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Found 16 papers, 3 papers with code
Efficient Online Learning of Optimal Rankings: Dimensionality Reduction via Gradient Descent
We consider a natural model of online preference aggregation, where sets of preferred items $R_1, R_2, \ldots, R_t$ along with a demand for $k_t$ items in each $R_t$, appear online.
Learning Powers of Poisson Binomial Distributions
The $k$'th power of this distribution, for $k$ in a range $[m]$, is the distribution of $P_k = \sum_{i=1}^n X_i^{(k)}$, where each Bernoulli random variable $X_i^{(k)}$ has $\mathbb{E}[X_i^{(k)}] = (p_i)^k$.
Optimal Learning of Mallows Block Model
The main result of the paper is a tight sample complexity bound for learning Mallows and Generalized Mallows Model.
Efficient Parameter Estimation of Truncated Boolean Product Distributions
A stunning consequence is that virtually any statistical task (e. g., learning in total variation distance, parameter estimation, uniformity or identity testing) that can be performed efficiently for Boolean product distributions, can also be performed from truncated samples, with a small increase in sample complexity.
Aggregating Incomplete and Noisy Rankings
We consider the problem of learning the true ordering of a set of alternatives from largely incomplete and noisy rankings.
Solving Inverse Problems for Spectral Energy Distributions with Deep Generative Networks
We propose an end-to-end approach for solving inverse problems for a class of complex astronomical signals, namely Spectral Energy Distributions (SEDs).
Efficient Online Learning for Dynamic k-Clustering
We study dynamic clustering problems from the perspective of online learning.
Learning Augmented Online Facility Location
We prove that the competitive ratio decreases smoothly from sublogarithmic in the number of demands to constant, as the error, i. e., the total distance of the predicted locations to the optimal facility locations, decreases towards zero.
Efficient Algorithms for Learning from Coarse Labels
Our main algorithmic result is that essentially any problem learnable from fine grained labels can also be learned efficiently when the coarse data are sufficiently informative.
Label Ranking through Nonparametric Regression
We introduce a generative model for Label Ranking, in noiseless and noisy nonparametric regression settings, and provide sample complexity bounds for learning algorithms in both cases.
Private and Non-private Uniformity Testing for Ranking Data
We study the problem of uniformity testing for statistical data that consists of rankings over $m$ items where the alternative class is restricted to Mallows models with single parameter.
Identity testing for Mallows model
In this paper, we devise identity tests for ranking data that is generated from Mallows model both in the \emph{asymptotic} and \emph{non-asymptotic} settings.
Differentially Private Regression with Unbounded Covariates
We provide computationally efficient, differentially private algorithms for the classical regression settings of Least Squares Fitting, Binary Regression and Linear Regression with unbounded covariates.
Perfect Sampling from Pairwise Comparisons
We design a Markov chain whose stationary distribution coincides with $\mathcal{D}$ and give an algorithm to obtain exact samples using the technique of Coupling from the Past.
Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Methods
Deep Neural Networks and Reinforcement Learning methods have empirically shown great promise in tackling challenging combinatorial problems.
Fairness in Ranking: Robustness through Randomization without the Protected Attribute
Second, there are multiple measures of fairness of rankings, and optimization-based methods utilizing a single measure of fairness of rankings may produce rankings that are unfair with respect to other measures.
