Paper analyzes robustness of MDPDE under INH setups.
problem Global reliability and breakdown behavior of MDPDE under INH.
method Asymptotic breakdown point analysis of MDPDE.
result Derives a theoretical lower bound for the asymptotic breakdown point.
Bayesian optimization improves molecule design by addressing three pitfalls.
problem Bayesian optimization pitfalls cause poor performance in molecule design.
method Identified and addressed three pitfalls: incorrect prior width, over-smoothing, and inadequate acquisition function maximization.
result Basic BO setup achieves highest performance on PMO benchmark.
EHVI outperforms scalarized EI in MOBO for molecule design.
problem Benchmarking MOBO strategies for molecule design.
method Compared EHVI against fixed-weight scalarized EI in MOBO.
result EHVI consistently outperforms scalarized EI in molecular optimization tasks.
Improved bounds for unbounded losses using transductive priors.
problem Sequential regression and classification with unbounded losses.
method Exponential weights algorithm with transductive priors.
result Statistical bounds independent of design vectors and optimal solution norm.
We implement an all-optical setup demonstrating kernel-based quantum machine learning for two-dimensional classification problems. In this hybrid approach, kernel evaluations are outsourced to projective measurements on suitably designed quantum states encoding the training data, while the model training is processed o…
In this paper, we consider a best action identification problem in the stochastic linear bandit setup with a fixed confident constraint. In the considered best action identification problem, instead of minimizing the accumulative regret as done in existing works, the learner aims to obtain an accurate estimate of the u…
Communication networks shared by many users are a widespread challenge nowadays. In this paper we address several aspects of this challenge simultaneously: learning unknown stochastic network characteristics, sharing resources with other users while keeping coordination overhead to a minimum. The proposed solution comb…
Design interventions to learn causal structure from observational data.
problem Identify causal structure from observational data, especially when direction of edges is unknown.
method Design non-adaptive interventions to maximize the number of identifiable edges, formulate as optimization problems.
result Proposed algorithms can efficiently identify causal structure from interventions, even for general causal structures.
Estimates piecewise polynomials and bounded variation functions using optimal decision trees.
problem Estimating piecewise smooth functions in general dimensions.
method Dyadic CART and Optimal Regression Tree (ORT) estimators for piecewise polynomials and bounded variation functions.
result Oracle inequalities and risk bounds for ORT estimators, demonstrating adaptivity and optimality.
Simplifies fixing Khovanov homology functoriality.
problem Fixing the sign indeterminacy in Khovanov homology.
method Adjusting signs of Reidemeister and Morse moves.
result Functoriality of Khovanov homology is fixed.
New algorithm for minimizing regret in adversarial dueling bandits.
problem Minimizing regret in dueling bandits with adversarial feedback.
method Introduced an algorithm with T-round regret of ildeO(K1/3T2/3). result Algorithm achieves Ω(K1/3T2/3) regret lower bound. We study revenue optimization learning algorithms for repeated second-price auctions with reserve where a seller interacts with multiple strategic bidders each of which holds a fixed private valuation for a good and seeks to maximize his expected future cumulative discounted surplus. We propose a novel algorithm that h…
We formalize the notion of nesting probabilistic programming queries and investigate the resulting statistical implications. We demonstrate that while query nesting allows the definition of models which could not otherwise be expressed, such as those involving agents reasoning about other agents, existing systems take …
Deep learning (DL) training-as-a-service (TaaS) is an important emerging industrial workload. The unique challenge of TaaS is that it must satisfy a wide range of customers who have no experience and resources to tune DL hyper-parameters, and meticulous tuning for each user's dataset is prohibitively expensive. Therefo…
We introduce new online and batch algorithms that are robust to data with missing features, a situation that arises in many practical applications. In the online setup, we allow for the comparison hypothesis to change as a function of the subset of features that is observed on any given round, extending the standard se…
New algebraic setup defines quantum link invariants.
problem Defining and controlling quantum link invariants.
method Quantum Schur--Weyl duality and variants.
result Global definitions of quantum polynomials.
Guarantees convergence for black-box variational inference without modifications.
problem Convergence guarantees for black-box variational inference.
method Analysis of log-smooth posterior densities, location-scale variational family, and convergence rates of algorithm design choices.
result Proximal stochastic gradient descent fixes suboptimal convergence rates and achieves strongest known guarantees.
