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Found 20 papers, 2 papers with code
A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health
Efforts to reduce maternal mortality rate, a key UN Sustainable Development target (SDG Target 3. 1), rely largely on preventative care programs to spread critical health information to high-risk populations.
Towards a Pretrained Model for Restless Bandits via Multi-arm Generalization
Restless multi-arm bandits (RMABs), a class of resource allocation problems with broad application in areas such as healthcare, online advertising, and anti-poaching, have recently been studied from a multi-agent reinforcement learning perspective.
Near Optimal Heteroscedastic Regression with Symbiotic Learning
Our work shows that we can estimate $\mathbf{w}^{*}$ in squared norm up to an error of $\tilde{O}\left(\|\mathbf{f}^{*}\|^2 \cdot \left(\frac{1}{n} + \left(\frac{d}{n}\right)^2\right)\right)$ and prove a matching lower bound (upto log factors).
Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization
We present Re-weighted Gradient Descent (RGD), a novel optimization technique that improves the performance of deep neural networks through dynamic sample importance weighting.
Indexability is Not Enough for Whittle: Improved, Near-Optimal Algorithms for Restless Bandits
Whittle index policies, which are based on Lagrangian relaxations, are widely used in these settings due to their simplicity and near-optimality under certain conditions.
Finite time analysis of temporal difference learning with linear function approximation: Tail averaging and regularisation
We study the finite-time behaviour of the popular temporal difference (TD) learning algorithm when combined with tail-averaging.
Multi-User Reinforcement Learning with Low Rank Rewards
In this work, we consider the problem of collaborative multi-user reinforcement learning.
Utilising the CLT Structure in Stochastic Gradient based Sampling : Improved Analysis and Faster Algorithms
We consider stochastic approximations of sampling algorithms, such as Stochastic Gradient Langevin Dynamics (SGLD) and the Random Batch Method (RBM) for Interacting Particle Dynamcs (IPD).
Introspective Experience Replay: Look Back When Surprised
In reinforcement learning (RL), experience replay-based sampling techniques play a crucial role in promoting convergence by eliminating spurious correlations.
Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs
The starting point of our work is the observation that in practice, Q-learning is used with two important modifications: (i) training with two networks, called online network and target network simultaneously (online target learning, or OTL) , and (ii) experience replay (ER) (Mnih et al., 2015).
The staircase property: How hierarchical structure can guide deep learning
This paper identifies a structural property of data distributions that enables deep neural networks to learn hierarchically.
Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems
In this work, we improve existing results for learning nonlinear systems in a number of ways: a) we provide the first offline algorithm that can learn non-linear dynamical systems without the mixing assumption, b) we significantly improve upon the sample complexity of existing results for mixing systems, c) in the much harder one-pass, streaming setting we study a SGD with Reverse Experience Replay ($\mathsf{SGD-RER}$) method, and demonstrate that for mixing systems, it achieves the same sample complexity as our offline algorithm, d) we justify the expansivity assumption by showing that for the popular ReLU link function -- a non-expansive but easy to learn link function with i. i. d.
Streaming Linear System Identification with Reverse Experience Replay
Thus, we provide the first -- to the best of our knowledge -- optimal SGD-style algorithm for the classical problem of linear system identification with a first order oracle.
A law of robustness for two-layers neural networks
We make a precise conjecture that, for any Lipschitz activation function and for most datasets, any two-layers neural network with $k$ neurons that perfectly fit the data must have its Lipschitz constant larger (up to a constant) than $\sqrt{n/k}$ where $n$ is the number of datapoints.
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms
Our improved rate serves as one of the first results where an algorithm outperforms SGD-DD on an interesting Markov chain and also provides one of the first theoretical analyses to support the use of experience replay in practice.
Sharp Representation Theorems for ReLU Networks with Precise Dependence on Depth
For each $D$, $\mathcal{G}_{D} \subseteq \mathcal{G}_{D+1}$ and as $D$ grows the class of functions $\mathcal{G}_{D}$ contains progressively less smooth functions.
A Corrective View of Neural Networks: Representation, Memorization and Learning
This technique yields several new representation and learning results for neural networks.
Making the Last Iterate of SGD Information Theoretically Optimal
While classical theoretical analysis of SGD for convex problems studies (suffix) \emph{averages} of iterates and obtains information theoretically optimal bounds on suboptimality, the \emph{last point} of SGD is, by far, the most preferred choice in practice.
SGD without Replacement: Sharper Rates for General Smooth Convex Functions
For {\em small} $K$, we show \sgdwor can achieve same convergence rate as \sgd for {\em general smooth strongly-convex} functions.
Optimal Single Sample Tests for Structured versus Unstructured Network Data
We develop a new approach that applies to both the Ising and Exponential Random Graph settings based on a general and natural statistical test.
