A PID-based feedback-control system improves multiple KPIs in RTB display advertising.
problem Challenges in simultaneously improving multiple KPIs in RTB campaigns.
method Sequential Control using PID-based feedback and importance metrics.
result Effective in simultaneously controlling multiple KPIs in both simulations and live traffic.
QBSD optimizes KPI forecasting for RAN networks with fast runtime and accuracy.
problem Efficiently forecasting KPIs for RAN networks with dynamic operating ranges.
method Quartile-Based Seasonality Decomposition (QBSD) for live single-step forecasting.
result QBSD outperforms other methods in runtime efficiency and forecast accuracy.
The biggest problem with the methods of machine learning used today in business analytics is that they do not generalize well and often fail when applied to new data. One of the possible approaches to this problem is to enrich these methods (which are almost exclusively based on statistical algorithms) with some intrin…
Proposes a Big Data framework for SC forecasting, including data preprocessing and machine learning.
problem Improving SC forecasting accuracy and efficiency.
method Data collection, preprocessing, machine learning model training, hyperparameter tuning, performance evaluation.
result Optimized SC forecasting models enhance workforce, inventory, and overall SC performance.
Proposes ATH for KPI anomaly detection based on local data properties.
problem Business outliers are not always anomalies; existing solutions are limited.
method Adaptive Thresholding Heuristic (ATH) dynamically adjusts detection thresholds.
result ATH minimizes false positives and adapts to concept drift.
A lightweight model predicts IT system KPIs from historical data.
problem Predicting future KPIs of interconnected IT systems is hard due to diverse and changing components.
method A weighted heterogeneous ensemble method combining neural network and mean predictor.
result Achieved R2 scores of 0.10 and 0.15 on test data. To ensure undisrupted business, large Internet companies need to closely monitor various KPIs (e.g., Page Views, number of online users, and number of orders) of its Web applications, to accurately detect anomalies and trigger timely troubleshooting/mitigation. However, anomaly detection for these seasonal KPIs with va…
Operating envelope is an important concept in industrial operations. Accurate identification for operating envelope can be extremely beneficial to stakeholders as it provides a set of operational parameters that optimizes some key performance indicators (KPI) such as product quality, operational safety, equipment effic…
PS-DME evaluates model performance and reliability after data-dependent selection.
problem Evaluating model performance and reliability when data is used for selection and evaluation.
method Post-selection distributional model evaluation (PS-DME) using e-values to control false coverage rate.
result PS-DME provides reliable comparison of model configurations across different reliability levels.
Time series anomaly detection is usually formulated as finding outlier data points relative to some usual data, which is also an important problem in industry and academia. To ensure systems working stably, internet companies, banks and other companies need to monitor time series, which is called KPI (Key Performance I…
Study proposes new methods to calculate probabilistic benchmarks in noisy data.
problem Identifying opportunities for improvement in comparable units with noisy data.
method 2-step methodology involving undersampling and relevance vector machine.
result Higher discrimination power achieved with macro-economic environment variables.
Hydropower plants are one of the most convenient option for power generation, as they generate energy exploiting a renewable source, they have relatively low operating and maintenance costs, and they may be used to provide ancillary services, exploiting the large reservoirs of available water. The recent advances in In…
In this paper, we propose an offline counterfactual policy estimation framework called Genie to optimize Sponsored Search Marketplace. Genie employs an open box simulation engine with click calibration model to compute the KPI impact of any modification to the system. From the experimental results on Bing traffic, we s…
The ad-trading desks of media-buying agencies are increasingly relying on complex algorithms for purchasing advertising inventory. In particular, Real-Time Bidding (RTB) algorithms respond to many auctions -- usually Vickrey auctions -- throughout the day for buying ad-inventory with the aim of maximizing one or severa…
We present a comparative study of different probabilistic forecasting techniques on the task of predicting the electrical load of secondary substations and cabinets located in a low voltage distribution grid, as well as their aggregated power profile. The methods are evaluated using standard KPIs for deterministic and …
This paper reviews and compares deep generative models for financial time series and VaR.
problem Forecasting risk factor distribution in financial markets.
method Apply multiple deep generative models (CGAN, CWGAN, Diffusion, Signature WGAN) and propose new methods for conditional time series generation.
result Top performing models are Historical Simulation, GARCH, and CWGAN.
