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Found 11 papers, 0 papers with code
Exploration is Harder than Prediction: Cryptographically Separating Reinforcement Learning from Supervised Learning
We also show that there is no computationally efficient algorithm for reward-directed RL in block MDPs, even when given access to an oracle for this regression problem.
Lasso with Latents: Efficient Estimation, Covariate Rescaling, and Computational-Statistical Gaps
It is well-known that the statistical performance of Lasso can suffer significantly when the covariates of interest have strong correlations.
Exploring and Learning in Sparse Linear MDPs without Computationally Intractable Oracles
The key assumption underlying linear Markov Decision Processes (MDPs) is that the learner has access to a known feature map $\phi(x, a)$ that maps state-action pairs to $d$-dimensional vectors, and that the rewards and transitions are linear functions in this representation.
Learning in Observable POMDPs, without Computationally Intractable Oracles
Much of reinforcement learning theory is built on top of oracles that are computationally hard to implement.
Provably Auditing Ordinary Least Squares in Low Dimensions
Measuring the stability of conclusions derived from Ordinary Least Squares linear regression is critically important, but most metrics either only measure local stability (i. e. against infinitesimal changes in the data), or are only interpretable under statistical assumptions.
Distributional Hardness Against Preconditioned Lasso via Erasure-Robust Designs
Surprisingly, at the heart of our lower bound is a new positive result in compressed sensing.
Planning in Observable POMDPs in Quasipolynomial Time
Our main result is a quasipolynomial-time algorithm for planning in (one-step) observable POMDPs.
Robust Generalized Method of Moments: A Finite Sample Viewpoint
For many inference problems in statistics and econometrics, the unknown parameter is identified by a set of moment conditions.
On the Power of Preconditioning in Sparse Linear Regression
First, we show that the preconditioned Lasso can solve a large class of sparse linear regression problems nearly optimally: it succeeds whenever the dependency structure of the covariates, in the sense of the Markov property, has low treewidth -- even if $\Sigma$ is highly ill-conditioned.
Truncated Linear Regression in High Dimensions
As a corollary, our guarantees imply a computationally efficient and information-theoretically optimal algorithm for compressed sensing with truncation, which may arise from measurement saturation effects.
Constant-Expansion Suffices for Compressed Sensing with Generative Priors
Using this theorem we can show that a matrix concentration inequality known as the Weight Distribution Condition (WDC), which was previously only known to hold for Gaussian matrices with logarithmic aspect ratio, in fact holds for constant aspect ratios too.
