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Found 29 papers, 0 papers with code
Learning-Augmented Algorithms with Explicit Predictors
Recent advances in algorithmic design show how to utilize predictions obtained by machine learning models from past and present data.
On Differentially Private Online Predictions
In this work we introduce an interactive variant of joint differential privacy towards handling online processes in which existing privacy definitions seem too restrictive.
Concurrent Shuffle Differential Privacy Under Continual Observation
the counter problem) and show that the concurrent shuffle model allows for significantly improved error compared to a standard (single) shuffle model.
Differentially-Private Bayes Consistency
We construct a universally Bayes consistent learning rule that satisfies differential privacy (DP).
Monotone Learning
Our transformation readily implies monotone learners in a variety of contexts: for example it extends Pestov's result to classification tasks with an arbitrary number of labels.
Differentially-Private Clustering of Easy Instances
Clustering is a fundamental problem in data analysis.
FriendlyCore: Practical Differentially Private Aggregation
Differentially private algorithms for common metric aggregation tasks, such as clustering or averaging, often have limited practicality due to their complexity or to the large number of data points that is required for accurate results.
Differentially Private Approximate Quantiles
In this work we study the problem of differentially private (DP) quantiles, in which given dataset $X$ and quantiles $q_1, ..., q_m \in [0, 1]$, we want to output $m$ quantile estimations which are as close as possible to the true quantiles and preserve DP.
Differentially Private Multi-Armed Bandits in the Shuffle Model
We give an $(\varepsilon,\delta)$-differentially private algorithm for the multi-armed bandit (MAB) problem in the shuffle model with a distribution-dependent regret of $O\left(\left(\sum_{a\in [k]:\Delta_a>0}\frac{\log T}{\Delta_a}\right)+\frac{k\sqrt{\log\frac{1}{\delta}}\log T}{\varepsilon}\right)$, and a distribution-independent regret of $O\left(\sqrt{kT\log T}+\frac{k\sqrt{\log\frac{1}{\delta}}\log T}{\varepsilon}\right)$, where $T$ is the number of rounds, $\Delta_a$ is the suboptimality gap of the arm $a$, and $k$ is the total number of arms.
Online Markov Decision Processes with Aggregate Bandit Feedback
We study a novel variant of online finite-horizon Markov Decision Processes with adversarially changing loss functions and initially unknown dynamics.
Separating Adaptive Streaming from Oblivious Streaming
We present a streaming problem for which every adversarially-robust streaming algorithm must use polynomial space, while there exists a classical (oblivious) streaming algorithm that uses only polylogarithmic space.
Data Structures and Algorithms
Locality Sensitive Hashing for Efficient Similar Polygon Retrieval
Find the vertical translation of a function $ f $ that is closest in $ L_1 $ distance to a function $ g $.
The Sparse Vector Technique, Revisited
This simple algorithm privately tests whether the value of a given query on a database is close to what we expect it to be.
Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity
We present a differentially private learner for halfspaces over a finite grid $G$ in $\mathbb{R}^d$ with sample complexity $\approx d^{2. 5}\cdot 2^{\log^*|G|}$, which improves the state-of-the-art result of [Beimel et al., COLT 2019] by a $d^2$ factor.
Locality Sensitive Hashing for Set-Queries, Motivated by Group Recommendations
For example, we can take $ s(p, x) $ to be the angular similarity between $ p $ and $ x $ (i. e., $1-{\angle (x, p)}/{\pi}$), and aggregate by arithmetic or geometric averaging, or taking the lowest similarity.
Adversarially Robust Streaming Algorithms via Differential Privacy
A streaming algorithm is said to be adversarially robust if its accuracy guarantees are maintained even when the data stream is chosen maliciously, by an adaptive adversary.
How to Find a Point in the Convex Hull Privately
We study the question of how to compute a point in the convex hull of an input set $S$ of $n$ points in ${\mathbb R}^d$ in a differentially private manner.
Near-optimal Regret Bounds for Stochastic Shortest Path
In this work we remove this dependence on the minimum cost---we give an algorithm that guarantees a regret bound of $\widetilde{O}(B_\star |S| \sqrt{|A| K})$, where $B_\star$ is an upper bound on the expected cost of the optimal policy, $S$ is the set of states, $A$ is the set of actions and $K$ is the number of episodes.
Privately Learning Thresholds: Closing the Exponential Gap
This problem has received much attention recently; unlike the non-private case, where the sample complexity is independent of the domain size and just depends on the desired accuracy and confidence, for private learning the sample complexity must depend on the domain size $X$ (even for approximate differential privacy).
Apprenticeship Learning via Frank-Wolfe
Specifically, we show that a variation of the FW method that is based on taking "away steps" achieves a linear rate of convergence when applied to AL and that a stochastic version of the FW algorithm can be used to avoid precise estimation of feature expectations.
Sample Complexity Bounds for Influence Maximization
Our main result is a surprising upper bound of $O( s \tau \epsilon^{-2} \ln \frac{n}{\delta})$ for a broad class of models that includes IC and LT models and their mixtures, where $n$ is the number of nodes and $\tau$ is the number of diffusion steps.
Thompson Sampling for Adversarial Bit Prediction
We also bound the regret of those sequences, the worse case sequences have regret $O(\sqrt{T})$ and the best case sequence have regret $O(1)$.
Unknown mixing times in apprenticeship and reinforcement learning
We derive and analyze learning algorithms for apprenticeship learning, policy evaluation, and policy gradient for average reward criteria.
Planning in Hierarchical Reinforcement Learning: Guarantees for Using Local Policies
We consider a settings of hierarchical reinforcement learning, in which the reward is a sum of components.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
Learning to Screen
(ii) In the second variant it is assumed that before the process starts, the algorithm has an access to a training set of $n$ items drawn independently from the same unknown distribution (e. g.\ data of candidates from previous recruitment seasons).
Differentially Private Learning of Geometric Concepts
We present differentially private efficient algorithms for learning union of polygons in the plane (which are not necessarily convex).
Hierarchical Reinforcement Learning: Approximating Optimal Discounted TSP Using Local Policies
In this work, we provide theoretical guarantees for reward decomposition in deterministic MDPs.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
Clustering Small Samples with Quality Guarantees: Adaptivity with One2all pps
At the core of our design is the {\em one2all} construction of multi-objective probability-proportional-to-size (pps) samples: Given a set $M$ of centroids and $\alpha \geq 1$, one2all efficiently assigns probabilities to points so that the clustering cost of {\em each} $Q$ with cost $V(Q) \geq V(M)/\alpha$ can be estimated well from a sample of size $O(\alpha |M|\epsilon^{-2})$.
Average Distance Queries through Weighted Samples in Graphs and Metric Spaces: High Scalability with Tight Statistical Guarantees
The estimate is based on a weighted sample of $O(\epsilon^{-2})$ pairs of points, which is computed using $O(n)$ distance computations.
