arXiv research

Pessimistic Minimax Value Iteration finds efficient NE policies from offline data.

New method estimates minimizer and minimum value of a regression function.

Study on estimating invertible functions with minimax analysis.

Study minimax-optimal rates for offline decision-making with function approximation.

New method for off-policy evaluation in POMDPs using future-dependent value functions.

New algorithms solve complex minimax problems without needing derivatives.

Paper proposes algorithms for solving nonconvex-nonconcave problems with complexity guarantees.

This paper analyzes how machine learning models resist adversarial attacks in nonparametric regression.

New method for evaluating policies in complex decision-making models with hidden variables.

We consider the problem of global optimization of an unknown non-convex smooth function with zeroth-order feedback. In this setup, an algorithm is allowed to adaptively query the underlying function at different locations and receives noisy evaluations of function values at the queried points (i.e. the algorithm has ac…

Study confirms learning rates for vector-valued spectral algorithms, proving consistency.

New algorithms estimate Q-functions under partial coverage and realizability, improving offline RL guarantees.

We study minimax methods for off-policy evaluation (OPE) using value functions and marginalized importance weights. Despite that they hold promises of overcoming the exponential variance in traditional importance sampling, several key problems remain: (1) They require function approximation and are generally biased. Fo…

New method estimates optimal Q-values with better accuracy for specific problems.

Papers learn from data to make decisions without interacting, improving on previous methods.

The paper optimizes risk-sensitive RL with CVaR, achieving near-minimax-optimal results.

The paper analyzes kNN density estimation's convergence rates under different conditions.

MOPI optimizes flexible set-valued mappings to achieve superior shape adaptivity in conformal prediction.

Study minimax regret in bilateral trade with heavy-tailed valuations.

New algorithm FLUTE achieves uniform-PAC convergence in RL with linear approx.

New RL algorithm achieves nearly optimal performance for linear MDPs.

New algorithm achieves optimal regret in average reward MDPs without prior bias information.

We study the problem of alleviating the instability issue in the GAN training procedure via new architecture design. The discrepancy between the minimax and maximin objective values could serve as a proxy for the difficulties that the alternating gradient descent encounters in the optimization of GANs. In this work, we…

New algorithms tackle robust RL with linear models, revealing unique challenges.

Paper tackles linear models with missing values, achieving minimax optimal results.

New RL method nearly optimally learns policies with generative models.

Study on $Q$-function estimation for continuous state-action MDPs, deriving rates and conditions.

Paper analyzes error bounds for learning with vector-valued RF, improving existing analyses.

Estimation of functions of $d$ variables is considered using ridge combinations of the form $\textstyle\sum_{k=1}^m c_{1,k} φ(\textstyle\sum_{j=1}^d c_{0,j,k}x_j-b_k)$ where the activation function $φ$ is a function with bounded value and derivative. These include single-hidden layer neural networks, polynomials, …

Minimax linkage was first introduced by Ao et al. [3] in 2004, as an alternative to standard linkage methods used in hierarchical clustering. Minimax linkage relies on distances to a prototype for each cluster; this prototype can be thought of as a representative object in the cluster, hence improving the interpretabil…

Study online learning with set-valued feedback, showing differences between deterministic and randomized approaches.

This paper studies continuum-armed bandits under Besov smoothness conditions and derives minimax rates.

We demonstrate an equivalence between reproducing kernel Hilbert space (RKHS) embeddings of conditional distributions and vector-valued regressors. This connection introduces a natural regularized loss function which the RKHS embeddings minimise, providing an intuitive understanding of the embeddings and a justificatio…

Paper tackles gradient-free minimax optimization with variance reduction for faster convergence.

Reinforcement learning (RL) has achieved remarkable performance in numerous sequential decision making and control tasks. However, a common problem is that learned nearly optimal policy always overfits to the training environment and may not be extended to situations never encountered during training. For practical app…

We consider the learning algorithms under general source condition with the polynomial decay of the eigenvalues of the integral operator in vector-valued function setting. We discuss the upper convergence rates of Tikhonov regularizer under general source condition corresponding to increasing monotone index function. T…

New RL algorithm tackles nonstationary MDPs with linear approximations and varying rewards.

Develops new instance-optimality concepts in differential privacy.

New algorithm reduces RL complexity with low switching costs.

We consider random-design linear prediction and related questions on the lower tail of random matrices. It is known that, under boundedness constraints, the minimax risk is of order $d/n$ in dimension $d$ with $n$ samples. Here, we study the minimax expected excess risk over the full linear class, depending on the dist…

A fundamental challenge in contextual bandits is to develop flexible, general-purpose algorithms with computational requirements no worse than classical supervised learning tasks such as classification and regression. Algorithms based on regression have shown promising empirical success, but theoretical guarantees have…

New methods estimate transport-growth pairs in unbalanced optimal transport.

Neural networks minimize error with shallow ReLU models for function estimation.

Despite the great empirical success of deep reinforcement learning, its theoretical foundation is less well understood. In this work, we make the first attempt to theoretically understand the deep Q-network (DQN) algorithm (Mnih et al., 2015) from both algorithmic and statistical perspectives. In specific, we focus on …

Study shows how to effectively predict functions on manifolds using kernel methods.

The paper analyzes the efficiency of gradient estimation methods in noisy function evaluations.

Estimates BV functions from noisy data using Voronoi diagrams.

TensorPlan shows an exponential lower bound for planning in MDPs with linearly realizable value functions.