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.
OMGD algorithm optimizes online convex optimization with switching costs and delayed gradients.
problem Optimizing online convex optimization with switching costs and delayed gradients.
method Proposed an online multiple gradient descent (OMGD) algorithm for quadratic and linear switching costs.
result OMGD achieves optimal dynamic regret in the limited information setting.
New algorithm reduces switching costs in multinomial logit bandit problems.
problem Minimizing switching costs in multinomial logit bandit problems.
method Proposed AT-DUCB and FH-DUCB algorithms with low assortment switching costs.
result AT-DUCB and FH-DUCB algorithms achieve almost optimal minimax regret with low switching costs.
This work addresses identifiability in sequential data with switching dynamics, introducing a new estimator.
problem Identifiability of sequential data with regime-switching dynamics under flexible assumptions.
method Introduces Ω Ω Ω SDS, a flow-based estimator for exact likelihood optimization. result Demonstrates improved disentanglement and more accurate forecasting compared to VAE-based estimators.
Paper analyzes minimax regret in constrained online convex optimization with limited switching opportunities.
problem Minimizing regret in online convex optimization with limited switching opportunities.
method Introduced fugal game relaxation and mini-batching algorithm to establish minimax regret bounds.
result Minimax regret of switching-constrained OCO is Θ(T / √K).
Study approximates financial market with discrete-time models.
problem Approximating continuous-time financial market models with discrete-time.
method Constructs discrete-time market models with Markov switching and proves convergence.
result Discrete-time models converge to continuous-time Black-Scholes model with Markov switching.
Study tackles balancing policy switching costs in offline RL.
problem Balancing the cost of policy switching in offline RL.
method Optimal transport ideas and Net Actor-Critic algorithm.
result Demonstrated efficiency on multiple RL benchmarks.
This paper addresses parameter estimation for wave equations with Markovian switching.
problem Parameter estimation for wave equations with abrupt changes.
method Bayesian statistical framework using discrete sparse Bayesian learning.
result Strong performance in parameter estimation for variable coefficient PDEs.
New algorithm reduces switching costs in RL beyond linear MDPs.
problem Costly policy switching in reinforcement learning.
method ELEANOR-LowSwitching algorithm for linear Bellman-complete MDPs.
result Achieves near-optimal regret with logarithmic switching cost.
We link disjoint longitudinal data for rare disease patients using latent representations and mixed-effects regression.
problem Analyzing treatment switches in rare diseases with limited data and changing measurement instruments.
method We embed item values into a shared latent space using variational autoencoders and apply mixed-effects regression to quantify treatment effects.
result Our approach allows for statistical inference and quantifies the impact of treatment switches in spinal muscular atrophy.
Paper tackles non-stationary bandits with various examples.
problem Non-stationary stochastic bandit problem with specific cases.
method Proposes a single algorithm for multiple non-stationary bandit problems.
result Unified solution for four different bandit problems.
We take initial steps in studying PAC-MDP algorithms with limited adaptivity, that is, algorithms that change its exploration policy as infrequently as possible during regret minimization. This is motivated by the difficulty of running fully adaptive algorithms in real-world applications (such as medical domains), and …
Efficient algorithms for online convex optimization with limited switching decisions.
problem Online convex optimization with limited switching decisions.
method Presented computationally efficient algorithms for both general and strongly convex losses.
result Regret bounds of O ( T / S ) O(T/S) O ( T / S ) for general convex losses and O ~ ( T / S 2 ) \widetilde O(T/S^2) O ( T / S 2 ) for strongly convex losses. Efficiently optimize GPs by reusing candidate solutions multiple times.
problem High computational cost of Gaussian process optimization due to unique historical points.
method Sticking to a candidate solution for multiple evaluation steps and limiting switches.
result Improved efficiency and practicality of Gaussian process optimization algorithms.
We consider the classical stochastic multi-armed bandit problem with a constraint that limits the total cost incurred by switching between actions to be no larger than a given switching budget. For this problem, we prove matching upper and lower bounds on the optimal (i.e., minimax) regret, and provide efficient rate-o…
This paper studies four trading algorithms of a professional trader at a multilateral trading facility, observing a realistic two-sided limit order book whose dynamics are driven by the order book events. The identity of the trader can be either internalizing or regular, either a hedge fund or a brokery agency. The spe…
Model shows stock markets can be inefficiently mispriced.
problem Limits of informationally efficient stock markets.
method Chartist-fundamentalist model with chartists and fundamentalists trading conditions.
result Stock markets can exhibit constant or oscillatory mispricing.
