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Found 17 papers, 4 papers with code
Information-Theoretic Generalization Bounds for Deep Neural Networks
This enables refining our generalization bounds to capture the contraction as a function of the network architecture parameters.
Entry-Specific Bounds for Low-Rank Matrix Completion under Highly Non-Uniform Sampling
Our bounds characterize the hardness of estimating each entry as a function of the localized sampling probabilities.
The SMART approach to instance-optimal online learning
We devise an online learning algorithm -- titled Switching via Monotone Adapted Regret Traces (SMART) -- that adapts to the data and achieves regret that is instance optimal, i. e., simultaneously competitive on every input sequence compared to the performance of the follow-the-leader (FTL) policy and the worst case guarantee of any other input policy.
Clustered Switchback Experiments: Near-Optimal Rates Under Spatiotemporal Interference
We consider experimentation in the presence of non-stationarity, inter-unit (spatial) interference, and carry-over effects (temporal interference), where we wish to estimate the global average treatment effect (GATE), the difference between average outcomes having exposed all units at all times to treatment or to control.
Matrix Estimation for Offline Reinforcement Learning with Low-Rank Structure
We consider offline Reinforcement Learning (RL), where the agent does not interact with the environment and must rely on offline data collected using a behavior policy.
Network Synthetic Interventions: A Causal Framework for Panel Data Under Network Interference
We propose an estimator, Network Synthetic Interventions (NSI), and show that it consistently estimates the mean outcomes for a unit under an arbitrary set of counterfactual treatments for the network.
Artificial Replay: A Meta-Algorithm for Harnessing Historical Data in Bandits
How best to incorporate historical data to "warm start" bandit algorithms is an open question: naively initializing reward estimates using all historical samples can suffer from spurious data and imbalanced data coverage, leading to computational and storage issues $\unicode{x2014}$ particularly salient in continuous action spaces.
Overcoming the Long Horizon Barrier for Sample-Efficient Reinforcement Learning with Latent Low-Rank Structure
The practicality of reinforcement learning algorithms has been limited due to poor scaling with respect to the problem size, as the sample complexity of learning an $\epsilon$-optimal policy is $\tilde{\Omega}\left(|S||A|H^3 / \epsilon^2\right)$ over worst case instances of an MDP with state space $S$, action space $A$, and horizon $H$.
Detection of Small Holes by the Scale-Invariant Robust Density-Aware Distance (RDAD) Filtration
A novel topological-data-analytical (TDA) method is proposed to distinguish, from noise, small holes surrounded by high-density regions of a probability density function.
Adaptive Discretization in Online Reinforcement Learning
In this paper we provide a unified theoretical analysis of tree-based hierarchical partitioning methods for online reinforcement learning, providing model-free and model-based algorithms.
Nonparametric Matrix Estimation with One-Sided Covariates
Consider the task of matrix estimation in which a dataset $X \in \mathbb{R}^{n\times m}$ is observed with sparsity $p$, and we would like to estimate $\mathbb{E}[X]$, where $\mathbb{E}[X_{ui}] = f(\alpha_u, \beta_i)$ for some Holder smooth function $f$.
Adaptive Discretization for Model-Based Reinforcement Learning
We introduce the technique of adaptive discretization to design an efficient model-based episodic reinforcement learning algorithm in large (potentially continuous) state-action spaces.
Model-based Reinforcement Learning
reinforcement-learning
+1
Tensor Estimation with Nearly Linear Samples Given Weak Side Information
Tensor completion exhibits an interesting computational-statistical gap in terms of the number of samples needed to perform tensor estimation.
Adaptive Discretization for Episodic Reinforcement Learning in Metric Spaces
We present an efficient algorithm for model-free episodic reinforcement learning on large (potentially continuous) state-action spaces.
Nonparametric Contextual Bandits in an Unknown Metric Space
Consider a nonparametric contextual multi-arm bandit problem where each arm $a \in [K]$ is associated to a nonparametric reward function $f_a: [0, 1] \to \mathbb{R}$ mapping from contexts to the expected reward.
Robust Max Entrywise Error Bounds for Tensor Estimation from Sparse Observations via Similarity Based Collaborative Filtering
We prove that the algorithm recovers a finite rank tensor with maximum entry-wise error (MEE) and mean-squared-error (MSE) decaying to $0$ as long as each entry is observed independently with probability $p = \Omega(n^{-3/2 + \kappa})$ for any arbitrarily small $\kappa > 0$.
Reducing Crowdsourcing to Graphon Estimation, Statistically
Inferring the correct answers to binary tasks based on multiple noisy answers in an unsupervised manner has emerged as the canonical question for micro-task crowdsourcing or more generally aggregating opinions.
