Ruslan Salakhutdinov

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UNIVERSITY OF TORONTO ∙ MIT ∙ Apple Inc. ∙ Carnegie Mellon University

Associate Professor, Machine Learning Department at Carnegie Mellon University

Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers

We propose a simple, practical, and intuitive approach for domain adapta...
06/24/2020 ∙ by Benjamin Eysenbach, et al. ∙ 15 ∙ share

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On Reward-Free Reinforcement Learning with Linear Function Approximation

Reward-free reinforcement learning (RL) is a framework which is suitable...
06/19/2020 ∙ by Ruosong Wang, et al. ∙ 0 ∙ share

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Demystifying Self-Supervised Learning: An Information-Theoretical Framework

Self-supervised representation learning adopts self-defined signals as s...
06/10/2020 ∙ by Yao-Hung Hubert Tsai, et al. ∙ 22 ∙ share

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Neural Methods for Point-wise Dependency Estimation

Since its inception, the neural estimation of mutual information (MI) ha...
06/09/2020 ∙ by Yao-Hung Hubert Tsai, et al. ∙ 13 ∙ share

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Feature Robust Optimal Transport for High-dimensional Data

Optimal transport is a machine learning technique with applications incl...
05/25/2020 ∙ by Mathis Petrovich, et al. ∙ 13 ∙ share

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Provably Efficient Reinforcement Learning with General Value Function Approximation

Value function approximation has demonstrated phenomenal empirical succe...
05/21/2020 ∙ by Ruosong Wang, et al. ∙ 13 ∙ share

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Guaranteeing Reproducibility in Deep Learning Competitions

To encourage the development of methods with reproducible and robust tra...
05/12/2020 ∙ by Brandon Houghton, et al. ∙ 3 ∙ share

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Exploring Controllable Text Generation Techniques

Neural controllable text generation is an important area gaining attenti...
05/04/2020 ∙ by Shrimai Prabhumoye, et al. ∙ 0 ∙ share

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Topological Sort for Sentence Ordering

Sentence ordering is the task of arranging the sentences of a given text...
05/01/2020 ∙ by Shrimai Prabhumoye, et al. ∙ 0 ∙ share

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Politeness Transfer: A Tag and Generate Approach

This paper introduces a new task of politeness transfer which involves c...
04/29/2020 ∙ by Aman Madaan, et al. ∙ 0 ∙ share

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Interpretable Multimodal Routing for Human Multimodal Language

The human language has heterogeneous sources of information, including t...
04/29/2020 ∙ by Yao-Hung Hubert Tsai, et al. ∙ 0 ∙ share

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Weakly-Supervised Reinforcement Learning for Controllable Behavior

Reinforcement learning (RL) is a powerful framework for learning to take...
04/06/2020 ∙ by Lisa Lee, et al. ∙ 11 ∙ share

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On Emergent Communication in Competitive Multi-Agent Teams

Several recent works have found the emergence of grounded compositional ...
03/04/2020 ∙ by Paul Pu Liang, et al. ∙ 10 ∙ share

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Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement

Multi-task reinforcement learning (RL) aims to simultaneously learn poli...
02/25/2020 ∙ by Benjamin Eysenbach, et al. ∙ 11 ∙ share

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Differentiable Reasoning over a Virtual Knowledge Base

We consider the task of answering complex multi-hop questions using a co...
02/25/2020 ∙ by Bhuwan Dhingra, et al. ∙ 0 ∙ share

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Learning Not to Learn in the Presence of Noisy Labels

Learning in the presence of label noise is a challenging yet important t...
02/16/2020 ∙ by Liu Ziyin, et al. ∙ 11 ∙ share

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Capsules with Inverted Dot-Product Attention Routing

We introduce a new routing algorithm for capsule networks, in which a ch...
02/12/2020 ∙ by Yao-Hung Hubert Tsai, et al. ∙ 5 ∙ share

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Think Locally, Act Globally: Federated Learning with Local and Global Representations

Federated learning is an emerging research paradigm to train models on p...
01/06/2020 ∙ by Paul Pu Liang, et al. ∙ 6 ∙ share

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Geometric Capsule Autoencoders for 3D Point Clouds

We propose a method to learn object representations from 3D point clouds...
12/06/2019 ∙ by Nitish Srivastava, et al. ∙ 21 ∙ share

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Worst Cases Policy Gradients

Recent advances in deep reinforcement learning have demonstrated the cap...
11/09/2019 ∙ by Yichuan Charlie Tang, et al. ∙ 20 ∙ share

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Multiple Futures Prediction

Temporal prediction is critical for making intelligent and robust decisi...
11/04/2019 ∙ by Yichuan Charlie Tang, et al. ∙ 23 ∙ share

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Enhanced Convolutional Neural Tangent Kernels

Recent research shows that for training with ℓ_2 loss, convolutional neu...
11/03/2019 ∙ by Zhiyuan Li, et al. ∙ 17 ∙ share

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Learning Data Manipulation for Augmentation and Weighting

Manipulating data, such as weighting data examples or augmenting with ne...
10/28/2019 ∙ by Zhiting Hu, et al. ∙ 30 ∙ share

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Complex Transformer: A Framework for Modeling Complex-Valued Sequence

While deep learning has received a surge of interest in a variety of fie...
10/22/2019 ∙ by Muqiao Yang, et al. ∙ 10 ∙ share

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Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks

Recent research shows that the following two models are equivalent: (a) ...
10/03/2019 ∙ by Sanjeev Arora, et al. ∙ 16 ∙ share

