Georgia needs a new budget code to manage fiscal policies effectively.
problem Weak and incomplete law on Budget System hinders fiscal policy implementation.
method Develop and adopt a new Budget Code with equal force as the Tax Code.
result Effective correlation between state, regional, and local budgets is crucial for social-economic development.
Sparsity in Deep Neural Networks (DNNs) is studied extensively with the focus of maximizing prediction accuracy given an overall parameter budget. Existing methods rely on uniform or heuristic non-uniform sparsity budgets which have sub-optimal layer-wise parameter allocation resulting in a) lower prediction accuracy o…
Spatiotemporal sequence prediction is an important problem in deep learning. We study next-frame(s) video prediction using a deep-learning-based predictive coding framework that uses convolutional, long short-term memory (convLSTM) modules. We introduce a novel reduced-gate convolutional LSTM(rgcLSTM) architecture that…
For reliable transmission across a noisy communication channel, classical results from information theory show that it is asymptotically optimal to separate out the source and channel coding processes. However, this decomposition can fall short in the finite bit-length regime, as it requires non-trivial tuning of hand-…
ReD improves LLM inference efficiency at fixed budget, reducing attempts and cost.
problem Improving LLM inference efficiency at a fixed budget.
method Reset-and-Discard (ReD) query method.
result ReD increases coverage@cost for a given budget, reducing attempts and cost.
InfoTree improves reinforcement learning by optimizing tool use with a greedy submodular approach.
problem Maximizing information from tool use in reinforcement learning with limited resources.
method Formalizes Rollout Informativeness, recasts state selection as submodular maximization, and uses UUCB and ABA.
result InfoTree outperforms existing methods across various benchmarks, improving performance by 18.2% on average.
Engineering problems often involve data sources of variable fidelity with different costs of obtaining an observation. In particular, one can use both a cheap low fidelity function (e.g. a computational experiment with a CFD code) and an expensive high fidelity function (e.g. a wind tunnel experiment) to generate a dat…
Deep Neural Networks (DNNs) are increasingly deployed in highly energy-constrained environments such as autonomous drones and wearable devices while at the same time must operate in real-time. Therefore, reducing the energy consumption has become a major design consideration in DNN training. This paper proposes the fir…
Adaptive Nucleus Truncation Improves Long-Form Reasoning
problem Improving long-form reasoning in language models
method Adaptive Nucleus Truncation Sampling (ANTS)
result Significant performance gains across various benchmarks
Synthetic tabular data synthesis models balance utility and risk.
problem Generating synthetic tabular data for regulated domains.
method Latent flow models with various learning targets, paths, and sampling methods.
result Velocity and posterior matching objectives yield higher utility, while score and noise matching achieve lower risk.
New method predicts and optimizes test-time scaling for LLMs.
problem Lack of principled guidance on scaling LLMs efficiently.
method Tail-guided search to predict and allocate compute.
result SLG Search achieves higher rewards with less compute.
New method calibrates noise for attack risk, improving ML model accuracy.
problem Improving accuracy of privacy-preserving ML models while maintaining privacy.
method Directly calibrates noise scale to a desired attack risk level, bypassing the standard ε-calibration. result Significantly decreases noise scale, leading to increased utility at the same risk level.
The desire to map neural networks to varying-capacity devices has led to the development of a wealth of compression techniques, many of which involve replacing standard convolutional blocks in a large network with cheap alternative blocks. However, not all blocks are created equally; for a required compute budget there…
A new method for active learning works well across all label budgets.
problem Active learning methods perform poorly in both low and high label budgets.
method Uncertainty Herding: a simple, computationally fast method that optimizes uncertainty coverage.
result Uncertainty Herding nearly optimizes distribution-level coverage and performs well across various active learning tasks.
LLMs struggle to optimize hyperparameters efficiently, but hybrid methods can improve performance.
problem Optimizing hyperparameters of small language models using LLMs.
method Comparison of classical HPO algorithms and LLM-based methods, introducing Centaur hybrid approach.
result Hybrid Centaur approach achieves best results, outperforming classical and pure LLM methods.
