Faster WIND accelerates iterative BOND for LLM alignment.
problem Iterative BOND is inefficient in practice due to sample and computation inefficiency.
method Unified game-theoretic connection to self-play alignment, WIND framework with efficient algorithms.
result WIND variant achieves superior sample efficiency and faster computation.
Framework for real-time win probability and player ability in sports.
problem Real-time evaluation and player assessment in team sports.
method Continuous dominance indicator (T-score) and T-process formulation.
result Decomposes competitive advantage into interpretable statistical components.
This work proves win rate is key to understanding preference learning.
problem Understanding preference learning from generative models.
method Analyzing preference learning methods as win rate optimization or non-WRO.
result Proves win rate is the only evaluation respecting preferences and prevalences.
The paper proposes a new method to forecast winning prices in real-time bidding.
problem Accurately forecasting winning prices in real-time bidding with limited data.
method The paper introduces a heteroscedastic fully parametric censored regression approach and a mixture density censored network.
result The proposed method significantly improves winning price forecasting compared to existing methods.
Calculates winning probability for three candidates based on support rates and information timing.
problem Determining optimal strategy for three candidates in an election.
method Closed-form solution using support rates, political spectrum positioning, time left, and information revelation rate.
result Optimal strategy can be complex, especially for candidates in the center of a polarized electorate.
New bid shading algorithm reduces costs by 55%.
problem Minimizing costs in online advertising auctions.
method Win-rate prediction and surplus maximization using logistic regression.
result 7% more profit for advertisers compared to benchmarks.
Paper confirms winning tickets have sharp minima, useful for generalization.
problem Explaining why over-parameterized models generalize well despite having sharp minima.
method PAC-Bayesian theory applied to analyze winning tickets and their generalization behavior.
result PAC-Bayesian theory confirms winning tickets have relatively sharp minima, a disadvantage for generalization.
The study analyzes games and social hierarchies, incorporating luck and depth of competition.
problem Analyzing patterns of wins and losses in games and social hierarchies.
method Generalized probabilistic models incorporating luck and depth of competition.
result Social competition tends to be deeper with many distinct levels, but there is often a chance of upset victories.
The Lucas critique has exposed the problem of the trade-off between changes in monetary policy and structural breaks in economic time series. The search for and characterisation of such breaks has been a major econometric task ever since. We have developed an integral technique similar to CUSUM using an empirical model…
The recently proposed Lottery Ticket Hypothesis of Frankle and Carbin (2019) suggests that the performance of over-parameterized deep networks is due to the random initialization seeding the network with a small fraction of favorable weights. These weights retain their dominant status throughout training -- in a very r…
Improved MACD trading strategies with other indicators for better performance.
problem Evaluating the effectiveness of MACD-based trading strategies in the US stock market.
method Backtested various MACD-based trading strategies on US stock indices using Python.
result Win-rate of MACD strategies improved with other momentum indicators, leading to a new VPVMA indicator.
State-space models are used in a wide range of time series analysis formulations. Kalman filtering and smoothing are work-horse algorithms in these settings. While classic algorithms assume Gaussian errors to simplify estimation, recent advances use a broader range of optimization formulations to allow outlier-robust e…
Unified framework for efficient Frank-Wolfe optimization of Dominant Set Clustering.
problem Optimizing Dominant Set Clustering with various Frank-Wolfe algorithms.
method Unified framework for pairwise, standard, and away-steps Frank-Wolfe algorithms, with explicit convergence rates.
result Explicit convergence rates for Frank-Wolfe methods in Dominant Set Clustering.
The success of lottery ticket initializations (Frankle and Carbin, 2019) suggests that small, sparsified networks can be trained so long as the network is initialized appropriately. Unfortunately, finding these "winning ticket" initializations is computationally expensive. One potential solution is to reuse the same wi…
SharedRep-RLHF learns shared traits for diverse groups, improving fairness and performance.
problem Uniform-reward RLHF fails to capture diverse preferences, leading to unfairness.
method SharedRep-RLHF learns shared traits among various groups, improving fairness and performance.
result SharedRep-RLHF outperforms MaxMin-RLHF by up to 20% in win rate.
DR-MCTS improves decision quality and sample efficiency in complex environments.
problem Improving decision quality and sample efficiency in complex environments.
method Integrates Doubly Robust off-policy estimation into Monte Carlo Tree Search (MCTS).
result DR-MCTS achieves superior performance in Tic-Tac-Toe and VirtualHome tasks.