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On Universal Approximation by Neural Networks with Uniform Guarantees on Approximation of Infinite Dimensional Maps

The study of universal approximation of arbitrary functions f: X→Y by ne...
10/03/2019 ∙ by William H. Guss, et al. ∙ 12 ∙ share

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LSMI-Sinkhorn: Semi-supervised Squared-Loss Mutual Information Estimation with Optimal Transport

Estimating mutual information is an important machine learning and stati...
09/05/2019 ∙ by Yanbin Liu, et al. ∙ 6 ∙ share

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Transformer Dissection: An Unified Understanding for Transformer's Attention via the Lens of Kernel

Transformer is a powerful architecture that achieves superior performanc...
08/30/2019 ∙ by Yao-Hung Hubert Tsai, et al. ∙ 32 ∙ share

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MineRL: A Large-Scale Dataset of Minecraft Demonstrations

The sample inefficiency of standard deep reinforcement learning methods ...
07/29/2019 ∙ by William H. Guss, et al. ∙ 7 ∙ share

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Learning Neural Networks with Adaptive Regularization

Feed-forward neural networks can be understood as a combination of an in...
07/14/2019 ∙ by Han Zhao, et al. ∙ 3 ∙ share

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Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization

There has been an increased interest in multimodal language processing i...
07/01/2019 ∙ by Paul Pu Liang, et al. ∙ 17 ∙ share

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Deep Gamblers: Learning to Abstain with Portfolio Theory

We deal with the selective classification problem (supervised-learning p...
06/29/2019 ∙ by Liu Ziyin, et al. ∙ 8 ∙ share

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XLNet: Generalized Autoregressive Pretraining for Language Understanding

With the capability of modeling bidirectional contexts, denoising autoen...
06/19/2019 ∙ by Zhilin Yang, et al. ∙ 0 ∙ share

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"My Way of Telling a Story": Persona based Grounded Story Generation

Visual storytelling is the task of generating stories based on a sequenc...
06/14/2019 ∙ by Shrimai Prabhumoye, et al. ∙ 0 ∙ share

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Efficient Exploration via State Marginal Matching

To solve tasks with sparse rewards, reinforcement learning algorithms mu...
06/12/2019 ∙ by Lisa Lee, et al. ∙ 0 ∙ share

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Search on the Replay Buffer: Bridging Planning and Reinforcement Learning

The history of learning for control has been an exciting back and forth ...
06/12/2019 ∙ by Benjamin Eysenbach, et al. ∙ 5 ∙ share

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Multimodal Transformer for Unaligned Multimodal Language Sequences

Human language is often multimodal, which comprehends a mixture of natur...
06/01/2019 ∙ by Yao-Hung Hubert Tsai, et al. ∙ 0 ∙ share

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Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels

While graph kernels (GKs) are easy to train and enjoy provable theoretic...
05/30/2019 ∙ by Simon S. Du, et al. ∙ 4 ∙ share

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Strong and Simple Baselines for Multimodal Utterance Embeddings

Human language is a rich multimodal signal consisting of spoken words, f...
05/14/2019 ∙ by Paul Pu Liang, et al. ∙ 0 ∙ share

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On Exact Computation with an Infinitely Wide Neural Net

How well does a classic deep net architecture like AlexNet or VGG19 clas...
04/26/2019 ∙ by Sanjeev Arora, et al. ∙ 0 ∙ share

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The MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors

Though deep reinforcement learning has led to breakthroughs in many diff...
04/22/2019 ∙ by William H. Guss, et al. ∙ 10 ∙ share

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Video Relationship Reasoning using Gated Spatio-Temporal Energy Graph

Visual relationship reasoning is a crucial yet challenging task for unde...
03/25/2019 ∙ by Yao-Hung Hubert Tsai, et al. ∙ 0 ∙ share

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Concurrent Meta Reinforcement Learning

State-of-the-art meta reinforcement learning algorithms typically assume...
03/07/2019 ∙ by Emilio Parisotto, et al. ∙ 14 ∙ share

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The Omniglot Challenge: A 3-Year Progress Report

Three years ago, we released the Omniglot dataset for developing more hu...
02/09/2019 ∙ by Brenden M. Lake, et al. ∙ 6 ∙ share

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Embodied Multimodal Multitask Learning

Recent efforts on training visual navigation agents conditioned on langu...
02/04/2019 ∙ by Devendra Singh Chaplot, et al. ∙ 36 ∙ share

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Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

Transformer networks have a potential of learning longer-term dependency...
01/09/2019 ∙ by Zihang Dai, et al. ∙ 2 ∙ share

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Connecting the Dots Between MLE and RL for Sequence Generation

Sequence generation models such as recurrent networks can be trained wit...
11/24/2018 ∙ by Bowen Tan, et al. ∙ 0 ∙ share

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Stackelberg GAN: Towards Provable Minimax Equilibrium via Multi-Generator Architectures

We study the problem of alleviating the instability issue in the GAN tra...
11/19/2018 ∙ by Hongyang Zhang, et al. ∙ 8 ∙ share

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On the Complexity of Exploration in Goal-Driven Navigation

Building agents that can explore their environments intelligently is a c...
11/16/2018 ∙ by Maruan Al-Shedivat, et al. ∙ 10 ∙ share

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Point Cloud GAN

Generative Adversarial Networks (GAN) can achieve promising performance ...
10/13/2018 ∙ by Chun-Liang Li, et al. ∙ 0 ∙ share

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Ruslan Salakhutdinov

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