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09/11/2023
An Empirical Study of NetOps Capability of Pre-Trained Large Language Models
Nowadays, the versatile capabilities of Pre-trained Large Language Model...
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07/21/2023
What can a Single Attention Layer Learn? A Study Through the Random Features Lens
Attention layers – which map a sequence of inputs to a sequence of outpu...
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07/06/2023
Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight
This paper studies the sample-efficiency of learning in Partially Observ...
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06/29/2023
Automatic Speech Recognition of Non-Native Child Speech for Language Learning Applications
Voicebots have provided a new avenue for supporting the development of l...
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06/07/2023
Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection
Neural sequence models based on the transformer architecture have demons...
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06/07/2023
An ASR-Based Tutor for Learning to Read: How to Optimize Feedback to First Graders
The interest in employing automatic speech recognition (ASR) in applicat...
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06/02/2023
Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations
In real-world reinforcement learning (RL) systems, various forms of impa...
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02/15/2023
Improved Online Conformal Prediction via Strongly Adaptive Online Learning
We study the problem of uncertainty quantification via prediction sets, ...
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02/13/2023
Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation
A unique challenge in Multi-Agent Reinforcement Learning (MARL) is the c...
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02/06/2023
Offline Learning in Markov Games with General Function Approximation
We study offline multi-agent reinforcement learning (RL) in Markov games...
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02/02/2023
Lower Bounds for Learning in Revealing POMDPs
This paper studies the fundamental limits of reinforcement learning (RL)...
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10/23/2022
Conformal Predictor for Improving Zero-shot Text Classification Efficiency
Pre-trained language models (PLMs) have been shown effective for zero-sh...
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10/20/2022
Learning Rationalizable Equilibria in Multiplayer Games
A natural goal in multiagent learning besides finding equilibria is to l...
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10/09/2022
The Role of Coverage in Online Reinforcement Learning
Coverage conditions – which assert that the data logging distribution ad...
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09/29/2022
Partially Observable RL with B-Stability: Unified Structural Condition and Sharp Sample-Efficient Algorithms
Partial Observability – where agents can only observe partial informatio...
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09/23/2022
Unified Algorithms for RL with Decision-Estimation Coefficients: No-Regret, PAC, and Reward-Free Learning
Finding unified complexity measures and algorithms for sample-efficient ...
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06/08/2022
Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials
A recent goal in the theory of deep learning is to identify how neural n...
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06/06/2022
Policy Optimization for Markov Games: Unified Framework and Faster Convergence
This paper studies policy optimization algorithms for multi-agent reinfo...
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05/30/2022
Efficient Φ-Regret Minimization in Extensive-Form Games via Online Mirror Descent
A conceptually appealing approach for learning Extensive-Form Games (EFG...
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05/15/2022
Sample-Efficient Learning of Correlated Equilibria in Extensive-Form Games
Imperfect-Information Extensive-Form Games (IIEFGs) is a prevalent model...
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02/22/2022
Efficient and Differentiable Conformal Prediction with General Function Classes
Quantifying the data uncertainty in learning tasks is often done by lear...
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02/13/2022
Application of Color Block Code in Image Scaling
Aiming at the high cost of embedding annotation watermark in a narrow sm...
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02/13/2022
Privacy protection based on mask template
Powerful recognition algorithms are widely used in the Internet or impor...
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02/03/2022
Near-Optimal Learning of Extensive-Form Games with Imperfect Information
This paper resolves the open question of designing near-optimal algorith...
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01/03/2022
Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning
Real economies can be modeled as a sequential imperfect-information game...
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10/08/2021
When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently?
Multi-agent reinforcement learning has made substantial empirical progre...
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09/23/2021
Cross-Lingual Language Model Meta-Pretraining
The success of pretrained cross-lingual language models relies on two es...
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06/10/2021
Understanding the Under-Coverage Bias in Uncertainty Estimation
Estimating the data uncertainty in regression tasks is often done by lea...
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06/09/2021
Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning
Recent theoretical work studies sample-efficient reinforcement learning ...
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03/30/2021
Multi-modal Trajectory Prediction for Autonomous Driving with Semantic Map and Dynamic Graph Attention Network
Predicting future trajectories of surrounding obstacles is a crucial tas...
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03/08/2021
Exact Gap between Generalization Error and Uniform Convergence in Random Feature Models
Recent work showed that there could be a large gap between the classical...
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02/23/2021
Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games
Real world applications such as economics and policy making often involv...
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02/22/2021
Localized Calibration: Metrics and Recalibration
Probabilistic classifiers output confidence scores along with their pred...
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02/15/2021
Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification
Modern machine learning models with high accuracy are often miscalibrate...
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02/02/2021
Near-Optimal Offline Reinforcement Learning via Double Variance Reduction
We consider the problem of offline reinforcement learning (RL) – a well-...
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10/12/2020
How Important is the Train-Validation Split in Meta-Learning?
Meta-learning aims to perform fast adaptation on a new task through lear...
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10/04/2020
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play
Model-based algorithms—algorithms that decouple learning of the model an...
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07/07/2020
Near Optimal Provable Uniform Convergence in Off-Policy Evaluation for Reinforcement Learning
The Off-Policy Evaluation aims at estimating the performance of target p...
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06/24/2020
Towards Understanding Hierarchical Learning: Benefits of Neural Representations
Deep neural networks can empirically perform efficient hierarchical lear...
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06/22/2020
Near-Optimal Reinforcement Learning with Self-Play
This paper considers the problem of designing optimal algorithms for rei...
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02/10/2020
Provable Self-Play Algorithms for Competitive Reinforcement Learning
Self-play, where the algorithm learns by playing against itself without ...
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02/10/2020
Taylorized Training: Towards Better Approximation of Neural Network Training at Finite Width
We propose Taylorized training as an initiative towards better understan...
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10/21/2019
Directed-Weighting Group Lasso for Eltwise Blocked CNN Pruning
Eltwise layer is a commonly used structure in the multi-branch deep lear...
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10/03/2019
Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks
Recent theoretical work has established connections between over-paramet...
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05/30/2019
Provably Efficient Q-Learning with Low Switching Cost
We take initial steps in studying PAC-MDP algorithms with limited adapti...
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03/01/2019
Proximal algorithms for constrained composite optimization, with applications to solving low-rank SDPs
We study a family of (potentially non-convex) constrained optimization p...
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10/25/2018
Subgradient Descent Learns Orthogonal Dictionaries
This paper concerns dictionary learning, i.e., sparse coding, a fundamen...
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10/01/2018
ProxQuant: Quantized Neural Networks via Proximal Operators
To make deep neural networks feasible in resource-constrained environmen...
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06/27/2018
Approximability of Discriminators Implies Diversity in GANs
While Generative Adversarial Networks (GANs) have empirically produced i...
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08/29/2017