PAC best arm identification with a deadline, improving efficiency over sequential methods.
problem Identifying an optimal arm under a fixed time constraint.
method Elastic Batch Racing (EBR) algorithm for (ε,δ)-PAC best arm identification under a deadline. result EBR is optimal with respect to two hardness results and outperforms sequential methods.
Study optimal portfolio for households with two goals: random and fixed deadlines.
problem Optimal portfolio choice for households managing random and fixed deadlines.
method Maximizes weighted sum of probabilities of funding both goals in a Black-Scholes market.
result Non-monotonic value function due to interaction between goals under forced funding.
Asymmetry PRISM outperforms CPU and GPU solvers for institutional rebalancing.
problem Institutional rebalancing with deadline constraints
method Asymmetry PRISM
result Asymmetry PRISM-CPU is 4.5x to 24.1x faster than the fastest completed reference row in the same lane.
TIP-Search optimizes market prediction accuracy and timeliness under uncertain load.
problem Real-time market prediction requires accurate predictions before a deadline.
method Filters feasible models, dispatches workers, trades accuracy for deadline risk.
result Optimized pool achieves 0.991 timely accuracy and 0.994 raw accuracy.
New framework for fair online allocation in continuous time with deadlines.
problem Fair allocation under deadlines in continuous-time online learning.
method Continuous-time utility maximization, dual ascent optimization for time averages.
result Achieves ildeO(B−1/2) regret bound in the absence of statistical knowledge. Paper evaluates deadline-ILS on insider trading contracts, finding it distinguishes signals from noise.
problem Deadlines in insider trading contracts and information leakage detection.
method Empirical evaluation using FFIC dataset, hazard-rate estimation, cross-market wallet analysis.
result Deadline-ILS distinguishes signal from proxy artefact, with a significant shift in magnitude.
New method controls false discoveries in online testing with deadlines.
problem Controlling false discoveries in online hypothesis testing with decision deadlines.
method Benjamini-Hochberg-type procedure over a moving window of hypotheses with adaptive threshold parameters.
result Controls false discovery rate at every stage and adaptively chosen stopping times.
Optimizes angular velocity transfers for rigid bodies under deadline constraints.
problem Stochastic guidance of spin states of rigid bodies over a hard deadline.
method Structural analysis of Kantorovich optimal coupling formulation for nonlinear dynamics.
result Derives the ground cost for optimal transport of angular velocity.
We study the optimal timing strategies for trading a mean-reverting price process with afinite deadline to enter and a separate finite deadline to exit the market. The price process is modeled by a diffusion with an affine drift that encapsulates a number of well-known models,including the Ornstein-Uhlenbeck (OU) model…
In today's economy, selling a new zero-marginal cost product is a real challenge, as it is difficult to determine a product's "correct" sales price based on its profit and dissemination. As an example, think of the price of a new app or video game. New sales mechanisms for selling this type of product need to be design…
Develops a new volatility model for prediction markets.
problem Volatility forecasting in prediction markets differs from standard asset markets.
method Combines Wright-Fisher and Glosten-Milgrom mechanisms to model binary prediction markets.
result Structural model outperforms standard ARCH/GARCH models in volatility forecasting.
Investor aims to meet financial goals with deadlines and target amounts, considering stock trading costs.
problem Goal-based portfolio selection with fixed transaction costs.
method Stochastic Perron's method to show value function is unique viscosity solution to quasi-variational inequalities. Existence of optimal strategy established.
result Optimal trading strategy differs significantly from frictionless case, revealing complex regions and strategies.
Labeling data (e.g., labeling the people, objects, actions and scene in images) comprehensively and efficiently is a widely needed but challenging task. Numerous models were proposed to label various data and many approaches were designed to enhance the ability of deep learning models or accelerate them. Unfortunately,…
Paper introduces TtT, market-implied transition time, from greenium term structure.
problem Estimating market-implied transition time to a low-carbon economy.
method Develops inference theory for TtT, introduces two stochastic models.
result Combines two-layer analysis for consistent estimation of diffusion parameters.
Develops a new volatility model for prediction markets.
problem Volatility forecasting in prediction markets differs from standard asset markets.
method Combines Wright-Fisher and Glosten-Milgrom mechanisms to model binary prediction markets.
result Structural model outperforms standard ARCH/GARCH models in volatility forecasting.
New algorithm solves complex mean-field Schrödinger bridge problem.
problem Designing a controller for diffusion processes with nonlocal interaction.
method Generalized Hopf-Cole transform and Sinkhorn-type algorithm.
result Convergence guarantees for the proposed algorithm under mild assumptions.
