2 citations · 2 across the 10 of their papers we have counts for
10 papers
Learning Nearest-Neighbor Maps from Adaptive Queries
Hadley Black, Geelon So
We study the problem of learning nearest-neighbor maps from adaptive queries, which is equivalent to the following problem of reconstructing a hidden set via a nearest-neighbor…
Hedging on the Frontier: Learning New Tasks with Few Samples
Tobias Wegel, Federico Di Gennaro, Geelon So +1
When a learner faces a new task with few samples, it must leverage any available side information. In practice, this often comes in the form of model evaluations on related tasks i…
Learnable Mixed Nash Equilibria are Collectively Rational
Geelon So, Yi-An Ma
We extend the study of learning in games to dynamics that exhibit non-asymptotic stability. We do so through the notion of uniform stability, which is concerned with equilibria of…
Actively Learning Halfspaces without Synthetic Data
Hadley Black, Kasper Green Larsen, Arya Mazumdar +2
In the classic point location problem, one is given an arbitrary dataset of points with query access to an unknown halfspace $f : \mathbb{R}^d \to \{0,…
On the sample complexity of semi-supervised multi-objective learning
Tobias Wegel, Geelon So, Junhyung Park +1
In multi-objective learning (MOL), several possibly competing prediction tasks must be solved jointly by a single model. Achieving good trade-offs may require a model class $\mathc…
Online Consistency of the Nearest Neighbor Rule
Sanjoy Dasgupta, Geelon So
In the realizable online setting, a learner is tasked with making predictions for a stream of instances, where the correct answer is revealed after each prediction. A learning rule…
Metric Learning from Limited Pairwise Preference Comparisons
Zhi Wang, Geelon So, Ramya Korlakai Vinayak
We study metric learning from preference comparisons under the ideal point model, in which a user prefers an item over another if it is closer to their latent ideal item. These ite…
Optimization on Pareto sets: On a theory of multi-objective optimization
Abhishek Roy, Geelon So, Yi-An Ma
In multi-objective optimization, a single decision vector must balance the trade-offs between many objectives. Solutions achieving an optimal trade-off are said to be Pareto optima…
Online nearest neighbor classification
Sanjoy Dasgupta, Geelon So
We study an instance of online non-parametric classification in the realizable setting. In particular, we consider the classical 1-nearest neighbor algorithm, and show that it achi…
Convergence of online -means
Sanjoy Dasgupta, Gaurav Mahajan, Geelon So
We prove asymptotic convergence for a general class of -means algorithms performed over streaming data from a distribution: the centers asymptotically converge to the set of sta…