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09/11/2023
Textbooks Are All You Need II: phi-1.5 technical report
We continue the investigation into the power of smaller Transformer-base...
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09/01/2023
Efficient RLHF: Reducing the Memory Usage of PPO
Reinforcement Learning with Human Feedback (RLHF) has revolutionized lan...
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06/27/2023
Length Generalization in Arithmetic Transformers
We examine how transformers cope with two challenges: learning basic int...
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06/20/2023
The Implicit Bias of Batch Normalization in Linear Models and Two-layer Linear Convolutional Neural Networks
We study the implicit bias of batch normalization trained by gradient de...
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06/09/2023
Specifying and Solving Robust Empirical Risk Minimization Problems Using CVXPY
We consider robust empirical risk minimization (ERM), where model parame...
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06/02/2023
Why Clean Generalization and Robust Overfitting Both Happen in Adversarial Training
Adversarial training is a standard method to train deep neural networks ...
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05/31/2023
Toward Understanding Why Adam Converges Faster Than SGD for Transformers
While stochastic gradient descent (SGD) is still the most popular optimi...
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05/24/2023
SPRING: GPT-4 Out-performs RL Algorithms by Studying Papers and Reasoning
Open-world survival games pose significant challenges for AI algorithms ...
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05/23/2023
Physics of Language Models: Part 1, Context-Free Grammar
We design experiments to study how generative language models, like GPT,...
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05/19/2023
The probability flow ODE is provably fast
We provide the first polynomial-time convergence guarantees for the prob...
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05/12/2023
TinyStories: How Small Can Language Models Be and Still Speak Coherent English?
Language models (LMs) are powerful tools for natural language processing...
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05/04/2023
Weighted Tallying Bandits: Overcoming Intractability via Repeated Exposure Optimality
In recommender system or crowdsourcing applications of online learning, ...
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05/03/2023
Plan, Eliminate, and Track – Language Models are Good Teachers for Embodied Agents
Pre-trained large language models (LLMs) capture procedural knowledge ab...
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04/07/2023
On the Importance of Contrastive Loss in Multimodal Learning
Recently, contrastive learning approaches (e.g., CLIP (Radford et al., 2...
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03/22/2023
Sparks of Artificial General Intelligence: Early experiments with GPT-4
Artificial intelligence (AI) researchers have been developing and refini...
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03/15/2023
The Benefits of Mixup for Feature Learning
Mixup, a simple data augmentation method that randomly mixes two data po...
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03/07/2023
How Do Transformers Learn Topic Structure: Towards a Mechanistic Understanding
While the successes of transformers across many domains are indisputable...
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02/09/2023
Read and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manuals
High sample complexity has long been a challenge for RL. On the other ha...
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02/07/2023
What Matters In The Structured Pruning of Generative Language Models?
Auto-regressive large language models such as GPT-3 require enormous com...
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11/04/2022
How Does Adaptive Optimization Impact Local Neural Network Geometry?
Adaptive optimization methods are well known to achieve superior converg...
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10/13/2022
Vision Transformers provably learn spatial structure
Vision Transformers (ViTs) have achieved comparable or superior performa...
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10/09/2022
Dissecting adaptive methods in GANs
Adaptive methods are a crucial component widely used for training genera...
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09/22/2022
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions
We provide theoretical convergence guarantees for score-based generative...
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08/04/2022
Towards Understanding Mixture of Experts in Deep Learning
The Mixture-of-Experts (MoE) layer, a sparsely-activated model controlle...
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07/13/2022
Towards understanding how momentum improves generalization in deep learning
Stochastic gradient descent (SGD) with momentum is widely used for train...
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05/31/2022
Learning (Very) Simple Generative Models Is Hard
Motivated by the recent empirical successes of deep generative models, w...
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05/12/2022
The Mechanism of Prediction Head in Non-contrastive Self-supervised Learning
Recently the surprising discovery of the Bootstrap Your Own Latent (BYOL...
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04/24/2022
Complete Policy Regret Bounds for Tallying Bandits
Policy regret is a well established notion of measuring the performance ...
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04/08/2022
Learning Polynomial Transformations
We consider the problem of learning high dimensional polynomial transfor...
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01/18/2022
Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs
Arguably the most fundamental question in the theory of generative adver...
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11/01/2021
Posterior Inference for Quantile Regression: Adaptation to Sparsity
Quantile regression is a powerful data analysis tool that accommodates h...
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11/01/2021
Settling the Horizon-Dependence of Sample Complexity in Reinforcement Learning
Recently there is a surge of interest in understanding the horizon-depen...
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09/29/2021
On the One-sided Convergence of Adam-type Algorithms in Non-convex Non-concave Min-max Optimization
Adam-type methods, the extension of adaptive gradient methods, have show...
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08/25/2021
Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization
Adaptive gradient methods such as Adam have gained increasing popularity...
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06/17/2021
LoRA: Low-Rank Adaptation of Large Language Models
The dominant paradigm of natural language processing consists of large-s...
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06/15/2021
Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity
Reinforcement learning (RL) is empirically successful in complex nonline...
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06/04/2021
Forward Super-Resolution: How Can GANs Learn Hierarchical Generative Models for Real-World Distributions
Generative adversarial networks (GANs) are among the most successful mod...
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05/31/2021
Toward Understanding the Feature Learning Process of Self-supervised Contrastive Learning
How can neural networks trained by contrastive learning extract features...
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02/26/2021
Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability
We empirically demonstrate that full-batch gradient descent on neural ne...
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01/01/2021
When Is Generalizable Reinforcement Learning Tractable?
Agents trained by reinforcement learning (RL) often fail to generalize b...
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12/17/2020
Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning
We formally study how Ensemble of deep learning models can improve test ...
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09/30/2020
A law of robustness for two-layers neural networks
We initiate the study of the inherent tradeoffs between the size of a ne...
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07/09/2020
Learning Over-Parametrized Two-Layer ReLU Neural Networks beyond NTK
We consider the dynamic of gradient descent for learning a two-layer neu...
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05/20/2020
Feature Purification: How Adversarial Training Performs Robust Deep Learning
Despite the great empirical success of adversarial training to defend de...
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03/09/2020
When can Wasserstein GANs minimize Wasserstein Distance?
Generative Adversarial Networks (GANs) are widely used models to learn c...
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01/13/2020
Backward Feature Correction: How Deep Learning Performs Deep Learning
How does a 110-layer ResNet learn a high-complexity classifier using rel...
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07/10/2019
Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks
Stochastic gradient descent with a large initial learning rate is a wide...
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06/25/2019
Complexity of Highly Parallel Non-Smooth Convex Optimization
A landmark result of non-smooth convex optimization is that gradient des...
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05/24/2019
What Can ResNet Learn Efficiently, Going Beyond Kernels?
How can neural networks such as ResNet efficiently learn CIFAR-10 with t...
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04/28/2019
