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Found 51 papers, 10 papers with code
Breaking the Curse of Many Agents: Provable Mean Embedding $Q$-Iteration for Mean-Field Reinforcement Learning
We highlight that MF-FQI algorithm enjoys a ``blessing of many agents'' property in the sense that a larger number of observed agents improves the performance of MF-FQI algorithm.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Federated Online and Bandit Convex Optimization
Furthermore, we delve into the more challenging setting of federated online optimization with bandit (zeroth-order) feedback, where the machines can only access values of the cost functions at the queried points.
On the Effect of Defections in Federated Learning and How to Prevent Them
Federated learning is a machine learning protocol that enables a large population of agents to collaborate over multiple rounds to produce a single consensus model.
Generative Diffusion Models for Lattice Field Theory
This study delves into the connection between machine learning and lattice field theory by linking generative diffusion models (DMs) with stochastic quantization, from a stochastic differential equation perspective.
Diffusion Models as Stochastic Quantization in Lattice Field Theory
In this work, we establish a direct connection between generative diffusion models (DMs) and stochastic quantization (SQ).
Privileged Knowledge Distillation for Sim-to-Real Policy Generalization
In this paper, we propose a novel single-stage privileged knowledge distillation method called the Historical Information Bottleneck (HIB) to narrow the sim-to-real gap.
Approaching epidemiological dynamics of COVID-19 with physics-informed neural networks
A physics-informed neural network (PINN) embedded with the susceptible-infected-removed (SIR) model is devised to understand the temporal evolution dynamics of infectious diseases.
Exploration in Model-based Reinforcement Learning with Randomized Reward
We further extend our theory to generalized function approximation and identified conditions for reward randomization to attain provably efficient exploration.
An Analysis of Attention via the Lens of Exchangeability and Latent Variable Models
In particular, such a representation instantiates the posterior distribution of the latent variable given input tokens, which plays a central role in predicting output labels and solving downstream tasks.
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning
Moreover, under the online setting, we propose novel upper confidence bound (UCB)-type algorithms that incorporate such a contrastive loss with online RL algorithms for MDPs or MGs.
Embed to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency
For a class of POMDPs with a low-rank structure in the transition kernel, ETC attains an $O(1/\epsilon^2)$ sample complexity that scales polynomially with the horizon and the intrinsic dimension (that is, the rank).
Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning
We show that such OOD sampling and pessimistic bootstrapping yields provable uncertainty quantifier in linear MDPs, thus providing the theoretical underpinning for PBRL.
GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges
However, although numerous algorithms are proposed based on fundus images or OCT volumes in computer-aided diagnosis, there are still few methods leveraging both of the modalities for the glaucoma assessment.
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback
As a result, client sampling plays an important role in FL systems as it affects the convergence rate of optimization algorithms used to train machine learning models.
Automatic differentiation approach for reconstructing spectral functions with neural networks
Reconstructing spectral functions from Euclidean Green's functions is an important inverse problem in physics.
Reconstructing spectral functions via automatic differentiation
Exploiting the neural networks' regularization as a non-local smoothness regulator of the spectral function, we represent spectral functions by neural networks and use the propagator's reconstruction error to optimize the network parameters unsupervisedly.
False Correlation Reduction for Offline Reinforcement Learning
Offline reinforcement learning (RL) harnesses the power of massive datasets for resolving sequential decision problems.
Dynamic Bottleneck for Robust Self-Supervised Exploration
Exploration methods based on pseudo-count of transitions or curiosity of dynamics have achieved promising results in solving reinforcement learning with sparse rewards.
Adaptive Differentially Private Empirical Risk Minimization
We propose an adaptive (stochastic) gradient perturbation method for differentially private empirical risk minimization.
A Principled Permutation Invariant Approach to Mean-Field Multi-Agent Reinforcement Learning
To exploit the permutation invariance therein, we propose the mean-field proximal policy optimization (MF-PPO) algorithm, at the core of which is a permutation- invariant actor-critic neural architecture.
Variance-reduced First-order Meta-learning for Natural Language Processing Tasks
The core of our algorithm is to introduce a novel variance reduction term to the gradient estimation when performing the task adaptation.
Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach
To exploit the permutation invariance therein, we propose the mean-field proximal policy optimization (MF-PPO) algorithm, at the core of which is a permutation-invariant actor-critic neural architecture.
Principled Exploration via Optimistic Bootstrapping and Backward Induction
In this paper, we propose a principled exploration method for DRL through Optimistic Bootstrapping and Backward Induction (OB2I).
Optimistic Exploration with Backward Bootstrapped Bonus for Deep Reinforcement Learning
However, such an approach is challenging in developing practical exploration algorithms for Deep Reinforcement Learning (DRL).
Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk
As the COVID-19 pandemic continues to ravage the world, it is of critical significance to provide a timely risk prediction of the COVID-19 in multi-level.
Variational Dynamic for Self-Supervised Exploration in Deep Reinforcement Learning
Efficient exploration remains a challenging problem in reinforcement learning, especially for tasks where extrinsic rewards from environments are sparse or even totally disregarded.
