Modeling vessel speed to balance efficiency and environmental risks in Arctic shipping.
problem Balancing vessel speed with environmental and ecological risks in Arctic shipping.
method Inverse control constrained optimization framework with risk parameters estimated from AIS data.
result Distinct decision-making patterns across vessel types and navigational statuses, with varying sensitivity to ice and whale risks.
Study identifies NFT whales driving the market with consistent high returns.
problem Lack of financial analysis of NFT trading ecosystem.
method Longitudinal study of 3.8M NFT transactions, classifying traders into whales, dolphins, and minnows.
result Top 0.1% of NFT traders (whales) drive the market with consistent, high returns.
Hybrid model improves wind speed prediction accuracy using MLP and WOA.
problem Improving wind speed prediction accuracy for renewable energy control.
method Combining MLP with Whale Optimization Algorithm (WOA) for data preprocessing and model optimization.
result The hybrid MLP-WOA model outperformed standalone MLP model in wind speed prediction accuracy.
Automatically detecting sound units of humpback whales in complex time-varying background noises is a current challenge for scientists. In this paper, we explore the applicability of Convolution Neural Network (CNN) method for this task. In the evaluation stage, we present 6 bi-class classification experimentations of …
Paper forecasts extreme Bitcoin volatility spikes using whale transactions and CryptoQuant data.
problem Forecasting extreme volatility spikes in Bitcoin market.
method Proposes Synthesizer Transformer model for forecasting.
result Model outperforms state-of-the-art models in forecasting extreme volatility spikes.
A Deep Zero-Inflated Model for Detecting North Atlantic Right Whale Presence
problem Balancing marine conservation and blue economy management
method Deep Zero-Inflated Bernoulli model
result Improved model adequacy and predictive performance
Study shows how high-budget agents can manipulate prediction markets.
problem Manipulation of prediction markets by high-budget agents.
method Agent-based simulations and analytic characterization of price dynamics.
result High-budget agents can temporarily shift prediction market prices.
Bitcoin reacts positively to USDT minting but not burning, showing state-dependence.
problem Understanding Bitcoin's response to Tether's supply changes.
method Analyzing Bitcoin's intraday price movements in response to USDT minting and burning events.
result Bitcoin's response to USDT minting events declines after 60 minutes and is influenced by investor sentiment and public announcements.
In 2012, JPMorgan accumulated a USD~6.2 billion loss on a credit derivatives portfolio, the so-called `London Whale', partly as a consequence of de-correlations of non-perfectly correlated positions that were supposed to hedge each other. Motivated by this case, we devise a factor model for correlations that allows for…
Research into automated systems for detecting and classifying marine mammals in acoustic recordings is expanding internationally due to the necessity to analyze large collections of data for conservation purposes. In this work, we present a Convolutional Neural Network that is capable of classifying the vocalizations o…
Method combines latent space exploration and causal inference to interpret unknown data.
problem Interpreting unknown communication system of sperm whales.
method Causal disentanglement with extreme values (CDEV) combining latent variable manipulation and causal inference.
result Sperm whales encode information using click number, timing regularity, and audio properties.
Nowadays, video game developers record every virtual action performed by their players. As each player can remain in the game for years, this results in an exceptionally rich dataset that can be used to understand and predict player behavior. In particular, this information may serve to identify the most valuable playe…
This paper presents a spermwhale' localization architecture using jointly a bag-of-features (BoF) approach and machine learning framework. BoF methods are known, especially in computer vision, to produce from a collection of local features a global representation invariant to principal signal transformations. Our idea …
Robust X-Learner improves HTE estimation in imbalanced and heavy-tailed data.
problem Estimating HTE in imbalanced and heavy-tailed data.
method Integrates γ-divergence objective and Proxy Hessian strategy into gradient boosting.
result Reduces PEHE metric by 98.6% in semi-synthetic Criteo Uplift dataset.
Dictionary based classifiers are a family of algorithms for time series classification (TSC), that focus on capturing the frequency of pattern occurrences in a time series. The ensemble based Bag of Symbolic Fourier Approximation Symbols (BOSS) was found to be a top performing TSC algorithm in a recent evaluation, as w…
Paper introduces SCI to distinguish market signals from coordination.
problem Unclear signals in prediction markets.
method Formalizes SCI, introduces weighted and time-varying extensions.
result Discriminates between market signals and coordination.
