Alexander C. Li

I'm a PhD student in the Machine Learning Department at Carnegie Mellon University. I'm advised by Deepak Pathak, and my work is supported by the NSF Graduate Research Fellowship.

Previously, I was an electrical engineering and computer science major at UC Berkeley, where I received my BS and MS. I was a researcher in the Berkeley Artificial Intelligence Research Lab and was advised by Pieter Abbeel, Lerrel Pinto, and Carlos Florensa.

If you're interested in collaborating, send me an email!

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I'm interested in generative models, generalization, optimization, and the role of data in deep learning.

Your Diffusion Model is Secretly a Zero-Shot Classifier
Alexander C. Li, Mihir Prabhudesai, Shivam Duggal, Ellis Brown, Deepak Pathak
ICCV 2023
arxiv / pdf / project page / code

We show that density estimates from text-to-image diffusion models like Stable Diffusion can be used for zero-shot classification without any additional training. Our generative approach to classification (Diffusion Classifier) outperforms alternative methods of extracting knowledge from diffusion models and has stronger multimodal reasoning abilities than competing discriminative approaches. Finally, we use Diffusion Classifier to extract standard classifiers from class-conditional diffusion models trained on ImageNet. Even though these diffusion models are trained with weak augmentations and no regularization, we find that they approach the performance of SOTA discriminative ImageNet classifiers.

Internet Explorer: Targeted Representation Learning on the Open Web
Alexander C. Li*, Ellis Brown*, Alexei A. Efros, Deepak Pathak
ICML 2023
arxiv / pdf / project page / code

We propose dynamically utilizing the Internet to quickly train a small-scale model that does extremely well on the task at hand. Our approach, called Internet Explorer, explores the web in a self-supervised manner to progressively find relevant examples that improve performance on a desired target dataset. It cycles between searching for images on the Internet with text queries, self-supervised training on downloaded images, determining which images were useful, and prioritizing what to search for next. We evaluate Internet Explorer across several datasets and show that it outperforms or matches CLIP oracle performance by using just a single GPU desktop to actively query the Internet for 40 hours.

Understanding Collapse in Non-Contrastive Siamese Representation Learning
Alexander C. Li, Alexei A. Efros, Deepak Pathak
ECCV 2022
arxiv / pdf / project page / code / bibtex

We empirically analyze non-contrastive self-supervised methods and find that SimSiam is extraordinarily sensitive to model size. In particular, SimSiam representations undergo partial dimensional collapse if the model is too small relative to the dataset size. We propose a metric to measure the degree of this collapse and show that it can be used to forecast the downstream task performance without any fine-tuning or labels. Finally, we demonstrate that shifting to a continual learning setting acts as a regularizer, prevents collapse, and can improve linear probe accuracy by up to 18 percentage points with ResNet-18 on ImageNet.

Functional Regularization for Reinforcement Learning via Learned Fourier Features
Alexander C. Li, Deepak Pathak
NeurIPS 2021
arxiv / pdf / project page / code / bibtex

We propose a simple architecture for RL that controls how quickly the network fits different frequencies in the training data. We explain this behavior using its neural tangent kernel, and use this to prioritize learning low-frequency functions and reduce networks' susceptibility to noise during optimization. Experiments on state-based and image-based RL benchmarks show improved sample-efficiency, as well as robustness to added bootstrap noise.

Generalized Hindsight for Reinforcement Learning
Alexander C. Li, Lerrel Pinto, Pieter Abbeel
NeurIPS 2020
arxiv / pdf / project page / code / bibtex

We present Generalized Hindsight: an approximate inverse reinforcement learning technique for relabeling behaviors with the right tasks. Given a behavior generated under one task, Generalized Hindsight finds a different task that the behavior is better suited for. Relabeling a trajectory with this different task and training with an off-policy RL algorithm improves performance on a suite of multi-task navigation and manipulation tasks.

Sub-policy Adaptation for Hierarchical Reinforcement Learning
Alexander C. Li*, Carlos Florensa*, Ignasi Clavera, Pieter Abbeel
International Conference on Learning Representations (ICLR), 2020
arxiv / pdf / project page / code / bibtex

We develop a new hierarchical RL algorithm that can efficiently adapt pre-trained skills on related tasks, and directly learn effective emergent skills by simultaneously training the entire hierarchy.

berkeley Co-head TA, CS 294-158: Deep Unsupervised Learning, Spring 2020

Head Content TA, EECS 126: Stochastic Processes, Fall 2019

TA, CS 188: Artificial Intelligence, Spring 2019

TA, CS 188: Artificial Intelligence, Fall 2018

Academic Intern, CS 189: Machine Learning, Spring 2018

Reader, CS 70: Discrete Mathematics & Probability, Fall 2017
Mentor, Google Code Corps 2017
Awards and Honors

Website template from Jon Barron.