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Found 15 papers, 10 papers with code
Parallel Sampling of Diffusion Models
Instead of reducing the number of denoising steps (trading quality for speed), in this paper we explore an orthogonal approach: can we run the denoising steps in parallel (trading compute for speed)?
Long Horizon Temperature Scaling
LHTS is compatible with all likelihood-based models, and optimizes for the long horizon likelihood of samples.
Training and Inference on Any-Order Autoregressive Models the Right Way
Conditional inference on arbitrary subsets of variables is a core problem in probabilistic inference with important applications such as masked language modeling and image inpainting.
Imitation Learning by Estimating Expertise of Demonstrators
In this work, we show that unsupervised learning over demonstrator expertise can lead to a consistent boost in the performance of imitation learning algorithms.
Conditional Imitation Learning for Multi-Agent Games
In this work, we study the problem of conditional multi-agent imitation learning, where we have access to joint trajectory demonstrations at training time, and we must interact with and adapt to new partners at test time.
PantheonRL: A MARL Library for Dynamic Training Interactions
We present PantheonRL, a multiagent reinforcement learning software package for dynamic training interactions such as round-robin, adaptive, and ad-hoc training.
HyperSPNs: Compact and Expressive Probabilistic Circuits
Probabilistic circuits (PCs) are a family of generative models which allows for the computation of exact likelihoods and marginals of its probability distributions.
Influencing Towards Stable Multi-Agent Interactions
Instead of reactively adapting to the other agent's (opponent or partner) behavior, we propose an algorithm to proactively influence the other agent's strategy to stabilize -- which can restrain the non-stationarity caused by the other agent.
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
On the Critical Role of Conventions in Adaptive Human-AI Collaboration
Humans can quickly adapt to new partners in collaborative tasks (e. g. playing basketball), because they understand which fundamental skills of the task (e. g. how to dribble, how to shoot) carry over across new partners.
Probabilistic Circuits for Variational Inference in Discrete Graphical Models
Inference in discrete graphical models with variational methods is difficult because of the inability to re-parameterize gradients of the Evidence Lower Bound (ELBO).
On Symbolically Encoding the Behavior of Random Forests
Recent work has shown that the input-output behavior of some machine learning systems can be captured symbolically using Boolean expressions or tractable Boolean circuits, which facilitates reasoning about the behavior of these systems.
On Tractable Representations of Binary Neural Networks
We consider the compilation of a binary neural network's decision function into tractable representations such as Ordered Binary Decision Diagrams (OBDDs) and Sentential Decision Diagrams (SDDs).
Smoothing Structured Decomposable Circuits
Further, for the important case of All-Marginals, we show a more efficient linear-time algorithm.
A Symbolic Approach to Explaining Bayesian Network Classifiers
We propose an approach for explaining Bayesian network classifiers, which is based on compiling such classifiers into decision functions that have a tractable and symbolic form.