New MCMC method tackles label-switching problem for clustering.
problem Label-switching problem impedes MCMC efficiency in discrete clustering.
method Formulated optimal transport couplings for partition space.
result Our method efficiently overcomes label-switching problem.
Model for cross-border markets with limited transmission capacities.
problem Limited transmission capacities between two countries' markets.
method Developed a regime-switching process model with high-frequency approximation.
result Analytic tractability allows computation of key market quantities.
Efficient RL algorithms for linear function approximation with limited adaptivity constraints.
problem Limited adaptivity in reinforcement learning with linear function approximation.
method Proposed two efficient online RL algorithms for episodic linear Markov decision processes under batch learning and rare policy switch models.
result Achieved efficient regret bounds for both batch learning and rare policy switch models, with substantial reduction in adaptivity.
Two algorithms achieve optimal regret with limited adaptivity in multinomial logistic bandits.
problem Achieving optimal regret with limited adaptivity in multinomial logistic bandits.
method Presented two algorithms, B-MNL-CB and RS-MNL, for batched and rarely-switching paradigms.
result Achieved i l d e O ( T ) ilde{O}(\sqrt{T}) i l d e O ( T ) regret with limited adaptivity. New polynomial invariants derived from birack and switch structures.
problem Polynomial invariants of braids.
method Switch structures, birack colorings, quiver-valued invariants.
result New polynomial invariants of braids.
In this paper, we study optimal switching problems under ambiguity. To characterize the optimal switching under ambiguity in the finite horizon, we use multidimensional reflected backward stochastic differential equations (multidimensional RBSDEs) and show that a value function of the optimal switching under ambiguity …
In this paper we introduce two new Hawkes processes, namely, compound and regime-switching compound Hawkes processes, to model the price processes in limit order books. We prove Law of Large Numbers and Functional Central Limit Theorems (FCLT) for both processes. The two FCLTs are applied to limit order books where we …
This work extends identifiability analysis to sequential latent variable models, focusing on Switching Dynamical Systems.
problem Identifying latent variables in sequential data models.
method Proved identifiability of Markov Switching Models and established conditions for Switching Dynamical Systems.
result Identifiability of latent variables and non-linear mappings in Switching Dynamical Systems up to affine transformations.
The problem of optimal switching between nonlinear autonomous subsystems is investigated in this study where the objective is not only bringing the states to close to the desired point, but also adjusting the switching pattern, in the sense of penalizing switching occurrences and assigning different preferences to util…
In this paper, we study various new Hawkes processes, namely, so-called general compound and regime-switching general compound Hawkes processes to model the price processes in the limit order books. We prove Law of Large Numbers (LLN) and Functional Central Limit Theorems (FCLT) for these processes. The latter two FCLT…
Code-switching, the alternation of languages within a conversation or utterance, is a common communicative phenomenon that occurs in multilingual communities across the world. This survey reviews computational approaches for code-switched Speech and Natural Language Processing. We motivate why processing code-switched …
New algorithms improve sampling from complex distributions.
problem Sampling from complex probability distributions efficiently.
method Regime-switching Langevin dynamics and Monte Carlo algorithms.
result Convergence guarantees and iteration complexities provided.
Squirrel switches between optimizers for better performance.
problem Finding the best optimizer for a given problem.
method Switches between different optimizers based on performance.
result Improves performance on various problems.
Modern information technology services largely depend on cloud infrastructures to provide their services. These cloud infrastructures are built on top of datacenter networks (DCNs) constructed with high-speed links, fast switching gear, and redundancy to offer better flexibility and resiliency. In this environment, net…
Optimizes control of hybrid systems with multiple switching processes.
problem Optimal control of hybrid systems with multiple Markov switching processes.
method Combines two separate Markov chains into one synthetic chain, derives HJB equations, and solves the portfolio choice problem.
result Derives explicit solutions and value functions for the optimal control problem.
New algorithm learns switching dynamics from multiple neural signals.
problem Learning accurate switching dynamical system models from multimodal neural data.
method Unsupervised learning algorithm for multiscale switching dynamical system models.
result Switching multiscale dynamical system models outperform single-scale models in behavior decoding.
Paper tackles utility maximization with job-switching and retirement constraints.
problem Maximizing utility with job-switching and retirement constraints.
method Dual-martingale approach and double obstacle problem theory.
result Characterization of optimal job-switching strategy and wealth boundaries.
