Alexander C. Li

I'm a third year 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 self-supervised learning, generalization, optimization, and the role of data in deep learning.

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.

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.

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.

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.

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.

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.

• Two Sigma PhD Fellowship Runner-Up (2nd place out of 160 nominees) (2023)
• National Science Foundation Graduate Research Fellowship (2020)
• 1st place at Citadel San Francisco Invitational Data Open (2019)
• Mark D. Weiser Excellence in Computing Scholarship (2019)
• Accel Scholar (2018)
• Quantedge Award for Academic Excellence: awarded to ~120 seniors at UC Berkeley for a 4.0 GPA (2018)
• Cal Alumni Association Leadership Award (2017)
• Dean's Honor List: Fall 2016, Spring 2017, Fall 2017, Spring 2018, Spring 2019
• Robert J. Kraft Award for Freshmen (2016)
• Regents’ and Chancellor’s Scholar: awarded to top <2% of entering students at Berkeley (2016)

Website template from Jon Barron.