
Efficient Transformers in Reinforcement Learning using ActorLearner Distillation
Many realworld applications such as robotics provide hard constraints o...
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Replacing Rewards with Examples: ExampleBased Policy Search via Recursive Classification
In the standard Markov decision process formalism, users specify tasks b...
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Selfsupervised Representation Learning with Relative Predictive Coding
This paper introduces Relative Predictive Coding (RPC), a new contrastiv...
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Instabilities of Offline RL with PreTrained Neural Representation
In offline reinforcement learning (RL), we seek to utilize offline data ...
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On Proximal Policy Optimization's Heavytailed Gradients
Modern policy gradient algorithms, notably Proximal Policy Optimization ...
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Reasoning Over Virtual Knowledge Bases With Open Predicate Relations
We present the Open Predicate Query Language (OPQL); a method for constr...
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The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors
Although deep reinforcement learning has led to breakthroughs in many di...
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Understanding the Tradeoffs in ClientSide Privacy for Speech Recognition
Existing approaches to ensuring privacy of user speech data primarily fo...
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CrossModal Generalization: Learning in Low Resource Modalities via MetaAlignment
The natural world is abundant with concepts expressed via visual, acoust...
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CLearning: Learning to Achieve Goals via Recursive Classification
We study the problem of predicting and controlling the future state dist...
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Close Category Generalization
Outofdistribution generalization is a core challenge in machine learni...
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Unsupervised Domain Adaptation for Visual Navigation
Advances in visual navigation methods have led to intelligent embodied n...
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Planning with Submodular Objective Functions
We study planning with submodular objective functions, where instead of ...
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Case Study: Deontological Ethics in NLP
Recent work in natural language processing (NLP) has focused on ethical ...
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Graph Adversarial Networks: Protecting Information against Adversarial Attacks
We study the problem of protecting information when learning with graph ...
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Revisiting LSTM Networks for SemiSupervised Text Classification via Mixed Objective Function
In this paper, we study bidirectional LSTM network for the task of text ...
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FewShot Learning with IntraClass Knowledge Transfer
We consider the fewshot classification task with an unbalanced dataset,...
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Towards Debiasing Sentence Representations
As natural language processing methods are increasingly deployed in real...
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OffDynamics Reinforcement Learning: Training for Transfer with Domain Classifiers
We propose a simple, practical, and intuitive approach for domain adapta...
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On RewardFree Reinforcement Learning with Linear Function Approximation
Rewardfree reinforcement learning (RL) is a framework which is suitable...
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Demystifying SelfSupervised Learning: An InformationTheoretical Framework
Selfsupervised representation learning adopts selfdefined signals as s...
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Neural Methods for Pointwise Dependency Estimation
Since its inception, the neural estimation of mutual information (MI) ha...
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Feature Robust Optimal Transport for Highdimensional Data
Optimal transport is a machine learning technique with applications incl...
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Provably Efficient Reinforcement Learning with General Value Function Approximation
Value function approximation has demonstrated phenomenal empirical succe...
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Guaranteeing Reproducibility in Deep Learning Competitions
To encourage the development of methods with reproducible and robust tra...
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Exploring Controllable Text Generation Techniques
Neural controllable text generation is an important area gaining attenti...
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Topological Sort for Sentence Ordering
Sentence ordering is the task of arranging the sentences of a given text...
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Politeness Transfer: A Tag and Generate Approach
This paper introduces a new task of politeness transfer which involves c...
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Interpretable Multimodal Routing for Human Multimodal Language
The human language has heterogeneous sources of information, including t...
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WeaklySupervised Reinforcement Learning for Controllable Behavior
Reinforcement learning (RL) is a powerful framework for learning to take...
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On Emergent Communication in Competitive MultiAgent Teams
Several recent works have found the emergence of grounded compositional ...
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Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement
Multitask reinforcement learning (RL) aims to simultaneously learn poli...
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Differentiable Reasoning over a Virtual Knowledge Base
We consider the task of answering complex multihop questions using a co...
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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...
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Capsules with Inverted DotProduct Attention Routing
We introduce a new routing algorithm for capsule networks, in which a ch...
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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...
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Geometric Capsule Autoencoders for 3D Point Clouds
We propose a method to learn object representations from 3D point clouds...
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Worst Cases Policy Gradients
Recent advances in deep reinforcement learning have demonstrated the cap...
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Multiple Futures Prediction
Temporal prediction is critical for making intelligent and robust decisi...
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Enhanced Convolutional Neural Tangent Kernels
Recent research shows that for training with ℓ_2 loss, convolutional neu...
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Learning Data Manipulation for Augmentation and Weighting
Manipulating data, such as weighting data examples or augmenting with ne...
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Complex Transformer: A Framework for Modeling ComplexValued Sequence
While deep learning has received a surge of interest in a variety of fie...
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Harnessing the Power of Infinitely Wide Deep Nets on Smalldata Tasks
Recent research shows that the following two models are equivalent: (a) ...
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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...
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LSMISinkhorn: Semisupervised SquaredLoss Mutual Information Estimation with Optimal Transport
Estimating mutual information is an important machine learning and stati...
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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...
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MineRL: A LargeScale Dataset of Minecraft Demonstrations
The sample inefficiency of standard deep reinforcement learning methods ...
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Learning Neural Networks with Adaptive Regularization
Feedforward neural networks can be understood as a combination of an in...
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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...
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Deep Gamblers: Learning to Abstain with Portfolio Theory
We deal with the selective classification problem (supervisedlearning p...
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Ruslan Salakhutdinov
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Associate Professor, Machine Learning Department at Carnegie Mellon University