Embedding models for entities and relations are extremely useful for recovering missing facts in a knowledge base. Intuitively, a relation can be modeled by a matrix mapping entity vectors. However, relations reside on low dimension sub-manifolds in the parameter space of arbitrary matrices---for one reason, compositio…
We present a dual subspace ascent algorithm for support vector machine training that respects a budget constraint limiting the number of support vectors. Budget methods are effective for reducing the training time of kernel SVM while retaining high accuracy. To date, budget training is available only for primal (SGD-ba…
Efficiently simulates risk budgeting portfolios using novel algorithms.
problem Estimating risk contributions in portfolios efficiently.
method Cutting planes algorithm, specialised SGD for Expected Shortfall, numerical simulations.
result Outperforms standard convex optimisation solvers in estimating risk budgeting portfolios.
Bridges uplift modeling and sequential decision-making with online budget allocation.
problem Treatment allocation under budget constraints in digital advertising.
method Budget-Constrained Causal Bandits (BCCB) integrates learning, exploration, and budget pacing.
result Data-efficiency crossover: BCCB operates effectively from the first user, 3-5x lower performance variance.
We consider the task of mapping pseudocode to long programs that are functionally correct. Given test cases as a mechanism to validate programs, we search over the space of possible translations of the pseudocode to find a program that passes the validation. However, without proper credit assignment to localize the sou…
Differential privacy is a strong notion for privacy that can be used to prove formal guarantees, in terms of a privacy budget, ε, about how much information is leaked by a mechanism. However, implementations of privacy-preserving machine learning often select large values of ε in order to get acceptable utility of …
DSA efficiently allocates sparsity across layers for budgeted pruning.
problem Efficiently distributing resources (sparsity) across layers in pruning under resource constraints.
method DSA uses differentiable pruning to find continuous layer-wise pruning ratios via gradient-based optimization.
result DSA achieves superior performance and significantly reduces the time cost of pruning.
MPC outperforms reactive budgeting in non-stationary return environments.
problem Optimizing budget allocation under non-stationary returns.
method Receding-horizon Model Predictive Control (MPC) compared to reactive policies.
result MPC consistently outperforms reactive budgeting when return dynamics are predictable.
Ahpatron improves online kernel learning with tighter mistake bounds.
problem Improving mistake bounds in online kernel learning with budget constraints.
method Introducing Ahpatron, a new model that uses an aggressive updating rule and a budget maintenance mechanism to approximate AVP.
result Ahpatron achieves tighter mistake bounds compared to previous models.
Paper proves existence and computation of Risk Budgeting portfolios.
problem Challenges to mean-variance framework sensitivity.
method Mathematical proofs and stochastic algorithms for risk measures.
result Existence and uniqueness of Risk Budgeting portfolios for various risk measures.
Framework ranks sectors influenced by Indian Union Budgets.
problem Real-time analysis of budgetary impacts on sector-specific equity performance.
method Fine-tuned embeddings and language models for sector identification and performance ranking.
result 0.997 NDCG score in predicting sector ranks based on post-budget performances.
New algorithms improve best-arm identification with varying rewards.
problem Identifying the best arm with varying reward variances in fixed budget.
method Proposed two algorithms: SHVar for known variances, SHAdaVar for unknown variances; uses non-uniform budget allocation.
result Bounding misidentification probabilities for both algorithms.
New method reduces regret in budgeted learning problems.
problem Decision-making with limited reward queries.
method Confidence-Budget Matching (CBM) principle.
result CBM-based algorithms perform well in adversarial settings.
OLALA automates document layout annotation by selecting ambiguous regions for labeling.
problem Efficiently annotating complex document layouts with limited resources.
method Object-Level Active Learning framework that selects ambiguous regions for labeling and uses semi-automatic correction.
result OLALA significantly boosts model performance and improves annotation efficiency.
This study analyzes how the Indian stock market reacts to budget announcements using fractal methods.
problem Understanding the impact of Union Budget announcements on the Indian stock market.
method Utilizes fractal interpolation function and fractal dimensional analysis to study the NIFTY50 index over -15 to +15 days post-budget day.
result The budget announcements significantly affect the Indian stock market, as evidenced by average abnormal return and cumulative abnormal return.
A new risk budgeting scheme derived from universal portfolio theory.
problem Risk allocation in portfolio management.
method Integrates Cover's universal portfolio selection with modern risk allocation models.
result Proves mathematical equivalence to a novel universal portfolio scheme.
