohen
ICLR 2020, [arXiv] -
Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks
Sanjeev Arora, Simon S. Du, Zhiyuan Li, Ruslan Salakhutdinov, Ruosong Wang, Dingli Yu
ICLR 2020, [arXiv] -
On Emergent Communication in Competitive Multi-Agent Teams
Paul Pu Liang, Jeffrey Chen, Ruslan Salakhutdinov, Louis-Philippe Morency, Satwik Kottur
AAMAS 2020. -
Modular Visual Navigation using Active Neural Mapping
Devendra Singh Chaplot, Saurabh Gupta, Abhinav Gupta, Ruslan Salakhutdinov
[pdf] [Videos], Winner of the Habitat Navigation Challenge at CVPR 2019. -
Concurrent Episodic Meta Reinforcement Learning
Emilio Parisotto, Soham Ghosh, Sai Bhargav Yalamanchi, Varsha Chinnaobireddy, Yuhuai Wu, Ruslan Salakhutdinov
[arXiv] -
Efficient Exploration via State Marginal Matching
Lisa Lee, Benjamin Eysenbach, Emilio Parisotto, Eric Xing, Sergey Levine, Ruslan Salakhutdinov
[arXiv] [Code] -
Worst cases policy gradients
Charlie Tang, Jian Zhang, Ruslan Salakhutdinov
CoRL 2019
-
Transformer Dissection: A Unified Understanding of Transformer Attention via the Lens of Kernel
Yao-Hung Hubert Tsai, Shaojie Bai, Makoto Yamada, Louis-Philippe Morency, Ruslan Salakhutdinov
EMNLP 2019, [arXiv] -
Multiple Futures Prediction
Charlie Tang, Ruslan Salakhutdinov
NeurIPS 2019, [arXiv]
-
Search on the Replay Buffer: Bridging Planning and Reinforcement Learning
Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine
NeurIPS 2019, [arXiv] [Code] -
Learning Data Manipulation for Augmentation and Weighting
Zhiting Hu, Bowen Tan, Ruslan Salakhutdinov, Tom Mitchell, Eric P. Xing
NeurIPS 2019
-
Mixtape: Breaking the Softmax Bottleneck Efficiently
Zhilin Yang, Thang Luong, Ruslan Salakhutdinov, Quoc V Le
NeurIPS 2019
-
XLNet: Generalized Autoregressive Pretraining for Language Understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le
NeurIPS 2019 [arXiv] [Code], oral,
-
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
Simon S. Du, Kangcheng Hou, Barnabás Póczos, Ruslan Salakhutdinov, Ruosong Wang, Keyulu Xu
NeurIPS 2019 [arXiv] -
On Exact Computation with an Infinitely Wide Neural Net
Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruslan Salakhutdinov, Ruosong Wang
NeurIPS 2019 [arXiv] -
Learning Neural Networks with Adaptive Regularization
Han Zhao, Yao-Hung Hubert Tsai, Ruslan Salakhutdinov, Geoffrey J. Gordon
NeurIPS 2019 [arXiv] -
Deep Gamblers: Learning to Abstain with Portfolio Theory
Liu Ziyin, Zhikang Wang, Paul Pu Liang, Ruslan Salakhutdinov, Louis-Philippe Morency, Masahito Ueda
NeurIPS 2019 [arXiv] -
MineRL: A Large-Scale Dataset of Minecraft Demonstrations
William H. Guss, Brandon Houghton, Nicholay Topin, Phillip Wang, Cayden Codel, Manuela Veloso, Ruslan Salakhutdinov
IJCAI 2019 [arXiv], [Web] -
My Way of Telling a Story": Persona based Grounded Story Generation
Shrimai Prabhumoye, Khyathi Chandu, Ruslan Salakhutdinov, Alan W Black.
Storytelling Workshop at ACL 2019 [arXiv], -
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov
ACL 2019 [arXiv], [Code] -
Multimodal Transformer for Unaligned Multimodal Language Sequences
Yao-Hung Hubert Tsai, Shaojie Bai, Paul Pu Liang, Zico Kolter, Louis-Philippe Morency, Ruslan Salakhutdinov
ACL 2019 [arXiv], [Code] -
Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization
Paul Pu Liang, Zhun Liu, Yao-Hung Hubert Tsai, Qibin Zhao, Ruslan Salakhutdinov, Louis-Philippe Morency
ACL 2019 [arXiv] -
The Omniglot Challenge: A 3-Year Progress Report
Brenden M. Lake, Ruslan Salakhutdinov, Joshua B. Tenenbaum
Current Opinion in Behavioral Sciences, 2019 [arXiv] -
Embodied Multimodal Multitask Learning
Devendra Singh Chaplot, Lisa Lee, Ruslan Salakhutdinov, Devi Parikh, Dhruv Batra
[arXiv] -
Video Relationship Reasoning using Gated Spatio-Temporal Energy Graph
Yao-Hung Hubert Tsai, Santosh Kumar Divvala, Louis-Philippe Morency, Ruslan Salakhutdinov, Ali Farhadi
CVPR 2019, [arXiv] -
A Strong and Simple Baseline for Multimodal Utterance Embeddings
Paul Pu Liang, Yao Chong Lim, Yao-Hung Hubert Tsai, Ruslan Salakhutdinov, Louis-Philippe Morency
NAACL 2019, [arXiv] [Code] -
Deep Neural Networks with Multi-Branch Architectures Are Intrinsically Less Non-Convex
Hongyang Zhang, Junru Shao, Ruslan Salakhutdinov
AIStats 2019, [arXiv] -
Learning Factorized Multimodal Representations
Yao Hung Tsai, Paul Pu Liang, Amir Ali Bagherzade, Louis-Philippe Morency, Ruslan Salakhutdinov
ICLR 2019, [arXiv] [Code] -
AutoLoss: Learning Discrete Schedule for Alternate Optimization
Haowen Xu, Hao Zhang, Zhiting Hu, Xiaodan Liang, Ruslan Salakhutdinov, Eric Xing
ICLR 2019, [arXiv] -
Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator
Makoto Yamada, Yi Wu, Yao Hung Tsai, Hirofumi Ohtai, Ruslan Salakhutdinov, Ichiro Takeeuchi, Kenji Fukumizu
ICLR 2019, [arXiv] -
Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function
Devendra Singh Sachan, Manzil Zaheer, Ruslan Salakhutdinov
AAAI 2019, [pdf] -
GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations
Zhilin Yang, Jake Zhao, Bhuwan Dhingra, Kaiming He, William Cohen, Ruslan Salakhutdinov, Yann LeCun
NeurIPS 2018, [arXiv] -
Deep Generative Models with Learnable Knowledge Constraints
Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Xiaodan Liang, Lianhui Qin, Haoye Dong, Eric Xing
NeurIPS 2018, [arXiv] -
How Many Samples are Needed to Learn a Convolutional Neural Network?
