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05/01/2023
On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring
A central problem in the theory of multi-agent reinforcement learning (M...
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11/14/2022
Linear Reinforcement Learning with Ball Structure Action Space
We study the problem of Reinforcement Learning (RL) with linear function...
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10/13/2022
Forecast Hedging and Calibration
Calibration means that forecasts and average realized frequencies are cl...
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10/13/2022
Smooth Calibration, Leaky Forecasts, Finite Recall, and Nash Dynamics
We propose to smooth out the calibration score, which measures how good ...
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09/11/2022
"Calibeating": Beating Forecasters at Their Own Game
In order to identify expertise, forecasters should not be tested by thei...
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07/18/2022
A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation
The current paper studies sample-efficient Reinforcement Learning (RL) i...
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12/03/2021
On Submodular Contextual Bandits
We consider the problem of contextual bandits where actions are subsets ...
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08/10/2021
The Benefits of Implicit Regularization from SGD in Least Squares Problems
Stochastic gradient descent (SGD) exhibits strong algorithmic regulariza...
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05/14/2021
Threshold Martingales and the Evolution of Forecasts
This paper introduces a martingale that characterizes two properties of ...
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10/22/2020
What are the Statistical Limits of Offline RL with Linear Function Approximation?
Offline reinforcement learning seeks to utilize offline (observational) ...
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11/20/2018
Coupled Recurrent Models for Polyphonic Music Composition
This work describes a novel recurrent model for music composition, which...
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07/16/2014
Large scale canonical correlation analysis with iterative least squares
Canonical Correlation Analysis (CCA) is a widely used statistical tool w...
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05/15/2014
Fast Ridge Regression with Randomized Principal Component Analysis and Gradient Descent
We propose a new two stage algorithm LING for large scale regression pro...
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09/25/2012
Optimal Weighting of Multi-View Data with Low Dimensional Hidden States
In Natural Language Processing (NLP) tasks, data often has the following...
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04/30/2012
A Spectral Algorithm for Latent Dirichlet Allocation
The problem of topic modeling can be seen as a generalization of the clu...
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03/28/2012
Spectral dimensionality reduction for HMMs
Hidden Markov Models (HMMs) can be accurately approximated using co-occu...
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05/04/2011