VA-LUCB identifies best arm with variance constraint, achieving optimal sample complexity.
problem Identifying the best arm with variance constraint under fixed confidence.
method Parameter-free algorithm VA-LUCB, analyzing sample complexity and proving lower bounds.
result Optimal sample complexity up to a logarithmic factor in HVA, demonstrated by experiments. We examine a fundamental problem that models various active sampling setups, such as network tomography. We analyze sampling of a multivariate normal distribution with an unknown expectation that needs to be estimated: in our setup it is possible to sample the distribution from a given set of linear functionals, and th…
This paper explores evasion attacks against Bayesian models.
problem Bayesian predictive models are vulnerable to evasion attacks.
method Developed gradient-based attacks for specific point predictions and entire posterior distributions.
result Optimal evasion attacks can be designed against Bayesian models.
Deep learning models forecast stock market orders over multiple time frames.
problem Forecasting stock market orders over varying time frames.
method Encoder-decoder models with sequence-to-sequence and Attention mechanisms, leveraging Intelligent Processing Units (IPUs) for faster training.
result Multi-horizon forecasting outperforms single-horizon models, especially for long prediction periods.
New algorithms for model selection in linear bandits adapt to instance complexity.
problem Adapting to the instance-dependent complexity of the true model in linear bandits.
method Design of algorithms in fixed confidence and fixed budget settings, leveraging experimental design and selection-validation procedures.
result Near instance optimal guarantees for model selection in linear bandits.
This paper proposes a new D2D data sharing approach to improve distributed machine learning training speed.
problem Straggler dilemma in distributed edge learning.
method Proposes a D2D data sharing approach to balance computation loads and optimize radio resource allocation.
result Significantly reduces training delay and enhances training accuracy in non-i.i.d. data environments.
Proposes a new method for efficient manifold denoising robust to high dimensional noise.
problem Efficiently denoise manifolds in high dimensional spaces with complicated noise.
method Landmark diffusion and optimal shrinkage under high dimensional noise and compact manifold setup.
result Systematic comparison with other algorithms on simulated and real datasets shows superior performance.
Machine learning, specifically LSTM, models quantum experiments efficiently.
problem Modeling complex quantum states with high-dimensional entanglement.
method Used a long short-term memory (LSTM) neural network to predict quantum experiment outcomes.
result LSTM neural networks can accurately predict quantum experiment outcomes without computing the states themselves.
Modern ML methods show unexpected behaviors that contradict classical statistics.
problem Modern machine learning methods exhibit behaviors at odds with classical statistical intuitions.
method Comparison between fixed and random design settings in ML and statistics.
result Moving from fixed to random designs reveals new insights into bias-variance tradeoffs and overfitting.
Although a key driver of Earth's climate system, global land-atmosphere energy fluxes are poorly constrained. Here we use machine learning to merge energy flux measurements from FLUXNET eddy covariance towers with remote sensing and meteorological data to estimate net radiation, latent and sensible heat and their uncer…
Method captures fabric mechanics from depth images without expensive setups.
problem Estimating mechanical parameters of fabrics accurately and efficiently.
method Sim-to-real strategy using learning-based framework trained on synthetic data.
result Metric correlates with human judgments of fabric drape similarity.
Estimates funding impact from an algorithmic relief rule, finding little effect on hospital activities.
problem Evaluating the impact of algorithmic policy decisions.
method Developed a treatment-effect estimator using algorithmic decisions as instruments.
result Funding from an algorithmic relief rule had little effect on COVID-19-related hospital activities.
Freya PAGE optimizes nonconvex optimization with heterogeneous, asynchronous workers.
problem Optimizing nonconvex finite-sum problems with varying worker processing times.
method Freya PAGE, a parallel method robust to stragglers and adaptive to slow computations.
result Freya PAGE offers improved time complexity guarantees compared to previous methods.
Improved AMM protocol supports diverse loan maturities in DeFi.
problem Challenges in designing AMMs for fixed-income lending with time-related complexities.
method Generalized BondMM protocol to support arbitrary maturities.
result BondMM-A protocol demonstrates superior performance in interest rate stability and financial robustness.
Sharp bounds on ERM's minimal error in regression.
problem Understanding ERM's performance in regression tasks.
method Sharp lower bounds for ERM in random and fixed design settings.
result ERM's performance depends on the global or local complexity of the model.
