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Found 36 papers, 10 papers with code
Diffusion Model for Data-Driven Black-Box Optimization
In this paper, we focus on diffusion models, a powerful generative AI technology, and investigate their potential for black-box optimization over complex structured variables.
Achieving $\tilde{O}(1/ε)$ Sample Complexity for Constrained Markov Decision Process
To be specific, our algorithm operates in the primal space and we resolve the primal LP for the CMDP problem at each period in an online manner, with \textit{adaptive} remaining resource capacities.
TriAug: Out-of-Distribution Detection for Imbalanced Breast Lesion in Ultrasound
Different diseases, such as histological subtypes of breast lesions, have severely varying incidence rates.
Decoupling Learning and Decision-Making: Breaking the $\mathcal{O}(\sqrt{T})$ Barrier in Online Resource Allocation with First-Order Methods
More importantly, for the first time, we show that first-order methods can attain regret $\mathcal{O}(T^{1/3})$ with this new framework.
Minimizing Sensor Allocation Cost for Crowdsensing On-street Parking Availability
This paper presents an integer programming-based optimal sensor allocation model to ensure the detection accuracy of the scheme while using the minimum number of sensing kits or probing vehicles.
A Homogenization Approach for Gradient-Dominated Stochastic Optimization
In this paper, we propose the stochastic homogeneous second-order descent method (SHSODM) for stochastic functions enjoying gradient dominance property based on a recently proposed homogenization approach.
Efficient Reinforcement Learning with Impaired Observability: Learning to Act with Delayed and Missing State Observations
We present algorithms and establish near-optimal regret upper and lower bounds, of the form $\tilde{\mathcal{O}}(\sqrt{{\rm poly}(H) SAK})$, for RL in the delayed and missing observation settings.
Pre-trained Mixed Integer Optimization through Multi-variable Cardinality Branching
We propose a new method to accelerate online Mixed Integer Optimization with Pre-trained machine learning models (PreMIO).
Stochastic Dimension-reduced Second-order Methods for Policy Optimization
In this paper, we propose several new stochastic second-order algorithms for policy optimization that only require gradient and Hessian-vector product in each iteration, making them computationally efficient and comparable to policy gradient methods.
Optimal Diagonal Preconditioning
Moreover, our implementation of customized solvers, combined with a random row/column sampling step, can find near-optimal diagonal preconditioners for matrices up to size 200, 000 in reasonable time, demonstrating their practical appeal.
DRSOM: A Dimension Reduced Second-Order Method
In this paper, we propose a Dimension-Reduced Second-Order Method (DRSOM) for convex and nonconvex (unconstrained) optimization.
Fine-grained Correlation Loss for Regression
In this work, we propose to revisit the classic regression tasks with novel investigations on directly optimizing the fine-grained correlation losses.
Stochastic Online Fisher Markets: Static Pricing Limits and Adaptive Enhancements
However, the efficacy of pricing schemes in achieving an equilibrium outcome in Fisher markets typically relies on complete knowledge of users' budgets and utilities and requires that transactions happen in a static market wherein all users are present simultaneously.
Fairer LP-based Online Allocation via Analytic Center
On a high level, we define the fairness in a way that a fair online algorithm should treat similar agents/customers similarly, and the decision made for similar agents/customers should be consistent over time.
An Adaptive State Aggregation Algorithm for Markov Decision Processes
Value iteration is a well-known method of solving Markov Decision Processes (MDPs) that is simple to implement and boasts strong theoretical convergence guarantees.
Distributed stochastic optimization with large delays
One of the most widely used methods for solving large-scale stochastic optimization problems is distributed asynchronous stochastic gradient descent (DASGD), a family of algorithms that result from parallelizing stochastic gradient descent on distributed computing architectures (possibly) asychronously.
Robustifying Conditional Portfolio Decisions via Optimal Transport
Despite the non-linearity of the objective function in the probability measure, we show that the distributionally robust portfolio allocation with side information problem can be reformulated as a finite-dimensional optimization problem.
From an Interior Point to a Corner Point: Smart Crossover
The crossover method, which aims at deriving an optimal extreme point from a suboptimal solution (the output of a starting method such as interior-point methods or first-order methods), is crucial in this process.
Optimization and Control 90C05
The Symmetry between Arms and Knapsacks: A Primal-Dual Approach for Bandits with Knapsacks
In this paper, we study the bandits with knapsacks (BwK) problem and develop a primal-dual based algorithm that achieves a problem-dependent logarithmic regret bound.