Simon Du, Yining Wang, Xiyu Zhai, Sivaraman Balakrishnani, Ruslan Salakhutdinov, Aarti Singh
NeurIPS 2018, [arXiv] -
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, Christopher Manning
EMNLP, 2018, [arXiv], [Code, Data] -
Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text
Haitian Sun, Bhuwan Dhingra, Manzil Zaheer, Kathryn Mazaitis, Ruslan Salakhutdinov, William Cohen
EMNLP, 2018, [arXiv], [Code] -
Investigating the Working of Text Classifiers
Devendra Singh Sachan, Manzil Zaheer, Ruslan Salakhutdinov
COLING 2018, [arXiv] -
Structured Control Nets for Deep Reinforcement Learning
Mario Srouji, Jian Zhang, Ruslan Salakhutdinov
ICML 2018, [arXiv] -
Gated Path Planning Networks
Lisa Lee, Emilio Parisotto, Devendra Singh Chaplot, Eric Xing, Ruslan Salakhutdinov
ICML 2018, [arXiv], [Code]. -
Transformation Autoregressive Networks
Junier B. Oliva, Avinava Dubey, Manzil Zaheer, BarnabásPoczos, Ruslan Salakhutdinov, Eric P. Xing, eff Schneider
ICML 2018, [arXiv] -
Learning Cognitive Models using Neural Networks,
oral, Devendra Singh Chaplot, Christopher MacLellan, Ruslan Salakhutdinov, Kenneth Koedinger.
[arXiv],, 19th International Conference on Artificial Intelligence in Education (AIED-18), London, UK -
Style Transfer Through Back-Translation
Shrimai Prabhumoye, Yulia Tsvetkov, Ruslan Salakhutdinov and Alan Black
ACL 2018 [arXiv], [Code]. -
Neural Models for Reasoning over Multiple Mentions using Coreference
Bhuwan Dhingra, Qiao Jin, Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov
NAACL, 2018 (Short Paper) [arXiv]. -
Global Pose Estimation with an Attention-based Recurrent Network
Emilio Parisotto, Devendra Singh Chaplot, Jian Zhang, Ruslan Salakhutdinov
CVPR 2018 workshop on Deep Learning for Visual SLAM [arXiv], 2018, best paper award . -
On Characterizing the Capacity of Neural Networks using Algebraic Topology
William H. Guss, Ruslan Salakhutdinov
[arXiv], 2018. -
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model
Zhilin Yang, Zihang Dai, Ruslan Salakhutdinov, William W. Cohen
ICLR 2018, oral, [arXiv], [code].
-
Active Neural Localization
Devendra Singh Chaplot, Emilio Parisotto, Ruslan Salakhutdinov
ICLR 2018 [arXiv], [code]. -
Neural Map: Structured Memory for Deep Reinforcement Learning
Emilio Parisotto, Ruslan Salakhutdinov
ICLR 2018, [arXiv], ICLR 2018. -
On Unifying Deep Generative Models
Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Eric P. Xing
ICLR 2018 [arXiv]. -
Selecting the Best in GANs Family: a Post Selection Inference Framework
Yao-Hung Hubert Tsai, Denny Wu, Makoto Yamada, Ruslan Salakhutdinov, Ichiro Takeuchi, Kenji Fukumizu
[arXiv], ICLR workshop 2018. -
A Generic Approach for Escaping Saddle Points
Sashank J Reddi, Manzil Zaheer, Suvrit Sra, Barnabas Poczos, Francis Bach, Ruslan Salakhutdinov, Alexander J Smola
[arXiv], AIStats 2018. -
Gated-Attention Architectures for Task-Oriented Language Grounding
Devendra Singh Chaplot, Kanthashree Mysore Sathyendra, Rama Kumar Pasumarthi, Dheeraj Rajagopal, Ruslan Salakhutdinov
[arXiv], AAAI 2018, oral. -
Knowledge-based Word Sense Disambiguation using Topic Models
Devendra Singh Chaplot, Ruslan Salakhutdinov
[pdf], AAAI 2018, oral.