Solves label switching in mixture models using optimal transport.
problem Label switching in mixture model posterior inference prevents meaningful statistics assessment.
method Proposes an algorithm leveraging optimal transport to compute posterior statistics in a quotient space.
result Demonstrates advantages over alternative approaches on simulated and real data.
New RL algorithm reduces policy switching cost to loglog(T) with similar regret.
problem Low policy switching cost in real-life RL applications.
method Stage-wise exploration and adaptive policy elimination.
result Regret of O ( H S A log log T ) O(HSA \log\log T) O ( H S A log log T ) with O ( H S A log log T ) O(HSA \log\log T) O ( H S A log log T ) switching cost. The paper explores dynamic regret with switching cost in online decision making.
problem The relation between dynamic regret and switching cost in online decision making.
method Investigates two classic online settings: Online Algorithms (OA) and Online Convex Optimization (OCO). Provides a new theoretical analysis framework.
result The switching cost impacts dynamic regret differently in OA and has no impact in OCO.
A new network learns market conditions and predicts stock performance.
problem Optimizing stock portfolio performance in the US equities market.
method Residual Switching Network combining two ResNets: a switching module and a main module.
result The residual switching network strategy outperformed other models with an average annual Sharpe ratio of 2.22.
Solves risk-aware optimal switching problems in discrete time.
problem Non-Markovian optimal switching problems with risk awareness and general filtration.
method Solves reflected backward stochastic difference equations.
result Existence and uniqueness of solutions for the problems.
Paper presents an efficient algorithm for linear MDP with low switching cost.
problem Large state space reinforcement learning problems with low switching cost.
method First algorithm for linear MDP with low switching cost, achieving near-optimal regret and switching cost.
result Regret bound of $\widetilde{O}\left(\sqrt{d^3H^4K}
ight)$ and near-optimal switching cost of $O\left(d H\log K
ight)$ .
Study strategic competition in commodity markets using impulse-switching controls.
problem Strategic competition between upstream and downstream firms in commodity markets.
method Non-zero-sum stochastic differential game with mixed impulse/switching controls.
result Multiple Nash equilibria found, depending on the number of switches by the downstream firm.
One type of switch simplifies operations on lattice knots.
problem Operations on lattice knots are complex.
method Reduced operations to one type of local switch.
result Simplified set of operations on lattice knots.
Optimal switching regret for all segmentations in online convex optimisation.
problem Non-stationary online convex optimisation problems.
method Developed an efficient algorithm to achieve optimal switching regret on every possible segmentation.
result Achieved asymptotically optimal switching regret on every possible segmentation simultaneously.
FedSGM tackles constrained federated learning with unified framework.
problem Functional constraints, communication bottlenecks, local updates, and partial client participation in federated learning.
method Unified framework based on switching gradient method, incorporating bi-directional error feedback, and soft switching for stability.
result Achieves O ( 1 / T ) \boldsymbol{\mathcal{O}}(1/\sqrt{T}) O ( 1/ T ) convergence rate with high-probability bounds decoupling from sampling noise. This paper tackles near-optimal adversarial RL with switching costs, providing algorithms and matching lower bounds.
problem Adversarial RL with switching costs, where loss distribution can be non-stationary or adversarial.
method Developed novel switching-reduced algorithms with matching lower bounds for known and unknown transition functions.
result Achieved near-optimal performance in adversarial RL with switching costs, matching theoretical lower bounds.
Develops a method to model neural dynamics with flexible yet interpretable latent states.
problem Capturing complex nonlinear dynamics in neural time series while maintaining interpretability.
method Gaussian Process Switching Linear Dynamical System (gpSLDS) that balances expressiveness and interpretability.
result Favorable performance in comparison to rSLDS on synthetic and real neuroscience data.
Algorithm for bandits with switching costs achieves optimal regret bounds.
problem Optimal regret bounds for stochastic and adversarial bandits with switching costs.
method Adaptation of Tsallis-INF algorithm with no prior knowledge of regime or time horizon.
result Achieves minimax optimal regret bounds in various settings.
Efficient algorithms for contextual slate bandits with limited adaptivity.
problem Contextual slate bandit problem with limited adaptivity.
method Proposed B-SlateGLinCB and RS-SlateGLinCB algorithms for batched and rarely-switching settings.
result Achieved regret bounds of O(Nd^(3/2)√T) and O(Nd√T) under diversity assumption.