3k citations · 24.2k across the 299 of their papers we have counts for
115 papers · 1 filter
Provably Optimal Learning Algorithms for Assistance Games
Nivasini Ananthakrishnan, Mark Bedaywi, Michael I. Jordan +2
This paper studies an online variant of the assistance games framework, where an informed agent and an uninformed agent repeatedly interact over timesteps to optimize a common…
Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation
Kaichao You, Ximei Wang, Mingsheng Long +1
Deep unsupervised domain adaptation (Deep UDA) methods successfully leverage rich labeled data in a source domain to boost the performance on related but unlabeled data in a target…
Delegating Data Collection in Decentralized Machine Learning
Nivasini Ananthakrishnan, Stephen Bates, Michael I. Jordan +1
Motivated by the emergence of decentralized machine learning (ML) ecosystems, we study the delegation of data collection. Taking the field of contract theory as our starting point,…
Provably Personalized and Robust Federated Learning
Mariel Werner, Lie He, Michael Jordan +2
Identifying clients with similar objectives and learning a model-per-cluster is an intuitive and interpretable approach to personalization in federated learning. However, doing so…
On Optimal Caching and Model Multiplexing for Large Model Inference
Banghua Zhu, Ying Sheng, Lianmin Zheng +3
Large Language Models (LLMs) and other large foundation models have achieved noteworthy success, but their size exacerbates existing resource consumption and latency challenges. In…
Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry
Serena Wang, Stephen Bates, P. M. Aronow +1
From the social sciences to machine learning, it has been well documented that metrics to be optimized are not always aligned with social welfare. In healthcare, Dranove et al. (20…
Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium
Chris Junchi Li, Dongruo Zhou, Quanquan Gu +1
We consider learning Nash equilibria in two-player zero-sum Markov Games with nonlinear function approximation, where the action-value function is approximated by a function in a R…
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees
Banghua Zhu, Lun Wang, Qi Pang +4
We propose Byzantine-robust federated learning protocols with nearly optimal statistical rates. In contrast to prior work, our proposed protocols improve the dimension dependence a…
Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets
Yifei Min, Tianhao Wang, Ruitu Xu +3
We study a Markov matching market involving a planner and a set of strategic agents on the two sides of the market. At each step, the agents are presented with a dynamical context,…
Improving Generalization via Uncertainty Driven Perturbations
Matteo Pagliardini, Gilberto Manunza, Martin Jaggi +2
Recently Shah et al., 2020 pointed out the pitfalls of the simplicity bias - the tendency of gradient-based algorithms to learn simple models - which include the model's high sensi…
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging
Anastasios N Angelopoulos, Amit P Kohli, Stephen Bates +5
Image-to-image regression is an important learning task, used frequently in biological imaging. Current algorithms, however, do not generally offer statistical guarantees that prot…
Robust Estimation for Nonparametric Families via Generative Adversarial Networks
Banghua Zhu, Jiantao Jiao, Michael I. Jordan
We provide a general framework for designing Generative Adversarial Networks (GANs) to solve high dimensional robust statistics problems, which aim at estimating unknown parameter…
Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach
Shuang Qiu, Boxiang Lyu, Qinglin Meng +3
Dynamic mechanism design studies how mechanism designers should allocate resources among agents in a time-varying environment. We consider the problem where the agents interact wit…
No-Regret Learning in Partially-Informed Auctions
Wenshuo Guo, Michael I. Jordan, Ellen Vitercik
Auctions with partially-revealed information about items are broadly employed in real-world applications, but the underlying mechanisms have limited theoretical support. In this wo…
Transfer Q-learning
Elynn Chen, Sai Li, Michael I. Jordan
Time-inhomogeneous finite-horizon Markov decision processes (MDP) are frequently employed to model decision-making in dynamic treatment regimes and other statistical reinforcement…
Conformal Prediction Under Feedback Covariate Shift for Biomolecular Design
Clara Fannjiang, Stephen Bates, Anastasios N. Angelopoulos +2
Many applications of machine learning methods involve an iterative protocol in which data are collected, a model is trained, and then outputs of that model are used to choose what…
Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopic Followers?
Han Zhong, Zhuoran Yang, Zhaoran Wang +1
We study multi-player general-sum Markov games with one of the players designated as the leader and the other players regarded as followers. In particular, we focus on the class of…
Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic
Yufeng Zhang, Siyu Chen, Zhuoran Yang +2
Actor-critic (AC) algorithms, empowered by neural networks, have had significant empirical success in recent years. However, most of the existing theoretical support for AC algorit…
ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning
Xiao-Yang Liu, Zechu Li, Zhuoran Yang +5
Deep reinforcement learning (DRL) has revolutionized learning and actuation in applications such as game playing and robotic control. The cost of data collection, i.e., generating…
An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit
Aldo Pacchiano, Peter Bartlett, Michael I. Jordan
We study the problem of information sharing and cooperation in Multi-Player Multi-Armed bandits. We propose the first algorithm that achieves logarithmic regret for this problem wh…
Cluster-and-Conquer: A Framework For Time-Series Forecasting
Reese Pathak, Rajat Sen, Nikhil Rao +3
We propose a three-stage framework for forecasting high-dimensional time-series data. Our method first estimates parameters for each univariate time series. Next, we use these para…
Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs
Kaichao You, Yong Liu, Ziyang Zhang +3
Model hubs with many pre-trained models (PTMs) have become a cornerstone of deep learning. Although built at a high cost, they remain \emph{under-exploited} -- practitioners usuall…
Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control
Anastasios N. Angelopoulos, Stephen Bates, Emmanuel J. Candès +2
We introduce a framework for calibrating machine learning models so that their predictions satisfy explicit, finite-sample statistical guarantees. Our calibration algorithms work w…
Learning Equilibria in Matching Markets from Bandit Feedback
Meena Jagadeesan, Alexander Wei, Yixin Wang +2
Large-scale, two-sided matching platforms must find market outcomes that align with user preferences while simultaneously learning these preferences from data. Classical notions of…