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July 18, 2021 Expo schedule » New in ML »

Timezone: Europe/Vienna »

Indicates the beginning of a session

 Tue Jul 20, 2021

Time Virtual
06:00 PM (Posters)
Decentralized Riemannian Gradient Descent on the Stiefel Manifold Virtual
PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization Virtual
On the Random Conjugate Kernel and Neural Tangent Kernel Virtual
Unbalanced minibatch Optimal Transport; applications to Domain Adaptation Virtual
Breaking the Limits of Message Passing Graph Neural Networks Virtual
Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity Virtual
Self-Tuning for Data-Efficient Deep Learning Virtual
PixelTransformer: Sample Conditioned Signal Generation Virtual
Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning Virtual
PID Accelerated Value Iteration Algorithm Virtual
Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More Virtual
Learning Curves for Analysis of Deep Networks Virtual
Fundamental Tradeoffs in Distributionally Adversarial Training Virtual
Winograd Algorithm for AdderNet Virtual
Scalable Computations of Wasserstein Barycenter via Input Convex Neural Networks Virtual
Asynchronous Decentralized Optimization With Implicit Stochastic Variance Reduction Virtual
Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation Virtual
Generative Adversarial Transformers Virtual
Efficient Differentiable Simulation of Articulated Bodies Virtual
Voice2Series: Reprogramming Acoustic Models for Time Series Classification Virtual
Learning Bounds for Open-Set Learning Virtual
Consistent Nonparametric Methods for Network Assisted Covariate Estimation Virtual
Slot Machines: Discovering Winning Combinations of Random Weights in Neural Networks Virtual
Multiplicative Noise and Heavy Tails in Stochastic Optimization Virtual
LTL2Action: Generalizing LTL Instructions for Multi-Task RL Virtual
Targeted Data Acquisition for Evolving Negotiation Agents Virtual
Size-Invariant Graph Representations for Graph Classification Extrapolations Virtual
Generative Particle Variational Inference via Estimation of Functional Gradients Virtual
Grid-Functioned Neural Networks Virtual
Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation Virtual
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions Virtual
Decoupling Value and Policy for Generalization in Reinforcement Learning Virtual
Fast Projection Onto Convex Smooth Constraints Virtual
Poisson-Randomised DirBN: Large Mutation is Needed in Dirichlet Belief Networks Virtual
Momentum Residual Neural Networks Virtual
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning Virtual
Skill Discovery for Exploration and Planning using Deep Skill Graphs Virtual
Parameter-free Locally Accelerated Conditional Gradients Virtual
Learning Task Informed Abstractions Virtual
Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization Virtual
Graph Mixture Density Networks Virtual
Hierarchical VAEs Know What They Don’t Know Virtual
Inverse Constrained Reinforcement Learning Virtual
Unsupervised Learning of Visual 3D Keypoints for Control Virtual
Coach-Player Multi-agent Reinforcement Learning for Dynamic Team Composition Virtual
State Entropy Maximization with Random Encoders for Efficient Exploration Virtual
Zoo-Tuning: Adaptive Transfer from A Zoo of Models Virtual
Bayesian Deep Learning via Subnetwork Inference Virtual
Learning Node Representations Using Stationary Flow Prediction on Large Payment and Cash Transaction Networks Virtual
Unifying Vision-and-Language Tasks via Text Generation Virtual
Generative Video Transformer: Can Objects be the Words? Virtual
Thinking Like Transformers Virtual
Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers Virtual
Demonstration-Conditioned Reinforcement Learning for Few-Shot Imitation Virtual
Global Optimality Beyond Two Layers: Training Deep ReLU Networks via Convex Programs Virtual
Directional Graph Networks Virtual
World Model as a Graph: Learning Latent Landmarks for Planning Virtual
Decision-Making Under Selective Labels: Optimal Finite-Domain Policies and Beyond Virtual
Interpretable Stability Bounds for Spectral Graph Filters Virtual
Learning Intra-Batch Connections for Deep Metric Learning Virtual
Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration Virtual
What Are Bayesian Neural Network Posteriors Really Like? Virtual
Stability and Convergence of Stochastic Gradient Clipping: Beyond Lipschitz Continuity and Smoothness Virtual
Provably Strict Generalisation Benefit for Equivariant Models Virtual
Reinforcement Learning with Prototypical Representations Virtual
Let's Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Framework Virtual
Dueling Convex Optimization Virtual
GLSearch: Maximum Common Subgraph Detection via Learning to Search Virtual
Interactive Learning from Activity Description Virtual
UnICORNN: A recurrent model for learning very long time dependencies Virtual
Connecting Sphere Manifolds Hierarchically for Regularization Virtual
Provably Efficient Learning of Transferable Rewards Virtual
Projection Robust Wasserstein Barycenters Virtual
A Functional Perspective on Learning Symmetric Functions with Neural Networks Virtual
Sparsifying Networks via Subdifferential Inclusion Virtual
Self Normalizing Flows Virtual
Zeroth-Order Non-Convex Learning via Hierarchical Dual Averaging Virtual
Not All Memories are Created Equal: Learning to Forget by Expiring Virtual
Muesli: Combining Improvements in Policy Optimization Virtual