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Found 18 papers, 5 papers with code
Optimal Best-Arm Identification in Bandits with Access to Offline Data
Learning paradigms based purely on offline data as well as those based solely on sequential online learning have been well-studied in the literature.
Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization
We present Re-weighted Gradient Descent (RGD), a novel optimization technique that improves the performance of deep neural networks through dynamic sample importance weighting.
End-to-End Neural Network Compression via $\frac{\ell_1}{\ell_2}$ Regularized Latency Surrogates
Our algorithm is versatile and can be used with many popular compression methods including pruning, low-rank factorization, and quantization.
Near Optimal Private and Robust Linear Regression
Under label-corruption, this is the first efficient linear regression algorithm to guarantee both $(\varepsilon,\delta)$-DP and robustness.
Optimal Algorithms for Latent Bandits with Cluster Structure
Instead, we propose LATTICE (Latent bAndiTs via maTrIx ComplEtion) which allows exploitation of the latent cluster structure to provide the minimax optimal regret of $\widetilde{O}(\sqrt{(\mathsf{M}+\mathsf{N})\mathsf{T}})$, when the number of clusters is $\widetilde{O}(1)$.
Building Robust Ensembles via Margin Boosting
Consequently, an emerging line of work has focused on learning an ensemble of neural networks to defend against adversarial attacks.
Boosted CVaR Classification
To learn such randomized classifiers, we propose the Boosted CVaR Classification framework which is motivated by a direct relationship between CVaR and a classical boosting algorithm called LPBoost.
Learning Minimax Estimators via Online Learning
We view the problem of designing minimax estimators as finding a mixed strategy Nash equilibrium of a zero-sum game.
Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games
For Lipschitz and smooth nonconvex-nonconcave games, our algorithm requires access to an optimization oracle which computes the perturbed best response.
Online Non-Convex Learning: Following the Perturbed Leader is Optimal
We show that the classical Follow the Perturbed Leader (FTPL) algorithm achieves optimal regret rate of $O(T^{-1/2})$ in this setting.
Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression
We provide a nearly linear time estimator which consistently estimates the true regression vector, even with $1-o(1)$ fraction of corruptions.
On the (In)fidelity and Sensitivity for Explanations
We analyze optimal explanations with respect to both these measures, and while the optimal explanation for sensitivity is a vacuous constant explanation, the optimal explanation for infidelity is a novel combination of two popular explanation methods.
Revisiting Adversarial Risk
Based on the modified definition, we show that there is no trade-off between adversarial and standard accuracies; there exist classifiers that are robust and achieve high standard accuracy.
Robust Estimation via Robust Gradient Estimation
We provide a new computationally-efficient class of estimators for risk minimization.
ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices
Such applications demand prediction models with small storage and computational complexity that do not compromise significantly on accuracy.
Ordinal Graphical Models: A Tale of Two Approaches
While there have been some work on tractable approximations, these do not come with strong statistical guarantees, and moreover are relatively computationally expensive.
Latent Feature Lasso
The latent feature model (LFM), proposed in (Griffiths \& Ghahramani, 2005), but possibly with earlier origins, is a generalization of a mixture model, where each instance is generated not from a single latent class but from a combination of latent features.
Vector-Space Markov Random Fields via Exponential Families
Specifically, VS-MRFs are the joint graphical model distributions where the node-conditional distributions belong to generic exponential families with general vector space domains.
