163 citations · 527 across the 28 of their papers we have counts for
16 papers · 1 filter
Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learning
Yifang Chen, Shuohang Wang, Ziyi Yang +6
Reinforcement learning with human feedback (RLHF), as a widely adopted approach in current large language model pipelines, is \textit{bottlenecked by the size of human preference d…
Contextual Bandit Applications in Customer Support Bot
Sandra Sajeev, Jade Huang, Nikos Karampatziakis +3
Virtual support agents have grown in popularity as a way for businesses to provide better and more accessible customer service. Some challenges in this domain include ambiguous use…
Off-policy Confidence Sequences
Nikos Karampatziakis, Paul Mineiro, Aaditya Ramdas
We develop confidence bounds that hold uniformly over time for off-policy evaluation in the contextual bandit setting. These confidence sequences are based on recent ideas from mar…
Lessons from Contextual Bandit Learning in a Customer Support Bot
Nikos Karampatziakis, Sebastian Kochman, Jade Huang +3
In this work, we describe practical lessons we have learned from successfully using contextual bandits (CBs) to improve key business metrics of the Microsoft Virtual Agent for cust…
Empirical Likelihood for Contextual Bandits
Nikos Karampatziakis, John Langford, Paul Mineiro
We propose an estimator and confidence interval for computing the value of a policy from off-policy data in the contextual bandit setting. To this end we apply empirical likelihood…
Log-time and Log-space Extreme Classification
Kalina Jasinska, Nikos Karampatziakis
We present LTLS, a technique for multiclass and multilabel prediction that can perform training and inference in logarithmic time and space. LTLS embeds large classification proble…
Fast Label Embeddings for Extremely Large Output Spaces
Paul Mineiro, Nikos Karampatziakis
Many modern multiclass and multilabel problems are characterized by increasingly large output spaces. For these problems, label embeddings have been shown to be a useful primitive…
Scalable Multilabel Prediction via Randomized Methods
Nikos Karampatziakis, Paul Mineiro
Modeling the dependence between outputs is a fundamental challenge in multilabel classification. In this work we show that a generic regularized nonlinearity mapping independent pr…
Fast Label Embeddings via Randomized Linear Algebra
Paul Mineiro, Nikos Karampatziakis
Many modern multiclass and multilabel problems are characterized by increasingly large output spaces. For these problems, label embeddings have been shown to be a useful primitive…
Efficient Online Bootstrapping for Large Scale Learning
Zhen Qin, Vaclav Petricek, Nikos Karampatziakis +2
Bootstrapping is a useful technique for estimating the uncertainty of a predictor, for example, confidence intervals for prediction. It is typically used on small to moderate sized…
Combining Structured and Unstructured Randomness in Large Scale PCA
Nikos Karampatziakis, Paul Mineiro
Principal Component Analysis (PCA) is a ubiquitous tool with many applications in machine learning including feature construction, subspace embedding, and outlier detection. In thi…
Least Squares Revisited: Scalable Approaches for Multi-class Prediction
Alekh Agarwal, Sham M. Kakade, Nikos Karampatziakis +2
This work provides simple algorithms for multi-class (and multi-label) prediction in settings where both the number of examples n and the data dimension d are relatively large. The…
Discriminative Features via Generalized Eigenvectors
Nikos Karampatziakis, Paul Mineiro
Representing examples in a way that is compatible with the underlying classifier can greatly enhance the performance of a learning system. In this paper we investigate scalable tec…
Loss-Proportional Subsampling for Subsequent ERM
Paul Mineiro, Nikos Karampatziakis
We propose a sampling scheme suitable for reducing a data set prior to selecting a hypothesis with minimum empirical risk. The sampling only considers a subset of the ultimate (unk…
Efficient Optimal Learning for Contextual Bandits
Miroslav Dudik, Daniel Hsu, Satyen Kale +4
We address the problem of learning in an online setting where the learner repeatedly observes features, selects among a set of actions, and receives reward for the action taken. We…
Online Importance Weight Aware Updates
Nikos Karampatziakis, John Langford
An importance weight quantifies the relative importance of one example over another, coming up in applications of boosting, asymmetric classification costs, reductions, and active…