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Found 19 papers, 6 papers with code
Universal Lower Bounds and Optimal Rates: Achieving Minimax Clustering Error in Sub-Exponential Mixture Models
In such mixtures, we establish that Bregman hard clustering, a variant of Lloyd's algorithm employing a Bregman divergence, is rate optimal.
Efficiently Escaping Saddle Points for Non-Convex Policy Optimization
Policy gradient (PG) is widely used in reinforcement learning due to its scalability and good performance.
Studying Lobby Influence in the European Parliament
In the absence of a ground-truth dataset of such links, we perform an indirect validation by comparing the discovered links with a dataset, which we curate, of retweet links between MEPs and lobbies, and with the publicly disclosed meetings of MEPs.
It's All Relative: Interpretable Models for Scoring Bias in Documents
In each case, we demonstrate that the outputs of the model can be explained and validated, even for the two domains that are outside the training-data domain.
Fast Interactive Search with a Scale-Free Comparison Oracle
A comparison-based search algorithm lets a user find a target item $t$ in a database by answering queries of the form, ``Which of items $i$ and $j$ is closer to $t$?''
When Does Bottom-up Beat Top-down in Hierarchical Community Detection?
We also establish that this bottom-up algorithm attains the information-theoretic threshold for exact recovery at intermediate levels of the hierarchy.
Self-Supervised Prototypical Transfer Learning for Few-Shot Classification
Building on these insights and on advances in self-supervised learning, we propose a transfer learning approach which constructs a metric embedding that clusters unlabeled prototypical samples and their augmentations closely together.
A User Study of Perceived Carbon Footprint
We propose a statistical model to understand people's perception of their carbon footprint.
Learning Hawkes Processes from a Handful of Events
It is also able to take into account the uncertainty in the model parameters by learning a posterior distribution over them.
Regression Networks for Meta-Learning Few-Shot Classification
We propose regression networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each class.
Scalable and Efficient Comparison-based Search without Features
We consider the problem of finding a target object $t$ using pairwise comparisons, by asking an oracle questions of the form \emph{"Which object from the pair $(i, j)$ is more similar to $t$?"}.
Pairwise Comparisons with Flexible Time-Dynamics
Inspired by applications in sports where the skill of players or teams competing against each other varies over time, we propose a probabilistic model of pairwise-comparison outcomes that can capture a wide range of time dynamics.
Analysis of a Canonical Labeling Algorithm for the Alignment of Correlated Erdős-Rényi Graphs
Graph alignment in two correlated random graphs refers to the task of identifying the correspondence between vertex sets of the graphs.
Can Who-Edits-What Predict Edit Survival?
As the number of contributors to online peer-production systems grows, it becomes increasingly important to predict whether the edits that users make will eventually be beneficial to the project.
ChoiceRank: Identifying Preferences from Node Traffic in Networks
We consider a setting where only aggregate node-level traffic is observed and tackle the task of learning edge transition probabilities.
The Player Kernel: Learning Team Strengths Based on Implicit Player Contributions
In this work, we draw attention to a connection between skill-based models of game outcomes and Gaussian process classification models.
Fast and Accurate Inference of Plackett–Luce Models
We show that the maximum-likelihood (ML) estimate of models derived from Luce's choice axiom (e. g., the Plackett-Luce model) can be expressed as the stationary distribution of a Markov chain.
Just Sort It! A Simple and Effective Approach to Active Preference Learning
We address the problem of learning a ranking by using adaptively chosen pairwise comparisons.
The Entropy of Conditional Markov Trajectories
To quantify the randomness of Markov trajectories with fixed initial and final states, Ekroot and Cover proposed a closed-form expression for the entropy of trajectories of an irreducible finite state Markov chain.
Information Theory Information Theory Applications
