2 citations · 9 across the 19 of their papers we have counts for
7 papers · 1 filter
Performance Control in Early Exiting to Deploy Large Models at the Same Cost of Smaller Ones
Mehrnaz Mofakhami, Reza Bayat, Ioannis Mitliagkas +2
Early Exiting (EE) is a promising technique for speeding up inference by adaptively allocating compute resources to data points based on their difficulty. The approach enables pred…
Context is Key: A Benchmark for Forecasting with Essential Textual Information
Andrew Robert Williams, Arjun Ashok, Étienne Marcotte +8
Forecasting is a critical task in decision-making across numerous domains. While historical numerical data provide a start, they fail to convey the complete context for reliable an…
Sample Compression Unleashed: New Generalization Bounds for Real Valued Losses
Mathieu Bazinet, Valentina Zantedeschi, Pascal Germain
The sample compression theory provides generalization guarantees for predictors that can be fully defined using a subset of the training dataset and a (short) message string, gener…
InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation
Gaurav Sahu, Abhay Puri, Juan Rodriguez +11
Data analytics is essential for extracting valuable insights from data that can assist organizations in making effective decisions. We introduce InsightBench, a benchmark dataset w…
RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content
Joao Monteiro, Pierre-Andre Noel, Etienne Marcotte +6
Large Language Models (LLMs) are trained on vast amounts of data, most of which is automatically scraped from the internet. This data includes encyclopedic documents that harbor a…
XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference
João Monteiro, Étienne Marcotte, Pierre-André Noël +5
In-context learning (ICL) approaches typically leverage prompting to condition decoder-only language model generation on reference information. Just-in-time processing of a context…
Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures
Paul Viallard, Rémi Emonet, Amaury Habrard +2
In statistical learning theory, a generalization bound usually involves a complexity measure imposed by the considered theoretical framework. This limits the scope of such bounds,…