Optimizes antenna tilt for better QoS in cellular networks.
problem Hard to learn optimal antenna tilt policies in real networks due to risk and simulation gap.
method Uses off-policy Contextual Multi-Armed-Bandit (CMAB) techniques to learn from existing data.
result Trained policies show consistent improvements over existing logging policies.
The paper adds explanation to predictive process monitoring.
problem Equipping predictive business process monitoring with explanation capabilities.
method Used game theory of Shapley Values to obtain robust explanations.
result First time explanations given in predictive business process monitoring.
Enabling cellular connectivity for drones introduces a wide set of challenges and opportunities. Communication of cellular-connected drones is influenced by 3-dimensional mobility and line-of-sight channel characteristics which results in higher number of handovers with increasing altitude. Our cell planning simulation…
With a new era of cloud and big data, Database Management Systems (DBMSs) have become more crucial in numerous enterprise business applications in all the industries. Accordingly, the importance of their proactive and preventive maintenance has also increased. However, detecting problems by predefined rules or stochast…
In financial field, a robust software system is of vital importance to ensure the smooth operation of financial transactions. However, many financial corporations still depend on operators to identify and eliminate the system failures when financial software systems break down. This traditional operation method is time…
Paper proposes transparent insurance models for PBMs.
problem PBMs' opaque business models and hidden profits.
method Quantitative estimates of two models with fixed premiums and fee-for-service.
result Proposes transparent models with fixed premiums and fee-for-service.
Survival strategy for crypto firms in bear markets using BTC-to-sats payments rail.
problem Downside risk in crypto reserves during bear markets.
method Conservative treasury policy, operating line monetizing holdings, BTC-to-sats payments rail.
result Sustained mNAV premium through cycles with disclosed KPIs.
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.
CJE calibrates cheap LLM judges against an oracle, achieving high accuracy at a fraction of the cost.
problem Inexpensive LLM judges can produce biased rankings, leading to unreliable outcomes.
method CJE uses a small oracle to calibrate cheap scores, then evaluates at scale with valid uncertainty.
result CJE achieves 99% pairwise ranking accuracy at 14x lower cost compared to a 16x oracle/judge cost ratio.
Bayesian optimization reduces computational effort in aircraft design optimization.
problem High computational cost in industrial aircraft design optimization.
method Constrained Bayesian optimization (Super Efficient Global Optimization with Mixture of Experts)
result Significant computational efficiency improvements over existing Isight optimizers.
Bayesian optimization method tackles combinatorial spaces, scalable for large data.
problem Optimization over combinatorial categorical spaces in natural sciences.
method Combines variational optimization and continuous relaxations for gradient-based optimization.
result Method performs comparably to state-of-the-art methods while scaling well.
New algorithm solves complex stopping problems with robust optimization.
problem Solving complex stochastic optimal stopping problems.
method Simulation-based robust optimization with exact reformulation as a zero-one bilinear program.
result Developed polynomial-time heuristics and algorithms for practical solution.
L2O uses ML to optimize traditional optimization techniques.
problem Real-world optimization problems with shared structures.
method Exploiting shared structures to enhance optimization techniques.
result Better or faster solutions through machine learning integration.
When hyperparameter optimization of a machine learning algorithm is repeated for multiple datasets it is possible to transfer knowledge to an optimization run on a new dataset. We develop a new hyperparameter-free ensemble model for Bayesian optimization that is a generalization of two existing transfer learning extens…
Meta algorithm solves multivariate optimization using univariate optimizers.
problem Multivariate global optimization problems.
method Meta algorithm combining univariate global optimizers.
result Meta algorithm provides robust regret guarantees.
A novel neural network approach for optimization problems.
problem Constrained optimization problems.
method Neural Optimization Machine (NOM) using a specially designed NN architecture and training procedure.
result Solves optimization problems efficiently, especially in high-dimensional spaces.
New algorithms ensure reproducibility and optimal convergence in convex optimization.
problem Trade-off between reproducibility and convergence rate in convex optimization.
method Regularization-based algorithms for smooth convex minimization and minimax optimization.
result Achieves optimal reproducibility and near-optimal gradient complexity for various oracle settings.