On the Global Optimality of Model-Agnostic Meta-Learning
Model-agnostic meta-learning (MAML) formulates meta-learning as a bilevel optimization problem, where the inner level solves each subtask based on a shared prior, while the outer level searches for the optimal shared prior by optimizing its aggregated performance over all the subtasks.
Provably Efficient Causal Reinforcement Learning with Confounded Observational Data
Empowered by expressive function approximators such as neural networks, deep reinforcement learning (DRL) achieves tremendous empirical successes.
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning
We highlight that MF-FQI algorithm enjoys a "blessing of many agents" property in the sense that a larger number of observed agents improves the performance of MF-FQI algorithm.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Revisiting Membership Inference Under Realistic Assumptions
Since previous inference attacks fail in imbalanced prior setting, we develop a new inference attack based on the intuition that inputs corresponding to training set members will be near a local minimum in the loss function, and show that an attack that combines this with thresholds on the per-instance loss can achieve high PPV even in settings where other attacks appear to be ineffective.
Improving Neural Language Generation with Spectrum Control
Recent Transformer-based models such as Transformer-XL and BERT have achieved huge success on various natural language processing tasks.
Statistical-Computational Tradeoff in Single Index Models
Using the statistical query model to characterize the computational cost of an algorithm, we show that when $\cov(Y, X^\top\beta^*)=0$ and $\cov(Y,(X^\top\beta^*)^2)>0$, no computationally tractable algorithms can achieve the information-theoretic limit of the minimax risk.
Efficient Privacy-Preserving Stochastic Nonconvex Optimization
While many solutions for privacy-preserving convex empirical risk minimization (ERM) have been developed, privacy-preserving nonconvex ERM remains a challenge.
A Knowledge Transfer Framework for Differentially Private Sparse Learning
We study the problem of estimating high dimensional models with underlying sparse structures while preserving the privacy of each training example.
Neural Policy Gradient Methods: Global Optimality and Rates of Convergence
In detail, we prove that neural natural policy gradient converges to a globally optimal policy at a sublinear rate.
Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization
We explore two popular methods of differential privacy, output perturbation and gradient perturbation, and advance the state-of-the-art for both methods in the distributed learning setting.
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery
We propose a primal-dual based framework for analyzing the global optimality of nonconvex low-rank matrix recovery.
Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization
We propose a nonconvex estimator for the covariate adjusted precision matrix estimation problem in the high dimensional regime, under sparsity constraints.
Learning One-hidden-layer ReLU Networks via Gradient Descent
We study the problem of learning one-hidden-layer neural networks with Rectified Linear Unit (ReLU) activation function, where the inputs are sampled from standard Gaussian distribution and the outputs are generated from a noisy teacher network.
DeepDeblur: Fast one-step blurry face images restoration
Comprehensive experiments demonstrate that our proposed method can handle various blur kenels and achieve state-of-the-art results for small size blurry face images restoration.
High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm
We apply our generic algorithm to two illustrative latent variable models: Gaussian mixture model and mixture of linear regression, and demonstrate the advantages of our algorithm by both theoretical analysis and numerical experiments.
Robust Gaussian Graphical Model Estimation with Arbitrary Corruption
In particular, we show that provided that the number of corrupted samples $n_2$ for each variable satisfies $n_2 \lesssim \sqrt{n}/\sqrt{\log d}$, where $n$ is the sample size and $d$ is the number of variables, the proposed robust precision matrix estimator attains the same statistical rate as the standard estimator for Gaussian graphical models.
A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery
We propose a generic framework based on a new stochastic variance-reduced gradient descent algorithm for accelerating nonconvex low-rank matrix recovery.
Robust Wirtinger Flow for Phase Retrieval with Arbitrary Corruption
We consider the robust phase retrieval problem of recovering the unknown signal from the magnitude-only measurements, where the measurements can be contaminated by both sparse arbitrary corruption and bounded random noise.
A Unified Framework for Low-Rank plus Sparse Matrix Recovery
We propose a unified framework to solve general low-rank plus sparse matrix recovery problems based on matrix factorization, which covers a broad family of objective functions satisfying the restricted strong convexity and smoothness conditions.
A Universal Variance Reduction-Based Catalyst for Nonconvex Low-Rank Matrix Recovery
We propose a generic framework based on a new stochastic variance-reduced gradient descent algorithm for accelerating nonconvex low-rank matrix recovery.
Stochastic Variance-reduced Gradient Descent for Low-rank Matrix Recovery from Linear Measurements
And in the noiseless setting, our algorithm is guaranteed to linearly converge to the unknown low-rank matrix and achieves exact recovery with optimal sample complexity.
An Aligned French-Chinese corpus of 10K segments from university educational material
This paper describes a corpus of nearly 10K French-Chinese aligned segments, produced by post-editing machine translated computer science courseware.
A Unified Computational and Statistical Framework for Nonconvex Low-Rank Matrix Estimation
In the general case with noisy observations, we show that our algorithm is guaranteed to linearly converge to the unknown low-rank matrix up to minimax optimal statistical error, provided an appropriate initial estimator.