Distributionally Robust Local Non-parametric Conditional Estimation
Conditional estimation given specific covariate values (i. e., local conditional estimation or functional estimation) is ubiquitously useful with applications in engineering, social and natural sciences.
A Mean-Field Theory for Learning the Schönberg Measure of Radial Basis Functions
In particular, we prove that in the scaling limits, the empirical measure of the Langevin particles converges to the law of a reflected It\^{o} diffusion-drift process.
Sequential Batch Learning in Finite-Action Linear Contextual Bandits
We study the sequential batch learning problem in linear contextual bandits with finite action sets, where the decision maker is constrained to split incoming individuals into (at most) a fixed number of batches and can only observe outcomes for the individuals within a batch at the batch's end.
Simple and Fast Algorithm for Binary Integer and Online Linear Programming
In this paper, we develop a simple and fast online algorithm for solving a class of binary integer linear programs (LPs) arisen in general resource allocation problem.
Data Structures and Algorithms Optimization and Control
Online Linear Programming: Dual Convergence, New Algorithms, and Regret Bounds
We study an online linear programming (OLP) problem under a random input model in which the columns of the constraint matrix along with the corresponding coefficients in the objective function are generated i. i. d.
Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity
In this paper, we settle the sampling complexity of solving discounted two-player turn-based zero-sum stochastic games up to polylogarithmic factors.
On a Randomized Multi-Block ADMM for Solving Selected Machine Learning Problems
In this paper, we apply this method to solving few selected machine learning problems related to convex quadratic optimization, such as Linear Regression, LASSO, Elastic-Net, and SVM.
Interior-Point Methods Strike Back: Solving the Wasserstein Barycenter Problem
Computing the Wasserstein barycenter of a set of probability measures under the optimal transport metric can quickly become prohibitive for traditional second-order algorithms, such as interior-point methods, as the support size of the measures increases.
Learning in Games with Lossy Feedback
We consider a game-theoretical multi-agent learning problem where the feedback information can be lost during the learning process and rewards are given by a broad class of games known as variationally stable games.
Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model
In this paper we consider the problem of computing an $\epsilon$-optimal policy of a discounted Markov Decision Process (DMDP) provided we can only access its transition function through a generative sampling model that given any state-action pair samples from the transition function in $O(1)$ time.
Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go?
One of the most widely used optimization methods for large-scale machine learning problems is distributed asynchronous stochastic gradient descent (DASGD).
Near-Optimal Time and Sample Complexities for Solving Discounted Markov Decision Process with a Generative Model
In this paper we consider the problem of computing an $\epsilon$-optimal policy of a discounted Markov Decision Process (DMDP) provided we can only access its transition function through a generative sampling model that given any state-action pair samples from the transition function in $O(1)$ time.
Optimization and Control
An ADMM-Based Interior-Point Method for Large-Scale Linear Programming
Due its connection to Newton's method, IPM is often classified as second-order method -- a genre that is attached with stability and accuracy at the expense of scalability.
Optimization and Control
Variance Reduced Value Iteration and Faster Algorithms for Solving Markov Decision Processes
Given a discounted Markov Decision Process (DMDP) with $|S|$ states, $|A|$ actions, discount factor $\gamma\in(0, 1)$, and rewards in the range $[-M, M]$, we show how to compute an $\epsilon$-optimal policy, with probability $1 - \delta$ in time \[ \tilde{O}\left( \left(|S|^2 |A| + \frac{|S| |A|}{(1 - \gamma)^3} \right) \log\left( \frac{M}{\epsilon} \right) \log\left( \frac{1}{\delta} \right) \right) ~ .
On the behavior of Lagrange multipliers in convex and non-convex infeasible interior point methods
Alternatively, in the convex case, if the primal feasibility is reduced too fast and the set of Lagrange multipliers is unbounded, then the Lagrange multiplier sequence generated will be unbounded.
Optimization and Control
Close the Gaps: A Learning-while-Doing Algorithm for a Class of Single-Product Revenue Management Problems
The relationship between the action and the demand rate is not known in advance.
A Dynamic Near-Optimal Algorithm for Online Linear Programming
A natural optimization model that formulates many online resource allocation and revenue management problems is the online linear program (LP) in which the constraint matrix is revealed column by column along with the corresponding objective coefficient.
