HardCoRe-NAS finds fitting neural networks adhering to hard resource constraints.
problem Finding fitting neural networks that adhere to hard resource constraints.
method Accurate formulation of resource requirement and scalable search method.
result HardCoRe-NAS generates state-of-the-art architectures strictly satisfying hard resource constraints.
The paper tackles online resource allocation with uncertain coefficients and chance constraints.
problem Online stochastic resource allocation problem with chance constraints.
method Linearization and primal-dual algorithms with heuristic corrections.
result Optimality gap and constraint violation are on the order of √n.
New algorithm optimizes online network resource allocation with long-term constraints.
problem Optimal resource reservation in communication networks with job transfers and budget limits.
method Randomized exponentially weighted method for long-term constraints.
result Upper bound for regret and cumulative constraint violations established.
A smart method predicts and optimizes decisions online with resource constraints.
problem Online decision-making with resource constraints.
method Combines prediction and optimization with dual update using mirror descent.
result Regret bounds and convergence rates for general convex feasible regions.
Optimizes resource allocation in MAB models with resource constraints.
problem Optimizing resource allocation under limited and replenishing resources.
method Derives asymptotic lower bounds and constructs optimal policies.
result Achieves optimal regret for feasible uniformly fast policies.
Algorithm tackles clustered contextual bandits with resource constraints.
problem Maximizing reward while respecting resource limits in unknown cluster contexts.
method Combines econometrics and bandit constraints techniques for sublinear regret.
result Achieves sublinear regret without needing all arm information.
Study on revenue management with limited switches, achieving strong performance and reduced switch counts.
problem Resource-constrained dynamic pricing with limited switching constraints.
method Developed algorithms for blind network revenue management and bandits with knapsacks, achieving optimal regret rates.
result Optimal regret rates are fully characterized by a piecewise-constant function of the switching budget and resource constraints.
TabNAS improves neural architecture search for tabular datasets by rejecting suboptimal architectures.
problem Finding optimal neural architectures for tabular datasets with resource constraints.
method Develops a reinforcement learning controller motivated by rejection sampling to handle resource constraints.
result TabNAS finds better models that obey resource constraints compared to previous methods.
Optimizes query routing to LLMs under cost and resource constraints.
problem Non-uniform or adversarial batching in per-query routing methods leads to cost inefficiency.
method Batch-level, resource-aware routing framework that jointly optimizes model assignment for each batch.
result Robust routing framework improves accuracy by 1-14% over non-robust methods.
Study non-stationary bandits with resource constraints.
problem Maximize reward in a non-stationary environment with resource constraints.
method Propose new non-stationarity measure and use primal-dual analysis.
result Upper and lower bounds for non-stationary BwK problem.
New framework guides resource usage to achieve sublinear regret in adversarial settings.
problem Achieving sublinear regret in online decision making with changing reward and cost distributions.
method General primal-dual methods guided by spending plans that ensure balanced resource usage.
result Achieves sublinear regret with respect to spending plans that balance resource usage.
State-augmented algorithm optimizes wireless network resource management.
problem Optimizing resource allocation in multi-user wireless networks.
method Proposes a state-augmented algorithm using dual variables.
result Feasible and near-optimal resource decisions achieved.
HATCH learns optimal recommendations with resource constraints.
problem Resource-constrained recommendation systems.
method Hierarchical adaptive contextual bandits with adaptive resource allocation.
result HATCH achieves a regret bound of O ( T ) O(\sqrt{T}) O ( T ) . Deep RL methods improve resource allocation in uncertain environments.
problem Optimizing resource allocation in dynamic, uncertain environments.
method Developed three DDPG-based approaches to handle constraints and combinatorial action spaces.
result Demonstrated improved performance over existing methods on real and semi-real data.
We present MorphNet, an approach to automate the design of neural network structures. MorphNet iteratively shrinks and expands a network, shrinking via a resource-weighted sparsifying regularizer on activations and expanding via a uniform multiplicative factor on all layers. In contrast to previous approaches, our meth…
This paper considers the design of optimal resource allocation policies in wireless communication systems which are generically modeled as a functional optimization problem with stochastic constraints. These optimization problems have the structure of a learning problem in which the statistical loss appears as a constr…
Paper tackles online DR-submodular maximization with stochastic constraints.
problem Maximizing utility while adhering to a cumulative resource constraint in an online setting.
method Proposes OLFW algorithm to solve the problem of online continuous DR-submodular maximization with linear stochastic constraints.
result Obtains sub-linear regret and constraint violation bounds.