ForesightFlow detects informed trading on prediction markets using an information leakage score.
problem Detecting informed trading on decentralized prediction markets.
method Developed an Information Leakage Score (ILS) framework to quantify the fraction of terminal information move priced in before public news events.
result The score connects label generation to proper-scoring-rule literature and reveals systematic biases in insider trading documentation.
This work studies the contraction coefficients of Schrödinger bridge problems in linear systems.
problem Optimally controlling the evolution of a system's state density over time.
method Analyzes and improves the convergence rates of dynamic Schrödinger systems via geometric and control-theoretic interpretations.
result New insights into improving computation of worst-case contraction coefficients by preconditioning.
This paper proposes a method to select project schedules with the lowest risk.
problem Selecting schedules that meet project deadlines while minimizing risk.
method Integrating aleatory uncertainty into project scheduling to quantify and compare risks.
result Proposes a method to select schedules with the lowest risk.
Schrödinger bridge solved with Weyl calculus for quadratic state cost.
problem Optimal control policy to steer joint state statistics.
method Weyl calculus in quantum mechanics for reaction-diffusion PDEs.
result Explicit Markov kernel for quadratic state cost found.
AIF improves physical AI agents' performance in dynamic environments.
problem Physical AI agents are less capable than biological agents in open-ended real-world environments.
method Developed from probability theory, Bayesian machine learning, variational inference, and Active Inference (AIF), grounded in the Free Energy Principle.
result AIF minimizes variational free energy and is well-suited to physical constraints.
RL-Exec uses reinforcement learning to optimize BTC-USD liquidation, outperforming traditional methods.
problem Optimizing liquidation strategies on BTC-USD limit-order books with transient impact and latency.
method PPO agent trained on historical BTC-USD limit-order book replays, incorporating impact resilience and fees.
result RL-Exec significantly outperforms TWAP and a VWAP-like baseline on BTC-USD liquidation, with performance improving with longer execution horizons.
New algorithm tackles resource allocation in multi-armed bandits to balance speed and throughput.
problem Balancing speed and throughput in stochastic multi-armed bandits with limited resources.
method Proposes an algorithm that trades off between information accumulation and throughput.
result Upper bounds the time taken to find the best arm with a given target success probability.
Efficient algorithm approximates discrete random variables with minimal Kolmogorov distance.
problem Estimating the probability of missing deadlines in series-parallel schedules.
method An efficient algorithm that computes a random variable with minimal Kolmogorov distance to a given discrete random variable.
result The algorithm efficiently approximates the probability of missing deadlines with minimal Kolmogorov distance.
We introduce an interactive market setup with sequential auctions where agents receive variegated signals with a known deadline. The effects of differential information and mutual learning on the allocation of overall profit \& loss (P\&L) and the pace of price discovery are analysed. We characterise the signal-based e…
New approach to goal-based investing using hedging and reinforcement learning.
problem Maximizing probability of reaching investment goals with varying risk aversion.
method Lower partial moments, quantile hedging, efficient hedging, reinforcement learning.
result Optimal investment policies for goal-based investing are equivalent.
A framework for goal-based investing with penalties for fund transfers.
problem Investors' mental accounting and multiple investment goals.
method Continuous-time portfolio selection with mental costs and penalties.
result The value function is the unique solution to a complex system of equations.
In this research we study a finite horizon optimal purchasing problem for items with a mean reverting price process. Under this model a fixed amount of identical items are bought under a given deadline, with the objective of minimizing the cost of their purchasing price and associated holding cost. We prove that the op…
In this paper, we consider same-day delivery with vehicles and drones. Customers make delivery requests over the course of the day, and the dispatcher dynamically dispatches vehicles and drones to deliver the goods to customers before their delivery deadline. Vehicles can deliver multiple packages in one route but trav…
Develops a model for gambling decisions under time inconsistency.
problem Time inconsistency in gambling decisions due to probability weighting in CPT.
method Formulates the problem as a mathematical program, derives optimal precommitted rule.
result Gambler may enter the casino even with limited play, behavior varies based on gains/losses.
We consider a decentralized learning problem, where a set of computing nodes aim at solving a non-convex optimization problem collaboratively. It is well-known that decentralized optimization schemes face two major system bottlenecks: stragglers' delay and communication overhead. In this paper, we tackle these bottlene…
Study shows time matters in automated trading, improving simple strategies over complex ones.
problem Effects of reaction speed and trading urgency on automated trading strategies.
method Simulated financial markets with public limit order book and continuous double auction matching. Examined reaction speed and trading urgency.
result Simple strategies outperform complex ones when considering reaction speed and trading urgency.
