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Found 15 papers, 9 papers with code
Diffusion World Model
We introduce Diffusion World Model (DWM), a conditional diffusion model capable of predicting multistep future states and rewards concurrently.
Guided Flows for Generative Modeling and Decision Making
Classifier-free guidance is a key component for enhancing the performance of conditional generative models across diverse tasks.
Dual RL: Unification and New Methods for Reinforcement and Imitation Learning
For offline RL, our analysis frames a recent offline RL method XQL in the dual framework, and we further propose a new method f-DVL that provides alternative choices to the Gumbel regression loss that fixes the known training instability issue of XQL.
Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories
For this setting, we develop and study a simple meta-algorithmic pipeline that learns an inverse dynamics model on the labelled data to obtain proxy-labels for the unlabelled data, followed by the use of any offline RL algorithm on the true and proxy-labelled trajectories.
Reliable Conditioning of Behavioral Cloning for Offline Reinforcement Learning
We study CWBC in the context of RvS (Emmons et al., 2021) and Decision Transformers (Chen et al., 2021), and show that CWBC significantly boosts their performance on various benchmarks.
Latent State Marginalization as a Low-cost Approach for Improving Exploration
While the maximum entropy (MaxEnt) reinforcement learning (RL) framework -- often touted for its exploration and robustness capabilities -- is usually motivated from a probabilistic perspective, the use of deep probabilistic models has not gained much traction in practice due to their inherent complexity.
Online Decision Transformer
Recent work has shown that offline reinforcement learning (RL) can be formulated as a sequence modeling problem (Chen et al., 2021; Janner et al., 2021) and solved via approaches similar to large-scale language modeling.
A Theorem of the Alternative for Personalized Federated Learning
A widely recognized difficulty in federated learning arises from the statistical heterogeneity among clients: local datasets often come from different but not entirely unrelated distributions, and personalization is, therefore, necessary to achieve optimal results from each individual's perspective.
Federated $f$-Differential Privacy
Federated learning (FL) is a training paradigm where the clients collaboratively learn models by repeatedly sharing information without compromising much on the privacy of their local sensitive data.
Near-Optimal Confidence Sequences for Bounded Random Variables
Many inference problems, such as sequential decision problems like A/B testing, adaptive sampling schemes like bandit selection, are often online in nature.
Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion
To address this question, we introduce a family of analytical and sharp privacy bounds under composition using the Edgeworth expansion in the framework of the recently proposed f-differential privacy.
ShadowSync: Performing Synchronization in the Background for Highly Scalable Distributed Training
Recommendation systems are often trained with a tremendous amount of data, and distributed training is the workhorse to shorten the training time.
Convergence Analysis for Rectangular Matrix Completion Using Burer-Monteiro Factorization and Gradient Descent
We address the rectangular matrix completion problem by lifting the unknown matrix to a positive semidefinite matrix in higher dimension, and optimizing a nonconvex objective over the semidefinite factor using a simple gradient descent scheme.
A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements
We propose a simple, scalable, and fast gradient descent algorithm to optimize a nonconvex objective for the rank minimization problem and a closely related family of semidefinite programs.
Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm
We consider the problem of recovering a low-rank tensor from its noisy observation.