Algorithm ensures privacy while strictly adhering to constraints.
problem Differential privacy with linear constraints that must be strictly followed.
method Developed an algorithm that releases a nearly-optimal solution satisfying constraints with probability 1.
result Achieved nearly optimal performance while preserving privacy and strictly adhering to constraints.
Study capacity constraints in continual learning with a simple model.
problem Understanding optimal resource allocation for agents with limited memory and compute resources.
method Analyzes a capacity-constrained linear-quadratic-Gaussian (LQG) sequential prediction problem and demonstrates optimal capacity allocation strategies.
result Derives a solution to the capacity-constrained LQG sequential prediction problem and shows how to optimally allocate capacity across sub-problems in the steady state.
Solves online resource allocation problems with budget constraints.
problem Maximizing revenue for e-commerce platforms under budget constraints.
method Integrated online optimization and learning algorithm for non-stationary Poisson processes.
result Effective and efficient solutions for constrained resource allocation problems.
Voltage control plays an important role in the operation of electricity distribution networks, especially with high penetration of distributed energy resources. These resources introduce significant and fast varying uncertainties. In this paper, we focus on reactive power compensation to control voltage in the presence…
A dueling bandit problem with resource constraints is solved using EXP3.
problem Maximizing rewards in dueling bandits with resource constraints.
method EXP3 algorithm considering resource consumptions.
result Achieves $ ilde{\mathcal{O}}\left({\frac{OPT^{(b)}}{B}}K^{1/3}T^{2/3}
ight)$ regret.
A new pricing controller handles resource constraints to infer target prices effectively.
problem Resource constraints prevent fixed-price inference, leading to support exclusion.
method Formalizes support-exclusion failure, designs a target-aware controller, and uses a realized information clock.
result The controller can certify feasible target bands and log continuous local densities, leading to polynomial rates of inference.
LinearAPT optimizes decision-making under resource constraints for a linear threshold problem.
problem Optimizing sequential decisions with a linear threshold under resource limitations.
method LinearAPT, an adaptive algorithm for fixed-budget TLB problem.
result LinearAPT achieves theoretical upper bounds and robust performance on various datasets.
OL4EL optimizes edge learning on resource-constrained servers.
problem Resource constraints on edge servers hinder effective distributed machine learning.
method Online Learning for EL (OL4EL) framework using budget-limited multi-armed bandit model.
result OL4EL significantly improves learning performance while conserving resources.
Study on collaboration vs. independent data collection in sensor networks.
problem Impact of sensor correlation on data collection strategies.
method Analysis of Fisher information and Cramer-Rao bound.
result Optimal strategy involves transferring non-immediate information for improved estimation.
Caratheodory's axiom limits arbitrage in resource-limited systems.
problem Non-arbitrage constraints in resource-limited financial systems.
method Preserving Caratheodory's axiom in resource-limited systems.
result Exponential family is the necessary geometric structure for both thermodynamics and finance.
Study allocates resources to strategic agents while balancing cost and incentives.
problem Dynamic allocation of reusable resources to strategic agents with private valuations under long-term cost constraints.
method Incentive-aware framework combining epoch-based lazy updates and randomized exploration rounds.
result Achieves i l d e O ( T ) ilde{\mathcal{O}}(\sqrt{T}) i l d e O ( T ) social welfare regret, satisfies all cost constraints, and ensures incentive alignment. Distill-Net creates efficient CNNs for IoT by distilling complex models.
problem Efficient inference of deep CNNs on resource-constrained IoT platforms.
method Application-specific distillation of deep CNNs.
result Efficient inference on resource-constrained platforms with high accuracy.
New algorithm tackles dynamic assortment optimization with knapsack constraints.
problem Optimizing retailer's assortment decisions under resource constraints with multi-nomial choice modeling.
method Epoch-based re-solving algorithm that transforms MNL's fractional structure into a linear program with slack variables.
result Regret scales logarithmically with time horizon and resource capacities.
Review of efficient neural networks for TinyML on resource-constrained devices.
problem Resource constraints on ultra-low power MCUs for deep learning models.
method Model compression, quantization, low-rank factorization, model pruning, hardware acceleration, algorithm-architecture co-design.
result Optimized neural network architectures for minimal resource utilization on MCUs.
Proposes rounding method for precise treatment effect estimation under budget constraints.
problem Resource-constrained experimental design for precise treatment effect estimation.
method Dependent randomized rounding procedure to convert assignment probabilities into binary treatment decisions.
result Improved estimator precision through variance reduction and efficient inference.