A heuristic minimizes tardy jobs' total weight on single-machine scheduling.
problem Minimizing tardy jobs' total weight on single-machine scheduling.
method Data-driven heuristic combining machine learning and problem-specific characteristics.
result Significantly outperforms state-of-the-art in optimality gap and adaptability.
YC Bench forecasts startup success in Y Combinator batches with a short-term metric.
problem Difficult forecasting of startup success due to sparse meaningful outcomes and slow evaluation cycles.
method Developed a live benchmark using publicly available traction signals and web visibility metrics.
result Revealed 6 out of 11 top performers at YC Demo Day with a simple proxy for prior brand recognition.
Kernel-based mean-field games use MMD penalties for interaction and target costs.
problem Optimizing mean-field games with specific cost functions.
method Kernel structure, random Fourier U-statistics, neural network training.
result Sample-level convergence theorem and rate of convergence proved.
A new algorithm tackles submodular bandit problems with multiple constraints.
problem Addressing diversified retrieval and online learning with budget constraints.
method Non-greedy algorithm focusing on upper-confidence bounds.
result High-probability upper bound of an approximation regret matching fast offline algorithm's ratio.
This work proposes an online learning approach to tighten constraints in stochastic control problems.
problem Solving chance-constrained stochastic optimal control problems is computationally challenging.
method Reformulate chance constraints as a binary regression problem and use a GP model to learn constraint-tightening parameters online.
result The approach tightens constraints more effectively, leading to lower costs in numerical experiments.
This paper tackles fair same-day delivery service by optimizing regional service rates.
problem Fairness in same-day delivery service for different neighborhoods.
method Partition service area into regions, use multi-objective Markov decision process and deep Q-learning.
result Our approach maximizes fairness across all regions while maintaining overall service rate.
We study constrained clustering, where constraints guide the clustering process. In existing works, two categories of constraints have been widely explored, namely pairwise and cardinality constraints. Pairwise constraints enforce the cluster labels of two instances to be the same (must-link constraints) or different (…
Simplifies neural network constraints with computationally efficient method.
problem Implementing hard output constraints in neural networks.
method Additional neural network layer for output constraints.
result Computational simplicity with complexity O(n*m) for linear constraints.
Reduces Lie (bi-)algebroids and Dirac manifolds using constraint vector bundles.
problem Reduction of Lie (bi-)algebroids and Dirac manifolds.
method Introduces constraint manifolds and constraint vector bundles; proves constraint Serre-Swan theorem; introduces Cartan calculus for constraint forms and multivector fields; shows compatibility with reduction.
result Reduction procedure for Lie (bi-)algebroids and Dirac manifolds.
Optimistic algorithm reduces regret and constraint violations in online convex optimization with adversarial constraints.
problem Online convex optimization with adversarial constraints.
method Improved algorithm using accurate predictions of loss and constraint functions.
result Improved bounds on regret and cumulative constraint violations.
Holistic GLMs add constraints for better model quality.
problem Improving classical linear regression models.
method Sparsity-inducing, sign-coherence, and linear constraints.
result Holistic GLMs reliably solve GLMs for various responses.
A new ML method teaches constraints directly to models.
problem Addressing safety and fairness in AI systems.
method Directly teaching constraint satisfaction to ML models using a constraint solver.
result Empirically, our approach performs well on fairness and synthetic constraints.
Study on-chain peak shaving to reduce Ethereum transaction costs.
problem Reducing transaction costs in blockchain networks, especially during congested periods.
method Analyzing transaction-level data from multiple firms across various industries to understand scheduling responses and cost management strategies.
result Firms' scheduling responses to congestion vary, leading to different fee savings and residual costs.
Paper tackles constrained bandit problems with a new learning framework.
problem Optimizing a black-box reward function subject to a black-box constraint function over a continuous space.
method Rectified Pessimistic-Optimistic Learning (RPOL) framework, incorporating optimistic and pessimistic GP bandit learning.
result RPOL achieves sublinear regret and minimal cumulative constraint violation.
In the present paper, the minimal investment risk for a portfolio optimization problem with imposed budget and investment concentration constraints is considered using replica analysis. Since the minimal investment risk is influenced by the investment concentration constraint (as well as the budget constraint), it is i…
Survey of Gaussian process constraints for modeling expensive data.
problem Modeling expensive data with physical constraints.
method Overview of various Gaussian process constraints and their implementation.
result Discussion of computational challenges introduced by constraints.