arXiv research

The paper tackles pure exploration in multi-armed bandits with low rank structure using oblivious sampling.

New insights into variable selection with different model assumptions.

New families of translation surfaces with multiple oblivious points discovered.

This work introduces oblivious fairness definitions for image generation.

Study shows efficient algorithms for noiseless linear regression require quadratic sample complexity in contamination rate.

New algorithms for GLMs with oblivious noise, identifying solutions even when half the data is corrupted.

Study non-oblivious adversarial bandits with delayed feedback and propose algorithms with improved regret bounds.

Classical multi-armed bandit problems use the expected value of an arm as a metric to evaluate its goodness. However, the expected value is a risk-neutral metric. In many applications like finance, one is interested in balancing the expected return of an arm (or portfolio) with the risk associated with that return. In …

New study shows data-oblivious attacks can outperform data-aware ones.

Stochastic games provide a framework for interactions among multiple agents and enable a myriad of applications. In these games, agents decide on actions simultaneously, the state of every agent moves to the next state, and each agent receives a reward. However, finding an equilibrium (if exists) in this game is often …

DOFEN improves DNN performance on tabular data benchmarks.

Optimal subspace embedding with near-optimal sparsity for high-dimensional data.

Develops efficient estimators for PCA and sparse regression in the presence of oblivious outliers.

Slow running or straggler tasks can significantly reduce computation speed in distributed computation. Recently, coding-theory-inspired approaches have been applied to mitigate the effect of straggling, through embedding redundancy in certain linear computational steps of the optimization algorithm, thus completing the…

Linear regression models contaminated by Gaussian noise (inlier) and possibly unbounded sparse outliers are common in many signal processing applications. Sparse recovery inspired robust regression (SRIRR) techniques are shown to deliver high quality estimation performance in such regression models. Unfortunately, most…

A new data-oblivious sketch for logistic regression reduces data size while maintaining approximation accuracy.

New algorithm speeds up polynomial kernel approximations.

Paper improves performance guarantees for Rademacher projections.

Study improves privacy-preserving online prediction from experts with speed-ups.

New algorithm achieves both static and dynamic regret optimally against an oblivious adversary for deterministic losses.

We investigate the problem of algorithmic fairness in the case where sensitive and non-sensitive features are available and one aims to generate new, `oblivious', features that closely approximate the non-sensitive features, and are only minimally dependent on the sensitive ones. We study this question in the context o…

Nowadays, deep neural networks (DNNs) have become the main instrument for machine learning tasks within a wide range of domains, including vision, NLP, and speech. Meanwhile, in an important case of heterogenous tabular data, the advantage of DNNs over shallow counterparts remains questionable. In particular, there is …

New algorithms optimize without knowing problem parameters.

Orthogonal matching pursuit (OMP) is a widely used algorithm for recovering sparse high dimensional vectors in linear regression models. The optimal performance of OMP requires \textit{a priori} knowledge of either the sparsity of regression vector or noise statistics. Both these statistics are rarely known \textit{a p…

New robust regression method works with fewer data points than previous methods.

Deep neural networks (DNNs) are powerful nonlinear architectures that are known to be robust to random perturbations of the input. However, these models are vulnerable to adversarial perturbations--small input changes crafted explicitly to fool the model. In this paper, we ask whether a DNN can distinguish adversarial …

Random projections (RP) are a popular tool for reducing dimensionality while preserving local geometry. In many applications the data set to be projected is given to us in advance, yet the current RP techniques do not make use of information about the data. In this paper, we provide a computationally light way to extra…

Sparse OSEs achieve optimal embedding dimension of O(d).

New algorithm recovers sparse signals robustly against Gaussian noise and adaptive adversaries.

Study the tradeoffs of bandit feedback in multiclass classification.

In this paper new general modewise Johnson-Lindenstrauss (JL) subspace embeddings are proposed that are both considerably faster to generate and easier to store than traditional JL embeddings when working with extremely large vectors and/or tensors. Corresponding embedding results are then proven for two different type…

We study the problem of maximizing a monotone set function subject to a cardinality constraint $k$ in the setting where some number of elements $τ$ is deleted from the returned set. The focus of this work is on the worst-case adversarial setting. While there exist constant-factor guarantees when the function is submodu…

Bandit algorithms struggle with consistent performance and robustness.

In this paper we investigate the problem of estimating the cluster tree for a density $f$ supported on or near a smooth $d$-dimensional manifold $M$ isometrically embedded in $\mathbb{R}^D$. We analyze a modified version of a $k$-nearest neighbor based algorithm recently proposed by Chaudhuri and Dasgupta. The main res…

This paper introduces TNTK to study infinite soft tree ensembles.

We give a fast oblivious L2-embedding of $A\in \mathbb{R}^{n x d}$ to $B\in \mathbb{R}^{r x d}$ satisfying $(1-\varepsilon)\|A x\|_2^2 \le \|B x\|_2^2 <= (1+\varepsilon) \|Ax\|_2^2.$ Our embedding dimension $r$ equals $d$, a constant independent of the distortion $\varepsilon$. We use as a black-box any L2-embedding $Π…

Improved upper bound for online calibrated forecasting of binary sequences.

New algorithm samples matrix rows proportional to their ℓ_p norm in a turnstile data stream.

The paper develops adaptive confidence intervals for Efron's Gaussian two-groups model with unknown contamination.

The paper improves smoothed analysis for online problems with adaptive adversaries.

The vulnerabilities of deep neural networks against adversarial examples have become a significant concern for deploying these models in sensitive domains. Devising a definitive defense against such attacks is proven to be challenging, and the methods relying on detecting adversarial samples are only valid when the att…

New algorithms improve online prediction from experts with privacy constraints.

In principal-agent models, a principal offers a contract to an agent to perform a certain task. The agent exerts a level of effort that maximizes her utility. The principal is oblivious to the agent's chosen level of effort, and conditions her wage only on possible outcomes. In this work, we consider a model in which t…

Paper introduces a new IS scheme for estimating distribution tails of complex models.

Self-supervised learning excels in topic modeling by being less model-specific.

The paper describes an application of Aggregating Algorithm to the problem of regression. It generalizes earlier results concerned with plain linear regression to kernel techniques and presents an on-line algorithm which performs nearly as well as any oblivious kernel predictor. The paper contains the derivation of an …

This work improves tensor decomposition methods, especially for large datasets.

Paper details Hilbert-curve for high-performance data mining.