New algorithm selects robust martingale for optimal stopping problems.
problem Optimal stopping problems in stochastic processes.
method Randomized dual martingale minimization algorithm.
result Efficiently selects Doob martingale as close as possible.
Numerical optimization is an important tool in the field of computational physics in general and in nano-optics in specific. It has attracted attention with the increase in complexity of structures that can be realized with nowadays nano-fabrication technologies for which a rational design is no longer feasible. Also, …
This paper shows how to combine optimal tests into log-optimal processes.
problem How to combine optimal sequential tests into log-optimal processes.
method Using a new class of WAIT e-processes, the paper aggregates asymptotically optimal sequential tests into asymptotically log-optimal processes.
result It is possible to aggregate asymptotically optimal sequential tests into asymptotically log-optimal e-processes.
Learning optimal feedback control laws capable of executing optimal trajectories is essential for many robotic applications. Such policies can be learned using reinforcement learning or planned using optimal control. While reinforcement learning is sample inefficient, optimal control only plans an optimal trajectory fr…
Paper studies optimal control for a specific geometric problem.
problem Optimal control problem associated with the Paneitz obstacle problem.
method Existence and regularity results for optimal controls.
result Existence of optimal controls and their properties.
New algorithm AG-OG optimizes separable convex-concave problems efficiently.
problem Efficiently solving separable convex-concave minimax optimization problems.
method Leverages Nesterov acceleration and optimistic gradient on component and coupling parts of the problem.
result Achieves optimal convergence rate for various settings including bilinearly coupled problems.
Adapts Bayesian optimization for mixed constraints in aircraft design.
problem Optimizing expensive black box functions with mixed constraints.
method Super efficient global optimization with upper trust bound for constraints, Gaussian process uncertainty, refinement procedure.
result Superior performance on aircraft design problem compared to state-of-the-art solvers.
Adam optimizer converges to zeros of a new vector field, not just gradient zeros.
problem Prove convergence rates for Adam optimizer in simple quadratic optimization problems.
method Introduced Adam vector field to analyze Adam optimizer's convergence.
result Established optimal convergence rates for Adam optimizer.
Optimal crypto asset routing with CFMMs, including fixed costs.
problem Optimizing order execution on a network of CFMMs with fixed costs.
method Convex optimization for no fixed costs, mixed-integer convex for fixed costs, heuristics for approximate solutions.
result Approximate solutions to optimal routing and arbitrage certification problems.
BOSH optimizes functions with stochastic evaluations more efficiently and precisely.
problem Optimizing functions with noisy evaluations can lead to suboptimal solutions.
method BOSH uses a hierarchical Gaussian process to generate a growing pool of realizations.
result BOSH provides more efficient and higher-precision optimization than standard BO.
VeLO learns versatile optimizers from deep learning tasks.
problem Training deep learning models with hand-designed optimizers.
method Meta-training a neural network optimizer on a wide variety of optimization tasks.
result The learned optimizer automatically adapts to different optimization tasks without hyperparameter tuning.
New learned optimizers outperform baselines by incorporating known and novel mechanisms.
problem Understanding how learned optimizers outperform traditional ones.
method Careful analysis and visualization of learned optimizers trained on various tasks.
result Learned optimizers incorporate known techniques like momentum and gradient clipping, as well as new forms of learning rate adaptation.
PAGE optimizes nonconvex problems with optimal convergence rates.
problem Nonconvex optimization problems.
method PAGE algorithm for achieving optimal convergence rates.
result PAGE achieves optimal convergence rates for nonconvex optimization.
A new method learns DAGs from data using permutation optimization.
problem Discovering latent DAGs from observational data.
method Optimizes over the Permutahedron to learn topological orderings and edges.
result Our method optimizes exact DAGs, is modular, and performs well on real-world data.
Develops a new method for efficient stochastic bilevel optimization.
problem Stochastic bilevel optimization problems in machine learning applications.
method Single-Timescale stochAstic BiLevEl optimization (STABLE) method.
result Achieves the same order of sample complexity as stochastic gradient descent for single-level optimization.