arXiv research

New method speeds up diffusion models inference to sub-linear time.

We provide a novel -- and to the best of our knowledge, the first -- algorithm for high dimensional sparse regression with constant fraction of corruptions in explanatory and/or response variables. Our algorithm recovers the true sparse parameters with sub-linear sample complexity, in the presence of a constant fractio…

We revisit the inductive matrix completion problem that aims to recover a rank-$r$ matrix with ambient dimension $d$ given $n$ features as the side prior information. The goal is to make use of the known $n$ features to reduce sample and computational complexities. We present and analyze a new gradient-based non-convex…

This paper proposes asal, a new GAN based active learning method that generates high entropy samples. Instead of directly annotating the synthetic samples, ASAL searches similar samples from the pool and includes them for training. Hence, the quality of new samples is high and annotations are reliable. To the best of o…

In recent years there has been an increasing interest in learning Bayesian networks from data. One of the most effective methods for learning such networks is based on the minimum description length (MDL) principle. Previous work has shown that this learning procedure is asymptotically successful: with probability one,…

In this work, we propose a robust approach to design distributed controllers for unknown-but-sparse linear and time-invariant systems. By leveraging modern techniques in distributed controller synthesis and structured linear inverse problems as applied to system identification, we show that near-optimal distributed con…

Optimal ReLU networks can memorize any separable set of points with a small number of parameters.

New algorithms achieve uniform-PAC guarantees for RL with bounded eluder dimension.

SURF simplifies distribution estimation with simple, robust, and fast algorithms.

A new algorithm improves stochastic linear bandit performance using residual bootstrap.

Bayesian optimisation algorithm for unknown search spaces with sub-linear regret.

Neural algorithms optimize arm selection with human preference feedback for complex reward functions.

Deep Retrieval learns a retrievable structure for efficient large-scale recommendations.

The paper develops a new method to test if two multidimensional distributions are equivalent or significantly different.

Efficient algorithms for $k$-means clustering frequently converge to suboptimal partitions, and given a partition, it is difficult to detect $k$-means optimality. In this paper, we develop an a posteriori certifier of approximate optimality for $k$-means clustering. The certifier is a sub-linear Monte Carlo algorithm b…

New NTK bounds show deep networks with minimum over-parameterization can still memorize and optimize.

Safe Gaussian Process Bandit Optimization with sub-linear regret bounds.

Study finds exact limits for sparse regression with fewer observations than usual.

Kernel $ε$-Greedy optimizes multi-armed bandits with covariates for sub-linear regret.

In this paper, we study a class of online optimization problems with long-term budget constraints where the objective functions are not necessarily concave (nor convex) but they instead satisfy the Diminishing Returns (DR) property. Specifically, a sequence of monotone DR-submodular objective functions $\{f_t(x)\}_{t=1…

Optimal Liouville theorem for minimal disks in any codimension.

We study the asymptotic properties of the adaptive Lasso in cointegration regressions in the case where all covariates are weakly exogenous. We assume the number of candidate I(1) variables is sub-linear with respect to the sample size (but possibly larger) and the number of candidate I(0) variables is polynomial with …

Paper tackles LDP bandits learning with improved results and sub-linear regret.

First robust bandit algorithm for contextual bandits with sub-linear regret.

In this paper, we introduce Ballooning Multi-Armed Bandits (BL-MAB), a novel extension of the classical stochastic MAB model. In the BL-MAB model, the set of available arms grows (or balloons) over time. In contrast to the classical MAB setting where the regret is computed with respect to the best arm overall, the regr…

New BO method optimizes functions efficiently even with unknown hyperparameters.

New algorithm tackles adversarial corruption in Lipschitz bandits with sub-linear regret.

VITS improves Thompson Sampling for contextual bandits with efficient approximate inference.

LNUCB-TA improves MAB performance by dynamically adjusting exploration rates and recognizing spatiotemporal patterns.

Latent DiTs improve data distribution recovery and inference efficiency under low-dimensional latent space.

In this work, we study the problem of aggregating a finite number of predictors for nonstationary sub-linear processes. We provide oracle inequalities relying essentially on three ingredients: (1) a uniform bound of the $\ell^1$ norm of the time varying sub-linear coefficients, (2) a Lipschitz assumption on the predict…

A new algorithm for restless bandits handles long-range dependencies.

A key quantity of interest in Bayesian inference are expectations of functions with respect to a posterior distribution. Markov Chain Monte Carlo is a fundamental tool to consistently compute these expectations via averaging samples drawn from an approximate posterior. However, its feasibility is being challenged in th…

Graph neural Thompson Sampling improves online decision-making for graph data.

This paper addresses robust CBs for linear SEMs with model fluctuations.

A new iterative low complexity algorithm has been presented for computing the Walsh-Hadamard transform (WHT) of an $N$ dimensional signal with a $K$-sparse WHT, where $N$ is a power of two and $K = O(N^α)$, scales sub-linearly in $N$ for some $0 < α< 1$. Assuming a random support model for the non-zero transform domain…

Avare improves optimization and sampling with adaptive importance sampling.

Log-linear models are arguably the most successful class of graphical models for large-scale applications because of their simplicity and tractability. Learning and inference with these models require calculating the partition function, which is a major bottleneck and intractable for large state spaces. Importance Samp…

In $\mathcal{X}$-armed bandit problem an agent sequentially interacts with environment which yields a reward based on the vector input the agent provides. The agent's goal is to maximise the sum of these rewards across some number of time steps. The problem and its variations have been a subject of numerous studies, su…

Paper introduces a neural network training algorithm for noisy data that achieves optimal parameters and replicates real-world behaviors.

We investigate the sample complexity of networks with bounds on the magnitude of its weights. In particular, we consider the class \[ H=\left\{W_t\circρ\circ \ldots\circρ\circ W_{1} :W_1,\ldots,W_{t-1}\in M_{d, d}, W_t\in M_{1,d}\right\} \] where the spectral norm of each $W_i$ is bounded by $O(1)$, the Frobenius norm …

New algorithms reduce slate bandit regret for large slates, outperforming existing methods.

Restless bandit problems assume time-varying reward distributions of the arms, which adds flexibility to the model but makes the analysis more challenging. We study learning algorithms over the unknown reward distributions and prove a sub-linear, $O(\sqrt{T}\log T)$, regret bound for a variant of Thompson sampling. Our…

In this paper, we consider the problem of preserving privacy in the online learning setting. We study the problem in the online convex programming (OCP) framework---a popular online learning setting with several interesting theoretical and practical implications---while using differential privacy as the formal privacy …

New algorithms for planning with adversarial changes in costs.

Paper tackles domain adaptation for contextual bandits with sub-linear regret.

Study of deep Stable neural networks with various activation functions.

New algorithm for partially observable contexts in finance.