The lottery ticket hypothesis finds multiple winning sub-networks in neural networks.
problem Finding a single winning sub-network in neural networks.
method Analyzing neural networks trained in isolation and on different tasks.
result Neural networks contain multiple sub-networks that match the accuracy of the original network, not just one.
We find a remarkable agreement between the statistics of a randomly divided interval and the observed statistical patterns and distributions found in horse racing betting markets. We compare the distribution of implied winning odds, the average true winning probabilities, the implied odds conditional on a win, and the …
Pretrained time-series models outperform train-from-scratch baselines in financial return forecasting.
problem Financial return forecasting
method Pretrained time-series foundation models
result Pretrained TSFMs dominate the ranking distribution, accounting for 8 of 10 task-level wins.
Adversarial policies beat superhuman Go AI systems.
problem Vulnerability of superhuman AI systems to adversarial attacks.
method Training adversarial policies to trick KataGo into making blunders.
result Adversarial policies achieve >97% win rate against KataGo at superhuman settings.
E-LTH finds winning tickets scalable across different network architectures.
problem Finding winning tickets in different network architectures efficiently.
method Tweaking winning tickets from one network to another.
result Winning tickets from one network can be stretched or squeezed into another network's subnetwork.
Emerging economies use countercyclical policies to manage crises and dominant currencies.
problem Managing economic crises and fluctuations in dominant currencies like USD and EUR.
method Theoretical analysis, case studies, econometric modeling.
result Emerging economies can stabilize growth with countercyclical monetary policies.
The lottery ticket hypothesis proposes that over-parameterization of deep neural networks (DNNs) aids training by increasing the probability of a "lucky" sub-network initialization being present rather than by helping the optimization process (Frankle & Carbin, 2019). Intriguingly, this phenomenon suggests that initial…
(Frankle & Carbin, 2019) shows that there exist winning tickets (small but critical subnetworks) for dense, randomly initialized networks, that can be trained alone to achieve comparable accuracies to the latter in a similar number of iterations. However, the identification of these winning tickets still requires the c…
Injecting human knowledge is an effective way to accelerate reinforcement learning (RL). However, these methods are underexplored. This paper presents our discovery that an abstract forward model (thought-game (TG)) combined with transfer learning (TL) is an effective way. We take StarCraft II as our study environment.…
Greedy pruning reduces neural networks by a logarithmic number of tickets, improving accuracy.
problem Pruning large neural networks to reduce size while maintaining accuracy.
method Greedy optimization-based pruning method with exponential decay guarantee.
result The discrepancy between pruned and original networks decays exponentially with network size.
In-game win probability models, which provide a sports team's likelihood of winning at each point in a game based on historical observations, are becoming increasingly popular. In baseball, basketball and American football, they have become important tools to enhance fan experience, to evaluate in-game decision-making,…
We present a powerful new loss function and training scheme for learning binary hash functions. In particular, we demonstrate our method by creating for the first time a neural network that outperforms state-of-the-art Haar wavelets and color layout descriptors at the task of automated scene matching. By accurately rel…
Although artificial neural networks have occasionally been used for Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) studies in the past, the literature has of late been dominated by other machine learning techniques such as random forests. However, a variety of new neural net techniques along with suc…
New method ranks multivariate distributions in SMOOP using q-dominance.
problem Lack of reliable methods to rank multivariate distributions in SMOOP.
method Introduces center-outward q-dominance and develops empirical test procedures.
result Proves q-dominance implies FSD and establishes a sample size threshold.
SRRM improves recursive transport surrogates in the small-discrepancy regime.
problem Insufficient understanding of recursive partitioning methods' statistical behavior and resolution in the small-discrepancy regime.
method Introduced Selective Recursive Rank Matching (SRRM) to improve the resolution of Recursive Rank Matching (RRM).
result SRRM yields a higher-fidelity practical surrogate for the Wasserstein distance at moderate additional computational cost.
This paper challenges the current metrics used for evaluating long-term forecasting models.
problem Current metrics focus on pointwise error reduction, ignoring structural properties.
method Proposes a multi-dimensional evaluation approach that includes statistical fidelity, structural coherence, and decision-level relevance.
result Current progress in forecasting may reflect specialization in benchmark configurations rather than deeper understanding of temporal dynamics.