Bayesian X-Learner calibrates uncertainty and robustness for CATE estimation under heavy-tailed data.
problem Estimating heterogeneous treatment effects with calibrated uncertainty and robustness to heavy-tailed outcomes.
method Bayesian X-Learner using cross-fitted doubly robust pseudo-outcomes and MCMC for a full posterior over CATE.
result Bayesian X-Learner achieves robust and calibrated CATE estimation on real and contaminated data.
LLM sandbox and persona dynamics create unethical reality gaps that shift risk to users.
problem Ethical issues arise from LLMs generating reality gaps that shift risk to uninformed users.
method Analyzes the ethical implications of LLM sandbox and persona dynamics, comparing them to financial regulation and compliance.
result Active generation of reality gaps is unethical as it shifts epistemic risk to users.
This study examines non-retail trading on Polymarket, revealing unique behavior patterns and structural limitations.
problem Lack of address-level quote-lifecycle data in Polymarket prediction markets.
method Empirical analysis of 13 million order-filled events using DBSCAN clustering on a six-feature fill-side vector.
result Non-retail behavior is uni-modal, contradicting previous archetypal hypotheses.
New algorithms reduce bilevel optimization complexity to ε^(-1.5).
problem Efficiently solving bilevel optimization problems in machine learning.
method Proposed two new algorithms: one using momentum-based recursive iterations, the other using recursive gradient estimations.
result Achieved computational complexity of ε^(-1.5), significantly faster than previous methods.
Learning to Optimize is a recently proposed framework for learning optimization algorithms using reinforcement learning. In this paper, we explore learning an optimization algorithm for training shallow neural nets. Such high-dimensional stochastic optimization problems present interesting challenges for existing reinf…
An optimal algorithm for multi-armed bandits with constraints.
problem Optimizing decisions in constrained multi-armed bandit problems.
method An index-based deterministic algorithm using Locatelli's anytime thresholding under known optimal value assumption.
result The algorithm achieves asymptotic optimality with probability approaching 1.
Paper proposes a reinforcement learning framework for efficient hyper-parameter tuning of stochastic optimization algorithms.
problem Efficient tuning of hyper-parameters for stochastic optimization algorithms.
method Modeling hyper-parameter tuning as a Markov decision process and using policy gradient algorithms.
result The proposed framework significantly reduces the time required for hyper-parameter tuning compared to Bayesian optimization.
New algorithms achieve optimal DP convex optimization with linear time and gradient computations.
problem Private stochastic convex optimization with optimal excess loss.
method Two new techniques: variable batch sizes and localization with stable optimization.
result Achieves optimal bound on excess loss with O(min{n,n2/d}) gradient computations. Two new algorithms optimize decentralized convex optimization with reduced communication rounds.
problem Decentralized minimization of smooth strongly convex functions in a network.
method Proposes two new algorithms based on accelerated Forward Backward methods.
result First algorithm is optimal in terms of communication rounds and gradient computations.
Study on randomized algorithms for optimal stopping problems.
problem Optimal stopping problems in randomized algorithms.
method Forward and backward Monte Carlo based optimisation algorithms.
result Proved convergence of the proposed algorithms and derived convergence rates.
MadEvolve optimizes trading algorithms using LLMs, achieving significant improvements in feature generation and trading strategy optimization.
problem Optimizing trading algorithms for better performance and feature generation.
method A framework inspired by Alpha-Evolve, using LLMs to evolve trading strategies and feature pipelines.
result Significant improvements in trading performance across various tasks, including feature generation and trading strategy optimization.
This thesis analyzes and improves convergence rates of bilevel optimization algorithms in machine learning.
problem Convergence analysis and algorithm design for bilevel optimization in machine learning.
method Comprehensive convergence rate analysis for both problem-based and algorithm-based bilevel optimization formulations.
result First lower bounds and matching upper bounds for bilevel optimization, and new stochastic algorithms with lower complexity.
Linear optimization is many times algorithmically simpler than non-linear convex optimization. Linear optimization over matroid polytopes, matching polytopes and path polytopes are example of problems for which we have simple and efficient combinatorial algorithms, but whose non-linear convex counterpart is harder and …
SMAC3 optimizes machine learning hyperparameters efficiently.
problem Optimizing hyperparameters for machine learning algorithms.
method Bayesian Optimization framework with facades for various use cases.
result Improves performance with minimal evaluations.