This work gives a simultaneous analysis of both the ordinary least squares estimator and the ridge regression estimator in the random design setting under mild assumptions on the covariate/response distributions. In particular, the analysis provides sharp results on the ``out-of-sample'' prediction error, as opposed to…
The study establishes minimax bounds for estimating operators from noisy samples.
problem Estimating unknown operators between Hilbert spaces from noisy data.
method Developed a minimax theory for uniformly bounded Lipschitz operators, proving lower and upper bounds.
result Sharp characterizations of minimax risk for generic Lipschitz operators, showing a curse of sample complexity.
Optimal benchmark design varies based on costs in financial manipulation.
problem Manipulation of price benchmarks in finance.
method Analyzes empirical pattern and cost structures to determine optimal benchmark design.
result The optimal benchmark depends on the relative sizes of fixed and variable costs.
New statistical mechanics analysis shows edge pruning outperforms node pruning in neural networks.
problem Theoretical understanding of neural network pruning effectiveness is lacking.
method Statistical mechanics analysis of a teacher-student framework.
result DPP node pruning method is superior to other methods, but edge pruning is better overall.
New method uses approximate KLD for intractable likelihood models.
problem Designing experiments for models with intractable likelihoods.
method Derive a lower bound of KLD utility, express it in terms of entropies, and evaluate efficiently.
result Demonstrated the performance of the proposed method through numerical examples.
The purpose of this study is to introduce new design-criteria for next-generation hyperparameter optimization software. The criteria we propose include (1) define-by-run API that allows users to construct the parameter search space dynamically, (2) efficient implementation of both searching and pruning strategies, and …
This study benchmarks anomaly detection methods in a functional setup.
problem Efficient anomaly detection in high-frequency sensor data.
method Functional analysis approach for multivariate data.
result Comparison of anomaly detection methods on real datasets.
New algorithm predicts piecewise regular functions online.
problem Online prediction of piecewise regular functions.
method Modified sleeping experts aggregation algorithm.
result Oracle risk bounds for all local regions.
REMAL: Residual Equilibrium Manifold Active Learning for Surrogate-Based Multidisciplinary Design Analysis
problem Multidisciplinary design analysis of coupled engineering systems requires solving equilibrium states where all disciplinary coupling variables are consistent.
method Residual manifold surrogate modeling framework for coupled systems.
result REMAL learns a surrogate model of the joint residual manifold via multitask Gaussian process models.
A CNN-DRL model improves learning in finance with scalable actions.
problem Finance environments with large action scales are hard for MLP-based DRL agents.
method Designed a CNN agent that uses historical data to adapt to large action scales.
result The CNN-DRL model remains stable and learns the environment better than MLP.
AHEAD improves financial market efficiency through ad-hoc auctions.
problem Improving financial market efficiency and reducing transaction costs.
method Introducing a new matching design (AHEAD) for electronic markets where participants can trade at a fixed price and trigger auctions when unsatisfied.
result A Nash equilibrium is achieved in the market, and ad-hoc auctions are more relevant and efficient than periodic auctions and continuous limit order books.
Translating potential disease biomarkers between multi-species 'omics' experiments is a new direction in biomedical research. The existing methods are limited to simple experimental setups such as basic healthy-diseased comparisons. Most of these methods also require an a priori matching of the variables (e.g., genes o…
Collateralization with daily margining has become a new standard in the post-crisis market. Although there appeared vast literature on a so-called multi-curve framework, a complete picture of a multi-currency setup with cross-currency basis can be rarely found since our initial attempts. This work gives its extension r…
A two-phase algorithm identifies the best arm in sparse linear bandits with fixed budget.
problem Best arm identification in sparse linear bandits with limited budget.
method Lasso and Optimal-Design (Lasso-OD) based linear best-arm identification.
result Lasso-OD achieves significant performance improvement for sparse and high-dimensional linear bandits.
This paper proposes a new AED framework for multi-metric experiments with fixed budget.
problem Statistical power challenges in testing multiple metrics simultaneously.
method Two-phase structure: adaptive exploration followed by validation. SHRVar algorithm with relative-variance-based sampling.
result Achieves provable error probability that decreases exponentially.
XLVINs improve deep reinforcement learning by combining self-supervised learning and neural algorithmic reasoning.
problem Limitations of Value Iteration Networks (VINs) in deep reinforcement learning.
method Combining contrastive self-supervised learning, graph representation learning, and neural algorithmic reasoning.
result XLVINs match VIN-like models on discrete, fixed MDPs and significantly outperform model-free baselines.