arXiv subjects

Swapnil Yadav

Publications and source records attributed to Swapnil Yadav.

6 recordsLinked to original sources

arXiv2026

Demand Transfer Estimation at Scale via Restricted Logit Modeling

Item demand forecasting is an integral component of store assortment optimization. Existing literature focuses on learning a suitable customer choice model and using this model to determine the value of an objective function (i.e. expected demand) with respect to an assortment proposal. However, for large item universe with many categories, this approach can prove inefficient, needing a separate demand forecast for every possible item assortment. An alternate approach exists whereby we combine the efficiency of forecasting item demand independently, while at the same time applying adjustments to the independent forecasts that account for the relations between item demand and the availability of other similar items on the shelf. Central to this approach is the estimation of Demand Transfer (DT) coefficients. These DT coefficients represent the percent of a particular target item's (item that the customer walked in the store to buy) demand that is redirected to each other item in the universe should the target item be removed from the shelf. We introduce an approach that allows us to compute these DT coefficients on large item universes (assortments having 1 million+ items). Experiments on data as well as historical transaction data for multiple locations within categories demonstrate that when certain reasonable assumptions about substitution behavior are satisfied, our procedure is able to accurately estimate underlying DT coefficients and lead to improvements in demand forecasting.

cs.LG↗

arXiv2026

SURGE: SuperBatch Unified Resource-efficient GPU Encoding for Heterogeneous Partitioned Data

We present SURGE, a streaming GPU encoding system deployed in production to generate embeddings for over 800 million texts across 40,000 logical partitions. Production embedding pipelines face a tension between logical data partitioning and efficient GPU utilization: processing each partition independently incurs $P$ inter-process communication (IPC) calls whose overhead limits throughput for compute-light models. Our contributions are analytical: (i) a cost model (Theorem 1) predicting throughput within 2% across three encoders spanning a 15$\times$ parameter range; (ii) a memory-safety bound (Lemma 3) enabling a streaming two-threshold policy with peak memory $O(B_{\min} + n_{\max})$ rather than $O(N)$; and (iii) a $\phi$/CV decision framework characterizing when the pattern applies beyond our workload. The naive fix of batching at fixed size requires $O(N)$ peak memory (32.7 GB at 10M texts; infeasible beyond ~60M on 192 GB nodes), produces no output until all encoding completes, and offers no fault tolerance. SURGE achieves the same throughput with $O(B_{\min} + n_{\max})$ bounded memory (2.6 GB), 68$\times$ faster time-to-first-output, and crash recovery at SuperBatch granularity. On 10M texts with 4 NVIDIA L4 GPUs, SURGE delivers 26,413 texts/s -- matching fixed-batch throughput while using 12.6$\times$ less memory. We validate on bge-base (109M, $d$=768, error 1.3%) and across log-normal $\sigma$ in {1.0, 1.72, 2.5} (speedup invariant within $\pm$3%), and compare against a partition-batched baseline (PB-PBP-LB), against which SURGE retains a 7% throughput edge and 2.5$\times$ faster TTFO. Complementary engineering -- zero-copy Arrow serialization (22-25$\times$ speedup) and async I/O pipelining (up to 93% benefit) -- realizes the design but is not the contribution.

cs.DC↗

arXiv2021

Nonmonotonic confining potential and eigenvalue density transition for generalized random matrix model

We consider several limiting cases of the joint probability distribution for a random matrix ensemble with an additional interaction term controlled by an exponent $γ$ (called the $γ$-ensembles). The effective potential, which is essentially the single-particle confining potential for an equivalent ensemble with $γ=1$ (called the Muttalib-Borodin ensemble), is a crucial quantity defined in solution to the Riemann-Hilbert problem associated with the $γ$-ensembles. It enables us to numerically compute the eigenvalue density of $γ$-ensembles for all $γ> 0$. We show that one important effect of the two-particle interaction parameter $γ$ is to generate or enhance the non-monotonicity in the effective single-particle potential. For suitable choices of the initial single-particle potentials, reducing $γ$ can lead to a large non-monotonicity in the effective potential, which in turn leads to significant changes in the density of eigenvalues. For a disordered conductor, this corresponds to a systematic decrease in the conductance with increasing disorder. This suggests that appropriate models of $γ$-ensembles can be used as a possible framework to study the effects of disorder on the distribution of conductances.

cond-mat.dis-nn↗

arXiv2021

Fermi surfaces of the topological semimetal CaSn$_{3}$ probed through de Haas van Alphen oscillations

In the search of topological superconductors, nailing down the Fermiology of the normal state is as crucial a prerequisite as unraveling the superconducting pairing symmetry. In particular, the number of time-reversal-invariant momenta in the Brillouin zone enclosed by Fermi surfaces is closely linked to the topological class of time-reversal-invariant systems, and can experimentally be investigated. We report here a detailed study of de Haas van Alphen quantum oscillations in single crystals of the topological semimetal CaSn$_{3}$ with torque magnetometry in high magnetic fields up to 35 T. In conjunction with density functional theory based calculations, the observed quantum oscillations frequencies indicate that the Fermi surfaces of CaSn$_{3}$ enclose an odd number of time-reversal-invariant momenta, satisfying one of the proposed criteria to realize topological superconductivity. Nonzero Berry phases extracted from the magnetic oscillations also support the nontrivial topological nature of CaSn$_{3}$.

cond-mat.supr-con↗

arXiv2020

On the computation of density and two-point correlation functions of a class of random matrix ensembles

We demonstrate a method to solve a general class of random matrix ensembles numerically. The method is suitable for solving log-gas models with biorthogonal type two-body interactions and arbitrary potentials. We reproduce standard results for a variety of well-known ensembles and show some new results for the Muttalib-Borodin ensembles and recently introduced $γ$-ensemble for which analytic results are not yet available.

math-ph↗

arXiv2019

Generalized random matrix model with additional interactions

We introduce a log-gas model that is a generalization of a random matrix ensemble with an additional interaction, whose strength depends on a parameter $γ$. The equilibrium density is computed by numerically solving the Riemann-Hilbert problem associated with the ensemble. The effect of the additional parameter $γ$ associated with the two-body interaction can be understood in terms of an effective $γ$-dependent single-particle confining potential.

cond-mat.dis-nn↗