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Found 27 papers, 2 papers with code
On Communication Cost of Distributed Statistical Estimation and Dimensionality
We conjecture that the tradeoff between communication and squared loss demonstrated by this protocol is essentially optimal up to logarithmic factor.
Random Coordinate Descent Methods for Minimizing Decomposable Submodular Functions
Submodular function minimization is a fundamental optimization problem that arises in several applications in machine learning and computer vision.
The Power of Randomization: Distributed Submodular Maximization on Massive Datasets
A wide variety of problems in machine learning, including exemplar clustering, document summarization, and sensor placement, can be cast as constrained submodular maximization problems.
Communication Lower Bounds for Statistical Estimation Problems via a Distributed Data Processing Inequality
We study the tradeoff between the statistical error and communication cost of distributed statistical estimation problems in high dimensions.
A New Framework for Distributed Submodular Maximization
A wide variety of problems in machine learning, including exemplar clustering, document summarization, and sensor placement, can be cast as constrained submodular maximization problems.
Heavy hitters via cluster-preserving clustering
Our main innovation is an efficient reduction from the heavy hitters to a clustering problem in which each heavy hitter is encoded as some form of noisy spectral cluster in a much bigger graph, and the goal is to identify every cluster.
A Reduction for Optimizing Lattice Submodular Functions with Diminishing Returns
A function $f: \mathbb{Z}_+^E \rightarrow \mathbb{R}_+$ is DR-submodular if it satisfies $f({\bf x} + \chi_i) -f ({\bf x}) \ge f({\bf y} + \chi_i) - f({\bf y})$ for all ${\bf x}\le {\bf y}, i\in E$.
Approximate Near Neighbors for General Symmetric Norms
We show that every symmetric normed space admits an efficient nearest neighbor search data structure with doubly-logarithmic approximation.
Decomposable Submodular Function Minimization: Discrete and Continuous
This paper investigates connections between discrete and continuous approaches for decomposable submodular function minimization.
Improved Algorithms for Collaborative PAC Learning
We study a recent model of collaborative PAC learning where $k$ players with $k$ different tasks collaborate to learn a single classifier that works for all tasks.
A Parallel Double Greedy Algorithm for Submodular Maximization
We study parallel algorithms for the problem of maximizing a non-negative submodular function.
Efficient Private Algorithms for Learning Large-Margin Halfspaces
We present new differentially private algorithms for learning a large-margin halfspace.
Parallel Algorithm for Non-Monotone DR-Submodular Maximization
For any desired accuracy $\epsilon$, our algorithm achieves a $1/e - \epsilon$ approximation using $O(\log{n} \log(1/\epsilon) / \epsilon^3)$ parallel rounds of function evaluations.
Adaptive Gradient Methods for Constrained Convex Optimization and Variational Inequalities
We provide new adaptive first-order methods for constrained convex optimization.
Fair and Useful Cohort Selection
A challenge in fair algorithm design is that, while there are compelling notions of individual fairness, these notions typically do not satisfy desirable composition properties, and downstream applications based on fair classifiers might not preserve fairness.
A note on differentially private clustering with large additive error
In this note, we describe a simple approach to obtain a differentially private algorithm for k-clustering with nearly the same multiplicative factor as any non-private counterpart at the cost of a large polynomial additive error.
Adaptive and Universal Algorithms for Variational Inequalities with Optimal Convergence
We show that our algorithms are universal and simultaneously achieve the optimal convergence rates in the non-smooth, smooth, and stochastic settings.
Projection-Free Bandit Optimization with Privacy Guarantees
We design differentially private algorithms for the bandit convex optimization problem in the projection-free setting.
Locally Private $k$-Means Clustering with Constant Multiplicative Approximation and Near-Optimal Additive Error
Recent work on this problem in the locally private setting achieves constant multiplicative approximation with additive error $\tilde{O} (n^{1/2 + a} \cdot k \cdot \max \{\sqrt{d}, \sqrt{k} \})$ and proves a lower bound of $\Omega(\sqrt{n})$ on the additive error for any solution with a constant number of rounds.
Adaptive Accelerated (Extra-)Gradient Methods with Variance Reduction
To address this problem, we propose two novel adaptive VR algorithms: Adaptive Variance Reduced Accelerated Extra-Gradient (AdaVRAE) and Adaptive Variance Reduced Accelerated Gradient (AdaVRAG).
Streaming Algorithm for Monotone k-Submodular Maximization with Cardinality Constraints
Maximizing a monotone k-submodular function subject to cardinality constraints is a general model for several applications ranging from influence maximization with multiple products to sensor placement with multiple sensor types and online ad allocation.
META-STORM: Generalized Fully-Adaptive Variance Reduced SGD for Unbounded Functions
There, STORM utilizes recursive momentum to achieve the VR effect and is then later made fully adaptive in STORM+ [Levy et al., '21], where full-adaptivity removes the requirement for obtaining certain problem-specific parameters such as the smoothness of the objective and bounds on the variance and norm of the stochastic gradients in order to set the step size.
On the Convergence of AdaGrad(Norm) on $\R^{d}$: Beyond Convexity, Non-Asymptotic Rate and Acceleration
Finally, we give new accelerated adaptive algorithms and their convergence guarantee in the deterministic setting with explicit dependency on the problem parameters, improving upon the asymptotic rate shown in previous works.
High Probability Convergence for Accelerated Stochastic Mirror Descent
In this work, we describe a generic approach to show convergence with high probability for stochastic convex optimization.
Identification of Negative Transfers in Multitask Learning Using Surrogate Models
This problem is computationally challenging since the number of subsets grows exponentially with the number of source tasks; efficient heuristics for subset selection do not always capture the relationship between task subsets and multitask learning performances.
Online and Streaming Algorithms for Constrained $k$-Submodular Maximization
Constrained $k$-submodular maximization is a general framework that captures many discrete optimization problems such as ad allocation, influence maximization, personalized recommendation, and many others.
Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages
We study the problem of private vector mean estimation in the shuffle model of privacy where $n$ users each have a unit vector $v^{(i)} \in\mathbb{R}^d$.