This paper is part of an ongoing investigation of "pragmatic information", defined in Weinberger (2002) as "the amount of information actually used in making a decision". Because a study of information rates led to the Noiseless and Noisy Coding Theorems, two of the most important results of Shannon's theory, we begin …
StarCraft II poses a grand challenge for reinforcement learning. The main difficulties of it include huge state and action space and a long-time horizon. In this paper, we investigate a hierarchical reinforcement learning approach for StarCraft II. The hierarchy involves two levels of abstraction. One is the macro-acti…
In the online multiple testing problem, p-values corresponding to different null hypotheses are observed one by one, and the decision of whether or not to reject the current hypothesis must be made immediately, after which the next p-value is observed. Alpha-investing algorithms to control the false discovery rate (FDR…
We explore a new way to evaluate generative models using insights from evaluation of competitive games between human players. We show experimentally that tournaments between generators and discriminators provide an effective way to evaluate generative models. We introduce two methods for summarizing tournament outcomes…
This paper discusses the gambling contest introduced in Seel & Strack (Gambling in contests, Discussion Paper Series of SFB/TR 15 Governance and the Efficiency of Economic Systems 375, Mar 2012.) and considers the impact of adding a penalty associated with failure to follow a winning strategy. The Seel & Strack model c…
Paper tackles learning win-win solutions in aggregation systems.
problem Learning approximate equilibrium solutions in aggregation systems to keep suppliers.
method Multi-Agent Reinforcement Learning (MARL) with variance reduction mechanism.
result Approach reduces value variance and moves joint solution closer to Nash Equilibrium.
The thesis explores IMP, a process that identifies winning tickets in DNNs, and its universality.
problem Understanding how winning subnetworks (tickets) perform across different problems.
method Iterative Magnitude Pruning (IMP) and comparison with Renormalisation Group (RG) theory.
result IMP identifies winning subnetworks that can perform similarly across various problems.
New insights into Bartnik mass from improvability of dominant energy scalar.
problem Characterizing Bartnik mass minimizing initial data sets.
method Introducing improvability concept, proving non-improvability consequences, and analyzing pp-wave counterexamples.
result Bartnik mass minimizing initial data sets are characterized, advancing conjectures.
Model analyzes RFQ markets using stochastic control to optimize dealer performance and inventory.
problem Optimizing market making in aggregator-routed RFQ markets with varying dealer performance scores.
method Two-tier stochastic control model that separates RFQ-level price competition from macro routing.
result Optimal controls can be expressed through derivatives of reduced Hamiltonians, leading to interpretable mappings from optimal win probabilities to optimal offsets.
New method schedules learning rate without stopping time, outperforming existing methods.
problem Learning rate schedules that require stopping time are outperformed.
method Schedule-Free approach that avoids stopping time and introduces no additional hyper-parameters.
result Exhibits state-of-the-art performance across various problems.
Abstract Coxeter groups have growth rates that are Perron numbers.
problem Understanding growth rates of Coxeter groups.
method Defined a class of Coxeter groups, ∞--spanned, and analyzed their growth rates. result For ∞--spanned Coxeter groups, geodesic growth rate strictly dominates word growth rate and appears to be a Perron number. TabPFN-2.5 boosts tabular AI performance, especially for large datasets.
problem Improving tabular AI models for large datasets and complex feature sets.
method Advanced tabular foundation model with data augmentation and distillation techniques.
result TabPFN-2.5 outperforms tuned tree-based models and AutoGluon 1.4 on benchmark datasets.
Model selection for time series forecasting can be biased by the distribution of scores.
problem Model selection for probabilistic forecasting on time series data.
method Using proper scoring rules to aggregate scores across multiple time series.
result The mean score is immune to the skewness of the score distribution.
Gamblers lose in long bets despite casino claims, study shows.
problem Gamblers losing in seemingly fair gambling scenarios.
method Theoretical and experimental analysis of two-armed Futurity slot machines.
result Casinos have a win rate greater than 50% in long bets, making them unfair.
The standard deviation and Gini mean difference order based on tail behavior.
problem Ordering between standard deviation and Gini mean difference for real-valued risks.
method Analysis of the mean excess function of the pairwise difference ∣X−X′∣. result Dominance regimes of SD and GMD are determined by tail behavior of the distribution.
SGD updates align with a low-rank subspace but do not lead to further loss reduction.
problem Understanding the training dynamics of deep neural networks, particularly the role of the dominant subspace.
method Exploring whether neural networks can be trained within the dominant subspace of the loss Hessian.
result SGD updates, when projected onto the dominant subspace, do not decrease the training loss further, suggesting spurious alignment.