Study compares various optimization algorithms for deep learning.
problem Challenges in optimizing deep neural networks as they grow deeper and datasets increase.
method Implemented and compared adaptive gradient methods for supervised and unsupervised tasks on four image datasets.
result Differences in behavior and performance of optimization algorithms during training.
New algorithm tackles multimodal bandits with optimal regret bounds.
problem Stochastic multi-armed bandit problem with multimodal rewards.
method Proposed computationally tractable algorithm for multimodal rewards.
result Asymptotically optimal algorithms implemented for the bandit problem.
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 reduces individual regret and communication costs in cooperative bandits.
problem Optimal individual and group regret in cooperative multi-agent bandits.
method Integrates a new communication policy into a learning algorithm.
result Achieves optimal individual regret and constant communication costs.
Many machine learning algorithms minimize a regularized risk, and stochastic optimization is widely used for this task. When working with massive data, it is desirable to perform stochastic optimization in parallel. Unfortunately, many existing stochastic optimization algorithms cannot be parallelized efficiently. In t…
Modified K-means ensures local optimality with same complexity.
problem Lack of rigorous analysis on local optimality guarantees of K-means.
method Proposed modifications to K-means ensuring local optimality.
result Proposed methods provide improved locally optimal solutions.
New algorithms achieve optimal robustness in stochastic convex optimization under contamination.
problem Determining optimal rates for robust stochastic convex optimization under ε-contamination. method Developed novel algorithms achieving minimax-optimal excess risk under ε-contamination model without stringent assumptions. result Achieved minimax-optimal excess risk (up to logarithmic factors) under ε-contamination model. Algorithm design is a laborious process and often requires many iterations of ideation and validation. In this paper, we explore automating algorithm design and present a method to learn an optimization algorithm, which we believe to be the first method that can automatically discover a better algorithm. We approach th…
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 RL algorithm learns optimal multi-threshold policies for MDPs.
problem Overcoming the curse of dimensionality in MDPs.
method Structure-aware RL algorithm exploiting multi-threshold optimal policies.
result The algorithm converges to the optimal policy asymptotically.
Adaptive algorithm AMSGrad converges for weakly convex constrained optimization problems.
problem Solving constrained stochastic optimization problems with weakly convex objectives.
method Analysis of AMSGrad algorithm for a specific class of problems.
result AMSGrad achieves a convergence rate of ildeO(t−1/4) for the norm of the gradient of the Moreau envelope. PAC-Bayesian theory applied to learning optimization algorithms with generalization guarantees.
problem Learning optimization algorithms with provable generalization guarantees and explicit trade-offs.
method PAC-Bayes theory applied to learning-to-optimize, reformulating the learning procedure into a one-dimensional minimization problem.
result Learned optimization algorithms outperform deterministic worst-case analysis algorithms, even in the limit case of guaranteed convergence.
Simpler majority vote of three classifiers achieves optimal error bounds.
problem Developing an optimal PAC learning algorithm in the realizable setting.
method Returning the majority vote of three ERM classifiers.
result Achieves optimal in-expectation bound on error.
Optimizes pure exploration in linear bandits with a new algorithm.
problem Best-arm identification in linear stochastic bandits.
method Developed the first asymptotically optimal algorithm for fixed-confidence pure exploration in linear bandits.
result Avoids the pitfall of a simple but difficult instance and bypasses the need to solve an optimal design problem.
Paper tackles non-stationary kernelized bandits with near-optimal algorithm.
problem Minimizing regret in a time-varying reward function.
method Near-optimal algorithm with a novel restarting phased elimination with random permutation (R-PERP).
result Regret upper bound matches the lower bound, making the algorithm near-optimal.
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.
Two algorithms find optimal points in decentralized optimization.
problem Decentralized non-convex stochastic optimization with composite objective functions.
method Prox-DASA and Prox-DASA-GT algorithms for finding ε-stationary points.
result Achieves comparable complexity without large batch sizes or complex per-iteration operations.
Establishes geometric convergence of iterative optimization algorithms.
problem Analyzes convergence of iterative optimization algorithms under general assumptions.
method General framework for iterative optimization algorithms, proving asymptotic geometric convergence and providing convergence rates.
result Asymptotic geometric convergence of iterative optimization algorithms with exact rate.