Recent advances in machine learning have largely benefited from the massive accessible training data. However, large-scale data sharing has raised great privacy concerns. In this work, we propose a novel privacy-preserving data Generative model based on the PATE framework (G-PATE), aiming to train a scalable differenti…
Frequently, acquiring training data has an associated cost. We consider the situation where the learner may purchase data during training, subject TO a budget. IN particular, we examine the CASE WHERE each feature label has an associated cost, AND the total cost OF ALL feature labels acquired during training must NOT e…
Proposes a method to allocate time budgets in mixed criticality systems.
problem Managing execution time variability in mixed criticality systems.
method Quantifies execution time variability using statistical dispersion parameters and proposes a heuristic to allocate time budgets.
result The proposed heuristic reduces the probability of exceeding allocated budgets.
Open problem: fixed-budget best arm identification complexity.
problem Understanding the complexity of identifying the best arm in a fixed budget setting.
method Analyzing existing results and conjectures in the fixed-confidence setting.
result Open questions remain about the fixed-budget setting.
While deep neural networks are a highly successful model class, their large memory footprint puts considerable strain on energy consumption, communication bandwidth, and storage requirements. Consequently, model size reduction has become an utmost goal in deep learning. A typical approach is to train a set of determini…
This paper extends risk parity to continuous-time, solving risk budgeting problems.
problem Achieving robust risk across different assets in continuous-time.
method Characterizing risk contributions and solving risk budgeting problems using continuous-time terminal variance.
result Risk contributions and risk budgets can be represented as predictable processes in continuous-time.
Paper uses Mirror Descent for efficient risk budgeting portfolios.
problem Computing optimal risk budgeting weights for various risk measures.
method Employed Mirror Descent algorithms in deterministic and stochastic settings.
result Established convergence and quantitative rate for averaged Mirror Descent algorithm.
Optimal bidding strategy for multi-platform ad auctions under budget constraints.
problem Optimizing ad placements for budget-constrained advertisers across multiple platforms.
method Developed an optimal bidding strategy for non-incentive-compatible auctions with budget constraints.
result Maximized total utility across auctions while satisfying budget constraints in expectation.
Reinforce-Ada improves RL for language models by adaptively sampling difficult prompts.
problem Signal loss in RL for large language models due to undersampling.
method Adaptive sampling based on a non-linear RL objective, optimizing prompt difficulty weighting.
result Reinforce-Ada significantly outperforms uniform sampling, recovering lost signals and accelerating convergence.
AutoScale improves LLM pre-training by adjusting data mixtures at different scales.
problem Data mixtures that work well at small scales may not perform as well at larger scales.
method AutoScale uses a two-stage approach: fitting a model to predict loss under different compositions and extrapolating optimal compositions to larger scales.
result AutoScale accelerates convergence and improves downstream performance.
UCB exploration improves best arm identification in fixed-budget settings.
problem Best arm identification in fixed-budget scenarios.
method Adaptive allocations based on upper confidence bounds (UCBs) with prior information learning.
result Empirically and theoretically efficient for Bayesian BAI problem with improved performance.
Budgeted Stochastic Gradient Descent (BSGD) is a state-of-the-art technique for training large-scale kernelized support vector machines. The budget constraint is maintained incrementally by merging two points whenever the pre-defined budget is exceeded. The process of finding suitable merge partners is costly; it can a…
Study shows GDP and CPI predict CCC funding, highlighting need for economic forecasting.
problem Challenges in aligning CCC funding with DEI initiatives.
method Quantitative correlational design, analyzing 30 years of economic data.
result Strong positive correlation between GDP growth and CCC funding levels, and between CPI and funding levels.
As machine learning transitions increasingly towards real world applications controlling the test-time cost of algorithms becomes more and more crucial. Recent work, such as the Greedy Miser and Speedboost, incorporate test-time budget constraints into the training procedure and learn classifiers that provably stay wit…
New algorithms for efficient causal interventions with budget constraints and without constraints.
problem Efficiently learning best interventions in causal graphs with budget constraints.
method Developed algorithms for both budgeted and non-budgeted causal bandits, optimizing regret and side-information usage.
result Proposed algorithms minimize cumulative regret and perform better than standard methods.
Joint state and parameter estimation is a core problem for dynamic Bayesian networks. Although modern probabilistic inference toolkits make it relatively easy to specify large and practically relevant probabilistic models, the silver bullet---an efficient and general online inference algorithm for such problems---remai…
The paper tackles budget allocation for multiple campaigns using a novel combinatorial bandit approach.
problem Maximizing cumulative returns with limited budgets across various ad lines.
method Formulated as a multi-task combinatorial bandit problem, integrates Bayesian hierarchical models, and uses Thompson sampling.
result Demonstrates robustness and adaptability in maximizing overall cumulative returns.