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deep dive
Goal representations for instruction following
BAIR blog
23 Nov 2023
How can we reconcile the ease of specifying tasks through natural language-based approaches with the performance improvements of goal-conditioned learning?
A comprehensive survey on rare event prediction
We review the rare event prediction literature and highlight open research questions and future directions in the field.
Test-time adaptation with slot-centric models
ML@CMU
25 Sep 2023
Improving out-of-distribution scene decomposition accuracy.
Training diffusion models with reinforcement learning
BAIR blog
15 Sep 2023
We show how diffusion models can be trained on downstream objectives directly using reinforcement learning.
A ‘black box’ AI system has been influencing criminal justice decisions for over two decades – it’s time to open it up
The Conversation
11 Aug 2023
Melissa Hamilton and Pamela Ugwudike investigate the use of automated decision-making systems in courts and prisons.
On the stepwise nature of self-supervised learning
BAIR blog
01 Aug 2023
Presenting a mathematical picture of the training process of large-scale SSL methods.
Navigating to objects in the real world
ML@CMU
24 Jul 2023
Research shows that modular learning is a reliable approach to navigate to objects.
Generating 3D molecular conformers via equivariant coarse-graining and aggregated attention
BAIR blog
14 Jul 2023
Introducing a variational encoder for molecular conformer generation.
GPT-4 + Stable-Diffusion = ?: Enhancing prompt understanding of text-to-image diffusion models with large language models
BAIR blog
26 Jun 2023
Our LLM-grounded model delivers improved prompt understanding in cases including negation, numeracy, and spatial relationships.
On privacy and personalization in federated learning: a retrospective on the US/UK PETs challenge
ML@CMU
05 Jun 2023
Studying the use of differential privacy in personalized, cross-silo federated learning.
TIDEE: An embodied agent that tidies up novel rooms using commonsense priors
ML@CMU
28 Apr 2023
We introduce a new benchmark to test agents in their ability to clean up messy scenes without any human instruction.
Koala: A dialogue model for academic research
BAIR blog
18 Apr 2023
In this post, we introduce Koala, a chatbot trained by fine-tuning Meta’s LLaMA on dialogue data gathered from the web.
Are model explanations useful in practice? Rethinking how to support human-ML interactions
ML@CMU
14 Apr 2023
This post describes a workflow for evaluating XAI methods, how this workflow was instantiated in two domains, and insights from these efforts.
Methods for addressing class imbalance in deep learning-based natural language processing
Sophie Henning and Annemarie Friedrich
30 Mar 2023
This blogpost gives an overview of class imbalance in NLP and surveys methods for addressing this.
RLPrompt: Optimizing discrete text prompts with reinforcement learning
ML@CMU
07 Mar 2023
We propose an efficient discrete prompt optimization approach with reinforcement learning.
Fully autonomous real-world reinforcement learning with applications to mobile manipulation
BAIR blog
07 Feb 2023
A system that learns to clean up a room directly with a real robot via continual learning.
Riemannian score-based generative modelling
Valentin De Bortoli
01 Feb 2023
The winners of a NeurIPS 2022 best paper award write about their work on generative modelling.
Bottom-up top-down detection transformers for open vocabulary object detection
ML@CMU
23 Jan 2023
We introduce a model that detects all objects that a phrase mentions.
Causal confounds in sequential decision making
ML@CMU
06 Dec 2022
Using techniques from causal inference, we derive provably correct and scalable algorithms for sequential decision making in certain settings.
Tackling diverse tasks with neural architecture search
ML@CMU
24 Oct 2022
We developed a Neural Architecture Search method that generates and trains task-specific convolutional neural networks.
Tracking any pixel in a video
ML@CMU
17 Oct 2022
We propose Persistent Independent Particles (PIPs), a new particle video method to track pixels in a video.
Keeping learning-based control safe by regulating distributional shift
BAIR blog
30 Sep 2022
We propose a new framework to reason about the safety of a learning-based controller with respect to its training distribution.
Recurrent model-free RL can be a strong baseline for many POMDPs
ML@CMU
23 Sep 2022
Considering an approach for dealing with realistic problems with noise and incomplete information.
Reverse engineering the NTK: towards first-principles architecture design
BAIR blog
12 Sep 2022
We propose a paradigm for bringing some principle to the art of architecture design.
Galaxies on graph neural networks
ML@CMU
05 Sep 2022
Using Graph Neural Networks, we trained Generative Adversarial Networks to correctly predict the coherent orientations of galaxies in a state-of-the-art cosmological simulation.
auton-survival: An open-source package for regression, counterfactual estimation, evaluation and phenotyping censored time-to-event data
ML@CMU
22 Aug 2022
We present auton-survival – a comprehensive Python code repository of user-friendly, machine learning tools for working with censored time-to-event data.
Why do policy gradient methods work so well in cooperative MARL? Evidence from policy representation
BAIR blog
15 Aug 2022
We show how policy gradient methods can converge to an optimal policy in certain cases, and that they can learn multi-modal policies.
Does AutoML work for diverse tasks?
ML@CMU
01 Aug 2022
Can the available AutoML tools quickly and painlessly attain near-expert performance on diverse learning tasks?
FIGS: Attaining XGBoost-level performance with the interpretability and speed of CART
BAIR blog
12 Jul 2022
In this blog post we cover a new method for fitting an interpretable model that takes the form of a sum of trees.
Deep attentive variational inference
ML@CMU
24 Jun 2022
The expressivity of current deep probabilistic models can be improved by selectively prioritizing statistical dependencies between latent variables that are potentially distant from each other.
Rethinking human-in-the-loop for artificial augmented intelligence
BAIR blog
17 Jun 2022
How do we build and evaluate an AI system for real-world applications?
Bootstrapped meta-learning – an interview with Sebastian Flennerhag
Lucy Smith
07 Jun 2022
ICLR2022 award winner tells us about how he and co-authors approached the meta-learning problem.
Designing societally beneficial reinforcement learning systems
BAIR blog
31 May 2022
Studying the risks associated with using reinforcement learning for real-world applications.
An experimental design perspective on model-based reinforcement learning
ML@CMU
19 May 2022
We propose a simple algorithm that is able to solve a wide variety of control tasks.
Should I use offline RL or imitation learning?
BAIR blog
17 May 2022
In this blog post, we aim to understand if, when and why offline RL is a better approach for tackling a variety of sequential decision-making problems.
Offline RL made easier: no TD learning, advantage reweighting, or transformers
BAIR blog
03 May 2022
We try to identify the essential elements of offline RL via supervised learning.
