Machine Learning Blog | ML@CMU | Carnegie Mellon University

federated learning machine learning Research

On Privacy and Personalization in Federated Learning: A Retrospective on the US/UK PETs Challenge

by Ken Liu / May 12, 2023

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…

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artificial intelligence computer vision machine learning Research

TIDEE: An Embodied Agent that Tidies Up Novel Rooms using Commonsense Priors

April 14, 2023

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…

43 2763

machine learning Research

Are Model Explanations Useful in Practice? Rethinking How to Support Human-ML Interactions.

March 31, 2023

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…

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artificial intelligence computer science machine learning natural language processing reinforcement learning Research

RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning

February 24, 2023

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)…

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computer vision machine learning Research

Bottom-up Top-Down Detection Transformers For Open Vocabulary Object Detection

January 13, 2023

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…

104 6491

machine learning Research

Causal Confounds in Sequential Decision Making

November 28, 2022

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…

160 9808

Research

How do Authors’ Perceptions about their Papers Compare with Co-authors’ Perceptions and Peer-review Decisions?

November 22, 2022

Alina Beygelzimer, Yann N. Dauphin, Percy Liang, Jennifer Wortman Vaughan(NeurIPS 2021 Program Chairs) Charvi Rastogi, Ivan Stelmakh, Zhenyu Xue, Hal Daumé III, Emma Pierson, and Nihar B. Shah There is a considerable body of research on peer review. Within the…

301 9129 3

automl deep learning machine learning Research

Tackling Diverse Tasks with Neural Architecture Search

October 14, 2022

DASH searches for the optimal kernel size and dilation rate efficiently from a large set of options for each convolutional layer in a CNN backbone. The resulting model can achieve task-specific feature extraction and work as well as hand-designed expert…

177 8680

computer vision deep learning machine learning Research

Tracking Any Pixel in a Video

September 9, 2022

We upgrade pixels into PIPs: “Persistent Independent Particles”. With this representation, we track any pixel over time, and overcome visibility issues with a learned temporal prior. Motion estimation is a fundamental task of computer vision, with extremely broad applications. By…

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