The aim of this workshop is to bring together researchers in the emerging topics related to learning and decision-making under budget constraints in uncertain and dynamic environments. These problems introduce a new trade off between prediction accuracy and prediction cost. Studying this tradeoff is an inherent challenge that needs to be investigated in a principled fashion in order to invent practically relevant machine learning algorithms. This problem lies at the intersection of ML, statistics, stochastic control, and information theory. Motivation: This problem has gained attention in several communities including machine learning (ML), signal processing (SP), information theory (IT), and computer science (CS). In ML it has also been referred to as resource constraint learning (and “test-time cost sensitive learning”) and has often been addressed with classifier cascades (e.g. Viola and Jones, 2004). In IT and SP this problem has been referred to as controlled sensing where the goal is to adaptively manage and control multiple degrees of freedom in an information-gathering systems to fulfill the goal of different inference tasks. This problem has been referred to as the discrete function evaluation problem in CS, with the goal of identifying distinct classes from a set of objects while minimizing the cost of testing to differentiate between objects.
Learning and decision-making problems under budget constraints arise in particular in real-world industrial applications ranging from medical diagnosis, to search engines and surveillance. In these applications budget constraints arise as a result of limits on computational cost, time, network-throughput and power-consumption. For instance, in search engines CPU cost during prediction-time must be budgeted to enabled business models such as online advertising. Additionally, search engines have time constraints at prediction-time as users are known to abandon the service is the response time of the search engine is not within a few tens of milliseconds. In another example, modern passenger screening systems impose constraints on throughput.
Learning under resource constraints departs from the traditional machine learning setting and introduces new exciting challenges. For instance, features are accompanied by costs (e.g. extraction time in search engines or true monetary values in medical diagnosis) and their amortized sum is constrained at test-time. Also, different search strategies in prediction can have widely varying computational costs (e.g., binary search, linear search, dynamic programming). In other settings, a system must maintain a throughput constraint to keep pace with arriving traffic.
All settings have in common that they introduce a new trade-off between prediction accuracy and prediction cost. Studying this tradeoff is an inherent challenge that needs to be investigated in a principled fashion in order to invent practically relevant machine learning algorithms. This problems lies at the intersection of ML, statistics, stochastic control and information theory. We aim to draw researchers working on foundational, algorithmic and application problems within these areas. In addition, we aim to supply benchmark datasets (which are typically hard to obtain) and encourage participants to evaluate and compare their novel algorithms.
In this workshop, we plan to focus on two special themes:
Date: July 11th, 2015 Tentative Invited Speakers: Jacob Abernethy (University of Michigan) Rich Caruana (Microsoft Research) Csaba Szepesvari (University of Alberta) Yoshua Bengio (Université de Montréal) Balazs Kegl (CNRS-University of Paris) Manik Varma (Microsoft Research)Ofer Dekel (Microsoft Research) Schedule: 9:00 am Introduction 9:20 am Matt Kusner Title: Dynamic Classification under Test-time Budgets 9:40 am Joe Wang Title: Resource Constrained Prediction using Directed Acyclic Graphs 10:00 am Jacob Abernethy Title: Actively Purchasing Data for Learning 10:00 am Coffee Break 10:40 am Ofer Dekel Title: Pruning Decision Forests 11:00 am Yoshua Bengio Title: Ideas for Smaller Footprint Deep Networks 11:40 am Balazs Kegl Title: Budgeting data scientists - Rapid Analytics and Model Prototyping 12:00 pm Lunch Break 02:10 pm Cedric Archambeau 02:30 pm Csaba Szepesvari Title: Online learning and prediction on a budget. 03:10 pm Manik Varma Title: Local Deep Kernel Learning 03:50 pm Coffee Break 04:30 pm Rich Caruana Title: Model Compression: Making High-Accuracy Models Smaller and Faster 05:10 pm Poster session Accepted Papers: Generatively Optimized Bayesian Network Classifiers Under Computational Constraints Sebastian Tschiatschek & Franz Pernkopf Bitwise Neural Networks Minje Kim & Paris Smaragdis Resource-Constrained Learnability Jacob Steinhardt, Gregory Valiant, Stefan Wager Dynamic Sensing: Better Classification under Acquisition Constraints Oran Richman & Shie Mannor Empirical Study: Energy Usage for Standard Machine Learning Prediction Aaron Verachtert, Wannes Meert, Jesse Davis, Hendrick Blockeel Large-Scale Semi-Supervised Learning with Online Spectral Graph Sparsification Daniele Calandriello, Alessandro Lazaric, Michal Valko Minimax Rates for Memory-Bounded Sparse Linear Regression Jacob Steinhardt & John Duchi Budgeted Random Forests Feng Nan, Joe Wang, Venkatesh Saligrama Organizers: Ralf Herbrich, Director of Machine Learning Science, Amazon, Venkatesh Saligrama, Professor, Boston University, Kilian Q. Weinberger, Associate Professor, Washington University in St. Louis, Joe Wang, Post-Doc, Boston University, Tolga Bolukbasi, PhD Candidate, Boston University, Matt J. Kusner, PhD Candidate, Washington University in St. Louis |