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Found 19 papers, 4 papers with code
Provable Policy Gradient Methods for Average-Reward Markov Potential Games
We prove that both algorithms based on independent policy gradient and independent natural policy gradient converge globally to a Nash equilibrium for the average reward criterion.
Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning
Though there has been a plethora of algorithms proposed for personalized supervised learning, discovering the structure of local data through personalized unsupervised learning is less explored.
Correlational Lagrangian Schrödinger Bridge: Learning Dynamics with Population-Level Regularization
Accurate modeling of system dynamics holds intriguing potential in broad scientific fields including cytodynamics and fluid mechanics.
From Function to Distribution Modeling: A PAC-Generative Approach to Offline Optimization
This paper considers the problem of offline optimization, where the objective function is unknown except for a collection of ``offline" data examples.
Harnessing the Power of Federated Learning in Federated Contextual Bandits
Federated learning (FL) has demonstrated great potential in revolutionizing distributed machine learning, and tremendous efforts have been made to extend it beyond the original focus on supervised learning.
Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation
We study robust reinforcement learning (RL) with the goal of determining a well-performing policy that is robust against model mismatch between the training simulator and the testing environment.
Exactly Tight Information-Theoretic Generalization Error Bound for the Quadratic Gaussian Problem
We provide a new information-theoretic generalization error bound that is exactly tight (i. e., matching even the constant) for the canonical quadratic Gaussian (location) problem.
Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning
We study policy optimization for Markov decision processes (MDPs) with multiple reward value functions, which are to be jointly optimized according to given criteria such as proportional fairness (smooth concave scalarization), hard constraints (constrained MDP), and max-min trade-off.
Approximate Top-$m$ Arm Identification with Heterogeneous Reward Variances
We study the effect of reward variance heterogeneity in the approximate top-$m$ arm identification setting.
On Top-$k$ Selection from $m$-wise Partial Rankings via Borda Counting
In the special case of $m=2$, i. e., pairwise comparison, the resultant bound is tighter than that given by Shah et al., leading to a reduced gap between the error probability upper and lower bounds.
Policy Optimization for Constrained MDPs with Provable Fast Global Convergence
We propose a new algorithm called policy mirror descent-primal dual (PMD-PD) algorithm that can provably achieve a faster $\mathcal{O}(\log(T)/T)$ convergence rate for both the optimality gap and the constraint violation.
Learning from Few Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales
Motivated by the problem of learning with small sample sizes, this paper shows how to incorporate into support-vector machines (SVMs) those properties that have made convolutional neural networks (CNNs) successful.
Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs
We show that when a strictly safe policy is known, then one can confine the system to zero constraint violation with arbitrarily high probability while keeping the reward regret of order $\tilde{\mathcal{O}}(\sqrt{K})$.
Individually Conditional Individual Mutual Information Bound on Generalization Error
We propose a new information-theoretic bound on generalization error based on a combination of the error decomposition technique of Bu et al. and the conditional mutual information (CMI) construction of Steinke and Zakynthinou.
On the Information Leakage in Private Information Retrieval Systems
We further characterize the optimal tradeoff between the minimum amount of common randomness and the total leakage.
Online Learning with Diverse User Preferences
We aim to show that when the user preferences are sufficiently diverse and each arm can be optimal for certain users, the O(log T) regret incurred by exploring the sub-optimal arms under the standard stochastic MAB setting can be reduced to a constant.
Cost-aware Cascading Bandits
For the online setting, we propose a Cost-aware Cas- cading Upper Confidence Bound (CC-UCB) algo- rithm, and show that the cumulative regret scales in O(log T ).
Cost-Aware Learning and Optimization for Opportunistic Spectrum Access
Our objective is to understand how the costs and reward of the actions would affect the optimal behavior of the user in both offline and online settings, and design the corresponding opportunistic spectrum access strategies to maximize the expected cumulative net reward (i. e., reward-minus-cost).
Regional Multi-Armed Bandits
We consider a variant of the classic multi-armed bandit problem where the expected reward of each arm is a function of an unknown parameter.
