ReLM enables writing tests that are guaranteed to come from the set of valid strings, such as dates. Without ReLM, LLMs are free to complete prompts with non-date answers, which are difficult to assess. TL;DR: While large language models (LLMs) have been touted for their ability to generate natural-sounding text, there are concerns around potential negative effects of LLMs such as data memorization, bias, and inappropriate language. We introduce ReLM (MLSys ’23), a system for validating and querying LLMs using standard regular expressions. We demonstrate via validation tasks on memorization, b ..read more

TL;DR: We study the use of differential privacy in personalized, cross-silo federated learning (NeurIPS’22), explain how these insights led us to develop a 1st place solution in the US/UK Privacy-Enhancing Technologies (PETs) Prize Challenge, and share challenges and lessons learned along the way. If you are feeling adventurous, checkout the extended version of this post with more technical details! How can we be better prepared for the next pandemic? Patient data collected by groups such as hospitals and health agencies is a critical tool for monitoring and preventing the spread of disease. U ..read more

Example of embodied commonsense reasoning. A robot proactively identifies a remote on the floor and knows it is out of place without instruction. Then, the robot figures out where to place it in the scene and manipulates it there. For robots to operate effectively in the world, they should be more than explicit step-by-step instruction followers. Robots should take actions in situations when there is a clear violation of the normal circumstances and be able to infer relevant context from partial instruction. Consider a situation where a home robot identifies a remote control which has fallen t ..read more

Figure 1. This blog post discusses the effectiveness of black-box model explanations in aiding end users to make decisions. We observe that explanations do not in fact help with concrete applications such as fraud detection and paper matching for peer review. Our work further motivates novel directions for developing and evaluating tools to support human-ML interactions. Model explanations have been touted as crucial information to facilitate human-ML interactions in many real-world applications where end users make decisions informed by ML predictions. For example, explanations are thought to ..read more

Figure 1: Overview of RL Prompt for discrete prompt optimization. All language models (LMs) are frozen. We build our policy network by training a task-specific multi-layer perceptron (MLP) network inserted into a frozen pre-trained LM. The figure above illustrates 1) generation of a prompt (left), 2) example usages in a masked LM for classification (top right) and a left-to-right LM for generation (bottom right), and 3) update of the MLP using RL reward signals (red arrows). TL;DR: Prompting enables large language models (LLMs) to perform various NLP tasks without changing the model. Discrete ..read more

We perform open vocabulary detection of the objects mentioned in the sentence using both bottom-up and top-down feedback. Object detection is the fundamental computer vision task of finding all “objects” that are present in a visual scene. However, this raises the question, what is an object? Typically, this question is side-stepped by defining a vocabulary of categories and then training a model to detect instances of this vocabulary. This means that if “apple” is not in this vocabulary, the model does not consider it as an object. The problem gets even worse when we try to integrate these ob ..read more

A standard assumption in sequential decision making is that we observe everything required to make good decisions. In practice however, this isn’t always the case. We discuss two specific examples (temporally correlated noise (a) and unobserved contexts (c)) that have stymied the use of IL/RL algorithms (in autonomous helicopters (b) and self-driving (d)). We derive provably correct algorithms for both of these problems that scale to continuous control problems. Reinforcement Learning (RL) and Imitation Learning (IL) methods have achieved impressive results in recent years like beating the wor ..read more

Figure 1: Modeled recommendation cycle. We demonstrate how enforcing group-fairness in every recommendation slate separately does not necessarily promote equity in second order variables of interest like network size. Connection recommendation is at the heart of user experience in many online social networks. Given a prompt such as ‘People you may know’, connection recommender systems suggest a list of users, and the recipient of the recommendation decides which of the users to connect with. In some instances, connection recommendations can account for more than 50% of the social network graph ..read more

Over the past decade, machine learning (ML) has grown rapidly in both popularity and complexity. Driven by advances in deep neural networks, ML is now being applied far beyond its traditional domains like computer vision and text processing, with applications in areas as diverse as solving partial differential equations (PDEs), tracking credit card fraud, and predicting medical conditions from gene sequences. However, progress in such areas has often required expert-driven development of complex neural network architectures, expensive hyperparameter tuning, or both. Given that such resource in ..read more