Budgeted hyper-parameter tuning algorithm improves performance.
problem Optimizing hyper-parameters with resource constraints.
method Sequential decision making, Bayesian model, action-value function.
result Superior performance across various budgets.
Optimizes wireless network resource management with state-augmented policies.
problem Optimizing network-wide utility with user performance constraints.
method State-augmented parameterization of RRM policy, using dual variables.
result Superior trade-off between mean, minimum, and 5th percentile rates.
New algorithm for contextual bandits with linear constraints using regression.
problem Contextual bandits with packing and covering constraints.
method Modular Lagrangian approach via regression.
result First vanishing-regret guarantees for CBwLC (or CBwK).
Automatically finds efficient multi-task models with less data.
problem Training separate models requires more data, parameters, and time.
method Compact search space for multi-task architectures, feature distillation for quick evaluation.
result Automatically identifies multi-task architectures that balance resource requirements and performance.
New method prunes neural networks while conserving resources.
problem Design efficient neural networks for edge devices with limited computational power.
method Iterative channel-wise pruning with holonomic constraint in Lagrangian multipliers framework.
result Reduction of 15.47 GMAC to 3.87 GMAC for VGG-16 with only 1% top-1 accuracy loss.
Band-limited training reduces resource usage without sacrificing accuracy.
problem Resource constraints in training Convolutional Neural Networks (CNNs).
method Artificially constraining the frequency spectra of convolutional filters during training.
result CNNs can leverage lower-frequency components effectively, reducing resource usage.
Study scaling of optimal solutions for reliability constraints in resource provisioning.
problem Achieving high reliability in resource provisioning under stringent requirements.
method Chance-constrained optimization, distributionally robust optimization, f-divergence balls, line search.
result Correct scaling properties of optimal decisions are preserved by using appropriate f-divergence balls, leading to conservative yet near-optimal solutions.
Optimizes resource allocation in a network with random job requests.
problem Minimizing costs while satisfying job requests within a budget.
method Formalizes as a repeated game, proposes an online saddle-point algorithm.
result Upper bounds for regret and constraint violations are derived.
This paper defines resource-constrained classifier performance and its impact on algorithm choice.
problem Classification tasks in resource-constrained settings where actions are limited.
method Defines resource-constrained classifier performance and discusses gains and lift.
result Gains and lift metrics can lead to different algorithm choices.
This work tackles maintenance planning with deep reinforcement learning under uncertainty.
problem Optimizing inspection and maintenance policies in deteriorating environments with incomplete information and constraints.
method Joint framework of constrained POMDPs and multi-agent DRL addressing challenges of state/action space, history, uncertainty, and constraints.
result The proposed framework outperforms existing methods in resource and risk-aware decision-making.
Paper uses RL to solve constrained combinatorial optimization problems.
problem Constrained combinatorial optimization problems.
method Extending NCO theory to CMDPs, using RL with penalty signals.
result Proposes a superior method for solving constrained problems.
Graph neural networks optimize radio resource management policies for wireless networks.
problem Optimizing user selection and power control in wireless networks with fairness constraints.
method Formulated as a Lagrangian dual problem, RRM policies are parameterized by a GNN architecture trained on channel conditions.
result The method achieves superior tradeoff between average and 5th percentile rates, demonstrating fairness.
Proposes a new framework for resource-limited recommendation.
problem Resource constraints affect user choices in recommendation tasks.
method Interest-behavior multiplicative network with MRRNNs and resource-limited branch.
result Framework effectively predicts user interactions considering resource limitations.
Method generates resource-optimized ML models for different platforms.
problem Deployment of deep learning models on edge platforms requires model reduction and retraining.
method Conditional Neural Architecture Search using Generative Adversarial Networks (GAN)
result Successfully generates resource-optimized ML models for different platforms.
Paper tackles non-monotonic resource utilization in sequential decision-making.
problem Sequential decision-making under uncertainty with resource constraints.
method Introduces a new MDP policy with constant regret against LP relaxation.
result Develops a learning algorithm with logarithmic regret for unknown outcome distributions.
New approach tackles resource constraints in bandit problems with weakly adaptive algorithms.
problem Maximizing rewards while adhering to general long-term constraints.
method Weakly adaptive primal and dual regret minimizers.
result Achieves sublinear constraints violations and competitive ratios in both stochastic and adversarial settings.